Imperfect drug penetration leads to spatial monotherapy and rapid evolution of multidrug resistance

Joint effects about antibiotics combined using with antibiotics or phytochemicals on Aeromonas hydrophila

Aeromonas hydrophila is highly prevalent in aquaculture animals and aquaculture environment. Due to the abuse of antibiotics, A. hydrophila can change the antibiotic resistance spectrum directly and affect human health indirectly. The use of combined drugs replacement therapy and the long-term coexistence with drug-resistant bacteria are the reality that human beings have to face in dealing with the problem of antibiotic resistance in the future. This study showed the characteristics and trends through abundant results of combined effects related with the combinations of antibiotic and the combinations of antibiotic and phytochemical on A. hydrophila, and revealed the antagonism probability of combinations of antibiotic and phytochemical is significantly higher than that of the combinations of antibiotic. Meanwhile, the combinations of antibiotic and phytochemical could protect the host cells which also achieved the same effectiveness as combination of antibiotics, and the enrichment pathway was proved to be relatively discrete. In addition, the possible mechanism about the reverse "U" shape of the combined effect curve on wild/antibiotic-resistant bacteria was clarified, and it was confirmed that the antagonism for the combinations of antibiotic and phytochemical might has the significance in inhibiting the evolution of bacterial resistance mutations. This study was aims to provide theoretical basis and some clues for the antibiotic resistance control associated with A. hydrophila.

Modeling spatial evolution of multi-drug resistance under drug environmental gradients

Multi-drug combinations to treat bacterial populations are at the forefront of approaches for infection control and prevention of antibiotic resistance. Although the evolution of antibiotic resistance has been theoretically studied with mathematical population dynamics models, extensions to spatial dynamics remain rare in the literature, including in particular spatial evolution of multi-drug resistance. In this study, we propose a reaction-diffusion system that describes the multi-drug evolution of bacteria based on a drug-concentration rescaling approach. We show how the resistance to drugs in space, and the consequent adaptation of growth rate, is governed by a Price equation with diffusion, integrating features of drug interactions and collateral resistances or sensitivities to the drugs. We study spatial versions of the model where the distribution of drugs is homogeneous across space, and where the drugs vary environmentally in a piecewise-constant, linear and nonlinear manner. Although in many evolution models, per capita growth rate is a natural surrogate for fitness, in spatially-extended, potentially heterogeneous habitats, fitness is an emergent property that potentially reflects additional complexities, from boundary conditions to the specific spatial variation of growth rates. Applying concepts from perturbation theory and reaction-diffusion equations, we propose an analytical metric for characterization of average mutant fitness in the spatial system based on the principal eigenvalue of our linear problem, λ1. This enables an accurate translation from drug spatial gradients and mutant antibiotic susceptibility traits to the relative advantage of each mutant across the environment. Our approach allows one to predict the precise outcomes of selection among mutants over space, ultimately from comparing their λ1 values, which encode a critical interplay between growth functions, movement traits, habitat size and boundary conditions. Such mathematical understanding opens new avenues for multi-drug therapeutic optimization.

Building pyramids against the evolutionary emergence of pathogens

Mutations allowing pathogens to escape host immunity promote the spread of infectious diseases in heterogeneous host populations and can lead to major epidemics. Understanding the conditions that slow down this evolution is key for the development of durable control strategies against pathogens. Here, we use theory and experiments to compare the efficacy of three strategies for the deployment of resistance: (i) a mixing strategy where the host population contains two single-resistant genotypes, (ii) a pyramiding strategy where the host carries a double-resistant genotype, (iii) a combining strategy where the host population is a mix of a single-resistant genotype and a double-resistant genotype. First, we use evolutionary epidemiology theory to clarify the interplay between demographic stochasticity and evolutionary dynamics to show that the pyramiding strategy always yields lower probability of evolutionary emergence. Second, we test experimentally these predictions with the introduction of bacteriophages into bacterial populations where we manipulated the diversity and the depth of immunity using a Clustered Regularly Interspaced Short Palindromic Repeats-CRISPR associated (CRISPR-Cas) system. These biological assays confirm that pyramiding multiple defences into the same host genotype and avoiding combination with single-defence genotypes is a robust way to reduce pathogen evolutionary emergence. The experimental validation of these theoretical recommendations has practical implications in various areas, including for the optimal deployment of resistance varieties in agriculture and for the design of durable vaccination strategies.

Antibiotic resistance: a global crisis, problems and solutions

Healthy state is priority in today's world which can be achieved using effective medicines. But due to overuse and misuse of antibiotics, a menace of resistance has increased in pathogenic microbes. World Health Organization (WHO) has announced ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp.) as the top priority pathogens as these have developed resistance against certain antibiotics. To combat such a global issue, it is utmost important to identify novel therapeutic strategies/agents as an alternate to such antibiotics. To name certain antibiotic adjuvants including: inhibitors of beta-lactamase, efflux pumps and permeabilizers for outer membrane can potentially solve the antibiotic resistance problems. In this regard, inhibitors of lytic domain of lytic transglycosylases provide a novel way to not only act as an alternate to antibiotics but also capable of restoring the efficiency of previously resistant antibiotics. Further, use of bacteriophages is another promising strategy to deal with antibiotic resistant pathogens. Taking in consideration the alternatives of antibiotics, a green synthesis nanoparticle-based therapy exemplifies a good option to combat microbial resistance. As horizontal gene transfer (HGT) in bacteria facilitates the evolution of new resistance strains, therefore identifying the mechanism of resistance and development of inhibitors against it can be a novel approach to combat such problems. In our perspective, host-directed therapy (HDT) represents another promising strategy in combating antimicrobial resistance (AMR). This approach involves targeting specific factors within host cells that pathogens rely on for their survival, either through replication or persistence. As many new drugs are under clinical trials it is advisable that more clinical data and antimicrobial stewardship programs should be conducted to fully assess the clinical efficacy and safety of new therapeutic agents.

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.

Long term management of people with post-tuberculosis lung disease

Post-tuberculosis lung disease (PTLD) is emerging as a significant area of global interest. As the number of patients surviving tuberculosis (TB) increases, the subsequent long-term repercussions have drawn increased attention due to their profound clinical and socioeconomic impacts. A primary obstacle to its comprehensive study has been its marked heterogeneity. The disease presents a spectrum of clinical manifestations which encompass tracheobronchial stenosis, bronchiectasis, granulomas with fibrosis, cavitation with associated aspergillosis, chronic pleural diseases, and small airway diseases-all persistent consequences of PTLD. The spectrum of symptoms a patient may experience varies based on the severity of the initial infection and the efficacy of the treatment received. As a result, the long-term management of PTLD necessitates a detailed and specific approach, addressing each manifestation individually-a tailored strategy. In the immediate aftermath (0-12 months after anti-TB chemotherapy), there should be an emphasis on monitoring for relapse, tracheobronchial stenosis, and smoking cessation. Subsequent management should focus on addressing hemoptysis, managing infection including aspergillosis, and TB-associated chronic obstructive pulmonary disease or restrictive lung function. There remains a vast expanse of knowledge to be discovered in PTLD. This review emphasizes the pressing need for comprehensive, consolidated guidelines for management of patients with PTLD.

