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In combination therapy, bacteria are challenged with two or more antibiotics simultaneously. Ideally, separate mutations are required to adapt to each of them, which is a priori expected to hinder the evolution of full resistance. Yet, the success of this strategy ultimately depends on how well the combination controls the growth of bacteria with and without resistance mutations. To design a combination treatment, we need to choose drugs and their doses and decide how many drugs get mixed. Which combinations are good? To answer this question, we set up a stochastic pharmacodynamic model and determine the probability to successfully eradicate a bacterial population. We consider bacteriostatic and two types of bactericidal drugs—those that kill independent of replication and those that kill during replication. To establish results for a null model, we consider non‐interacting drugs and implement the two most common models for drug independence—Loewe additivity and Bliss independence. Our results show that combination therapy is almost always better in limiting the evolution of resistance than administering just one drug, even though we keep the total drug dose constant for a 'fair' comparison. Yet, exceptions exist for drugs with steep dose–response curves. Combining a bacteriostatic and a bactericidal drug which can kill non‐replicating cells is particularly beneficial. Our results suggest that a 50:50 drug ratio—even if not always optimal—is usually a good and safe choice. Applying three or four drugs is beneficial for treatment of strains with large mutation rates but adding more drugs otherwise only provides a marginal benefit or even a disadvantage. By systematically addressing key elements of treatment design, our study provides a basis for future models which take further factors into account. It also highlights conceptual challenges with translating the traditional concepts of drug independence to the single‐cell level. [ABSTRACT FROM AUTHOR]

The evolution of antimicrobial resistance (AMR), driven by the misuse of drugs – but also by the rationale with which antimicrobials are currently used – is a global health problem. The main measures against AMR are the prudent use of antimicrobials, rapid diagnostics, and the development of new drugs. Forecasting the evolution of AMR, we argue, could add to the arsenal of measures to control and reduce the spread of AMR. While many examples of forecasting or prediction are scattered throughout the literature, it is not yet seriously considered for addressing AMR. Forecasting, while challenging, will contribute to the sustainable use of antimicrobials and hence make them economically more viable. It will also inform the treatment of persistent infections. Operationalizing the forecasting of evolving resistance will provide an additional tool to combat the spread of AMR; it will require the development of bespoke technology, adapted computational approaches, stakeholder involvement, and a shift in mindset from a posteriori to a priori measures. Antimicrobial resistance (AMR) is a major global health issue. Current measures for tackling it comprise mainly the prudent use of drugs, the development of new drugs, and rapid diagnostics. Relatively little attention has been given to forecasting the evolution of resistance. Here, we argue that forecasting has the potential to be a great asset in our arsenal of measures to tackle AMR. We argue that, if successfully implemented, forecasting resistance will help to resolve the antibiotic crisis in three ways: it will (i) guide a more sustainable use (and therefore lifespan) of antibiotics and incentivize investment in drug development, (ii) reduce the spread of AMR genes and pathogenic microbes in the environment and between patients, and (iii) allow more efficient treatment of persistent infections, reducing the continued evolution of resistance. We identify two important challenges that need to be addressed for the successful establishment of forecasting: (i) the development of bespoke technology that allows stakeholders to empirically assess the risks of resistance evolving during the process of drug development and therapeutic/preventive use, and (ii) the transformative shift in mindset from the current praxis of mostly addressing the problem of antibiotic resistance a posteriori to a concept of a priori estimating, and acting on, the risks of resistance. [ABSTRACT FROM AUTHOR]

Recent theory and experiments have reported a reproducible tendency for the coexistence of microbial species under controlled environmental conditions. This observation has been explained in the context of competition for resources and metabolic complementarity given that, in microbial communities (MCs), many excreted by-products of metabolism may also be resources. MCs therefore play a key role in promoting their own stability and in shaping the niches of the constituent taxa. We suggest that an intermediate level of organization between the species and the community level may be pervasive, where tightly knit metabolic interactions create discrete consortia that are stably maintained. We call these units Metabolically Cohesive Consortia (MeCoCos) and we discuss the environmental context in which we expect their formation, and the ecological and evolutionary consequences of their existence. We argue that the ability to identify MeCoCos would open new avenues to link the species-, community- and ecosystemlevel properties, with consequences for our understanding of microbial ecology and evolution, and an improved ability to predict ecosystem functioning in the wild. [ABSTRACT FROM AUTHOR]

The stress-induced mutagenesis hypothesis postulates that in response to stress, bacteria increase their genome-wide mutation rate, in turn increasing the chances that a descendant is able to better withstand the stress. This has implications for antibiotic treatment: exposure to subinhibitory doses of antibiotics has been reported to increase bacterial mutation rates and thus probably the rate at which resistance mutations appear and lead to treatment failure. More generally, the hypothesis posits that stress increases evolvability (the ability of a population to generate adaptive genetic diversity) and thus accelerates evolution. Measuring mutation rates under stress, however, is problematic, because existing methods assume there is no death. Yet subinhibitory stress levels may induce a substantial death rate. Death events need to be compensated by extra replication to reach a given population size, thus providing more opportunities to acquire mutations. We show that ignoring death leads to a systematic overestimation of mutation rates under stress. We developed a system based on plasmid segregation that allows us to measure death and division rates simultaneously in bacterial populations. Using this system, we found that a substantial death rate occurs at the tested subinhibitory concentrations previously reported to increase mutation rate. Taking this death rate into account lowers and sometimes removes the signal for stress-induced mutagenesis. Moreover, even when antibiotics increase mutation rate, we show that subinhibitory treatments do not increase genetic diversity and evolvability, again because of effects of the antibiotics on population dynamics. We conclude that antibiotic-induced mutagenesis is overestimated because of death and that understanding evolvability under stress requires accounting for the effects of stress on population dynamics as much as on mutation rate. Our goal here is dual: we show that population dynamics and, in particular, the numbers of cell division are crucial but neglected parameters in the evolvability of a population, and we provide experimental and computational tools and methods to study evolvability under stress, leading to a reassessment of the magnitude and significance of the stress-induced mutagenesis paradigm. [ABSTRACT FROM AUTHOR]

