What is a quasispecies?

Viral genome sequencing methods: benefits and pitfalls of current approaches

Whole genome sequencing of viruses provides high-resolution molecular insights, enhancing our understanding of viral genome function and phylogeny. Beyond fundamental research, viral sequencing is increasingly vital for pathogen surveillance, epidemiology, and clinical applications. As sequencing methods rapidly evolve, the diversity of viral genomics applications and catalogued genomes continues to expand. Advances in long-read, single molecule, real-time sequencing methodologies present opportunities to sequence contiguous, haplotype resolved viral genomes in a range of research and applied settings. Here we present an overview of nucleic acid sequencing methods and their applications in studying viral genomes. We emphasise the advantages of different viral sequencing approaches, with a particular focus on the benefits of third-generation sequencing technologies in elucidating viral evolution, transmission networks, and pathogenesis.

Endogenous retroviruses in multiple sclerosis: A network-based etiopathogenic model

The present perspective article proposes an etiopathological model for multiple sclerosis pathogenesis and progression associated with the activation of human endogenous retroviruses. We reviewed preclinical, clinical, epidemiological, and evolutionary evidence indicating how the complex, multi-level interplay of genetic traits and environmental factors contributes to multiple sclerosis. We propose that endogenous retroviruses transactivation acts as a critical node in disease development. We also discuss the rationale for combined anti-retroviral therapy in multiple sclerosis as a disease-modifying therapeutic strategy. Finally, we propose that the immuno-pathogenic process triggered by endogenous retrovirus activation can be extended to aging and aging-related neurodegeneration. In this regard, endogenous retroviruses can be envisioned to act as epigenetic noise, favoring the proliferation of disorganized cellular subpopulations and accelerating system-specific "aging". Since inflammation and aging are two sides of the same coin (plastic dis-adaptation to external stimuli with system-specific degree of freedom), the two conditions may be epiphenomenal products of increased epigenomic entropy. Inflammation accelerates organ-specific aging, disrupting communication throughout critical systems of the body and producing symptoms. Overlapping neurological symptoms and syndromes may emerge from the activity of shared molecular networks that respond to endogenous retroviruses' reactivation.

Diverse mutant selection windows shape spatial heterogeneity in evolving populations

Mutant selection windows (MSWs), the range of drug concentrations that select for drug-resistant mutants, have long been used as a model for predicting drug resistance and designing optimal dosing strategies in infectious disease. The canonical MSW model offers comparisons between two subtypes at a time: drug-sensitive and drug-resistant. In contrast, the fitness landscape model with N alleles, which maps genotype to fitness, allows comparisons between N genotypes simultaneously, but does not encode continuous drug response data. In clinical settings, there may be a wide range of drug concentrations selecting for a variety of genotypes in both cancer and infectious diseases. Therefore, there is a need for a more robust model of the pathogen response to therapy to predict resistance and design new therapeutic approaches. Fitness seascapes, which model genotype-by-environment interactions, permit multiple MSW comparisons simultaneously by encoding genotype-specific dose-response data. By comparing dose-response curves, one can visualize the range of drug concentrations where one genotype is selected over another. In this work, we show how N-allele fitness seascapes allow for N * 2N-1 unique MSW comparisons. In spatial drug diffusion models, we demonstrate how fitness seascapes reveal spatially heterogeneous MSWs, extending the MSW model to more fully reflect the selection of drug resistant genotypes. Furthermore, using synthetic data and empirical dose-response data in cancer, we find that the spatial structure of MSWs shapes the evolution of drug resistance in an agent-based model. By simulating a tumor treated with cyclic drug therapy, we find that mutant selection windows introduced by drug diffusion promote the proliferation of drug resistant cells. Our work highlights the importance and utility of considering dose-dependent fitness seascapes in evolutionary medicine.

Thermal Inactivation of Escherichia Phage OSYSP and Host Strain Escherichia coli O157:H7 EDL933: A Comparative Kinetic Analysis

Lytic bacteriophages are promising biocontrol agents against pathogenic bacteria for food and therapeutic applications. Investigating the feasibility of combining phage and physical lethal agents, such as heat, as an effective hurdle combination could lead to beneficial applications. The current research was initiated to compare the thermal inactivation kinetics of a lytic phage (Escherichia phage OSYSP) and its host (Shiga toxin-producing Escherichia coli O157:H7 EDL933), considering they have different critical thermal targets in their structures. To provide a basis for comparison, thermal inactivation kinetics were determined on suspensions of these agents in buffered peptone water using a thermally controlled circulating water bath. Results showed that the bacteriophage virions have a remarkable heat resistance (p < 0.05) compared to their host cells. The D-values of the populations of phage (PFU/mL) and EDL933 strain (CFU/mL) were 166.7 and 7.3 min at 55°C, compared to 44.4 and 0.3 min at 60°C, respectively. Additionally, D-values were significantly (p < 0.05) more influenced by temperature changes in the case of E. coli O157:H7 EDL933 (z-value 3.7°C) compared to that for phage OSYSP (z-value 7.7°C). When the phage suspension was heat-treated in a thermal cycler instead of a water bath, no significant differences between the two treatment procedures (p > 0.05) in estimating virus D- and z-values were observed. Based on these findings, it may be feasible to combine phage OSYSP with mild heat during processing of food to selectively inactivate E. coli O157:H7 EDL933 and subsequently maintain product safety during storage by the surviving phage population; however, the feasibility of this application needs to be investigated. Additionally, the relatively heat-resistant phage OSYSP could qualify as a biological indicator to validate thermal treatments of minimally processed foods in which E. coli O157:H7 EDL933 is the pathogen-of-concern.

Characterization of differential susceptibility and differential infectivity epidemic models

Heterogeneity in susceptibility and infectivity is a central issue in epidemiology. Although the latter has received some attention recently, the former is often neglected in modeling of epidemic systems. Moreover, very few studies consider both of these heterogeneities. This paper is concerned with the characterization of epidemic models with differential susceptibility and differential infectivity under a general setup. Specifically, we investigate the global asymptotic behavior of equilibria of these systems when the network configuration of the Susceptible-Infectious interactions is strongly connected. These results prove two conjectures by Bonzi et al. (J Math Biol 62:39-64, 2011) and Hyman and Li (Math Biosci Eng 3:89-100, 2006). Moreover, we consider the scenario in which the strong connectivity hypothesis is dropped. In this case, the model exhibits a wider range of dynamical behavior, including the rise of boundary and interior equilibria, all based on the topology of network connectivity. Finally, a model with multidirectional transitions between infectious classes is presented and completely analyzed.

Diverse mutant selection windows shape spatial heterogeneity in evolving populations

Mutant selection windows (MSWs), the range of drug concentrations that select for drug-resistant mutants, have long been used as a model for predicting drug resistance and designing optimal dosing strategies in infectious disease. The canonical MSW model offers comparisons between two subtypes at a time: drug-sensitive and drug-resistant. In contrast, the fitness landscape model with N alleles, which maps genotype to fitness, allows comparisons between N genotypes simultaneously, but does not encode continuous drug response data. In clinical settings, there may be a wide range of drug concentrations selecting for a variety of genotypes. Therefore, there is a need for a more robust model of the pathogen response to therapy to predict resistance and design new therapeutic approaches. Fitness seascapes, which model genotype-by-environment interactions, permit multiple MSW comparisons simultaneously by encoding genotype-specific dose-response data. By comparing dose-response curves, one can visualize the range of drug concentrations where one genotype is selected over another. In this work, we show how N-allele fitness seascapes allow for N*2N-1 unique MSW comparisons. In spatial drug diffusion models, we demonstrate how fitness seascapes reveal spatially heterogeneous MSWs, extending the MSW model to more accurately reflect the selection fo drug resistant genotypes. Furthermore, we find that the spatial structure of MSWs shapes the evolution of drug resistance in an agent-based model. Our work highlights the importance and utility of considering dose-dependent fitness seascapes in evolutionary medicine.