Modeling spatial evolution of multi-drug resistance under drug environmental gradients

Multi-drug combinations to treat bacterial populations are at the forefront of approaches for infection control and prevention of antibiotic resistance. Although the evolution of antibiotic resistance has been theoretically studied with mathematical population dynamics models, extensions to spatial dynamics remain rare in the literature, including in particular spatial evolution of multi-drug resistance. In this study, we propose a reaction-diffusion system that describes the multi-drug evolution of bacteria, based on a rescaling approach (Gjini and Wood, 2021). We show how the resistance to drugs in space, and the consequent adaptation of growth rate is governed by a Price equation with diffusion. The covariance terms in this equation integrate features of drug interactions and collateral resistances or sensitivities to the drugs. We study spatial versions of the model where the distribution of drugs is homogeneous across space, and where the drugs vary environmentally in a piecewise-constant, linear and nonlinear manner. Applying concepts from perturbation theory and reaction-diffusion equations, we propose an analytical characterization of average mutant fitness in the spatial system based on the principal eigenvalue of our linear problem. This enables an accurate translation from drug spatial gradients and mutant antibiotic susceptibility traits, to the relative advantage of each mutant across the environment. Such a mathematical understanding allows to predict the precise outcomes of selection over space, ultimately from the fundamental balance between growth and movement traits, and their diversity in a population.

Cross-feeding affects the target of resistance evolution to an antifungal drug

Pathogenic fungi are a cause of growing concern. Developing an efficient and safe antifungal is challenging because of the similar biological properties of fungal and host cells. Consequently, there is an urgent need to better understand the mechanisms underlying antifungal resistance to prolong the efficacy of current molecules. A major step in this direction would be to be able to predict or even prevent the acquisition of resistance. We leverage the power of experimental evolution to quantify the diversity of paths to resistance to the antifungal 5-fluorocytosine (5-FC), commercially known as flucytosine. We generated hundreds of independent 5-FC resistant mutants derived from two genetic backgrounds from wild isolates of Saccharomyces cerevisiae. Through automated pin-spotting, whole-genome and amplicon sequencing, we identified the most likely causes of resistance for most strains. Approximately a third of all resistant mutants evolved resistance through a pleiotropic drug response, a potentially novel mechanism in response to 5-FC, marked by cross-resistance to fluconazole. These cross-resistant mutants are characterized by a loss of respiration and a strong tradeoff in drug-free media. For the majority of the remaining two thirds, resistance was acquired through loss-of-function mutations in FUR1, which encodes an important enzyme in the metabolism of 5-FC. We describe conditions in which mutations affecting this particular step of the metabolic pathway are favored over known resistance mutations affecting a step upstream, such as the well-known target cytosine deaminase encoded by FCY1. This observation suggests that ecological interactions may dictate the identity of resistance hotspots.

Assisted percolation of slow-spreading mutants in heterogeneous environments

Environmental heterogeneity can drive genetic heterogeneity in expanding populations; mutant strains may emerge that trade overall growth rate for an improved ability to survive in patches that are hostile to the wild type. This evolutionary dynamic is of practical importance when seeking to prevent the emergence of damaging traits. We show that a subcritical slow-spreading mutant can attain dominance even when the density of patches is below their percolation threshold and predict this transition using geometrical arguments. This work demonstrates a phenomenon of "assisted percolation", where one subcritical process assists another to achieve supercriticality.

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.

Higher plasma interleukin - 6 levels are associated with lung cavitation in drug-resistant tuberculosis

BACKGROUND

Lung cavitation is associated with heightened TB transmission and poor treatment outcomes. This study aimed to determine the relationship between systemic inflammation and lung cavitation in drug-resistant TB patients with and without HIV co-infection.

METHODS

Plasma samples were obtained from 128 participants from the CAPRISA 020 Individualized M(X)drug-resistant TB Treatment Strategy Study (InDEX) prior to treatment initiation. Lung cavitation was present in 61 of the 128 drug-resistant TB patients with 93 being co-infected with HIV. The plasma cytokine and chemokine levels were measured using the 27-Plex Human Cytokine immunoassay. Modified Poisson regression models were used to determine the association between plasma cytokine/chemokine expression and lung cavitation in individuals with drug-resistant TB.

RESULTS

Higher Interleukin-6 plasma levels (adjusted risk ratio [aRR] 1.405, 95% confidence interval [CI] 1.079-1.829, p = 0.011) were associated with a higher risk of lung cavitation in the multivariable model adjusting for age, sex, body mass index, HIV status, smoking and previous history of TB. Smoking was associated with an increased risk of lung cavitation (aRR 1.784, 95% CI 1.167-2.729, p = 0.008). An HIV positive status and a higher body mass index, were associated with reduced risk of lung cavitation (aRR 0.537, 95% CI 0.371-0.775, p = 0.001 and aRR 0.927, 95% CI 0.874-0.983, p = 0.012 respectively).

CONCLUSION

High plasma interleukin-6 levels are associated with an increased risk of cavitary TB highlighting the role of interleukin-6 in the immunopathology of drug-resistant TB.

Isolation and characterization of a novel mycobacteriophage Kashi-VT1 infecting Mycobacterium species

Mycobacteriophages are viruses that infect members of genus Mycobacterium. Because of the rise in antibiotic resistance in mycobacterial diseases such as tuberculosis, mycobacteriophages have received renewed attention as alternative therapeutic agents. Mycobacteriophages are highly diverse, and, on the basis of their genome sequences, they are grouped into 30 clusters and 10 singletons. In this article, we have described the isolation and characterization of a novel mycobacteriophage Kashi-VT1 (KVT1) infecting Mycobacterium >smegmatis mc2 155 (M. smegmatis) and Mycobacterium fortuitum isolated from Varanasi, India. KVT1 is a cluster K1 temperate phage that belongs to Siphoviridae family as visualized in transmission electron microscopy. The phage genome is 61,010 base pairs with 66.5% Guanine/Cytosine (GC) content, encoding 101 putative open reading frames. The KVT1 genome encodes an immunity repressor, a tyrosine integrase, and an excise protein, which are the characteristics of temperate phages. It also contains genes encoding holin, lysin A, and lysin B involved in host cell lysis. The one-step growth curve demonstrated that KVT1 has a latency time of 90 min and an average burst size of 101 phage particles per infected cell. It can withstand a temperature of up to 45°C and has a maximum viability between pH 8 and 9. Some mycobacteriophages from cluster K are known to infect the pathogenic Mycobacterium tuberculosis (M. tuberculosis); hence, KVT1 holds potential for the phage therapy against tuberculosis, and it can also be engineered to convert into an exclusively lytic phage.

Advances in Lipid-Based Codelivery Systems for Cancer and Inflammatory Diseases

Combination therapy targeting multiple therapeutic targets is a favorable strategy to achieve better therapeutic outcomes in cancer and inflammatory diseases. Codelivery is a subfield of drug delivery that aims to achieve combined delivery of diverse therapeutic cargoes within the same delivery system, thereby ensuring delivery to the same site and providing an opportunity to tailor the release kinetics as desired. Among the wide range of materials being investigated in the design of codelivery systems, lipids have stood out on account of their low toxicity, biocompatibility, and ease of formulation scale-up. This review highlights the advances of the last decade in lipid-based codelivery systems focusing on the codelivery of drug-drug, drug-nucleic acid, nucleic acid-nucleic acid, and protein therapeutic-based combinations for targeted therapy in cancer and inflammatory diseases.