Disease tolerance is a defense strategy against infections that aims at maintaining host health even at high pathogen replication or load. Tolerance mechanisms are currently intensively studied with the long-term goal of exploiting them therapeutically. Because tolerance-based treatment imposes less selective pressure on the pathogen it has been hypothesised to be “evolution-proof”. However, the primary public health goal is to reduce the incidence and mortality associated with a disease. From this perspective, tolerance-based treatment bears the risk of increasing the prevalence of the disease, which may lead to increased mortality. We assessed the promise of tolerance-based treatment strategies using mathematical models. Conventional treatment was implemented as an increased recovery rate, while tolerance-based treatment was assumed to reduce the disease-related mortality of infected hosts without affecting recovery. We investigated the endemic phase of two types of infections: acute and chronic. Additionally, we considered the effect of pathogen resistance against conventional treatment. We show that, for low coverage of tolerance-based treatment, chronic infections can cause even more deaths than without treatment. Overall, we found that conventional treatment always outperforms tolerance-based treatment, even when we allow the emergence of pathogen resistance. Our results cast doubt on the potential benefit of tolerance-based over conventional treatment. Any clinical application of tolerance-based treatment of infectious diseases has to consider the associated detrimental epidemiological feedback. [ABSTRACT FROM AUTHOR]

Bacteria are infected by mobile genetic elements like plasmids and virulent phages, and those infections significantly impact bacterial ecology and evolution. Recent discoveries reveal that some plasmids carry anti-phage immune systems like CRISPR-Cas, suggesting that plasmids may participate in the coevolutionary arms race between virulent phages and bacteria. Intuitively, this seems reasonable as virulent phages kill the plasmid's obligate host. However, the efficiency of CRISPR-Cas systems carried by plasmids can be expected to be lower than those carried by the chromosome due to continuous segregation loss, creating susceptible cells for phage amplification. To evaluate the anti-phage protection efficiency of CRISPR-Cas on plasmids, we develop a stochastic model describing the dynamics of a virulent phage infection against which a conjugative plasmid defends using CRISPR-Cas. We show that CRISPR-Cas on plasmids provides robust protection, except in limited parameter sets. In these cases, high segregation loss favours phage outbreaks by generating a population of defenceless cells on which the phage can evolve and escape CRISPR-Cas immunity. We show that the phage's ability to exploit segregation loss depends strongly on the evolvability of both CRISPR-Cas and the phage itself. [ABSTRACT FROM AUTHOR]

In HIV patients, the set-point viral load (SPVL) is the most widely used predictor of disease severity. Yet SPVL varies over several orders of magnitude between patients. The heritability of SPVL quantifies how much of the variation in SPVL is due to transmissible viral genetics. There is currently no clear consensus on the value of SPVL heritability, as multiple studies have reported apparently discrepant estimates. Here we illustrate that the discrepancies in estimates are most likely due to differences in the estimation methods, rather than the study populations. Importantly, phylogenetic estimates run the risk of being strongly confounded by unrealistic model assumptions. Care must be taken when interpreting and comparing the different estimates to each other. [ABSTRACT FROM AUTHOR]

The sexually transmitted bacterium Neisseria gonorrhoeae has developed resistance to all antibiotic classes that have been used for treatment and strains resistant to multiple antibiotic classes have evolved. In many countries, there is only one antibiotic remaining for empirical N. gonorrhoeae treatment, and antibiotic management to counteract resistance spread is urgently needed. Understanding dynamics and drivers of resistance spread can provide an improved rationale for antibiotic management. In our study, we first used antibiotic resistance surveillance data to estimate the rates at which antibiotic-resistant N. gonorrhoeae spread in two host populations, heterosexual men (HetM) and men who have sex with men (MSM). We found higher rates of spread for MSM (0.86 to 2.38 y−1, mean doubling time: 6 months) compared to HetM (0.24 to 0.86 y−1, mean doubling time: 16 months). We then developed a dynamic transmission model to reproduce the observed dynamics of N. gonorrhoeae transmission in populations of heterosexual men and women (HMW) and MSM. We parameterized the model using sexual behavior data and calibrated it to N. gonorrhoeae prevalence and incidence data. In the model, antibiotic-resistant N. gonorrhoeae spread with a median rate of 0.88 y−1 in HMW and 3.12 y−1 in MSM. These rates correspond to median doubling times of 9 (HMW) and 3 (MSM) months. Assuming no fitness costs, the model shows the difference in the host population’s treatment rate rather than the difference in the number of sexual partners explains the differential spread of resistance. As higher treatment rates result in faster spread of antibiotic resistance, treatment recommendations for N. gonorrhoeae should carefully balance prevention of infection and avoidance of resistance spread. [ABSTRACT FROM AUTHOR]

Set point viral load in HIV patients ranges over several orders of magnitude and is a key determinant of disease progression in HIV. A number of recent studies have reported high heritability of set point viral load implying that viral genetic factors contribute substantially to the overall variation in viral load. The high heritability is surprising given the diversity of host factors associated with controlling viral infection. Here we develop an analytical model that describes the temporal changes of the distribution of set point viral load as a function of heritability. This model shows that high heritability is the most parsimonious explanation for the observed variance of set point viral load. Our results thus not only reinforce the credibility of previous estimates of heritability but also shed new light onto mechanisms of viral pathogenesis. [ABSTRACT FROM AUTHOR]