Evolutionary safety of lethal mutagenesis driven by antiviral treatment

Nucleoside analogs are a major class of antiviral drugs. Some act by increasing the viral mutation rate causing lethal mutagenesis of the virus. Their mutagenic capacity, however, may lead to an evolutionary safety concern. We define evolutionary safety as a probabilistic assurance that the treatment will not generate an increased number of mutants. We develop a mathematical framework to estimate the total mutant load produced with and without mutagenic treatment. We predict rates of appearance of such virus mutants as a function of the timing of treatment and the immune competence of patients, employing realistic assumptions about the vulnerability of the viral genome and its potential to generate viable mutants. We focus on the case study of Molnupiravir, which is an FDA-approved treatment against Coronavirus Disease-2019 (COVID-19). We estimate that Molnupiravir is narrowly evolutionarily safe, subject to the current estimate of parameters. Evolutionary safety can be improved by restricting treatment with this drug to individuals with a low immunological clearance rate and, in future, by designing treatments that lead to a greater increase in mutation rate. We report a simple mathematical rule to determine the fold increase in mutation rate required to obtain evolutionary safety that is also applicable to other pathogen-treatment combinations.

Quasispecies Nature of RNA Viruses: Lessons from the Past

Viral quasispecies are distinct but closely related mutants formed by the disparity in viral genomes due to recombination, mutations, competition, and selection pressure. Theoretical derivation for the origin of a quasispecies is owed to the error-prone replication by polymerase and mutants of RNA replicators. Here, we briefly addressed the theoretical and mathematical origin of quasispecies and their dynamics. The impact of quasispecies for major salient human pathogens is reviewed. In the current global scenario, rapid changes in geographical landscapes favor the origin and selection of mutants. It comes as no surprise that a cauldron of mutants poses a significant risk to public health, capable of causing pandemics. Mutation rates in RNA viruses are magnitudes higher than in DNA organisms, explaining their enhanced virulence and evolvability. RNA viruses cause the most devastating pandemics; for example, members of the Orthomyxoviridae family caused the great influenza pandemic (1918 flu or Spanish flu), the SARS (severe acute respiratory syndrome) and MERS (Middle East respiratory syndrome) outbreak, and the human immunodeficiency viruses (HIV), lentiviruses of the Retroviridae family, caused worldwide devastation. Rapidly evolving RNA virus populations are a daunting challenge for the designing of effective control measures like vaccines. Developing awareness of the evolutionary dispositions of RNA viral mutant spectra and what influences their adaptation and virulence will help curtail outbreaks of past and future pathogens.

Hepatitis E virus species barriers: seeking viral and host determinants

The intimate relationship between virus and host cell can result in highly adapted viruses that are restricted to a single host. However, some viruses have the ability to infect multiple host species. Remarkably, hepatitis E viruses (HEV) comprise genotypes that are either 'single-host' or 'multi-host' genotypes, a trait that raises fundamental questions: Why do different genotypes differ in their host range, despite a high degree of genomic similarity? What are the underlying molecular determinants that shape species barriers? Here, we review the current knowledge of viral and host determinants that may affect the evolutionary trajectories of HEV. We also provide a perspective on techniques and methods that address open questions of HEV host range and adaptation.

Transcriptomics and RNA-Based Therapeutics as Potential Approaches to Manage SARS-CoV-2 Infection

SARS-CoV-2 is a coronavirus family member that appeared in China in December 2019 and caused the disease called COVID-19, which was declared a pandemic in 2020 by the World Health Organization. In recent months, great efforts have been made in the field of basic and clinical research to understand the biology and infection processes of SARS-CoV-2. In particular, transcriptome analysis has contributed to generating new knowledge of the viral sequences and intracellular signaling pathways that regulate the infection and pathogenesis of SARS-CoV-2, generating new information about its biology. Furthermore, transcriptomics approaches including spatial transcriptomics, single-cell transcriptomics and direct RNA sequencing have been used for clinical applications in monitoring, detection, diagnosis, and treatment to generate new clinical predictive models for SARS-CoV-2. Consequently, RNA-based therapeutics and their relationship with SARS-CoV-2 have emerged as promising strategies to battle the SARS-CoV-2 pandemic with the assistance of novel approaches such as CRISPR-CAS, ASOs, and siRNA systems. Lastly, we discuss the importance of precision public health in the management of patients infected with SARS-CoV-2 and establish that the fusion of transcriptomics, RNA-based therapeutics, and precision public health will allow a linkage for developing health systems that facilitate the acquisition of relevant clinical strategies for rapid decision making to assist in the management and treatment of the SARS-CoV-2-infected population to combat this global public health problem.

A general approach to identify low-frequency variants within influenza samples collected during routine surveillance

Influenza viruses exhibit considerable diversity between hosts. Additionally, different quasispecies can be found within the same host. High-throughput sequencing technologies can be used to sequence a patient-derived virus population at sufficient depths to identify low-frequency variants (LFV) present in a quasispecies, but many challenges remain for reliable LFV detection because of experimental errors introduced during sample preparation and sequencing. High genomic copy numbers and extensive sequencing depths are required to differentiate false positive from real LFV, especially at low allelic frequencies (AFs). This study proposes a general approach for identifying LFV in patient-derived samples obtained during routine surveillance. Firstly, validated thresholds were determined for LFV detection, whilst balancing both the cost and feasibility of reliable LFV detection in clinical samples. Using a genetically well-defined population of influenza A viruses, thresholds of at least 10⁴ genomes per microlitre and AF of ≥5 % were established as detection limits. Secondly, a subset of 59 retained influenza A (H3N2) samples from the 2016-2017 Belgian influenza season was composed. Thirdly, as a proof of concept for the added value of LFV for routine influenza monitoring, potential associations between patient data and whole genome sequencing data were investigated. A significant association was found between a high prevalence of LFV and disease severity. This study provides a general methodology for influenza LFV detection, which can also be adopted by other national influenza reference centres and for other viruses such as SARS-CoV-2. Additionally, this study suggests that the current relevance of LFV for routine influenza surveillance programmes might be undervalued.

Evaluating the structure-coefficient theorem of evolutionary game theory

In order to accommodate the empirical fact that population structures are rarely simple, modern studies of evolutionary dynamics allow for complicated and highly heterogeneous spatial structures. As a result, one of the most difficult obstacles lies in making analytical deductions, either qualitative or quantitative, about the long-term outcomes of evolution. The "structure-coefficient" theorem is a well-known approach to this problem for mutation-selection processes under weak selection, but a general method of evaluating the terms it comprises is lacking. Here, we provide such a method for populations of fixed (but arbitrary) size and structure, using easily interpretable demographic measures. This method encompasses a large family of evolutionary update mechanisms and extends the theorem to allow for asymmetric contests to provide a better understanding of the mutation-selection balance under more realistic circumstances. We apply the method to study social goods produced and distributed among individuals in spatially heterogeneous populations, where asymmetric interactions emerge naturally and the outcome of selection varies dramatically, depending on the nature of the social good, the spatial topology, and the frequency with which mutations arise.