Intracellular Experimental Evolution of Francisella tularensis Subsp. holarctica Live Vaccine Strain (LVS) to Antimicrobial Resistance

In vitro experimental evolution has complemented clinical studies as an excellent tool to identify genetic changes responsible for the de novo evolution of antimicrobial resistance. However, the in vivo context for adaptation contributes to the success of particular evolutionary trajectories, especially in intracellular niches where the adaptive landscape of virulence and resistance are strongly coupled. In this work, we designed an ex vivo evolution approach to identify evolutionary trajectories responsible for antibiotic resistance in the Live Vaccine Strain (LVS) of Francisella tularensis subsp. holarctica while being passaged to increasing ciprofloxacin (CIP) and doxycycline (DOX) concentrations within macrophages. Overall, adaptation within macrophages advanced much slower when compared to previous in vitro evolution studies reflecting a limiting capacity for the expansion of adaptive mutations within the macrophage. Longitudinal genomic analysis identified resistance conferring gyrase mutations outside the Quinolone Resistance Determining Region. Strikingly, FupA/B mutations that are uniquely associated with in vitro CIP resistance in Francisella were not observed ex vivo, reflecting the coupling of intracellular survival and resistance during intracellular adaptation. To our knowledge, this is the first experimental study demonstrating the ability to conduct experimental evolution to antimicrobial resistance within macrophages. The results provide evidence of differences in mutational profiles of populations adapted to the same antibiotic in different environments/cellular compartments and underscore the significance of host mediated stress during resistance evolution.

Inflammation-mediated tissue damage in pulmonary tuberculosis and host-directed therapeutic strategies

Treatment of tuberculosis (TB) involves the administration of anti-mycobacterial drugs for several months. The emergence of drug-resistant strains of Mycobacterium tuberculosis (Mtb, the causative agent) together with increased disease severity in people with co-morbidities such as diabetes mellitus and HIV have hampered efforts to reduce case fatality. In severe disease, TB pathology is largely attributable to over-exuberant host immune responses targeted at controlling bacterial replication. Non-resolving inflammation driven by host pro-inflammatory mediators in response to high bacterial load leads to pulmonary pathology including cavitation and fibrosis. The need to improve clinical outcomes and reduce treatment times has led to a two-pronged approach involving the development of novel antimicrobials as well as host-directed therapies (HDT) that favourably modulate immune responses to Mtb. HDT strategies incorporate aspects of immune modulation aimed at downregulating non-productive inflammatory responses and augmenting antimicrobial effector mechanisms to minimise pulmonary pathology and accelerate symptom resolution. HDT in combination with existing antimycobacterial agents offers a potentially promising strategy to improve the long-term outcome for TB patients. In this review, we describe components of the host immune response that contribute to inflammation and tissue damage in pulmonary TB, including cytokines, matrix metalloproteinases, lipid mediators, and neutrophil extracellular traps. We then proceed to review HDT directed at these pathways.

High concordance in plasma and CSF HIV-1 drug resistance mutations despite high cases of CSF viral escape in individuals with HIV-associated cryptococcal meningitis in Botswana

OBJECTIVES

We compared the patterns of HIV-1 drug resistance mutations between the CSF and plasma of individuals with HIV-associated cryptococcal meningitis.

METHODS

This is a cross-sectional study of archived CSF and plasma samples collected from ART-exposed participants recruited in the Phase 3 AmBisome Therapy Induction Optimisation randomized controlled trial (ISRCTN72509687) conducted in Botswana between 2018 and 2021. HIV-1 RT and protease genes were genotyped using next-generation sequencing and HIV-1 drug resistance mutations were compared between the CSF and plasma compartments stratified by thresholds of ≥20% and <20%.

RESULTS

Overall, 66.7% (16/24) of participants had at least one HIV-1 drug resistance mutation in the CSF and/or plasma. A total of 15/22 (68.2%) participants had HIV-1 drug resistance mutations at ≥20% threshold in the plasma and of those, 11 (73.3%) had been on ART longer than 6 months. HIV-1 drug resistance mutations were highly concordant between the CSF and plasma at ≥20% threshold despite a substantial number of individuals experiencing CSF viral escape and with only 54.5% with CSF WBC count ≥20 cells/mm3. Minority HIV-1 drug resistance mutations were detected in 20.8% (5/24) of participants. There were no mutations in the CSF that were not detected in the plasma.

CONCLUSIONS

There was high concordance in HIV-1 drug resistance mutations in the CSF and plasma, suggesting intercompartmental mixing and possibly a lack of compartmentalization. Some individuals harboured minority HIV-1 drug resistance mutations, demonstrating the need to employ more sensitive genotyping methods such as next-generation sequencing for the detection of low-abundance mutations.

Synergistic cytotoxicity of perifosine and ABT-737 to colon cancer cells

An acidic environment and hypoxia within the tumour are hallmarks of cancer that contribute to cell resistance to therapy. Deregulation of the PI3K/Akt pathway is common in colon cancer. Numerous Akt-targeted therapies are being developed, the activity of Akt-inhibitors is, however, strongly pH-dependent. Combination therapy thus represents an opportunity to increase their efficacy. In this study, the cytotoxicity of the Akt inhibitor perifosine and the Bcl-2/Bcl-xL inhibitor ABT-737 was tested in colon cancer HT-29 and HCT-116 cells cultured in monolayer or in the form of spheroids. The efficacy of single drugs and their combination was analysed in different tumour-specific environments including acidosis and hypoxia using a series of viability assays. Changes in protein content and distribution were determined by immunoblotting and a "peeling analysis" of immunohistochemical signals. While the cytotoxicity of single agents was influenced by the tumour-specific microenvironment, perifosine and ABT-737 in combination synergistically induced apoptosis in cells cultured in both 2D and 3D independently on pH and oxygen level. Thus, the combined therapy of perifosine and ABT-737 could be considered as a potential treatment strategy for colon cancer.

Antibiotic Cycling Affects Resistance Evolution Independently of Collateral Sensitivity

Antibiotic cycling has been proposed as a promising approach to slow down resistance evolution against currently employed antibiotics. It remains unclear, however, to which extent the decreased resistance evolution is the result of collateral sensitivity, an evolutionary trade-off where resistance to one antibiotic enhances the sensitivity to the second, or due to additional effects of the evolved genetic background, in which mutations accumulated during treatment with a first antibiotic alter the emergence and spread of resistance against a second antibiotic via other mechanisms. Also, the influence of antibiotic exposure patterns on the outcome of drug cycling is unknown. Here, we systematically assessed the effects of the evolved genetic background by focusing on the first switch between two antibiotics against Salmonella Typhimurium, with cefotaxime fixed as the first and a broad variety of other drugs as the second antibiotic. By normalizing the antibiotic concentrations to eliminate the effects of collateral sensitivity, we demonstrated a clear contribution of the evolved genetic background beyond collateral sensitivity, which either enhanced or reduced the adaptive potential depending on the specific drug combination. We further demonstrated that the gradient strength with which cefotaxime was applied affected both cefotaxime resistance evolution and adaptation to second antibiotics, an effect that was associated with higher levels of clonal interference and reduced cost of resistance in populations evolved under weaker cefotaxime gradients. Overall, our work highlights that drug cycling can affect resistance evolution independently of collateral sensitivity, in a manner that is contingent on the antibiotic exposure pattern.