Quantifying epidemiological dynamics is crucial for understanding and forecasting the spread of an epidemic. The coalescent and the birth-death model are used interchangeably to infer epidemiological parameters from the genealogical relationships of the pathogen population under study, which in turn are inferred from the pathogen genetic sequencing data. To compare the performance of these widely applied models, we performed a simulation study. We simulated phylogenetic trees under the constant rate birth-death model and the coalescent model with a deterministic exponentially growing infected population. For each tree, we re-estimated the epidemiological parameters using both a birth-death and a coalescent based method, implemented as an MCMC procedure in BEAST v2.0. In our analyses that estimate the growth rate of an epidemic based on simulated birth-death trees, the point estimates such as the maximum a posteriori/maximum likelihood estimates are not very different. However, the estimates of uncertainty are very different. The birth-death model had a higher coverage than the coalescent model, i.e. contained the true value in the highest posterior density (HPD) interval more often (2–13% vs. 31–75% error). The coverage of the coalescent decreases with decreasing basic reproductive ratio and increasing sampling probability of infecteds. We hypothesize that the biases in the coalescent are due to the assumption of deterministic rather than stochastic population size changes. Both methods performed reasonably well when analyzing trees simulated under the coalescent. The methods can also identify other key epidemiological parameters as long as one of the parameters is fixed to its true value. In summary, when using genetic data to estimate epidemic dynamics, our results suggest that the birth-death method will be less sensitive to population fluctuations of early outbreaks than the coalescent method that assumes a deterministic exponentially growing infected population. [ABSTRACT FROM AUTHOR]

Antibiotic resistance provides evolutionary rescue for bacterial populations under the threat of extinction through antibiotics. It can arise de novo through mutation in the population, or be obtained from other bacterial populations via the transfer of a resistance-conferring plasmid. We use stochastic modeling methods to establish whether the most likely source of rescue is via a plasmid or via the chromosome, and show that contrary to what is assumed plasmids are not necessarily beneficial locations for resistance genes. Competition at the plasmid level of selection is of great importance-the spread of a resistant plasmid in the population can be slowed or entirely stopped by a nonresistant version of the same plasmid. We suggest that future studies on antibiotic-resistant plasmids should explicitly consider competition at this level of selection. [ABSTRACT FROM AUTHOR]

Abstract: Mobile genetic elements such as plasmids are increasingly becoming thought of as evolutionarily important. Being horizontally transmissible is generally assumed to be beneficial for a gene. Using several simple modelling approaches we show that in fact being horizontally transferable is just as important for fixation as being beneficial to the host, in line with other results. We find fixation probability is approximately , where is the increased (vertical) fitness provided by the gene, and the rate of horizontal transfer when rare. This result comes about because when the gene is rare, almost all individuals in the population are possible recipients of horizontal transfer. The ability to horizontally transfer could thus cause a deleterious gene to become fixed in a population even without hitchhiking. Our findings provide further evidence for the importance and ubiquity of mobile genetic elements, particularly in microorganisms. [Copyright &y& Elsevier]

Abstract: Human sexual networks exhibit a heterogeneous structure where few individuals have many partners and many individuals have few partners. Network theory predicts that the spread of sexually transmitted infections (STI) on such networks should exhibit striking properties (e.g. rapid spread). However, these properties cannot be found in epidemiological data. Current network models typically assume a constant STI transmission risk per partnership, which is unrealistic because it implies that sexual activity is proportional to the number of partners and that individuals have the same activity with each partner. We develop a framework that allows us to weight any sexual network based on biological assumptions. Our results indicate that STI spreading on the resulting weighted networks do not have heterogeneous-related properties, which is consistent with data and earlier studies. [Copyright &y& Elsevier]

Antagonistic coevolution between hosts and parasites can involve rapid fluctuations of genotype frequencies that are known as Red Queen dynamics. Under such dynamics, recombination in the hosts may be advantageous because genetic shuffling can quickly produce disproportionately fit offspring (the Red Queen hypothesis). Previous models investigating these dynamics have assumed rather simple models of genetic interactions between hosts and parasites. Here, we assess the robustness of earlier theoretical predictions about the Red Queen with respect to the underlying host-parasite interactions. To this end, we created large numbers of random interaction matrices, analysed the resulting dynamics through simulation, and ascertained whether recombination was favoured or disfavoured. We observed Red Queen dynamics in many of our simulations provided the interaction matrices exhibited sufficient 'antagonicity'. In agreement with previous studies, strong selection on either hosts or parasites favours selection for increased recombination. However, fast changes in the sign of linkage disequilibrium or epistasis were only infrequently observed and do not appear to be a necessary condition for the Red Queen hypothesis to work. Indeed, recombination was often favoured even though the linkage disequilibrium remained of constant sign throughout the simulations. We conclude that Red Queen-type dynamics involving persistent fluctuations in host and parasite genotype frequencies appear to not be an artefact of specific assumptions about host-parasite fitness interactions, but emerge readily with the general interactions studied here. Our results also indicate that although recombination is often favoured, some of the factors previously thought to be important in this process such as linkage disequilibrium fluctuations need to be reassessed when fitness interactions between hosts and parasites are complex. [ABSTRACT FROM AUTHOR]

The article analyzes dynamic shifts in the HIV-1 quasispecies to estimate the in vivo fitness cost of enfuvirtide resistance. According to the authors, the HIV-1 quasispecies undergoes dynamic changes in response to withdrawal and reinitiation of fusion inhibitor therapy. They add that the relative stability of plasma HIV-1 titers during decay of V38A suggests that factors other than viral fitness likely define viral load set-point in patients with advanced disease.