The mutational dynamics of the SARS-CoV-2 virus in serial passages in vitro

Since its outbreak in 2019, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) keeps surprising the medical community by evolving diverse immune escape mutations in a rapid and effective manner. To gain deeper insight into mutation frequency and dynamics, we isolated ten ancestral strains of SARS-CoV-2 and performed consecutive serial incubation in ten replications in a suitable and common cell line and subsequently analysed them using RT-qPCR and whole genome sequencing. Along those lines we hoped to gain fundamental insights into the evolutionary capacity of SARS-CoV-2 in vitro. Our results identified a series of adaptive genetic changes, ranging from unique convergent substitutional mutations and hitherto undescribed insertions. The region coding for spike proved to be a mutational hotspot, evolving a number of mutational changes including the already known substitutions at positions S:484 and S:501. We discussed the evolution of all specific adaptations as well as possible reasons for the seemingly inhomogeneous potential of SARS-CoV-2 in the adaptation to cell culture. The combination of serial passage in vitro with whole genome sequencing uncovers the immense mutational potential of some SARS-CoV-2 strains. The observed genetic changes of SARS-CoV-2 in vitro could not be explained solely by selectively neutral mutations but possibly resulted from the action of directional selection accumulating favourable genetic changes in the evolving variants, along the path of increasing potency of the strain. Competition among a high number of quasi-species in the SARS-CoV-2 in vitro population gene pool may reinforce directional selection and boost the speed of evolutionary change.

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Ten genetically similar strains evolved very differently in serial passage in vitro.

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Observed mutations included substitutions at important spike positions.

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The three strains with the highest replication rates developed two convergent mutations.

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Via directional selection favourable genetic changes are accumulated.

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Competition among many quasi-species boosts the speed of evolutionary change.

Quasispecies of SARS-CoV-2 revealed by single nucleotide polymorphisms (SNPs) analysis

New SARS-CoV-2 mutants have been continuously indentified with enhanced transmission ever since its outbreak in early 2020. As an RNA virus, SARS-CoV-2 has a high mutation rate due to the low fidelity of RNA polymerase. To study the single nucleotide polymorphisms (SNPs) dynamics of SARS-CoV-2, 158 SNPs with high confidence were identified by deep meta-transcriptomic sequencing, and the most common SNP type was C > T. Analyses of intra-host population diversity revealed that intra-host quasispecies' composition varies with time during the early onset of symptoms, which implicates viral evolution during infection. Network analysis of co-occurring SNPs revealed the most abundant non-synonymous SNP 22,638 in the S glycoprotein RBD region and 28,144 in the ORF8 region. Furthermore, SARS-CoV-2 variations differ in an individual's respiratory tissue (nose, throat, BALF, or sputum), suggesting independent compartmentalization of SARS-CoV-2 populations in patients. The positive selection analysis of the SARS-CoV-2 genome uncovered the positive selected amino acid G251V on ORF3a. Alternative allele frequency spectrum (AAFS) of all variants revealed that ORF8 could bear alternate alleles with high frequency. Overall, the results show the quasispecies' profile of SARS-CoV-2 in the respiratory tract in the first two months after the outbreak.

Experimental and mathematical insights on the interactions between poliovirus and a defective interfering genome

During replication, RNA viruses accumulate genome alterations, such as mutations and deletions. The interactions between individual variants can determine the fitness of the virus population and, thus, the outcome of infection. To investigate the effects of defective interfering genomes (DI) on wild-type (WT) poliovirus replication, we developed an ordinary differential equation model, which enables exploring the parameter space of the WT and DI competition. We also experimentally examined virus and DI replication kinetics during co-infection, and used these data to infer model parameters. Our model identifies, and our experimental measurements confirm, that the efficiencies of DI genome replication and encapsidation are two most critical parameters determining the outcome of WT replication. However, an equilibrium can be established which enables WT to replicate, albeit to reduced levels.

SARS-CoV-2 Variant Identification Using a Genome Tiling Array and Genotyping Probes

With over three million deaths worldwide attributed to the respiratory disease COVID-19 caused by the novel coronavirus SARS-CoV-2, it is essential that continued efforts be made to track the evolution and spread of the virus globally. We previously presented a rapid and cost-effective method to sequence the entire SARS-CoV-2 genome with 95% coverage and 99.9% accuracy. This method is advantageous for identifying and tracking variants in the SARS-CoV-2 genome when compared to traditional short read sequencing methods which can be time consuming and costly. Herein we present the addition of genotyping probes to our DNA chip which target known SARS-CoV-2 variants. The incorporation of the genotyping probe sets along with the advent of a moving average filter have improved our sequencing coverage and accuracy of the SARS-CoV-2 genome.

Avian Influenza H7N9 Virus Adaptation to Human Hosts

Avian influenza virus A (H7N9), after circulating in avian hosts for decades, was identified as a human pathogen in 2013. Herein, amino acid substitutions possibly essential for human adaptation were identified by comparing the 4706 aligned overlapping nonamer position sequences (1–9, 2–10, etc.) of the reported 2014 and 2017 avian and human H7N9 datasets. The initial set of virus sequences (as of year 2014) exhibited a total of 109 avian-to-human (A2H) signature amino acid substitutions. Each represented the most prevalent substitution at a given avian virus nonamer position that was selectively adapted as the corresponding index (most prevalent sequence) of the human viruses. The majority of these avian substitutions were long-standing in the evolution of H7N9, and only 17 were first detected in 2013 as possibly essential for the initial human adaptation. Strikingly, continued evolution of the avian H7N9 virus has resulted in avian and human protein sequences that are almost identical. This rapid and continued adaptation of the avian H7N9 virus to the human host, with near identity of the avian and human viruses, is associated with increased human infection and a predicted greater risk of human-to-human transmission.

Next-Generation Sequencing Analysis of the Within-Host Genetic Diversity of Influenza A(H1N1)pdm09 Viruses in the Upper and Lower Respiratory Tracts of Patients with Severe Influenza

The D222G/N substitution in the hemagglutinin (HA) protein of influenza A(H1N1)pdm09 virus has been reported to be associated with disease severity and mortality in numerous previous studies. In the present study, 75% of lower respiratory samples contained heterogeneous influenza populations that carried different amino acids at position 222 of the HA protein, whereas all upper respiratory samples only contained the wild-type 222D.

The influenza A(H1N1)pdm09 virus emerged in April 2009 with an unusual incidence of severe disease and mortality, and currently circulates as a seasonal influenza virus. Previous studies using consensus viral genome sequencing data have overlooked the viral genomic and phenotypic diversity. Next-generation sequencing (NGS) may instead be used to characterize viral populations in an unbiased manner and to measure within-host genetic diversity. In this study, we used NGS analysis to investigate the within-host genetic diversity of influenza A(H1N1)pdm09 virus in the upper and lower respiratory samples from nine patients who were admitted to the intensive care unit (ICU). A total of 47 amino acid substitution positions were found to differ between the upper and lower respiratory tract samples from all patients. However, the D222G/N substitution in hemagglutinin (HA) protein was the only amino acid substitution common to multiple patients. Furthermore, the substitution was detected only in the six samples from the lower respiratory tract. Therefore, it is important to investigate influenza A(H1N1)pdm09 virus populations using multiple paired samples from the upper and lower respiratory tract to avoid overlooking potentially important substitutions, especially in patients with severe disease.