How bacteria navigate varying environments

Collective sensing and phenotypic diversification aid response to environmental fluctuations

The risk of drug resistance during long-acting antimicrobial therapy

The emergence of drug resistance during antimicrobial therapy is a major global health problem, especially for chronic infections like human immunodeficiency virus, hepatitis B and C, and tuberculosis. Sub-optimal adherence to long-term treatment is an important contributor to resistance risk. New long-acting drugs are being developed for weekly, monthly or less frequent dosing to improve adherence, but may lead to long-term exposure to intermediate drug levels. In this study, we analyse the effect of dosing frequency on the risk of resistance evolving during time-varying drug levels. We find that long-acting therapies can increase, decrease or have little effect on resistance, depending on the source (pre-existing or de novo) and degree of resistance, and rates of drug absorption and clearance. Long-acting therapies with rapid drug absorption, slow clearance and strong wild-type inhibition tend to reduce resistance caused by partially resistant strains in the early stages of treatment even if they do not improve adherence. However, if subpopulations of microbes persist and can reactivate during sub-optimal treatment, longer-acting therapies may substantially increase the resistance risk. Our results show that drug kinetics affect selection for resistance in a complicated manner, and that pathogen-specific models are needed to evaluate the benefits of new long-acting therapies.

HIV drug resistance in various body compartments

PURPOSE OF REVIEW

HIV drug resistance testing using blood plasma or dried blood spots forms part of international guidelines. However, as the clinical utility of assessing drug resistance in other body compartments is less well established, we review this for blood cells and samples from other body compartments.

RECENT EVIDENCE

Although clinical benefit is not clear, drug resistance testing in blood cells is often performed when patients with suppressed plasma viral loads require a treatment substitution. In patients with HIV neurocognitive disease, cerebral spinal fluid (CSF) drug resistance is rarely discordant with plasma but has nevertheless been used to guide antiretroviral drug substitutions. Cases with HIV drug resistance in genital fluids have been documented but this does not appear to indicate transmission risk when blood plasma viral loads are suppressed.

SUMMARY

Drug-resistant variants, which may be selected in tissues under conditions of variable adherence and drug penetration, appear to disseminate quickly, and become detectable in blood. This may explain why drug resistance discordance between plasma and these compartments is rarely found. Partial compartmentalization of HIV populations is well established for the CSF and the genital tract but other than blood plasma, evidence is lacking to support drug resistance testing in body compartments.

The role of spatial structure in multi-deme models of evolutionary rescue

Genetic variation and population sizes are critical factors for successful adaptation to novel environmental conditions. Gene flow between sub-populations is a potent mechanism to provide such variation and can hence facilitate adaptation, for instance by increasing genetic variation or via the introduction of beneficial variants. On the other hand, if gene flow between different habitats is too strong, locally beneficial alleles may not be able to establish permanently. In the context of evolutionary rescue, intermediate levels of gene flow are therefore often optimal for maximizing a species chance for survival in metapopulations without spatial structure. To which extent and under which conditions gene flow facilitates or hinders evolutionary rescue in spatially structured populations remains unresolved. We address this question by studying the differences between evolutionary rescue in the island model and in the stepping stone model in a gradually deteriorating habitat. We show that evolutionary rescue is modulated by the rate of gene flow between different habitats, which in turn depends strongly on the spatial structure and the pattern of environmental deterioration. We use these insights to show that in many cases spatially structured models can be translated into a simpler island model using an appropriately scaled effective migration rate.

Yersinia pseudotuberculosis doxycycline tolerance strategies include modulating expression of genes involved in cell permeability and tRNA modifications

Antibiotic tolerance is typically associated with a phenotypic change within a bacterial population, resulting in a transient decrease in antibiotic susceptibility that can contribute to treatment failure and recurrent infections. Although tolerant cells may emerge prior to treatment, the stress of prolonged antibiotic exposure can also promote tolerance. Here, we sought to determine how Yersinia pseudotuberculosis responds to doxycycline exposure, to then verify if these gene expression changes could promote doxycycline tolerance in culture and in our mouse model of infection. Only four genes were differentially regulated in response to a physiologically-relevant dose of doxycycline: osmB and ompF were upregulated, tusB and cnfy were downregulated; differential expression also occurred during doxycycline treatment in the mouse. ompF, tusB and cnfy were also differentially regulated in response to chloramphenicol, indicating these could be general responses to ribosomal inhibition. cnfy has previously been associated with persistence and was not a major focus here. We found deletion of the OmpF porin resulted in increased antibiotic accumulation, suggesting expression may promote diffusion of doxycycline out of the cell, while OsmB lipoprotein had a minor impact on antibiotic permeability. Overexpression of tusB significantly impaired bacterial survival in culture and in the mouse, suggesting that tRNA modification by tusB, and the resulting impacts on translational machinery, promotes survival during treatment with an antibiotic classically viewed as bacteriostatic. We believe this may be the first observation of bactericidal activity of doxycycline under physiological conditions, which was revealed by reversing tusB downregulation.

Mutational Switch-Backs Can Accelerate Evolution of Francisella to a Combination of Ciprofloxacin and Doxycycline

Combination antimicrobial therapy has been considered a promising strategy to combat the evolution of antimicrobial resistance. Francisella tularensis is the causative agent of tularemia and in addition to being found in the nature, is recognized as a threat agent that requires vigilance. We investigated the evolutionary outcome of adapting the Live Vaccine Strain (LVS) of F. tularensis subsp. holarctica to two non-interacting drugs, ciprofloxacin and doxycycline, individually, sequentially, and in combination. Despite their individual efficacies and independence of mechanisms, evolution to the combination arose on a shorter time scale than evolution to the two drugs sequentially. We conducted a longitudinal mutational analysis of the populations evolving to the drug combination, genetically reconstructed the identified evolutionary pathway, and carried out biochemical validation. We discovered that, after the appearance of an initial weak generalist mutation (FupA/B), each successive mutation alternated between adaptation to one drug or the other. In combination, these mutations allowed the population to more efficiently ascend the fitness peak through a series of evolutionary switch-backs. Clonal interference, weak pleiotropy, and positive epistasis also contributed to combinatorial evolution. This finding suggests that the use of this non-interacting drug pair against F. tularensis may render both drugs ineffective because of mutational switch-backs that accelerate evolution of dual resistance.

Evolution during primary HIV infection does not require adaptive immune selection

Modern HIV research depends crucially on both viral sequencing and population measurements. To directly link mechanistic biological processes and evolutionary dynamics during HIV infection, we developed multiple within-host phylodynamic models of HIV primary infection for comparative validation against viral load and evolutionary dynamics data. The optimal model of primary infection required no positive selection, suggesting that the host adaptive immune system reduces viral load but surprisingly does not drive observed viral evolution. Rather, the fitness (infectivity) of mutant variants is drawn from an exponential distribution in which most variants are slightly less infectious than their parents (nearly neutral evolution). This distribution was not largely different from either in vivo fitness distributions recorded beyond primary infection or in vitro distributions that are observed without adaptive immunity, suggesting the intrinsic viral fitness distribution may drive evolution. Simulated phylogenetic trees also agree with independent data and illuminate how phylogenetic inference must consider viral and immune-cell population dynamics to gain accurate mechanistic insights.