It is not clear how biological pathways evolve to mediate a certain physiological response and why they show a level of complexity that is generally above the minimum required to achieve such a response. One possibility is that pathway complexity increases due to the nature of evolutionary mechanisms. Here, we analyze this possibility by using mathematical models of biological pathways and evolutionary simulations. Starting with a population of small pathways of three proteins, we let the population evolve with mutations that affect pathway structure through duplication or deletion of existing proteins, deletion or creation of interactions among them, or addition of new proteins. Our simulations show that such mutational events, coupled with a selective pressure, leads to growth of pathways. These results indicate that pathways could be driven toward complexity via simple evolutionary mechanisms and that complexity can arise without any specific selective pressure for it. Furthermore, we find that the level of complexity that pathways evolve toward depends on the selection criteria. In general, we find that final pathway size tends to be lower when pathways evolve under stringent selection criteria. This leads to the counterintuitive conclusion that simple response requirements on a pathway would facilitate its evolution toward higher complexity. [ABSTRACT FROM AUTHOR]

The emergence of drug resistance mutations in human immunodeficiency virus (HIV) has been a major setback in the treatment of infected patients. Besides the high mutation rate, recombination has been conjectured to have an important impact on the emergence of drug resistance. Population genetic theory suggests that in populations limited in size recombination may facilitate the acquisition of beneficial mutations. The viral population in an infected patient may indeed represent such a population limited in size, since current estimates of the effective population size range from 500 to 105. To address the effects of limited population size, we therefore expand a previously described deterministic population genetic model of HIV replication by incorporating the stochastic processes that occur in finite populations of infected cells. Using parameter estimates from the literature, we simulate the evolution of drug-resistant viral strains. The simulations show that recombination has only a minor effect on the rate of acquisition of drug resistance mutations in populations with effective population sizes as small as 1,000, since in these populations, viral strains typically fix beneficial mutations sequentially. However, for intermediate effective population sizes (104 to 105), recombination can accelerate the evolution of drug resistance by up to 25%. Furthermore, a reduction in population size caused by drug therapy can be overcome by a higher viral mutation rate, leading to a faster evolution of drug resistance. [ABSTRACT FROM AUTHOR]

Over the course of infection, the coreceptor usage of the HIV virus changes from a preference for CCR5 to a preference for CXCR4 in ∼50% of infected individuals. The change in coreceptor usage is the result of the complex interaction of the viral population with various cell populations of the immune system. Although many of the molecular processes involved in viral attachment and entry have been resolved, the population dynamical mechanisms leading to the emergence of CXCR4-using HIV variants in some infected individuals are not yet understood. Here, we review various hypotheses that have been proposed to explain the change of HIV coreceptor usage in the course of infection, and conclude that any corroboration or rejection of these hypotheses requires a quantitative analysis of the interaction between the virus and immune cells. [Copyright &y& Elsevier]

The recently discovered mammalian enzymes, APOBEC3G and 3F, induce guanine-to-adenine hypermutation in retroviruses. However, the preference of adenine over guanine in retroviral codon usage is not correlated with the presence or absence of APOBEC3G or its viral inhibitor (Vif), and its pattern does not reflect the biochemical properties of APOBEC3G action. The guanine–adenine bias of retroviruses is thus probably not a result of host-induced mutational pressure, but rather reflects a general predisposition associated with reverse transcription. [Copyright &y& Elsevier]

Abstract: Efficient immune responses against pathogens are frequently characterized by the simultaneous targeting of multiple epitopes. However, it remains unclear how the targeting of multiple epitopes is maintained in the face of competition for antigenic stimulation. Here, we investigate this question by using mathematical models of the population dynamics of a viral pathogen, antigen presentation sites and T-cells. We first show that direct competition for access to antigen presenting sites and indirect competition through killing of the pathogen select for dominance of the T-cell response with the highest affinity for its epitope. We then incorporate in our model that epitopes can become down-modulated following interaction with epitope specific T-cells. We demonstrate that epitope down-modulation leads to differentiation of epitope presentation on antigen presenting sites. This differentiation promotes the coexistence of multiple epitope specific responses. Hence, we propose that the functional relevance of epitope down-modulation may be to enable the persistence of a broad immune response despite competition for antigenic stimulation. [Copyright &y& Elsevier]

Reproductive strategies such as sexual reproduction and recombination that involve the shuffling of parental genomes for the production of offspring are ubiquitous in nature. However, their evolutionary benefit remains unclear. Many theories have identified potential benefits, but progress is hampered by the scarcity of relevant data. One class of theories is based on the assumption that mutations affecting fitness exhibit negative epistasis. Retroviruses recombine frequently and thus provide a unique opportunity to test these theories. Using amino acid sequence data and fitness values from 9466 human immunodeficiency virus 1 (HIV-1) isolates, we find in contrast to these theories strong statistical evidence for a predominance of positive epistasis in HIV-1. [ABSTRACT FROM AUTHOR]

Although limited by a single resource, microbial pop- ulations that grow for long periods in continuous culture(chemostat) frequently evolve stable polymorphisms. These polymorphisms may be maintained by cross-feeding, where one strain partially degrades the primary energy resource and excretes an intermediate that is used as an energy resource by a second strain. It is unclear what selective advantage cross-feeding strains have over a single competitor that completely degrades the primary resource. Here we show that cross-feeding may evolve in microbial populations as a consequence of the following optimization principles: the rate of ATP production is max- imized, the concentration of enzymes of the pathway is minimized, and the concentration of intermediates of the pathway is minimized. [ABSTRACT FROM AUTHOR]

Examines the emergence of minor populations of human immunodeficiency virus type 1 carrying the M184V and L90M mutations in subjects undergoing structured treatment interruptions. Administration of anti-retroviral therapy to research subjects under a four cycles of two-week structured treatment interruption; Detection of drug-resistant variants; Emergence of drug-resistance mutations during HIV-1 replication.