IMPORTANCE The D222G/N substitution in the hemagglutinin (HA) protein of influenza A(H1N1)pdm09 virus has been reported to be associated with disease severity and mortality in numerous previous studies. In the present study, 75% of lower respiratory samples contained heterogeneous influenza populations that carried different amino acids at position 222 of the HA protein, whereas all upper respiratory samples only contained the wild-type 222D. These results suggest the influenza A(H1N1)pdm09 virus has diversified inside the host owing to differences in tissue specificity. In this study, the within-host genetic diversity of influenza A(H1N1)pdm09 virus was investigated for the first time using next-generation sequencing analysis of the viral whole-genome in samples extracted from the upper and lower respiratory tracts of patients with severe disease.

Inter- Versus Intra-Host Sequence Diversity of pH1N1 and Associated Clinical Outcomes

The diversity of RNA viruses dictates their evolution in a particular host, community or environment. Here, we reported within- and between-host pH1N1virus diversity at consensus and sub-consensus levels over a three-year period (2015-2017) and its implications on disease severity. A total of 90 nasal samples positive for the pH1N1 virus were deep-sequenced and analyzed to detect low-frequency variants (LFVs) and haplotypes. Parallel evolution of LFVs was seen in the hemagglutinin (HA) gene across three scales: among patients (33%), across years (22%), and at global scale. Remarkably, investigating the emergence of LFVs at the consensus level demonstrated that within-host virus evolution recapitulates evolutionary dynamics seen at the global scale. Analysis of virus diversity at the HA haplotype level revealed the clustering of low-frequency haplotypes from early 2015 with dominant strains of 2016, indicating rapid haplotype evolution. Haplotype sharing was also noticed in all years, strongly suggesting haplotype transmission among patients infected during a specific influenza season. Finally, more than half of patients with severe symptoms harbored a larger number of haplotypes, mostly in patients under the age of five. Therefore, patient age, haplotype diversity, and the presence of certain LFVs should be considered when interpreting illness severity. In addition to its importance in understanding virus evolution, sub-consensus virus diversity together with whole genome sequencing is essential to explain variabilities in clinical outcomes that cannot be explained by either analysis alone.

Model genotype-phenotype mappings and the algorithmic structure of evolution

Cancers are complex dynamic systems that undergo evolution and selection. Personalized medicine approaches in the clinic increasingly rely on predictions of tumour response to one or more therapies; these predictions are complicated by the inevitable evolution of the tumour. Despite enormous amounts of data on the mutational status of cancers and numerous therapies developed in recent decades to target these mutations, many of these treatments fail after a time due to the development of resistance in the tumour. The emergence of these resistant phenotypes is not easily predicted from genomic data, since the relationship between genotypes and phenotypes, termed the genotype-phenotype (GP) mapping, is neither injective nor functional. We present a review of models of this mapping within a generalized evolutionary framework that takes into account the relation between genotype, phenotype, environment and fitness. Different modelling approaches are described and compared, and many evolutionary results are shown to be conserved across studies despite using different underlying model systems. In addition, several areas for future work that remain understudied are identified, including plasticity and bet-hedging. The GP-mapping provides a pathway for understanding the potential routes of evolution taken by cancers, which will be necessary knowledge for improving personalized therapies.

Typicality in quasispecies evolution in high dimensions

We study quasispecies and closely related evolutionary dynamics like the replicator-mutator equation in high dimensions. In particular, we show that under certain conditions, the average fitness of almost all quasispecies of a given dimension becomes independent of mutational probabilities in high dimensional sequence spaces. This result is a consequence of concentration of measure on a high dimensional hypersphere and its extension to Lipschitz functions known as the Levy's Lemma. Our results naturally extend to other functional capabilities that can be described as Lipschitz functions and whose input parameters are the frequencies of individual constituents of the quasispecies. In order to show this, we give a generalization of Levy's Lemma and discuss possible biological consequences of our work.

Systems protobiology: origin of life in lipid catalytic networks

Life is that which replicates and evolves, but there is no consensus on how life emerged. We advocate a systems protobiology view, whereby the first replicators were assemblies of spontaneously accreting, heterogeneous and mostly non-canonical amphiphiles. This view is substantiated by rigorous chemical kinetics simulations of the graded autocatalysis replication domain (GARD) model, based on the notion that the replication or reproduction of compositional information predated that of sequence information. GARD reveals the emergence of privileged non-equilibrium assemblies (composomes), which portray catalysis-based homeostatic (concentration-preserving) growth. Such a process, along with occasional assembly fission, embodies cell-like reproduction. GARD pre-RNA evolution is evidenced in the selection of different composomes within a sparse fitness landscape, in response to environmental chemical changes. These observations refute claims that GARD assemblies (or other mutually catalytic networks in the metabolism first scenario) cannot evolve. Composomes represent both a genotype and a selectable phenotype, anteceding present-day biology in which the two are mostly separated. Detailed GARD analyses show attractor-like transitions from random assemblies to self-organized composomes, with negative entropy change, thus establishing composomes as dissipative systems—hallmarks of life. We show a preliminary new version of our model, metabolic GARD (M-GARD), in which lipid covalent modifications are orchestrated by non-enzymatic lipid catalysts, themselves compositionally reproduced. M-GARD fills the gap of the lack of true metabolism in basic GARD, and is rewardingly supported by a published experimental instance of a lipid-based mutually catalytic network. Anticipating near-future far-reaching progress of molecular dynamics, M-GARD is slated to quantitatively depict elaborate protocells, with orchestrated reproduction of both lipid bilayer and lumenal content. Finally, a GARD analysis in a whole-planet context offers the potential for estimating the probability of life's emergence. The invigorated GARD scrutiny presented in this review enhances the validity of autocatalytic sets as a bona fide early evolution scenario and provides essential infrastructure for a paradigm shift towards a systems protobiology view of life's origin.

Direct RNA nanopore sequencing of full-length coronavirus genomes provides novel insights into structural variants and enables modification analysis

Sequence analyses of RNA virus genomes remain challenging owing to the exceptional genetic plasticity of these viruses. Because of high mutation and recombination rates, genome replication by viral RNA-dependent RNA polymerases leads to populations of closely related viruses, so-called "quasispecies." Standard (short-read) sequencing technologies are ill-suited to reconstruct large numbers of full-length haplotypes of (1) RNA virus genomes and (2) subgenome-length (sg) RNAs composed of noncontiguous genome regions. Here, we used a full-length, direct RNA sequencing (DRS) approach based on nanopores to characterize viral RNAs produced in cells infected with a human coronavirus. By using DRS, we were able to map the longest (∼26-kb) contiguous read to the viral reference genome. By combining Illumina and Oxford Nanopore sequencing, we reconstructed a highly accurate consensus sequence of the human coronavirus (HCoV)-229E genome (27.3 kb). Furthermore, by using long reads that did not require an assembly step, we were able to identify, in infected cells, diverse and novel HCoV-229E sg RNAs that remain to be characterized. Also, the DRS approach, which circumvents reverse transcription and amplification of RNA, allowed us to detect methylation sites in viral RNAs. Our work paves the way for haplotype-based analyses of viral quasispecies by showing the feasibility of intra-sample haplotype separation. Even though several technical challenges remain to be addressed to exploit the potential of the nanopore technology fully, our work illustrates that DRS may significantly advance genomic studies of complex virus populations, including predictions on long-range interactions in individual full-length viral RNA haplotypes.

