Clonal Expansion of a Streptococcus pneumoniae Serotype 3 Capsule Variant Sequence Type 700 With Enhanced Vaccine Escape Potential After 13-Valent Pneumococcal Conjugate Vaccine Introduction.

BACKGROUND: Streptococcus pneumoniae serotype 3 remains a problem globally. Malawi introduced 13-valent pneumococcal conjugate vaccine (PCV13) in 2011, but there has been no direct protection against serotype 3 carriage. We explored whether vaccine escape by serotype 3 is due to clonal expansion of a lineage with a competitive advantage. METHODS: The distribution of serotype 3 Global Pneumococcal Sequence Clusters (GPSCs) and sequence types (STs) globally was assessed using sequences from the Global Pneumococcal Sequencing Project. Whole-genome sequences of 135 serotype 3 carriage isolates from Blantyre, Malawi (2015-2019) were analyzed. Comparative analysis of the capsule locus, entire genomes, antimicrobial resistance, and phylogenetic reconstructions were undertaken. Opsonophagocytosis was evaluated using serum samples from vaccinated adults and children. RESULTS: Serotype 3 GPSC10-ST700 isolates were most prominent in Malawi. Compared with the prototypical serotype 3 capsular polysaccharide locus sequence, 6 genes are absent, with retention of capsule polysaccharide biosynthesis. This lineage is characterized by increased antimicrobial resistance and lower susceptibility to opsonophagocytic killing. CONCLUSIONS: A serotype 3 variant in Malawi has genotypic and phenotypic characteristics that could enhance vaccine escape and clonal expansion after post-PCV13 introduction. Genomic surveillance among high-burden populations is essential to improve the effectiveness of next-generation pneumococcal vaccines.

Distinguishing Immunologic and Behavioral Effects of Vaccination.

The interpretation of vaccine efficacy estimands is subtle, even in randomized trials designed to quantify the immunologic effects of vaccination. In this article, we introduce terminology to distinguish between different vaccine efficacy estimands and clarify their interpretations. This allows us to explicitly consider the immunologic and behavioral effects of vaccination, and establish that policy-relevant estimands can differ substantially from those commonly reported in vaccine trials. We further show that a conventional vaccine trial allows the identification and estimation of different vaccine estimands under plausible conditions if one additional post-treatment variable is measured. Specifically, we utilize a "belief variable" that indicates the treatment an individual believed they had received. The belief variable is similar to "blinding assessment" variables that are occasionally collected in placebo-controlled trials in other fields. We illustrate the relations between the different estimands, and their practical relevance, in numerical examples based on an influenza vaccine trial.

Risk averse reproduction numbers improve resurgence detection.

The effective reproduction number R is a prominent statistic for inferring the transmissibility of infectious diseases and effectiveness of interventions. R purportedly provides an easy-to-interpret threshold for deducing whether an epidemic will grow (R>1) or decline (R<1). We posit that this interpretation can be misleading and statistically overconfident when applied to infections accumulated from groups featuring heterogeneous dynamics. These groups may be delineated by geography, infectiousness or sociodemographic factors. In these settings, R implicitly weights the dynamics of the groups by their number of circulating infections. We find that this weighting can cause delayed detection of outbreak resurgence and premature signalling of epidemic control because it underrepresents the risks from highly transmissible groups. Applying E-optimal experimental design theory, we develop a weighting algorithm to minimise these issues, yielding the risk averse reproduction number E. Using simulations, analytic approaches and real-world COVID-19 data stratified at the city and district level, we show that E meaningfully summarises transmission dynamics across groups, balancing bias from the averaging underlying R with variance from directly using local group estimates. An E>1generates timely resurgence signals (upweighting risky groups), while an E<1ensures local outbreaks are under control. We propose E as an alternative to R for informing policy and assessing transmissibility at large scales (e.g., state-wide or nationally), where R is commonly computed but well-mixed or homogeneity assumptions break down.

Prediction of sustained opioid use in children and adolescents using machine learning.