Although the steady-state virus load during HIV-1 infection is remarkably stable within a patient, it displays variation over several orders of magnitude between patients. Despite intensive research, the host and virus factors that are responsible for the observed variation remain poorly understood. Comparison of model predictions with clinical data suggests that most of the variation in steady-state virus load between patients reflects variation of the net rate at which activated CD4 cells are produced. [Copyright &y& Elsevier]

Maturing T cells with a high affinity for self-antigens presented in the thymus are deleted in the process of negative selection. Although the expression of various ‘tissue-specific’ antigens has been described in the thymus, it is still controversial what fraction of all self-antigens induces tolerance by this mechanism. We demonstrate that the limited duration of the negative selection phase imposes a constraint on the number of self-peptides that can be reliably selected against. The analysis supports the theory that negative selection is confined to the subset of peptides produced by dendritic cells. [Copyright &y& Elsevier]

The evolutionary transition from single cells toward multicellular forms of life represents one of the major transitions in the evolution of complex organisms. In this transition, single autonomously reproducing cells became parts of larger reproducing entities that eventually constituted a new unit of selection. The first step in the evolutionary transition to multicellularity likely was the evolution of simple, undifferentiated cell clusters. However, what the selective advantage of such cell clusters may have been remains unclear. Here, we argue that in populations of unicellular organisms with cooperative behavior, clustering may be beneficial by reducing interactions with noncooperative individuals. In support of this hypothesis, we present a set of computer simulations showing that clustering can evolve as a biological, heritable trait for cells that cooperate in the use of external energy resources. Following the evolution of simple cell clusters, further benefits could have arisen from the exchange of resources between cells of a cluster. [ABSTRACT FROM AUTHOR]

The human immunodeficiency virus (HIV) infects a wide range of human cells. Cell entry is mediated through the CD4 receptor and a variety of coreceptors, most importantly the chemokine receptors CCR5 and CXCR4. Some antiretroviral agents selectively inhibit different HIV phenotypes depending on their coreceptor usage. Here, we analyse mathematical models, which describe the in vivo interaction of HIV phenotypes, differing in their coreceptor usage, with two target cell types (naive and memory CD4+ T cells). In particular, we investigate how the selection pressures on CCR5- and CXCR4-using HIV variants change as a result of treatment with coreceptor-specific antiretroviral agents. Our main result is that CXCR4 inhibitors increase the selection pressure in favor of the emergence of CCR5-using variants, thus selecting for coexistence of CXCR4- and CCR5-using variants, whereas CCR5 inhibitors increase the selection pressure against CCR5-using variants, thus selecting against coexistence. These results shed new light on the potential risks and benefits of coreceptor inhibitors. [Copyright &y& Elsevier]

Studies the growth of drug-resistant human immunodeficiency viruses (HIV) during the course of HIV drug therapy. Use of deterministic and stochastic models to investigate the origin of multidrug resistance; Quantification of probabilities that resistant mutants pre-exist and that resistant mutants are generated during therapy.

The authors reflect on the 10 anniversary of the journal "PLOS Computational Biology" in 2015 and importance of computational biology in biological research. Topics discussed include transformation and innovations in the computational biology field, importance of Computational biology in the biological research ranging from molecules to ecosystems, and advances in computational biology due to increase in the availability of experimental and computational data.

SummaryBackgroundIn chronic HIV-1 infection, dynamic equilibrium exists between viral production and clearance. The half-life of free virions can be estimated by inhibiting virion production with antiretroviral agents and modelling the resulting decline in plasma HIV-1 RNA. To define HIV-1 and hepatitis C virus (HCV) dynamics, we used plasma apheresis to increase virion clearance temporarily while leaving virion production unaffected.MethodsPlasma virus loads were measured frequently before, during, and after apheresis in four HIV-1-infected patients, two of whom were also co-infected with HCV. Rates of virion clearance were derived by non-linear least-square fitting of plasma virus load to a model of viral dynamics.FindingsVirion clearance rate constants were 0.0063/min (9.1/day) to 0.025/min (36.0/day; half-life 28-110 min) for HIV-1 and 0.0038/min (5.5/day) to 0.0069/min (9.9/day; half-life 100-182 min) for HCV. These values provided estimates of daily particle production of 9.3 log[sub 10]-10.2 log[sub 10] particles for HIV-1 and 11.6 log[sub 10]-13.0 log[sub 10] particles for HCV.InterpretationOur findings confirm that HIV-1 and HCV are produced and cleared extremely rapidly. New estimates for HIV-1 clearance are up to ten times higher than previous ones, whereas HCV clearance is similar to previous estimates. [ABSTRACT FROM AUTHOR]

Proposes criteria for the evaluation of the population-wide effects of treatment protocols for directly transmitted bacterial infections. Discussion of different usage patterns for single and multiple antibiotic therapy; Mathematical models analyzed; Goal of analyzing the models.

Discusses the understanding of virus population dynamics, role of the immune system in limiting virus abundance, dynamics of viral drug resistance, and the question of whether virus infection can be eliminated from individual patients by drug treatment. Characterization of dynamics of different types of infected cells in HIV-infected patients; Exploration of emergence of resistant virus; Analysis of kinetics of multiple-drug therapy.

Examines viral dynamics using a mathematical model following treatment of human immunodeficiency virus type 1 and hepatitis B virus. Description of basic models for short-term HIV dynamic; Analysis on reverse transcriptase inhibitors actions on blocking infections growth; Protease inhibitors and combined drug treatment; Pharmacological delay of drug application.