Direct RNA nanopore sequencing of full-length coronavirus genomes provides novel insights into structural variants and enables modification analysis

Sequence analyses of RNA virus genomes remain challenging owing to the exceptional genetic plasticity of these viruses. Because of high mutation and recombination rates, genome replication by viral RNA-dependent RNA polymerases leads to populations of closely related viruses, so-called “quasispecies.” Standard (short-read) sequencing technologies are ill-suited to reconstruct large numbers of full-length haplotypes of (1) RNA virus genomes and (2) subgenome-length (sg) RNAs composed of noncontiguous genome regions. Here, we used a full-length, direct RNA sequencing (DRS) approach based on nanopores to characterize viral RNAs produced in cells infected with a human coronavirus. By using DRS, we were able to map the longest (∼26-kb) contiguous read to the viral reference genome. By combining Illumina and Oxford Nanopore sequencing, we reconstructed a highly accurate consensus sequence of the human coronavirus (HCoV)-229E genome (27.3 kb). Furthermore, by using long reads that did not require an assembly step, we were able to identify, in infected cells, diverse and novel HCoV-229E sg RNAs that remain to be characterized. Also, the DRS approach, which circumvents reverse transcription and amplification of RNA, allowed us to detect methylation sites in viral RNAs. Our work paves the way for haplotype-based analyses of viral quasispecies by showing the feasibility of intra-sample haplotype separation. Even though several technical challenges remain to be addressed to exploit the potential of the nanopore technology fully, our work illustrates that DRS may significantly advance genomic studies of complex virus populations, including predictions on long-range interactions in individual full-length viral RNA haplotypes.

Metagenomes of a Freshwater Charavirus from British Columbia Provide a Window into Ancient Lineages of Viruses

Charophyte algae, not chlorophyte algae, are the ancestors of ‘higher plants’; hence, viruses infecting charophytes may be related to those that first infected higher plants. Streamwaters from British Columbia, Canada, yielded single-stranded RNA metagenomes of Charavirus canadensis (CV-Can), that are similar in genomic architecture, length (9593 nt), nucleotide identity (63.4%), and encoded amino-acid sequence identity (53.0%) to those of Charavirus australis (CV-Aus). The sequences of their RNA-dependent RNA-polymerases (RdRp) resemble those found in benyviruses, their helicases those of hepaciviruses and hepegiviruses, and their coat-proteins (CP) those of tobamoviruses; all from the alphavirus/flavivirus branch of the ‘global RNA virome’. The 5’-terminus of the CV-Can genome, but not that of CV-Aus, is complete and encodes a methyltransferase domain. Comparisons of CP sequences suggests that Canadian and Australian charaviruses diverged 29–46 million years ago (mya); whereas, the CPs of charaviruses and tobamoviruses last shared a common ancestor 212 mya, and the RdRps of charaviruses and benyviruses 396 mya. CV-Can is sporadically abundant in low-nutrient freshwater rivers in British Columbia, where Chara braunii, a close relative of C. australis, occurs, and which may be its natural host. Charaviruses, like their hosts, are ancient and widely distributed, and thus provide a window to the viromes of early eukaryotes and, even, Archaea.

Evolutionary and ecological insights into the emergence of arthropod-borne viruses

The emergence of arthropod-borne viruses (arboviruses) is of global concern as they can rapidly spread across countries and to new continents as the recent examples of chikungunya virus and Zika virus have demonstrated. Whereas the global movement patterns of emerging arboviruses are comparatively well studied, there is little knowledge on initial emergence processes that enable sylvatic (enzootic) viruses to leave their natural amplification cycle and infect humans or livestock, often also involving infection of anthropophilic vector species. Emerging arboviruses almost exclusively originate in highly biodiverse ecosystems of tropical countries. Changes in host population diversity and density can affect pathogen transmission patterns and are likely to influence arbovirus emergence processes. This review focuses on concepts from disease ecology, explaining the interplay between biodiversity and pathogen emergence.

Application of deep sequencing methods for inferring viral population diversity

The first deep sequencing method was announced in 2005. Due to an increasing number of sequencing data and a reduction in the costs of each sequencing dataset, this innovative technique was soon applied to genetic investigations of viral genome diversity in various viruses, particularly RNA viruses. These deep sequencing findings documented viral epidemiology and evolution and provided high-resolution data on the genetic changes in viral populations. Here, we review deep sequencing platforms that have been applied in viral quasispecies studies. Further, we discuss recent deep sequencing studies on viral inter- and intrahost evolution, drug resistance, and humoral immune selection, especially in emerging and re-emerging viruses. Deep sequencing methods are becoming the standard for providing comprehensive results of viral population diversity, and their applications are discussed.

Mapping the Peaks: Fitness Landscapes of the Fittest and the Flattest

Populations exposed to a high mutation rate harbor abundant deleterious genetic variation, leading to depressed mean fitness. This reduction in mean fitness presents an opportunity for selection to restore fitness through the evolution of mutational robustness. In extreme cases, selection for mutational robustness can lead to flat genotypes (with low fitness but high robustness) outcompeting fit genotypes (with high fitness but low robustness)-a phenomenon known as survival of the flattest. While this effect was previously explored using the digital evolution system Avida, a complete analysis of the local fitness landscapes of fit and flat genotypes has been lacking, leading to uncertainty about the genetic basis of the survival-of-the-flattest effect. Here, we repeated the survival-of-the-flattest study and analyzed the mutational neighborhoods of fit and flat genotypes. We found that the flat genotypes, compared to the fit genotypes, had a reduced likelihood of deleterious mutations as well as an increased likelihood of neutral and, surprisingly, of lethal mutations. This trend holds for mutants one to four substitutions away from the wild-type sequence. We also found that flat genotypes have, on average, no epistasis between mutations, while fit genotypes have, on average, positive epistasis. Our results demonstrate that the genetic causes of mutational robustness on complex fitness landscapes are multifaceted. While the traditional idea of the survival of the flattest emphasized the evolution of increased neutrality, others have argued for increased mutational sensitivity in response to strong mutational loads. Our results show that both increased neutrality and increased lethality can lead to the evolution of mutational robustness. Furthermore, strong negative epistasis is not required for mutational sensitivity to lead to mutational robustness. Overall, these results suggest that mutational robustness is achieved by minimizing heritable deleterious variation.

Inter- and Intra-Host Nucleotide Variations in Hepatitis A Virus in Culture and Clinical Samples Detected by Next-Generation Sequencing

The accurate virus detection, strain discrimination, and source attribution of contaminated food items remains a persistent challenge because of the high mutation rates anticipated to occur in foodborne RNA viruses, such as hepatitis A virus (HAV). This has led to predictions of the existence of more than one sequence variant between the hosts (inter-host) or within an individual host (intra-host). However, there have been no reports of intra-host variants from an infected single individual, and little is known about the accuracy of the single nucleotide variations (SNVs) calling with various methods. In this study, the presence and identity of viral SNVs, either between HAV clinical specimens or among a series of samples derived from HAV clone1-infected FRhK4 cells, were determined following analyses of nucleotide sequences generated using next-generation sequencing (NGS) and pyrosequencing methods. The results demonstrate the co-existence of inter- and intra-host variants both in the clinical specimens and the cultured samples. The discovery and confirmation of multi-viral RNAs in an infected individual is dependent on the strain discrimination at the SNV level, and critical for successful outbreak traceback and source attribution investigations. The detection of SNVs in a time series of HAV infected FRhK4 cells improved our understanding on the mutation dynamics determined probably by different selective pressures. Additionally, it demonstrated that NGS could potentially provide a valuable investigative approach toward SNV detection and identification for other RNA viruses.