BACKGROUND: Opioid misuse in the paediatric population is understudied. This study aimed to develop a machine learning classifier to differentiate between occasional and sustained opioid users among children and adolescents in outpatient settings. METHODS: Data for 29,335 patients under 19 yr with recorded opioid purchases were collected from medical records. Machine learning methods were applied to predict sustained opioid use within 1, 2, or 3 yr after first opioid use, using sociodemographic information, medical history, and healthcare usage variables collected near the time of first prescription fulfilment. The models' performance was evaluated with classification and calibration metrics, and a decision curve analysis. An online tool was deployed for model self-exploration and visualisation. RESULTS: The models demonstrated good performance, with a 1-yr follow-up model achieving a sensitivity of 0.772, a specificity of 0.703, and an ROC-AUC of 0.792 on an independent test set, with calibration intercept and slope of 0.00 and 1.02, respectively. Decision curve analysis revealed the clinical benefit of using the model relative to other strategies. SHAP analysis (SHapley Additive exPlanations) identified influential variables, including the number of diagnoses, medical images, laboratory tests, and type of opioid used. CONCLUSIONS: Our model showed promising performance in predicting sustained opioid use among paediatric patients. The online risk prediction tool can facilitate compliance to such tools by clinicians. This study presents the potential of machine learning in identifying at-risk paediatric populations for sustained opioid use, potentially contributing to secondary prevention of opioid abuse.

Allergic to bureaucracy? Regulatory allergenicity assessments of novel food: Motivations, challenges, compromises, and possibilities.

New sources of proteins are essential to meet the demands of the growing world population and evolving food trends. Assessing the allergenicity of proteins in novel food (NF) poses a significant food safety regulatory challenge. The Codex Alimentarius Commission presented an allergenicity assessment protocol for genetically modified (GM) foods, which can also be adapted for NF. Since no single laboratory test can adequately predict the allergenic potential of NF, the protocol follows a weight-of-evidence approach, evaluated by experts, as part of a risk management process. Regulatory bodies worldwide have adopted this safety protocol, which, among other things, promotes global harmonization. This review unravels the reliability and various motivations, terms, concepts, and approaches of allergenicity assessments, aiming to enhance understanding among manufacturers and the public. Health Canada, Food Safety Commission JAPAN, and Food Standards Australia New Zealand were surveyed, focusing on the European Food Safety Authority and the US Food Safety Administration for examples of scientific opinions regarding allergenicity assessments for novel and GM foods, from 2019 to 2023. According to our findings, current regulatory allergenicity assessments for NF approval primarily rely on literature reviews. Only a few of the NF assessments proactively presented additional tests. We recommend conducting bioinformatic analyses on NF when a panel of experts deems that there is insufficient prior scientific research.

Estimating the impact of cefuroxime versus cefazolin and amoxicillin/clavulanate use on future collateral resistance: a retrospective comparison.

BACKGROUND: Quantitative estimates of collateral resistance induced by antibiotic use are scarce. OBJECTIVES: To estimate the effects of treatment with amoxicillin/clavulanate or cefazolin, compared with cefuroxime, on future resistance to ceftazidime among hospitalized patients. METHODS: A retrospective analysis of patients with positive bacterial cultures hospitalized in an Israeli hospital during 2016-19 was conducted. Patients were restricted to those treated with amoxicillin/clavulanate, cefazolin or cefuroxime and re-hospitalized with a positive bacterial culture during the following year. Matching was performed using exact, Mahalanobis and propensity score matching. Each patient in the amoxicillin/clavulanate and cefazolin groups was matched to a single patient from the cefuroxime group, yielding 185:185 and 298:298 matched patients. Logistic regression and the g-formula (standardization) were used to estimate the OR, risk difference (RD) and number needed to harm (NNH). RESULTS: Cefuroxime induced significantly higher resistance to ceftazidime than amoxicillin/clavulanate or cefazolin; the marginal OR was 1.76 (95% CI = 1.16-2.83) compared with amoxicillin/clavulanate and 1.98 (95% CI = 1.41-2.8) compared with cefazolin and the RD was 0.118 (95% CI = 0.031-0.215) compared with amoxicillin/clavulanate and 0.131 (95% CI = 0.058-0.197) compared with cefazolin. We also estimated the NNH; replacing amoxicillin/clavulanate or cefazolin with cefuroxime would yield ceftazidime resistance in 1 more patient for every 8.5 (95% CI = 4.66-32.14) or 7.6 (95% CI = 5.1-17.3) patients re-hospitalized in the following year, respectively. CONCLUSIONS: Our results indicate that treatment with amoxicillin/clavulanate or cefazolin is preferable to cefuroxime, in terms of future collateral resistance. The results presented here are a first step towards quantitative estimations of the ecological damage caused by different antibiotics.