Reports on the usage of mathematical models in the measuring of the HIV-1 infection. In-depth look at the dynamics of drug-resistance; Role of reverse-transcriptase inhibitors in the prevention of the reverse transcription of viral genomic RNA; Details on HIV replication kinetics.

Reports the use of rhesus macaques in studying lymphocyte turnover in an effort to better understand the mechanism of cell killing and the extent of lymphocyte regeneration in human immunodeficiency virus infection. Details of the experiments; How simian immunodeficiency virus (SIV) was associated with a heightened lymphocyte turnover; Questions raised from the results.

Bacteria have adaptive immunity against viruses (phages) in the form of CRISPR-Cas immune systems. Currently, 6 types of CRISPR-Cas systems are known and the molecular study of three of these has revealed important molecular differences. It is unknown if and how these molecular differences change the outcome of phage infection and the evolutionary pressure the CRISPR-Cas systems faces. To determine the importance of these molecular differences, we model a phage outbreak entering a population defending exclusively with a type I/II or a type III CRISPR-Cas system. We show that for type III CRISPR-Cas systems, rapid phage extinction is driven by the probability to acquire at least one resistance spacer. However, for type I/II CRISPR-Cas systems, rapid phage extinction is characterized by an a threshold-like behaviour: any acquisition probability below this threshold leads to phage survival whereas any acquisition probability above it, results in phage extinction. We also show that in the absence of autoimmunity, high acquisition rates evolve. However, when CRISPR-Cas systems are prone to autoimmunity, intermediate levels of acquisition are optimal during a phage outbreak. As we predict an optimal probability of spacer acquisition 2 factors of magnitude above the one that has been measured, we discuss the origin of such a discrepancy. Finally, we show that in a biologically relevant parameter range, a type III CRISPR-Cas system can outcompete a type I/II CRISPR-Cas system with a slightly higher probability of acquisition. Author summary: CRISPR-Cas systems are adaptive immune systems that use a complex 3-step molecular mechanism to defend prokaryotes against phages. Viral infections of populations defending themselves with CRISPR-Cas can result in rapid phage extinction or in medium-term phage maintenance. To investigate what controls the fate of the phage population, we use mathematical modeling of type I/II and type III CRISPR-Cas systems, and show that two parameters control the epidemiological short-term outcome: the type of CRISPR-Cas systems and CRISPR-Cas probability of resistance acquisition. Furthermore, the latter impacts host fitness. From this, we derive that 1) for both types, CRISPR-Cas acquisition probability is a key predictor of the efficiency and of the cost of a CRISPR-Cas system, 2) during an outbreak, there is an optimal probability of resistance acquisition balancing the cost of autoimmunity and immune efficiency and 3) type I/II CRISPR-Cas systems are likely to evolve higher acquisition probability than type III. [ABSTRACT FROM AUTHOR]

• The presence of an immunological barrier in cancer- immune system interaction (CISI) is consistent with the bistability patterns in that system. • In CISI models, bistability patterns are consistent with immunosuppressive effects dominating immunoproliferative effects. • Bistability could be harnessed to devise effective combination immunotherapy approaches. Cancer immunotherapies rely on how interactions between cancer and immune system cells are constituted. The more essential to the emergence of the dynamical behavior of cancer growth these interactions are, the more effectively they may be used as mechanisms for interventions. Mathematical modeling can help unearth such connections, and help explain how they shape the dynamics of cancer growth. Here, we explored whether there exist simple, consistent properties of cancer-immune system interaction (CISI) models that might be harnessed to devise effective immunotherapy approaches. We did this for a family of three related models of increasing complexity. To this end, we developed a base model of CISI, which captures some essential features of the more complex models built on it. We find that the base model and its derivates can plausibly reproduce biological behavior that is consistent with the notion of an immunological barrier. This behavior is also in accord with situations in which the suppressive effects exerted by cancer cells on immune cells dominate their proliferative effects. Under these circumstances, the model family may display a pattern of bistability , where two distinct, stable states (a cancer-free, and a full-grown cancer state) are possible. Increasing the effectiveness of immune-caused cancer cell killing may remove the basis for bistability, and abruptly tip the dynamics of the system into a cancer-free state. Additionally, in combination with the administration of immune effector cells, modifications in cancer cell killing may be harnessed for immunotherapy without the need for resolving the bistability. We use these ideas to test immunotherapeutic interventions in silico in a stochastic version of the base model. This bistability-reliant approach to cancer interventions might offer advantages over those that comprise gradual declines in cancer cell numbers. [ABSTRACT FROM AUTHOR]

Given the considerable challenges to the rapid development of an effective vaccine against influenza, antiviral agents will play an important role as a first-line defense if a new pandemic occurs. The large-scale use of drugs for chemoprophylaxis and treatment will impose strong selection for the evolution of drug-resistant strains. The ensuing transmission of those strains could substantially limit the effectiveness of the drugs as a first-line defense. Summarizing recent data on the rate at which the treatment of influenza infection generates resistance de novo and on the transmission fitness of resistant virus, we discuss possible implications for the epidemiological spread of drug resistance in the context of an established population dynamic model. [ABSTRACT FROM AUTHOR]

Reports on a virus model which suggests that viral mutation rate is optimized in an evolutionary trade-off between adaptability and genomic integrity. Requirement of genetic variation in evolution by natural selection; Incorporation of the immune response in quasispecies models; Calculation of the optimal genomic mutation rate.