Stochastic investigation of HIV infection and the emergence of drug resistance

Drug-resistant HIV-1 has caused a growing concern in clinic and public health. Although combination antiretroviral therapy can contribute massively to the suppression of viral loads in patients with HIV-1, it cannot lead to viral eradication. Continuing viral replication during sub-optimal therapy (due to poor adherence or other reasons) may lead to the accumulation of drug resistance mutations, resulting in an increased risk of disease progression. Many studies also suggest that events occurring during the early stage of HIV-1 infection (i.e., the first few hours to days following HIV exposure) may determine whether the infection can be successfully established. However, the numbers of infected cells and viruses during the early stage are extremely low and stochasticity may play a critical role in dictating the fate of infection. In this paper, we use stochastic models to investigate viral infection and the emergence of drug resistance of HIV-1. The stochastic model is formulated by a continuous-time Markov chain (CTMC), which is derived based on an ordinary differential equation model proposed by Kitayimbwa et al. that includes both forward and backward mutations. An analytic estimate of the probability of the clearance of HIV infection of the CTMC model near the infection-free equilibrium is obtained by a multitype branching process approximation. The analytical predictions are validated by numerical simulations. Unlike the deterministic dynamics where the basic reproduction number R₀ serves as a sharp threshold parameter (i.e., the disease dies out if R₀<1 and persists if R₀>1), the stochastic models indicate that there is always a positive probability for HIV infection to be eradicated in patients. In the presence of antiretroviral therapy, our results show that the chance of clearance of the infection tends to increase although drug resistance is likely to emerge.

Manipulating Intratumoral Fenton Chemistry for Enhanced Chemodynamic and Chemodynamic-Synergized Multimodal Therapy

Chemodynamic therapy (CDT) uses the tumor microenvironment-assisted intratumoral Fenton reaction for generating highly toxic hydroxyl free radicals (•OH) to achieve selective tumor treatment. However, the limited intratumoral Fenton reaction efficiency restricts the therapeutic efficacy of CDT. Recent years have witnessed the impressive development of various strategies to increase the efficiency of intratumoral Fenton reaction. The introduction of these reinforcement strategies can dramatically improve the treatment efficiency of CDT and further promote the development of enhanced CDT (ECDT)-based multimodal anticancer treatments. In this review, the authors systematically introduce these reinforcement strategies, from their basic working principles, reinforcement mechanisms to their representative clinical applications. Then, ECDT-based multimodal anticancer therapy is discussed, including how to integrate these emerging Fenton reinforcement strategies for accelerating the development of multimodal anticancer therapy, as well as the synergistic mechanisms of ECDT and other treatment methods. Eventually, future direction and challenges of ECDT and ECDT-based multimodal synergistic therapies are elaborated, highlighting the key scientific problems and unsolved technical bottlenecks to facilitate clinical translation.

Spatial segregation and cooperation in radially expanding microbial colonies under antibiotic stress

Antibiotic resistance in microbial communities reflects a combination of processes operating at different scales. In this work, we investigate the spatiotemporal dynamics of bacterial colonies comprised of drug-resistant and drug-sensitive cells undergoing range expansion under antibiotic stress. Using the opportunistic pathogen Enterococcus faecalis with plasmid-encoded β-lactamase, we track colony expansion dynamics and visualize spatial patterns in fluorescently labeled populations exposed to antibiotics. We find that the radial expansion rate of mixed communities is approximately constant over a wide range of drug concentrations and initial population compositions. Imaging of the final populations shows that resistance to ampicillin is cooperative, with sensitive cells surviving in the presence of resistant cells at otherwise lethal concentrations. The populations exhibit a diverse range of spatial segregation patterns that depend on drug concentration and initial conditions. Mathematical models indicate that the observed dynamics are consistent with global cooperation, despite the fact that β-lactamase remains cell-associated. Experiments confirm that resistant colonies provide a protective effect to sensitive cells on length scales multiple times the size of a single colony, and populations seeded with (on average) no more than a single resistant cell can produce mixed communities in the presence of the drug. While biophysical models of drug degradation suggest that individual resistant cells offer only short-range protection to neighboring cells, we show that long-range protection may arise from synergistic effects of multiple resistant cells, providing surprisingly large protection zones even at small population fractions.

Understanding patterns of HIV multi-drug resistance through models of temporal and spatial drug heterogeneity

Triple-drug therapies have transformed HIV from a fatal condition to a chronic one. These therapies should prevent HIV drug resistance evolution, because one or more drugs suppress any partially resistant viruses. In practice, such therapies drastically reduced, but did not eliminate, resistance evolution. In this article, we reanalyze published data from an evolutionary perspective and demonstrate several intriguing patterns about HIV resistance evolution - resistance evolves (1) even after years on successful therapy, (2) sequentially, often via one mutation at a time and (3) in a partially predictable order. We describe how these observations might emerge under two models of HIV drugs varying in space or time. Despite decades of work in this area, much opportunity remains to create models with realistic parameters for three drugs, and to match model outcomes to resistance rates and genetic patterns from individuals on triple-drug therapy. Further, lessons from HIV may inform other systems.

The within-host evolution of antimicrobial resistance in Mycobacterium tuberculosis

Tuberculosis (TB) has been responsible for the greatest number of human deaths due to an infectious disease in general, and due to antimicrobial resistance (AMR) in particular. The etiological agents of human TB are a closely-related group of human-adapted bacteria that belong to the Mycobacterium tuberculosis complex (MTBC). Understanding how MTBC populations evolve within-host may allow for improved TB treatment and control strategies. In this review, we highlight recent works that have shed light on how AMR evolves in MTBC populations within individual patients. We discuss the role of heteroresistance in AMR evolution, and review the bacterial, patient and environmental factors that likely modulate the magnitude of heteroresistance within-host. We further highlight recent works on the dynamics of MTBC genetic diversity within-host, and discuss how spatial substructures in patients' lungs, spatiotemporal heterogeneity in antimicrobial concentrations and phenotypic drug tolerance likely modulates the dynamics of MTBC genetic diversity in patients during treatment. We note the general characteristics that are shared between how the MTBC and other bacterial pathogens evolve in humans, and highlight the characteristics unique to the MTBC.

Price equation captures the role of drug interactions and collateral effects in the evolution of multidrug resistance

Bacterial adaptation to antibiotic combinations depends on the joint inhibitory effects of the two drugs (drug interaction [DI]) and how resistance to one drug impacts resistance to the other (collateral effects [CE]). Here we model these evolutionary dynamics on two-dimensional phenotype spaces that leverage scaling relations between the drug-response surfaces of drug-sensitive (ancestral) and drug-resistant (mutant) populations. We show that evolved resistance to the component drugs - and in turn, the adaptation of growth rate - is governed by a Price equation whose covariance terms encode geometric features of both the two-drug-response surface (DI) in ancestral cells and the correlations between resistance levels to those drugs (CE). Within this framework, mean evolutionary trajectories reduce to a type of weighted gradient dynamics, with the drug interaction dictating the shape of the underlying landscape and the collateral effects constraining the motion on those landscapes. We also demonstrate how constraints on available mutational pathways can be incorporated into the framework, adding a third key driver of evolution. Our results clarify the complex relationship between drug interactions and collateral effects in multidrug environments and illustrate how specific dosage combinations can shift the weighting of these two effects, leading to different and temporally explicit selective outcomes.