Processes on the emergent landscapes of biochemical reaction networks and heterogeneous cell population dynamics: differentiation in living matters

The notion of an attractor has been widely employed in thinking about the nonlinear dynamics of organisms and biological phenomena as systems and as processes. The notion of a landscape with valleys and mountains encoding multiple attractors, however, has a rigorous foundation only for closed, thermodynamically non-driven, chemical systems, such as a protein. Recent advances in the theory of nonlinear stochastic dynamical systems and its applications to mesoscopic reaction networks, one reaction at a time, have provided a new basis for a landscape of open, driven biochemical reaction systems under sustained chemostat. The theory is equally applicable not only to intracellular dynamics of biochemical regulatory networks within an individual cell but also to tissue dynamics of heterogeneous interacting cell populations. The landscape for an individual cell, applicable to a population of isogenic non-interacting cells under the same environmental conditions, is defined on the counting space of intracellular chemical compositions x = (x₁,x₂, … ,x_N ) in a cell, where x_ℓ is the concentration of the ℓth biochemical species. Equivalently, for heterogeneous cell population dynamics x_ℓ is the number density of cells of the ℓth cell type. One of the insights derived from the landscape perspective is that the life history of an individual organism, which occurs on the hillsides of a landscape, is nearly deterministic and 'programmed', while population-wise an asynchronous non-equilibrium steady state resides mostly in the lowlands of the landscape. We argue that a dynamic 'blue-sky' bifurcation, as a representation of Waddington's landscape, is a more robust mechanism for a cell fate decision and subsequent differentiation than the widely pictured pitch-fork bifurcation. We revisit, in terms of the chemostatic driving forces upon active, living matter, the notions of near-equilibrium thermodynamic branches versus far-from-equilibrium states. The emergent landscape perspective permits a quantitative discussion of a wide range of biological phenomena as nonlinear, stochastic dynamics.

Lethal mutagenesis of an RNA plant virus via lethal defection

Lethal mutagenesis is an antiviral therapy that relies on increasing the viral mutation rate with mutagenic nucleoside or base analogues. Currently, the molecular mechanisms that lead to virus extinction through enhanced mutagenesis are not fully understood. Increasing experimental evidence supports the lethal defection model of lethal mutagenesis of RNA viruses, where replication-competent-defectors drive infective virus towards extinction. Here, we address lethal mutagenesis in vivo using 5-fluorouracil (5-FU) during the establishment of tobacco mosaic virus (TMV) systemic infections in N. tabacum. The results show that 5-FU decreased the infectivity of TMV without affecting its viral load. Analysis of molecular clones spanning two genomic regions showed an increase of the FU-related base transitions A → G and U → C. Although the mutation frequency or the number of mutations per molecule did not increase, the complexity of the mutant spectra and the distribution of the mutations were altered. Overall, our results suggest that 5-FU antiviral effect on TMV is associated with the perturbation of the mutation-selection balance in the genomic region of the RNA-dependent RNA polymerase (RdRp). Our work supports the lethal defection model for lethal mutagenesis in vivo in a plant RNA virus and opens the way to study lethal mutagens in plant-virus systems.

Critical Mutation Rate has an Exponential Dependence on Population Size for Eukaryotic-length Genomes with Crossover

The critical mutation rate (CMR) determines the shift between survival-of-the-fittest and survival of individuals with greater mutational robustness ("flattest"). We identify an inverse relationship between CMR and sequence length in an in silico system with a two-peak fitness landscape; CMR decreases to no more than five orders of magnitude above estimates of eukaryotic per base mutation rate. We confirm the CMR reduces exponentially at low population sizes, irrespective of peak radius and distance, and increases with the number of genetic crossovers. We also identify an inverse relationship between CMR and the number of genes, confirming that, for a similar number of genes to that for the plant Arabidopsis thaliana (25,000), the CMR is close to its known wild-type mutation rate; mutation rates for additional organisms were also found to be within one order of magnitude of the CMR. This is the first time such a simulation model has been assigned input and produced output within range for a given biological organism. The decrease in CMR with population size previously observed is maintained; there is potential for the model to influence understanding of populations undergoing bottleneck, stress, and conservation strategy for populations near extinction.

Discovering Complete Quasispecies in Bacterial Genomes

Mobile genetic elements can be found in almost all genomes. Possibly the most common nonautonomous mobile genetic elements in bacteria are repetitive extragenic palindromic doublets forming hairpins (REPINs) that can occur hundreds of times within a genome. The sum of all REPINs in a genome can be viewed as an evolving population because REPINs replicate and mutate. In contrast to most other biological populations, we know the exact composition of the REPIN population and the sequence of each member of the population. Here, we model the evolution of REPINs as quasispecies. We fit our quasispecies model to 10 different REPIN populations from 10 different bacterial strains and estimate effective duplication rates. Our estimated duplication rates range from ∼5 × 10-9 to 15 × 10-9 duplications per bacterial generation per REPIN. The small range and the low level of the REPIN duplication rates suggest a universal trade-off between the survival of the REPIN population and the reduction of the mutational load for the host genome. The REPIN populations we investigated also possess features typical of other natural populations. One population shows hallmarks of a population that is going extinct, another population seems to be growing in size, and we also see an example of competition between two REPIN populations.

Unravelling the complexity and redundancy of carbon catabolic repression in Pseudomonas fluorescens SBW25

The two-component system CbrAB is the principal regulator for cellular metabolic balance in Pseudomonas fluorescens SBW25 and is necessary for growth on many substrates including xylose. To understand the regulatory linkage between CbrAB and genes for xylose utilization (xut), we performed transposon mutagenesis of ΔcbrB to select for Xut⁺ suppressors. This led to identification of crc and hfq. Subsequent genetic and biochemical analysis showed that Crc and Hfq are key mediators of succinate-provoked carbon catabolite repression (CCR). Specifically, Crc/Hfq sequentially bind to mRNAs of both the transcriptional activator and structural genes involved in xylose catabolism. However, in the absence of succinate, repression is relieved through competitive binding by two ncRNAs, CrcY and CrcZ, whose expression is activated by CbrAB. These findings provoke a model for CCR in which it is assumed that crc and hfq are functionally complementary, whereas crcY and crcZ are genetically redundant. Inactivation of either crcY or crcZ produced no effects on bacterial fitness in laboratory media, however, results of mathematical modelling predict that the co-existence of crcY and crcZ requires separate functional identity. Finally, we provide empirical evidence that CCR is advantageous in nutrient-complex environments where preferred carbon sources are present at high concentrations but fluctuate in their availability.

Second-order cooperation: Cooperative offspring as a living public good arising from second-order selection on non-cooperative individuals

Switching rate between cooperating and non-cooperating genotypes is a crucial social evolution factor, often neglected by game theory-inspired theoretical and experimental frameworks. We show that the evolution of alleles increasing the mutation or phenotypic switching rates toward cooperation is in itself a social dilemma. Although cooperative offspring are often unlikely to reproduce, due to high cost of cooperation, they can be seen both as a living public good and a part of the extended parental phenotype. The competition between individuals that generate cooperators and ones that do not is often more relevant than the competition between cooperators and non-cooperators. The dilemma of second-order cooperation we describe relates directly to eusociality, but can be also interpreted as a division of labor or a soma-germline distinction. The results of our simulations shine a new light on what Darwin had already termed a "special difficulty" of evolutionary theory and describe a novel type of cooperation dynamics.