Background: Next-generation sequencing has considerably increased the number of genomes available in the public domain. However, efforts to use these genomes for surveillance of antimicrobial resistance have thus far been limited and geographically heterogeneous. We inferred global resistance trends in Escherichia coli in food animals using genomes from public databases.Methods: We retrieved 7632 E. coli genomes from public databases (NCBI, PATRIC and EnteroBase) and screened for antimicrobial resistance genes (ARGs) using ResFinder. Selection bias towards resistance, virulence or specific strains was accounted for by screening BioProject descriptions. Temporal trends for MDR, resistance to antimicrobial classes and ARG prevalence were inferred using generalized linear models for all genomes, including those not subjected to selection bias.Results: MDR increased by 1.6 times between 1980 and 2018, as genomes carried, on average, ARGs conferring resistance to 2.65 antimicrobials in swine, 2.22 in poultry and 1.58 in bovines. Highest resistance levels were observed for tetracyclines (42.2%-69.1%), penicillins (19.4%-47.5%) and streptomycin (28.6%-56.6%). Resistance trends were consistent after accounting for selection bias, although lower mean absolute resistance estimates were associated with genomes not subjected to selection bias (difference of 3.16%±3.58% across years, hosts and antimicrobial classes). We observed an increase in extended-spectrum cephalosporin ARG blaCMY-2 and a progressive substitution of tetB by tetA. Estimates of resistance prevalence inferred from genomes in the public domain were in good agreement with reports from systematic phenotypic surveillance.Conclusions: Our analysis illustrates the potential of using the growing volume of genomes in public databases to track AMR trends globally. [ABSTRACT FROM AUTHOR]

The test-trace-isolate-quarantine (TTIQ) strategy, where confirmed-positive pathogen carriers are isolated from the community and their recent close contacts are identified and pre-emptively quarantined, is used to break chains of transmission during a disease outbreak. The protocol is frequently followed after an individual presents with disease symptoms, at which point they will be tested for the pathogen. This TTIQ strategy, along with hygiene and social distancing measures, make up the non-pharmaceutical interventions that are utilised to suppress the ongoing COVID-19 pandemic. Here we develop a tractable mathematical model of disease transmission and the TTIQ intervention to quantify how the probability of detecting and isolating a case following symptom onset, the fraction of contacts that are identified and quarantined, and the delays inherent to these processes impact epidemic growth. In the model, the timing of disease transmission and symptom onset, as well as the frequency of asymptomatic cases, is based on empirical distributions of SARS-CoV-2 infection dynamics, while the isolation of confirmed cases and quarantine of their contacts is implemented by truncating their respective infectious periods. We find that a successful TTIQ strategy requires intensive testing: the majority of transmission is prevented by isolating symptomatic individuals and doing so in a short amount of time. Despite the lesser impact, additional contact tracing and quarantine increases the parameter space in which an epidemic is controllable and is necessary to control epidemics with a high reproductive number. TTIQ could remain an important intervention for the foreseeable future of the COVID-19 pandemic due to slow vaccine rollout and highly-transmissible variants with the potential for vaccine escape. Our results can be used to assess how TTIQ can be improved and optimised, and the methodology represents an improvement over previous quantification methods that is applicable to future epidemic scenarios. [ABSTRACT FROM AUTHOR]

The evolutionary pressures that determine the location (chromosomal or plasmid‐borne) of bacterial genes are not fully understood. We investigate these pressures through mathematical modeling in the context of antibiotic resistance, which is often found on plasmids. Our central finding is that gene location is under positive frequency‐dependent selection: the higher the frequency of one form of resistance compared to the other, the higher its relative fitness. This can keep moderately beneficial genes on plasmids, despite occasional plasmid loss. For these genes, positive frequency dependence leads to a priority effect: whichever form is acquired first—through either mutation or horizontal gene transfer—has time to increase in frequency and thus becomes difficult to displace. Higher rates of horizontal transfer of plasmid‐borne than chromosomal genes therefore predict moderately beneficial genes will be found on plasmids. Gene flow between plasmid and chromosome allows chromosomal forms to arise, but positive frequency‐dependent selection prevents these from establishing. Further modeling shows that this effect is particularly pronounced when genes are shared across a large number of species, suggesting that antibiotic resistance genes are often found on plasmids because they are moderately beneficial across many species. We also revisit previous theoretical work—relating to the role of local adaptation in explaining gene location and to plasmid persistence—in light of our findings. [ABSTRACT FROM AUTHOR]

Large-scale serological testing in the population is essential to determine the true extent of the current SARS-CoV-2 pandemic. Serological tests measure antibody responses against pathogens and use predefined cutoff levels that dichotomize the quantitative test measures into sero-positives and negatives and use this as a proxy for past infection. With the imperfect assays that are currently available to test for past SARS-CoV-2 infection, the fraction of seropositive individuals in serosurveys is a biased estimator of the cumulative incidence and is usually corrected to account for the sensitivity and specificity. Here we use an inference method—referred to as mixture-model approach—for the estimation of the cumulative incidence that does not require to define cutoffs by integrating the quantitative test measures directly into the statistical inference procedure. We confirm that the mixture model outperforms the methods based on cutoffs, leading to less bias and error in estimates of the cumulative incidence. We illustrate how the mixture model can be used to optimize the design of serosurveys with imperfect serological tests. We also provide guidance on the number of control and case sera that are required to quantify the test's ambiguity sufficiently to enable the reliable estimation of the cumulative incidence. Lastly, we show how this approach can be used to estimate the cumulative incidence of classes of infections with an unknown distribution of quantitative test measures. This is a very promising application of the mixture-model approach that could identify the elusive fraction of asymptomatic SARS-CoV-2 infections. An R-package implementing the inference methods used in this paper is provided. Our study advocates using serological tests without cutoffs, especially if they are used to determine parameters characterizing populations rather than individuals. This approach circumvents some of the shortcomings of cutoff-based methods at exactly the low cumulative incidence levels and test accuracies that we are currently facing in SARS-CoV-2 serosurveys. Author summary: As other pathogens, SARS-CoV-2 elicits antibody responses in infected people that can be detected in their blood serum as early as a week after the infection until long after recovery. The presence of SARS-CoV-2 specific antibodies can therefore be used as a marker of past infection, and the prevalence of seropositive people, i.e. people with specific antibodies, is a key measure to determine the extent of the SARS-CoV-2 pandemic. The serological tests, however, are usually not perfect, yielding false positive and false negative results. Here we exploit an approach that refrains from classifying people as seropositive or negative, but rather compares the antibody level of an individual to that of confirmed cases and controls. This approach leads to more reliable estimates of cumulative incidence, especially for the low prevalence and low test accuracies that we face during the current SARS-CoV-2 pandemic. We also show how this approach can be extended to infer the presence of specific types of cases that have not been used for validating the test, such as people that underwent a mild or asymptomatic infection. [ABSTRACT FROM AUTHOR]