Coupled Source-Sink Habitats Produce Spatial and Temporal Variation of Cancer Cell Molecular Properties as an Alternative to Branched Clonal Evolution and Stem Cell Paradigms

Intratumoral molecular cancer cell heterogeneity is conventionally ascribed to the accumulation of random mutations that occasionally generate fitter phenotypes. This model is built upon the “mutation-selection” paradigm in which mutations drive ever-fitter cancer cells independent of environmental circumstances. An alternative model posits spatio-temporal variation (e.g., blood flow heterogeneity) drives speciation by selecting for cancer cells adapted to each different environment. Here, spatial genetic variation is the consequence rather than the cause of intratumoral evolution. In nature, spatially heterogenous environments are frequently coupled through migration. Drawing from ecological models, we investigate adjacent well-perfused and poorly-perfused tumor regions as “source” and “sink” habitats, respectively. The source habitat has a high carrying capacity resulting in more emigration than immigration. Sink habitats may support a small (“soft-sink”) or no (“hard-sink”) local population. Ecologically, sink habitats can reduce the population size of the source habitat so that, for example, the density of cancer cells directly around blood vessels may be lower than expected. Evolutionarily, sink habitats can exert a selective pressure favoring traits different from those in the source habitat so that, for example, cancer cells adjacent to blood vessels may be suboptimally adapted for that habitat. Soft sinks favor a generalist cancer cell type that moves between the environment but can, under some circumstances, produce speciation events forming source and sink habitat specialists resulting in significant molecular variation in cancer cells separated by small distances. Finally, sink habitats, with limited blood supply, may receive reduced concentrations of systemic drug treatments; and local hypoxia and acidosis may further decrease drug efficacy allowing cells to survive treatment and evolve resistance. In such cases, the sink transforms into the source habitat for resistant cancer cells, leading to treatment failure and tumor progression. We note these dynamics will result in spatial variations in molecular properties as an alternative to the conventional branched evolution model and will result in cellular migration as well as variation in cancer cell phenotype and proliferation currently described by the stem cell paradigm.

The Genomic Basis of Rapid Adaptation to Antibiotic Combination Therapy in Pseudomonas aeruginosa

Combination therapy is a common antibiotic treatment strategy that aims at minimizing the risk of resistance evolution in several infectious diseases. Nonetheless, evidence supporting its efficacy against the nosocomial opportunistic pathogen Pseudomonas aeruginosa remains elusive. Identification of the possible evolutionary paths to resistance in multidrug environments can help to explain treatment outcome. For this purpose, we here performed whole-genome sequencing of 127 previously evolved populations of P. aeruginosa adapted to sublethal doses of distinct antibiotic combinations and corresponding single-drug treatments, and experimentally characterized several of the identified variants. We found that alterations in the regulation of efflux pumps are the most favored mechanism of resistance, regardless of the environment. Unexpectedly, we repeatedly identified intergenic variants in the adapted populations, often with no additional mutations and usually associated with genes involved in efflux pump expression, possibly indicating a regulatory function of the intergenic regions. The experimental analysis of these variants demonstrated that the intergenic changes caused similar increases in resistance against single and multidrug treatments as those seen for efflux regulatory gene mutants. Surprisingly, we could find no substantial fitness costs for a majority of these variants, most likely enhancing their competitiveness toward sensitive cells, even in antibiotic-free environments. We conclude that the regulation of efflux is a central target of antibiotic-mediated selection in P. aeruginosa and that, importantly, changes in intergenic regions may represent a usually neglected alternative process underlying bacterial resistance evolution, which clearly deserves further attention in the future.

Evolution in alternating environments with tunable interlandscape correlations

Natural populations are often exposed to temporally varying environments. Evolutionary dynamics in varying environments have been extensively studied, although understanding the effects of varying selection pressures remains challenging. Here, we investigate how cycling between a pair of statistically related fitness landscapes affects the evolved fitness of an asexually reproducing population. We construct pairs of fitness landscapes that share global fitness features but are correlated with one another in a tunable way, resulting in landscape pairs with specific correlations. We find that switching between these landscape pairs, depending on the ruggedness of the landscape and the interlandscape correlation, can either increase or decrease steady-state fitness relative to evolution in single environments. In addition, we show that switching between rugged landscapes often selects for increased fitness in both landscapes, even in situations where the landscapes themselves are anticorrelated. We demonstrate that positively correlated landscapes often possess a shared maximum in both landscapes that allows the population to step through sub-optimal local fitness maxima that often trap single landscape evolution trajectories. Finally, we demonstrate that switching between anticorrelated paired landscapes leads to ergodic-like dynamics where each genotype is populated with nonzero probability, dramatically lowering the steady-state fitness in comparison to single landscape evolution.

Lactic Acidosis Interferes With Toxicity of Perifosine to Colorectal Cancer Spheroids: Multimodal Imaging Analysis

Colorectal cancer (CRC) is a disease with constantly increasing incidence and high mortality. The treatment efficacy could be curtailed by drug resistance resulting from poor drug penetration into tumor tissue and the tumor-specific microenvironment, such as hypoxia and acidosis. Furthermore, CRC tumors can be exposed to different pH depending on the position in the intestinal tract. CRC tumors often share upregulation of the Akt signaling pathway. In this study, we investigated the role of external pH in control of cytotoxicity of perifosine, the Akt signaling pathway inhibitor, to CRC cells using 2D and 3D tumor models. In 3D settings, we employed an innovative strategy for simultaneous detection of spatial drug distribution and biological markers of proliferation/apoptosis using a combination of mass spectrometry imaging and immunohistochemistry. In 3D conditions, low and heterogeneous penetration of perifosine into the inner parts of the spheroids was observed. The depth of penetration depended on the treatment duration but not on the external pH. However, pH alteration in the tumor microenvironment affected the distribution of proliferation- and apoptosis-specific markers in the perifosine-treated spheroid. Accurate co-registration of perifosine distribution and biological response in the same spheroid section revealed dynamic changes in apoptotic and proliferative markers occurring not only in the perifosine-exposed cells, but also in the perifosine-free regions. Cytotoxicity of perifosine to both 2D and 3D cultures decreased in an acidic environment below pH 6.7. External pH affects cytotoxicity of the other Akt inhibitor, MK-2206, in a similar way. Our innovative approach for accurate determination of drug efficiency in 3D tumor tissue revealed that cytotoxicity of Akt inhibitors to CRC cells is strongly dependent on pH of the tumor microenvironment. Therefore, the effect of pH should be considered during the design and pre-clinical/clinical testing of the Akt-targeted cancer therapy.

HIV-1C env and gag Variation in the Cerebrospinal Fluid and Plasma of Patients with HIV-Associated Cryptococcal Meningitis in Botswana

HIV-1 compartmentalization in reservoir sites remains a barrier to complete HIV eradication. It is unclear whether there is variation in HIV-1 env and gag between cerebrospinal fluid (CSF) and plasma of individuals with HIV-associated cryptococcal meningitis (CM). We compared HIV-1 env characteristics and the gag cytotoxic T-lymphocyte (CTL) escape mutations from CSF and plasma samples. Employing population-based Sanger sequencing, we sequenced HIV-1 env from CSF of 25 patients and plasma of 26 patients. For gag, 15 CSF and 21 plasma samples were successfully sequenced. Of these, 18 and 9 were paired env and gag CSF/plasma samples, respectively. There was no statistically significant difference in the proportion of CCR5-using strains in the CSF and plasma, (p = 0.50). Discordant CSF/plasma virus co-receptor use was found in 2/18 pairs (11.1%). The polymorphisms in the HIV-1 V3 loop were concordant between the two compartments. From the HIV-1 gag sequences, three pairs had discordant CTL escape mutations in three different epitopes of the nine analyzed. These findings suggest little variation in the HIV-1 env between plasma and CSF and that the CCR5-using strains predominate in both compartments. HIV-1 gag CTL escape mutations also displayed little variation in CSF and plasma suggesting similar CTL selective pressure.