Recent advances in inferring viral diversity from high-throughput sequencing data

Rapidly evolving RNA viruses prevail within a host as a collection of closely related variants, referred to as viral quasispecies. Advances in high-throughput sequencing (HTS) technologies have facilitated the assessment of the genetic diversity of such virus populations at an unprecedented level of detail. However, analysis of HTS data from virus populations is challenging due to short, error-prone reads. In order to account for uncertainties originating from these limitations, several computational and statistical methods have been developed for studying the genetic heterogeneity of virus population. Here, we review methods for the analysis of HTS reads, including approaches to local diversity estimation and global haplotype reconstruction. Challenges posed by aligning reads, as well as the impact of reference biases on diversity estimates are also discussed. In addition, we address some of the experimental approaches designed to improve the biological signal-to-noise ratio. In the future, computational methods for the analysis of heterogeneous virus populations are likely to continue being complemented by technological developments.

Analysis of Quasispecies of Avain Leukosis Virus Subgroup J Using Sanger and High-throughput Sequencing

Background

Avian leukosis viruses subgroup J (ALV-J) exists as a complex mixture of different, but closely related genomes named quasispecies subjected to continuous change according to the Principles of Darwinian evolution.

Method

The present study seeks to compare conventional Sanger sequencing with deep sequencing using MiSeq platform to study quasispecies dynamics of ALV-J.

Results

The accuracy and reproducibility of MiSeq sequencing was determined better than Sanger sequencing by running each experiment in duplicate. According to the mutational rate of single position and the ability to distinguish dominant quasispecies with two sequencing methods, conventional Sanger sequencing technique displayed high randomness due to few sequencing samples, while deep sequencing could reflect the composition of the quasispecies more accurately. In the mean time, the research of quasispecies via Sanger sequencing was simulated and analyzed with the aid of re-sampling strategy with replacement for 1000 times repeat from high-throughput sequencing data, which indicated that the higher antibody titer, the higher sequence entropy, the harder analyzing with the conventional Sanger sequencing, resulted in lower ratios of dominant variants.

Conclusions

In sum, deep sequencing is better suited for detecting rare variants comprehensively. The simulation of Sanger sequencing that we propose here will also help to standardize quasispecies researching under different selection pressure based on next-generation sequencing data.

Diversification of self-replicating molecules

How new species emerge in nature is still incompletely understood and difficult to study directly. Self-replicating molecules provide a simple model that allows us to capture the fundamental processes that occur in species formation. We have been able to monitor in real time and at a molecular level the diversification of self-replicating molecules into two distinct sets that compete for two different building blocks ('food') and so capture an important aspect of the process by which species may arise. The results show that the second replicator set is a descendant of the first and that both sets are kinetic products that oppose the thermodynamic preference of the system. The sets occupy related but complementary food niches. As diversification into sets takes place on the timescale of weeks and can be investigated at the molecular level, this work opens up new opportunities for experimentally investigating the process through which species arise both in real time and with enhanced detail.

g-FLUA2H: a web-based application to study the dynamics of animal-to-human mutation transmission for influenza viruses

g-FLUA2H is a web-based application focused on the analysis of the dynamics of influenza virus animal-to-human (A2H) mutation transmissions. The application only requires the viral protein sequences from both the animal and human host populations as input datasets. The comparative analyses between the co-aligned sequences of the two viral populations is based on a sliding window approach of size nine for statistical significance and data application to the major histocompatibility complex (MHC) and T-cell receptor (TCR) immune response mechanisms. The sequences at each of the aligned overlapping nonamer positions for the respective virus hosts are classified as four patterns of characteristic diversity motifs, as a basis for quantitative analyses: (i) "index", the most prevalent sequence; (ii) "major" variant, the second most common sequence and the single most prevalent variant of the index, with at least one amino acid mutation; (iii) "minor" variants, multiple different sequences, each with an incidence (percent occurrence) less than that of the major variant; and (iv) "unique" variants, each with only one occurrence in the alignment. The diversity motifs and their incidences at each of the nonamer positions allow evaluation of the mutation transmission dynamics and selectivity of the viral sequences in relation to the animal and the human hosts. g-FLUA2H is facilitated by a grid back-end for parallel processing of large sequence datasets. The web-application is publicly available at http://bioinfo.perdanauniversity.edu.my/g-FLUA2H. It can be used for a detailed characterization of the composition and incidence of mutations present in the proteomes of influenza viruses from animal and human host populations, for a better understanding of host tropism.

Empirical fitness models for hepatitis C virus immunogen design

Hepatitis C virus (HCV) afflicts 170 million people worldwide, 2%-3% of the global population, and kills 350 000 each year. Prophylactic vaccination offers the most realistic and cost effective hope of controlling this epidemic in the developing world where expensive drug therapies are not available. Despite 20 years of research, the high mutability of the virus and lack of knowledge of what constitutes effective immune responses have impeded development of an effective vaccine. Coupling data mining of sequence databases with spin glass models from statistical physics, we have developed a computational approach to translate clinical sequence databases into empirical fitness landscapes quantifying the replicative capacity of the virus as a function of its amino acid sequence. These landscapes explicitly connect viral genotype to phenotypic fitness, and reveal vulnerable immunological targets within the viral proteome that can be exploited to rationally design vaccine immunogens. We have recovered the empirical fitness landscape for the HCV RNA-dependent RNA polymerase (protein NS5B) responsible for viral genome replication, and validated the predictions of our model by demonstrating excellent accord with experimental measurements and clinical observations. We have used our landscapes to perform exhaustive in silico screening of 16.8 million T-cell immunogen candidates to identify 86 optimal formulations. By reducing the search space of immunogen candidates by over five orders of magnitude, our approach can offer valuable savings in time, expense, and labor for experimental vaccine development and accelerate the search for a HCV vaccine.

ABBREVIATIONS

HCV-hepatitis C virus, HLA-human leukocyte antigen, CTL-cytotoxic T lymphocyte, NS5B-nonstructural protein 5B, MSA-multiple sequence alignment, PEG-IFN-pegylated interferon.

Steering Evolution with Sequential Therapy to Prevent the Emergence of Bacterial Antibiotic Resistance

The increasing rate of antibiotic resistance and slowing discovery of novel antibiotic treatments presents a growing threat to public health. Here, we consider a simple model of evolution in asexually reproducing populations which considers adaptation as a biased random walk on a fitness landscape. This model associates the global properties of the fitness landscape with the algebraic properties of a Markov chain transition matrix and allows us to derive general results on the non-commutativity and irreversibility of natural selection as well as antibiotic cycling strategies. Using this formalism, we analyze 15 empirical fitness landscapes of E. coli under selection by different β-lactam antibiotics and demonstrate that the emergence of resistance to a given antibiotic can be either hindered or promoted by different sequences of drug application. Specifically, we demonstrate that the majority, approximately 70%, of sequential drug treatments with 2-4 drugs promote resistance to the final antibiotic. Further, we derive optimal drug application sequences with which we can probabilistically 'steer' the population through genotype space to avoid the emergence of resistance. This suggests a new strategy in the war against antibiotic-resistant organisms: drug sequencing to shepherd evolution through genotype space to states from which resistance cannot emerge and by which to maximize the chance of successful therapy.

Analysis of the genetic diversity of influenza A viruses using next-generation DNA sequencing

BACKGROUND

Influenza viruses exist as a large group of closely related viral genomes, also called quasispecies. The composition of this influenza viral quasispecies can be determined by an accurate and sensitive sequencing technique and data analysis pipeline. We compared the suitability of two benchtop next-generation sequencers for whole genome influenza A quasispecies analysis: the Illumina MiSeq sequencing-by-synthesis and the Ion Torrent PGM semiconductor sequencing technique.