Incidence and risk factors of hospitalisations for respiratory syncytial virus among children aged less than 2 years.

The study aim was to examine the incidence and risk factors of respiratory syncytial virus (RSV) bronchiolitis hospitalisations and disease severity among infants. We compared demographic and health characteristics of children aged 0-23 hospitalised for RSV bronchiolitis (cases, n = 1227) during 2008-2018 and control children (n = 554) of the same age admitted for non-respiratory disease. RSV antigen was detected in nasal swabs by immunochromatography. Multiple logistic regression models were applied. The average annual incidence of hospitalisation for RSV bronchiolitis was 12.6 per 1000 and 1.7 per 1000 (P < 0.001) among infants and toddlers, respectively, with winter seasonality (November-March). The risk of hospitalisation for RSV bronchiolitis increased among children aged 0-5 months (OR 7.66; 95% CI 5.61-10.45) and 6-11 months (OR 12.88, 95% CI 8.48-19.55), compared to those aged 12-23 months. Additional risk factors were living in low vs. higher socio-economic status towns (OR 1.49; 95% CI 1.14-1.95), having chronic medical conditions (OR 2.75; 95% CI 1.61-4.70), birth month (October-January vs. June-September) (OR 2.19; 95% CI 1.60-2.99) and history of stay in neonatal intensive care unit at birth (OR 2.37; 95% CI 1.27-4.41). Male children and those who had pneumonia were more likely to have severe RSV bronchiolitis. In conclusion, the burden of hospitalisations for RSV bronchiolitis is high, especially in young infants. Effective preventive measures such as RSV active vaccines can reduce the risk of hospitalisations for RSV bronchiolitis among these vulnerable groups.

Predicting microbial growth in a mixed culture from growth curve data.

Determining the fitness of specific microbial genotypes has extensive application in microbial genetics, evolution, and biotechnology. While estimates from growth curves are simple and allow high throughput, they are inaccurate and do not account for interactions between costs and benefits accruing over different parts of a growth cycle. For this reason, pairwise competition experiments are the current "gold standard" for accurate estimation of fitness. However, competition experiments require distinct markers, making them difficult to perform between isolates derived from a common ancestor or between isolates of nonmodel organisms. In addition, competition experiments require that competing strains be grown in the same environment, so they cannot be used to infer the fitness consequence of different environmental perturbations on the same genotype. Finally, competition experiments typically consider only the end-points of a period of competition so that they do not readily provide information on the growth differences that underlie competitive ability. Here, we describe a computational approach for predicting density-dependent microbial growth in a mixed culture utilizing data from monoculture and mixed-culture growth curves. We validate this approach using 2 different experiments with Escherichia coli and demonstrate its application for estimating relative fitness. Our approach provides an effective way to predict growth and infer relative fitness in mixed cultures.

Predicting Antibiotic Resistance in Hospitalized Patients by Applying Machine Learning to Electronic Medical Records.

BACKGROUND: Computerized decision support systems are becoming increasingly prevalent with advances in data collection and machine learning (ML) algorithms. However, they are scarcely used for empiric antibiotic therapy. Here, we predict the antibiotic resistance profiles of bacterial infections of hospitalized patients using ML algorithms applied to patients' electronic medical records (EMRs). METHODS: The data included antibiotic resistance results of bacterial cultures from hospitalized patients, alongside their EMRs. Five antibiotics were examined: ceftazidime (n = 2942), gentamicin (n = 4360), imipenem (n = 2235), ofloxacin (n = 3117), and sulfamethoxazole-trimethoprim (n = 3544). We applied lasso logistic regression, neural networks, gradient boosted trees, and an ensemble that combined all 3 algorithms, to predict antibiotic resistance. Variable influence was gauged by permutation tests and Shapely Additive Explanations analysis. RESULTS: The ensemble outperformed the separate models and produced accurate predictions on test set data. When no knowledge regarding the infecting bacterial species was assumed, the ensemble yielded area under the receiver-operating characteristic (auROC) scores of 0.73-0.79 for different antibiotics. Including information regarding the bacterial species improved the auROCs to 0.8-0.88. Variables' effects on predictions were assessed and found to be consistent with previously identified risk factors for antibiotic resistance. CONCLUSIONS: We demonstrate the potential of ML to predict antibiotic resistance of bacterial infections of hospitalized patients. Moreover, we show that rapidly gained information regarding the infecting bacterial species can improve predictions substantially. Clinicians should consider the implementation of such systems to aid correct empiric therapy and to potentially reduce antibiotic misuse.