The Moran process on 2-chromatic graphs

Resources are rarely distributed uniformly within a population. Heterogeneity in the concentration of a drug, the quality of breeding sites, or wealth can all affect evolutionary dynamics. In this study, we represent a collection of properties affecting the fitness at a given location using a color. A green node is rich in resources while a red node is poorer. More colors can represent a broader spectrum of resource qualities. For a population evolving according to the birth-death Moran model, the first question we address is which structures, identified by graph connectivity and graph coloring, are evolutionarily equivalent. We prove that all properly two-colored, undirected, regular graphs are evolutionarily equivalent (where “properly colored” means that no two neighbors have the same color). We then compare the effects of background heterogeneity on properly two-colored graphs to those with alternative schemes in which the colors are permuted. Finally, we discuss dynamic coloring as a model for spatiotemporal resource fluctuations, and we illustrate that random dynamic colorings often diminish the effects of background heterogeneity relative to a proper two-coloring.

Heterogeneity in environmental conditions can have profound effects on long-term evolutionary outcomes in structured populations. We consider a population evolving on a colored graph, wherein the color of a node represents the resources at that location. Using a combination of analytical and numerical methods, we quantify the effects of background heterogeneity on a population’s dynamics. In addition to considering the notion of an “optimal” coloring with respect to mutant invasion, we also study the effects of dynamic spatial redistribution of resources as the population evolves. Although the effects of static background heterogeneity can be quite striking, these effects are often attenuated by the movement (or “flow”) of the underlying resources.

The population genomics of within-host Mycobacterium tuberculosis

Recent progress in genomic sequencing from patient samples has allowed for the first detailed insight into the within-host genetic diversity of Mycobacterium tuberculosis (M.TB), revealing remarkably low levels of variation. While this has often been attributed to low mutation rates, other factors have been described, including resistance evolution (i.e., selective sweeps), widespread purifying and background selection, and, more recently, progeny skew. Here we review recent findings pertaining to the processes governing the evolutionary dynamics of M.TB, discuss their implications for improving our understanding of this important human pathogen, and make recommendations for future work. Significantly, this emerging evolutionary framework involving the joint estimation of demographic, selective, and reproductive processes is forming a new paradigm for the study of within-host pathogen evolution that will be widely applicable across organisms.

Competition delays multi-drug resistance evolution during combination therapy

Combination therapies have shown remarkable success in preventing the evolution of resistance to multiple drugs, including HIV, tuberculosis, and cancer. Nevertheless, the rise in drug resistance still remains an important challenge. The capability to accurately predict the emergence of resistance, either to one or multiple drugs, may help to improve treatment options. Existing theoretical approaches often focus on exponential growth laws, which may not be realistic when scarce resources and competition limit growth. In this work, we study the emergence of single and double drug resistance in a model of combination therapy of two drugs. The model describes a sensitive strain, two types of single-resistant strains, and a double-resistant strain. We compare the probability that resistance emerges for three growth laws: exponential growth, logistic growth without competition between strains, and logistic growth with competition between strains. Using mathematical estimates and numerical simulations, we show that between-strain competition only affects the emergence of single resistance when resources are scarce. In contrast, the probability of double resistance is affected by between-strain competition over a wider space of resource availability. This indicates that competition between different resistant strains may be pertinent to identifying strategies for suppressing drug resistance, and that exponential models may overestimate the emergence of resistance to multiple drugs. A by-product of our work is an efficient strategy to evaluate probabilities of single and double resistance in models with multiple sequential mutations. This may be useful for a range of other problems in which the probability of resistance is of interest.

Differences in human immunodeficiency virus-1C viral load and drug resistance mutation between plasma and cerebrospinal fluid in patients with human immunodeficiency virus-associated cryptococcal meningitis in Botswana

To determine effects of cryptococcal meningitis (CM) on human immunodeficiency virus (HIV)-1C cerebrospinal fluid (CSF) viral escape, CSF/plasma viral discordance, and drug resistance mutation (DRM) discordance between CSF and plasma compartments, we compared CSF and plasma viral load (VL) and DRMs in individuals with HIV-associated CM in Botswana.This cross-sectional study utilized 45 paired CSF/plasma samples from participants in a CM treatment trial (2014-2016). HIV-1 VL was determined and HIV-1 protease and reverse transcriptase genotyping performed. DRMs were determined using the Stanford HIV database. CSF viral escape was defined as HIV-1 ribonucleic acid ≥0.5 log10 higher in CSF than plasma and VL discordance as CSF VL > plasma VL.HIV-1 VL was successfully measured in 39/45 pairs, with insufficient sample volume in 6; 34/39 (87.2%) participants had detectable HIV-1 in plasma and CSF, median 5.1 (interquartile range: 4.7-5.7) and 4.6 (interquartile range:3.7-4.9) log10 copies/mL, respectively (P≤.001). CSF viral escape was present in 1/34 (2.9%) and VL discordance in 6/34 (17.6%). Discordance was not associated with CD4 count, antiretroviral status, fungal burden, CSF lymphocyte percentage nor mental status. Twenty-six of 45 (57.8%) CSF/plasma pairs were successfully sequenced. HIV-1 DRM discordance was found in 3/26 (11.5%); 1 had I84IT and another had M46MI in CSF only. The third had K101E in plasma and V106 M in CSF.Our findings suggest that HIV-1 escape and DRM discordance may occur at lower rates in participants with advanced HIV-disease and CM compared to those with HIV associated neurocognitive impairment.

Population structure across scales facilitates coexistence and spatial heterogeneity of antibiotic-resistant infections

Antibiotic-resistant infections are a growing threat to human health, but basic features of the eco-evolutionary dynamics remain unexplained. Most prominently, there is no clear mechanism for the long-term coexistence of both drug-sensitive and resistant strains at intermediate levels, a ubiquitous pattern seen in surveillance data. Here we show that accounting for structured or spatially-heterogeneous host populations and variability in antibiotic consumption can lead to persistent coexistence over a wide range of treatment coverages, drug efficacies, costs of resistance, and mixing patterns. Moreover, this mechanism can explain other puzzling spatiotemporal features of drug-resistance epidemiology that have received less attention, such as large differences in the prevalence of resistance between geographical regions with similar antibiotic consumption or that neighbor one another. We find that the same amount of antibiotic use can lead to very different levels of resistance depending on how treatment is distributed in a transmission network. We also identify parameter regimes in which population structure alone cannot support coexistence, suggesting the need for other mechanisms to explain the epidemiology of antibiotic resistance. Our analysis identifies key features of host population structure that can be used to assess resistance risk and highlights the need to include spatial or demographic heterogeneity in models to guide resistance management.

A multiscale model of virus pandemic: Heterogeneous interactive entities in a globally connected world

This paper is devoted to the multidisciplinary modelling of a pandemic initiated by an aggressive virus, specifically the so-called SARS-CoV-2 Severe Acute Respiratory Syndrome, corona virus n.2. The study is developed within a multiscale framework accounting for the interaction of different spatial scales, from the small scale of the virus itself and cells, to the large scale of individuals and further up to the collective behaviour of populations. An interdisciplinary vision is developed thanks to the contributions of epidemiologists, immunologists and economists as well as those of mathematical modellers. The first part of the contents is devoted to understanding the complex features of the system and to the design of a modelling rationale. The modelling approach is treated in the second part of the paper by showing both how the virus propagates into infected individuals, successfully and not successfully recovered, and also the spatial patterns, which are subsequently studied by kinetic and lattice models. The third part reports the contribution of research in the fields of virology, epidemiology, immune competition, and economy focussed also on social behaviours. Finally, a critical analysis is proposed looking ahead to research perspectives.