RESULTS

We first compared the accuracy and sensitivity of both sequencers using plasmid DNA and different ratios of wild type and mutant plasmid. Illumina MiSeq sequencing reads were one and a half times more accurate than those of the Ion Torrent PGM. The majority of sequencing errors were substitutions on the Illumina MiSeq and insertions and deletions, mostly in homopolymer regions, on the Ion Torrent PGM. To evaluate the suitability of the two techniques for determining the genome diversity of influenza A virus, we generated plasmid-derived PR8 virus and grew this virus in vitro. We also optimized an RT-PCR protocol to obtain uniform coverage of all eight genomic RNA segments. The sequencing reads obtained with both sequencers could successfully be assembled de novo into the segmented influenza virus genome. After mapping of the reads to the reference genome, we found that the detection limit for reliable recognition of variants in the viral genome required a frequency of 0.5% or higher. This threshold exceeds the background error rate resulting from the RT-PCR reaction and the sequencing method. Most of the variants in the PR8 virus genome were present in hemagglutinin, and these mutations were detected by both sequencers.

CONCLUSIONS

Our approach underlines the power and limitations of two commonly used next-generation sequencers for the analysis of influenza virus gene diversity. We conclude that the Illumina MiSeq platform is better suited for detecting variant sequences whereas the Ion Torrent PGM platform has a shorter turnaround time. The data analysis pipeline that we propose here will also help to standardize variant calling in small RNA genomes based on next-generation sequencing data.

Drake’s rule as a consequence of approaching channel capacity

How mutations accumulate in genomes is the central question of molecular evolution theories. However, our understanding of this process is far from complete. Drake’s rule is a notoriously universal property of genomes from microbes to mammals—the number of (functional) mutations per-genome per-generation is approximately constant within a phylum, despite the orders of magnitude differences in genome sizes and diverse populations’ properties. So far, there is no concise explanation for this phenomenon. A formal model for the storage of genetic information suggests that a genome of any species operates near its maximum informational storage capacity, and the mutation rate per-genome per-generation is near its upper limit, providing a simple explanation for the rule with minimal assumptions.

A validated mathematical model of tumor growth including tumor-host interaction, cell-mediated immune response and chemotherapy

We consider a dynamical model of cancer growth including three interacting cell populations of tumor cells, healthy host cells and immune effector cells. The tumor-immune and the tumor-host interactions are characterized to reproduce experimental results. A thorough dynamical analysis of the model is carried out, showing its capability to explain theoretical and empirical knowledge about tumor development. A chemotherapy treatment reproducing different experiments is also introduced. We believe that this simple model can serve as a foundation for the development of more complicated and specific cancer models.

A model for genome size evolution

We present a model for genome size evolution that takes into account both local mutations such as small insertions and small deletions, and large chromosomal rearrangements such as duplications and large deletions. We introduce the possibility of undergoing several mutations within one generation. The model, albeit minimalist, reveals a non-trivial spontaneous dynamics of genome size: in the absence of selection, an arbitrary large part of genomes remains beneath a finite size, even for a duplication rate 2.6-fold higher than the rate of large deletions, and even if there is also a systematic bias toward small insertions compared to small deletions. Specifically, we show that the condition of existence of an asymptotic stationary distribution for genome size non-trivially depends on the rates and mean sizes of the different mutation types. We also give upper bounds for the median and other quantiles of the genome size distribution, and argue that these bounds cannot be overcome by selection. Taken together, our results show that the spontaneous dynamics of genome size naturally prevents it from growing infinitely, even in cases where intuition would suggest an infinite growth. Using quantitative numerical examples, we show that, in practice, a shrinkage bias appears very quickly in genomes undergoing mutation accumulation, even though DNA gains and losses appear to be perfectly symmetrical at first sight. We discuss this spontaneous dynamics in the light of the other evolutionary forces proposed in the literature and argue that it provides them a stability-related size limit below which they can act.

Influenza A virus acquires enhanced pathogenicity and transmissibility after serial passages in swine

Genetic and phylogenetic analyses suggest that the pandemic H1N1/2009 virus was derived from well-established swine influenza lineages; however, there is no convincing evidence that the pandemic virus was generated from a direct precursor in pigs. Furthermore, the evolutionary dynamics of influenza virus in pigs have not been well documented. Here, we subjected a recombinant virus (rH1N1) with the same constellation makeup as the pandemic H1N1/2009 virus to nine serial passages in pigs. The severity of infection sequentially increased with each passage. Deep sequencing of viral quasispecies from the ninth passage found five consensus amino acid mutations: PB1 A469T, PA 1129T, NA N329D, NS1 N205K, and NEP T48N. Mutations in the hemagglutinin (HA) protein, however, differed greatly between the upper and lower respiratory tracts. Three representative viral clones with the five consensus mutations were selected for functional evaluation. Relative to the parental virus, the three viral clones showed enhanced replication and polymerase activity in vitro and enhanced replication, pathogenicity, and transmissibility in pigs, guinea pigs, and ferrets in vivo. Specifically, two mutants of rH1N1 (PB1 A469T and a combination of NS1 N205K and NEP T48N) were identified as determinants of transmissibility in guinea pigs. Crucially, one mutant viral clone with the five consensus mutations, which also carried D187E, K211E, and S289N mutations in its HA, additionally was able to infect ferrets by airborne transmission as effectively as the pandemic virus. Our findings demonstrate that influenza virus can acquire viral characteristics that are similar to those of the pandemic virus after limited serial passages in pigs. Importance: We demonstrate here that an engineered reassortant swine influenza virus, with the same gene constellation pattern as the pandemic H1N1/2009 virus and subjected to only nine serial passages in pigs, acquired greatly enhanced virulence and transmissibility. In particular, one representative pathogenic passaged virus clone, which carried three mutations in the HA gene and five consensus mutations in PB1, PA, NA, NS1, and NEP genes, additionally was able to confer respiratory droplet transmission as effectively as the pandemic H1N1/2009 virus. Our findings suggest that pigs can readily induce adaptive mutational changes to a precursor pandemic-like virus to transform it into a highly virulent and infectious form akin to that of the pandemic H1N1/2009 virus, which underlines the potential direct role of pigs in promoting influenza A virus pathogenicity and transmissibility.

Full-length haplotype reconstruction to infer the structure of heterogeneous virus populations

Next-generation sequencing (NGS) technologies enable new insights into the diversity of virus populations within their hosts. Diversity estimation is currently restricted to single-nucleotide variants or to local fragments of no more than a few hundred nucleotides defined by the length of sequence reads. To study complex heterogeneous virus populations comprehensively, novel methods are required that allow for complete reconstruction of the individual viral haplotypes. Here, we show that assembly of whole viral genomes of ∼8600 nucleotides length is feasible from mixtures of heterogeneous HIV-1 strains derived from defined combinations of cloned virus strains and from clinical samples of an HIV-1 superinfected individual. Haplotype reconstruction was achieved using optimized experimental protocols and computational methods for amplification, sequencing and assembly. We comparatively assessed the performance of the three NGS platforms 454 Life Sciences/Roche, Illumina and Pacific Biosciences for this task. Our results prove and delineate the feasibility of NGS-based full-length viral haplotype reconstruction and provide new tools for studying evolution and pathogenesis of viruses.