Potential impact of individual exposure histories to endemic human coronaviruses on age-dependent severity of COVID-19.

BACKGROUND: Cross-reactivity to SARS-CoV-2 from exposure to endemic human coronaviruses (eHCoV) is gaining increasing attention as a possible driver of both protection against infection and COVID-19 severity. Here we explore the potential role of cross-reactivity induced by eHCoVs on age-specific COVID-19 severity in a mathematical model of eHCoV and SARS-CoV-2 transmission. METHODS: We use an individual-based model, calibrated to prior knowledge of eHCoV dynamics, to fully track individual histories of exposure to eHCoVs. We also model the emergent dynamics of SARS-CoV-2 and the risk of hospitalisation upon infection. RESULTS: We hypothesise that primary exposure with any eHCoV confers temporary cross-protection against severe SARS-CoV-2 infection, while life-long re-exposure to the same eHCoV diminishes cross-protection, and increases the potential for disease severity. We show numerically that our proposed mechanism can explain age patterns of COVID-19 hospitalisation in EU/EEA countries and the UK. We further show that some of the observed variation in health care capacity and testing efforts is compatible with country-specific differences in hospitalisation rates under this model. CONCLUSIONS: This study provides a "proof of possibility" for certain biological and epidemiological mechanisms that could potentially drive COVID-19-related variation across age groups. Our findings call for further research on the role of cross-reactivity to eHCoVs and highlight data interpretation challenges arising from health care capacity and SARS-CoV-2 testing.

Revealing antibiotic cross-resistance patterns in hospitalized patients through Bayesian network modelling.

OBJECTIVES: Microbial resistance exhibits dependency patterns between different antibiotics, termed cross-resistance and collateral sensitivity. These patterns differ between experimental and clinical settings. It is unclear whether the differences result from biological reasons or from confounding, biasing results found in clinical settings. We set out to elucidate the underlying dependency patterns between resistance to different antibiotics from clinical data, while accounting for patient characteristics and previous antibiotic usage. METHODS: Additive Bayesian network modelling was employed to simultaneously estimate relationships between variables in a dataset of bacterial cultures derived from hospitalized patients and tested for resistance to multiple antibiotics. Data contained resistance results, patient demographics and previous antibiotic usage, for five bacterial species: Escherichia coli (n = 1054), Klebsiella pneumoniae (n = 664), Pseudomonas aeruginosa (n = 571), CoNS (n = 495) and Proteus mirabilis (n = 415). RESULTS: All links between resistance to the various antibiotics were positive. Multiple direct links between resistance of antibiotics from different classes were observed across bacterial species. For example, resistance to gentamicin in E. coli was directly linked with resistance to ciprofloxacin (OR = 8.39, 95% credible interval 5.58-13.30) and sulfamethoxazole/trimethoprim (OR = 2.95, 95% credible interval 1.97-4.51). In addition, resistance to various antibiotics was directly linked with previous antibiotic usage. CONCLUSIONS: Robust relationships among resistance to antibiotics belonging to different classes, as well as resistance being linked to having taken antibiotics of a different class, exist even when taking into account multiple covariate dependencies. These relationships could help inform choices of antibiotic treatment in clinical settings.

Vaccination can drive an increase in frequencies of antibiotic resistance among nonvaccine serotypes of Streptococcus pneumoniae.

The bacterial pathogen Streptococcus pneumoniae is a major public health concern, being responsible for more than 1.5 million deaths annually through pneumonia, meningitis, and septicemia. Available vaccines target only a subset of serotypes, so vaccination is often accompanied by a rise in the frequency of nonvaccine serotypes. Epidemiological studies suggest that such a change in serotype frequencies is often coupled with an increase of antibiotic resistance among nonvaccine serotypes. Building on previous multilocus models for bacterial pathogen population structure, we have developed a theoretical framework incorporating variation of serotype and antibiotic resistance to examine how their associations may be affected by vaccination. Using this framework, we find that vaccination can result in a rapid increase in the frequency of preexisting resistant variants of nonvaccine serotypes due to the removal of competition from vaccine serotypes.