For the Greater (Bacterial) Good: Heterogeneous Expression of Energetically Costly Virulence Factors

Bacterial populations are phenotypically heterogeneous, which allows subsets of cells to survive and thrive following changes in environmental conditions. For bacterial pathogens, changes within the host environment occur over the course of the immune response to infection and can result in exposure to host-derived, secreted antimicrobials or force direct interactions with immune cells. Many recent studies have shown host cell interactions promote virulence factor expression, forcing subsets of bacterial cells to battle the host response, while other bacteria reap the benefits of this pacification. It still remains unclear whether virulence factor expression is truly energetically costly within host tissues and whether expression is sufficient to impact the growth kinetics of virulence factor-expressing cells. However, it is clear that slow-growing subsets of bacteria emerge during infection and that these subsets are particularly difficult to eliminate with antibiotics. This minireview will focus on our current understanding of heterogenous virulence factor expression and discuss the evidence that supports or refutes the hypothesis that virulence factor expression is linked to slowed growth and antibiotic tolerance.

Assessing the impact of HIV treatment interruptions using stochastic cellular Automata

Chronic HIV infection causes a progressive decrease in the ability to maintain homeostasis resulting, after some time, in eventual break down of immune functions. Recent clinical research has shed light on a significant contribution of the lymphatic tissues, where HIV causes accumulation of collagen, (fibrosis). Specifically, where tissue is populated by certain types of functional stromal cells designated Fibroblastic Reticular Cells (FRCs), these have been found to play a crucial role in balancing out apoptosis and regeneration of naïve T-cells through 2-way cellular signaling. Tissue fibrosis not only impedes this signaling, effectively reducing T-cell levels through increased apoptosis of cells of both T- and FRC type but has been found to be irreversible by current HIV standard treatment (cART). While the therapy aims to block the viral lifecycle, cART-associated increase of T-cell levels in blood appears to conceal existing FRC impairment through fibrosis. This hidden impairment can lead to adverse consequences if treatment is interrupted, e.g. due to poor adherence (missing doses) or through periods recovering from drug toxicities. Formal clinical studies on treatment interruption have indicated possible adverse effects, but quantification of those effects in relation to interruption protocol and patient predisposition remains unclear. Accordingly, the impact of treatment interruption on lymphatic tissue structure and T-cell levels is explored here by means of computer simulation. A novel Stochastic Cellular Automata model is proposed, which utilizes all sources of clinical detail available to us (though sparse in part) for model parametrization. Sources are explicitly referenced and conflicting evidence from previous studies explored. The main focus is on (i) spatial aspects of collagen build up, together with (ii) collagen increase after repeated treatment interruptions to explore the dynamics of HIV-induced fibrosis and T-cell loss.

Site-Specific, Stoichiometric-Controlled, PEGylated Conjugates of Fibroblast Growth Factor 2 (FGF2) with Hydrophilic Auristatin Y for Highly Selective Killing of Cancer Cells Overproducing Fibroblast Growth Factor Receptor 1 (FGFR1)

In spite of significant progress in the field of targeted anticancer therapy, the FDA has approved only five ADC-based drugs. Hence the search for new targeted anticancer agents is an unfulfilled necessity. Here, we present novel types of protein–drug conjugates (PDCs) that exhibit superior anticancer activities. Instead of a monoclonal antibody, we used fibroblast growth factor 2 (FGF2) as a targeting molecule. FGF2 is a natural ligand of fibroblast growth factor receptor 1 (FGFR1), a transmembrane receptor overproduced in various types of cancers. We synthesized site-specific and stoichiometric-controlled conjugates of FGF2 with a highly potent, hydrophilic derivative of auristatin called auristatin Y. To increase the hydrophilicity and hydrodynamic radius of conjugates, we employed PEG4 and PEG27 molecules as a spacer between the targeting molecule and the cytotoxic payload. All conjugates were selective to FGFR1-positive cell lines, effectively internalized via the FGFR1-dependent pathway, and exhibited a highly cytotoxic effect only on FGFR1-positive cancer cell lines.

Cavitary tuberculosis: the gateway of disease transmission

Tuberculosis continues to be a major threat to global health. Cavitation is a dangerous consequence of pulmonary tuberculosis associated with poor outcomes, treatment relapse, higher transmission rates, and development of drug resistance. However, in the antibiotic era, cavities are often identified as the most extreme outcome of treatment failure and are one of the least-studied aspects of tuberculosis. We review the epidemiology, clinical features, and concurrent standards of care for individuals with cavitary tuberculosis. We also discuss developments in the understanding of tuberculosis cavities as dynamic physical and biochemical structures that interface the host response with a unique mycobacterial niche to drive tuberculosis-associated morbidity and transmission. Advances in preclinical models and non-invasive imaging can provide valuable insights into the drivers of cavitation. These insights will guide the development of specific pharmacological interventions to prevent cavitation and improve lung function for individuals with tuberculosis.

Evolutionary causes and consequences of bacterial antibiotic persistence

Antibiotic treatment failure is of growing concern. Genetically encoded resistance is key in driving this process. However, there is increasing evidence that bacterial antibiotic persistence, a non-genetically encoded and reversible loss of antibiotic susceptibility, contributes to treatment failure and emergence of resistant strains as well. In this Review, we discuss the evolutionary forces that may drive the selection for antibiotic persistence. We review how some aspects of antibiotic persistence have been directly selected for whereas others result from indirect selection in disparate ecological contexts. We then discuss the consequences of antibiotic persistence on pathogen evolution. Persisters can facilitate the evolution of antibiotic resistance and virulence. Finally, we propose practical means to prevent persister formation and how this may help to slow down the evolution of virulence and resistance in pathogens.

From CENTRAL to SENTRAL (SErum aNgiogenesis cenTRAL): Circulating Predictive Biomarkers to Anti-VEGFR Therapy

Background: In the last decade, a series of analyses failed to identify predictive biomarkers of resistance/susceptibility for anti-angiogenic drugs in metastatic colorectal cancer (mCRC). We conducted an exploratory preplanned analysis of serum pro-angiogenic factors (SErum aNgiogenesis-cenTRAL) in 72 mCRC patients enrolled in the phase II CENTRAL (ColorEctalavastiNTRiAlLdh) trial, with the aim to identify potential predictive factors for sensitivity/resistance to first line folinic acid-fluorouracil-irinotecan regimen (FOLFIRI) plus bevacizumab. Methods: First-line FOLFIRI/bevacizumab patients were prospectively assessed for the following circulating pro-angiogenic factors, evaluated with ELISA (enzyme-linked immunosorbent assay)-based technique at baseline and at every cycle: Vascular endothelial growth factor A (VEGF-A), hepatocyte growth factor (HGF), stromal derived factor-1 (SDF-1), placental derived growth factor (PlGF), fibroblast growth factor-2 (FGF-2), monocyte chemotactic protein-3 (MCP-3), interleukin-8 (IL-8). Results: Changes in circulating FGF-2 levels among different blood samples seemed to correlate with clinical outcome. Patients who experienced an increase in FGF-2 levels at the second cycle of chemotherapy compared to baseline, had a median Progression Free Survival (mPFS) of 12.85 vs. 7.57 months (Hazard Ratio—HR: 0.73, 95% Confidence Interval—CI: 0.43-1.27, p = 0.23). Similar results were seen when comparing FGF-2 concentrations between baseline and eight-week time point (mPFS 12.98 vs. 8.00 months, HR: 0.78, 95% CI: 0.46–1.33, p = 0.35). Conclusions: Our pre-planned, prospective analysis suggests that circulating FGF-2 levels’ early increase could be used as a marker to identify patients who are more likely to gain benefit from FOLFIRI/bevacizumab first-line therapy.