Background: Accurate estimation of the effective reproductive number ( R e ) of epidemic outbreaks is of central relevance to public health policy and decision making. We present estimateR, an R package for the estimation of the reproductive number through time from delayed observations of infection events. Such delayed observations include confirmed cases, hospitalizations or deaths. The package implements the methodology of Huisman et al. but modularizes the R e estimation procedure to allow easy implementation of new alternatives to the currently available methods. Users can tailor their analyses according to their particular use case by choosing among implemented options. Results: The estimateR R package allows users to estimate the effective reproductive number of an epidemic outbreak based on observed cases, hospitalization, death or any other type of event documenting past infections, in a fast and timely fashion. We validated the implementation with a simulation study: estimateR yielded estimates comparable to alternative publicly available methods while being around two orders of magnitude faster. We then applied estimateR to empirical case-confirmation incidence data for COVID-19 in nine countries and for dengue fever in Brazil; in parallel, estimateR is already being applied (i) to SARS-CoV-2 measurements in wastewater data and (ii) to study influenza transmission based on wastewater and clinical data in other studies. In summary, this R package provides a fast and flexible implementation to estimate the effective reproductive number for various diseases and datasets. Conclusions: The estimateR R package is a modular and extendable tool designed for outbreak surveillance and retrospective outbreak investigation. It extends the method developed for COVID-19 by Huisman et al. and makes it available for a variety of pathogens, outbreak scenarios, and observation types. Estimates obtained with estimateR can be interpreted directly or used to inform more complex epidemic models (e.g. for forecasting) on the value of R e . [ABSTRACT FROM AUTHOR]

ABSTRACT: We propose an evolutionary perspective to classify and characterize the diverse systems of adaptive immunity that have been discovered across all major domains of life. We put forward a new function‐based classification according to the way information is acquired by the immune systems: Darwinian immunity (currently known from, but not necessarily limited to, vertebrates) relies on the Darwinian process of clonal selection to ‘learn’ by cumulative trial‐and‐error feedback; Lamarckian immunity uses templated targeting (guided adaptation) to internalize heritable information on potential threats; finally, shotgun immunity operates through somatic mechanisms of variable targeting without feedback. We argue that the origin of Darwinian (but not Lamarckian or shotgun) immunity represents a radical innovation in the evolution of individuality and complexity, and propose to add it to the list of major evolutionary transitions. While transitions to higher‐level units entail the suppression of selection at lower levels, Darwinian immunity re‐opens cell‐level selection within the multicellular organism, under the control of mechanisms that direct, rather than suppress, cell‐level evolution for the benefit of the individual. From a conceptual point of view, the origin of Darwinian immunity can be regarded as the most radical transition in the history of life, in which evolution by natural selection has literally re‐invented itself. Furthermore, the combination of clonal selection and somatic receptor diversity enabled a transition from limited to practically unlimited capacity to store information about the antigenic environment. The origin of Darwinian immunity therefore comprises both a transition in individuality and the emergence of a new information system – the two hallmarks of major evolutionary transitions. Finally, we present an evolutionary scenario for the origin of Darwinian immunity in vertebrates. We propose a revival of the concept of the ‘Big Bang’ of vertebrate immunity, arguing that its origin involved a ‘difficult’ (i.e. low‐probability) evolutionary transition that might have occurred only once, in a common ancestor of all vertebrates. In contrast to the original concept, we argue that the limiting innovation was not the generation of somatic diversity, but the regulatory circuitry needed for the safe operation of amplifiable immune responses with somatically acquired targeting. Regulatory complexity increased abruptly by genomic duplications at the root of the vertebrate lineage, creating a rare opportunity to establish such circuitry. We discuss the selection forces that might have acted at the origin of the transition, and in the subsequent stepwise evolution leading to the modern immune systems of extant vertebrates. [ABSTRACT FROM AUTHOR]

Host population structure has a major influence on epidemiological dynamics. However, in particular for sexually transmitted diseases, quantitative data on population contact structure are hard to obtain. Here, we introduce a new method that quantifies host population structure based on phylogenetic trees, which are obtained from pathogen genetic sequence data. Our method is based on a maximum-likelihood framework and uses a multi-type branching process, under which each host is assigned to a type (subpopulation). In a simulation study, we show that our method produces accurate parameter estimates for phylogenetic trees in which each tip is assigned to a type, as well for phylogenetic trees in which the type of the tip is unknown. We apply the method to a Latvian HIV-1 dataset, quantifying the impact of the intravenous drug user epidemic on the heterosexual epidemic (known tip states), and identifying superspreader dynamics within the men-having-sex-with-men epidemic (unknown tip states). [ABSTRACT FROM AUTHOR]