Hepatitis B virus seroepidemiology data for Africa: Modelling intervention strategies based on a systematic review and meta-analysis.

BACKGROUND: International Sustainable Development Goals (SDGs) for elimination of hepatitis B virus (HBV) infection set ambitious targets for 2030. In African populations, infant immunisation has been fundamental to reducing incident infections in children, but overall population prevalence of chronic hepatitis B (CHB) infection remains high. In high-prevalence populations, adult catch-up vaccination has sometimes been deployed, but an alternative Test and Treat (T&T) approach could be used as an intervention to interrupt transmission. Universal T&T has not been previously evaluated as a population intervention for HBV infection, despite high-profile data supporting its success with human immunodeficiency virus (HIV). METHODS AND FINDINGS: We set out to investigate the relationship between prevalence of HBV infection and exposure in Africa, undertaking a systematic literature review in November 2019. We identified published seroepidemiology data representing the period 1995-2019 from PubMed and Web of Science, including studies of adults that reported prevalence of both hepatitis B surface antigen (HBsAg; prevalence of HBV infection) and antibody to hepatitis B core antigen (anti-HBc; prevalence of HBV exposure). We identified 96 studies representing 39 African countries, with a median cohort size of 370 participants and a median participant age of 34 years. Using weighted linear regression analysis, we found a strong relationship between the prevalence of infection (HBsAg) and exposure (anti-HBc) (R2 = 0.45, p < 0.001). Region-specific differences were present, with estimated CHB prevalence in Northern Africa typically 30% to 40% lower (p = 0.007) than in Southern Africa for statistically similar exposure rates, demonstrating the need for intervention strategies to be tailored to individual settings. We applied a previously published mathematical model to investigate the effect of interventions in a high-prevalence setting. The most marked and sustained impact was projected with a T&T strategy, with a predicted reduction of 33% prevalence by 20 years (95% CI 30%-37%) and 62% at 50 years (95% CI 57%-68%), followed by routine neonatal vaccination and prevention of mother to child transmission (PMTCT; at 100% coverage). In contrast, the impact of catch-up vaccination in adults had a negligible and transient effect on population prevalence. The study is constrained by gaps in the published data, such that we could not model the impact of antiviral therapy based on stratification by specific clinical criteria and our model framework does not include explicit age-specific or risk-group assumptions regarding force of transmission. CONCLUSIONS: The unique data set collected in this study highlights how regional epidemiology data for HBV can provide insights into patterns of transmission, and it provides an evidence base for future quantitative research into the most effective local interventions. In combination with robust neonatal immunisation programmes, ongoing PMTCT efforts, and the vaccination of high-risk groups, diagnosing and treating HBV infection is likely to be of most impact in driving advances towards elimination targets at a population level.

Characterising West Nile virus epidemiology in Israel using a transmission suitability index.

BackgroundClimate is a major factor in the epidemiology of West Nile virus (WNV), a pathogen increasingly pervasive worldwide. Cases increased during 2018 in Israel, the United States and Europe.AimWe set to retrospectively understand the spatial and temporal determinants of WNV transmission in Israel, as a case study for the possible effects of climate on virus spread.MethodsWe employed a suitability index to WNV, parameterising it with prior knowledge pertaining to a bird reservoir and Culex species, using local time series of temperature and humidity as inputs. The predicted suitability index was compared with confirmed WNV cases in Israel (2016-2018).ResultsThe suitability index was highly associated with WNV cases in Israel, with correlation coefficients of 0.91 (p value = 4 × 10- 5), 0.68 (p = 0.016) and 0.9 (p = 2 × 10- 4) in 2016, 2017 and 2018, respectively. The fluctuations in the number of WNV cases between the years were explained by higher area under the index curve. A new WNV seasonal mode was identified in the south-east of Israel, along the Great Rift Valley, characterised by two yearly peaks (spring and autumn), distinct from the already known single summer peak in the rest of Israel.ConclusionsBy producing a detailed geotemporal estimate of transmission potential and its determinants in Israel, our study promotes a better understanding of WNV epidemiology and has the potential to inform future public health responses. The proposed approach further provides opportunities for retrospective and prospective mechanistic modelling of WNV epidemiology and its associated climatic drivers.

Flowers respond to pollinator sound within minutes by increasing nectar sugar concentration.

Can plants sense natural airborne sounds and respond to them rapidly? We show that Oenothera drummondii flowers, exposed to playback sound of a flying bee or to synthetic sound signals at similar frequencies, produce sweeter nectar within 3 min, potentially increasing the chances of cross pollination. We found that the flowers vibrated mechanically in response to these sounds, suggesting a plausible mechanism where the flower serves as an auditory sensory organ. Both the vibration and the nectar response were frequency-specific: the flowers responded and vibrated to pollinator sounds, but not to higher frequency sound. Our results document for the first time that plants can rapidly respond to pollinator sounds in an ecologically relevant way. Potential implications include plant resource allocation, the evolution of flower shape and the evolution of pollinators sound. Finally, our results suggest that plants may be affected by other sounds as well, including anthropogenic ones.

Key issues review: evolution on rugged adaptive landscapes.

Adaptive landscapes represent a mapping between genotype and fitness. Rugged adaptive landscapes contain two or more adaptive peaks: allele combinations with higher fitness than any of their neighbors in the genetic space. How do populations evolve on such rugged landscapes? Evolutionary biologists have struggled with this question since it was first introduced in the 1930s by Sewall Wright. Discoveries in the fields of genetics and biochemistry inspired various mathematical models of adaptive landscapes. The development of landscape models led to numerous theoretical studies analyzing evolution on rugged landscapes under different biological conditions. The large body of theoretical work suggests that adaptive landscapes are major determinants of the progress and outcome of evolutionary processes. Recent technological advances in molecular biology and microbiology allow experimenters to measure adaptive values of large sets of allele combinations and construct empirical adaptive landscapes for the first time. Such empirical landscapes have already been generated in bacteria, yeast, viruses, and fungi, and are contributing to new insights about evolution on adaptive landscapes. In this Key Issues Review we will: (i) introduce the concept of adaptive landscapes; (ii) review the major theoretical studies of evolution on rugged landscapes; (iii) review some of the recently obtained empirical adaptive landscapes; (iv) discuss recent mathematical and statistical analyses motivated by empirical adaptive landscapes, as well as provide the reader with instructions and source code to implement simulations of evolution on adaptive landscapes; and (v) discuss possible future directions for this exciting field.

With a little help from my friends: cooperation can accelerate the rate of adaptive valley crossing.

BACKGROUND: Natural selection favors changes that lead to genotypes possessing high fitness. A conflict arises when several mutations are required for adaptation, but each mutation is separately deleterious. The process of a population evolving from a genotype encoding for a local fitness maximum to a higher fitness genotype is termed an adaptive peak shift. RESULTS: Here we suggest cooperative behavior as a factor that can facilitate adaptive peak shifts. We model cooperation in a public goods scenario, wherein each individual contributes resources that are later equally redistributed among all cooperating individuals. We use mathematical modeling and stochastic simulations to study the effect of cooperation on peak shifts in both panmictic and structured populations. Our results show that cooperation can substantially affect the rate of complex adaptation. Furthermore, we show that cooperation increases the population diversity throughout the peak shift process, thus increasing the robustness of the population to sudden environmental changes. CONCLUSIONS: We provide a new explanation to adaptive valley crossing in natural populations and suggest that the long term evolution of a species depends on its social behavior.

Bimodal regulation of ICR1 levels generates self-organizing auxin distribution.

Auxin polar transport, local maxima, and gradients have become an important model system for studying self-organization. Auxin distribution is regulated by auxin-dependent positive feedback loops that are not well-understood at the molecular level. Previously, we showed the involvement of the RHO of Plants (ROP) effector INTERACTOR of CONSTITUTIVELY active ROP 1 (ICR1) in regulation of auxin transport and that ICR1 levels are posttranscriptionally repressed at the site of maximum auxin accumulation at the root tip. Here, we show that bimodal regulation of ICR1 levels by auxin is essential for regulating formation of auxin local maxima and gradients. ICR1 levels increase concomitant with increase in auxin response in lateral root primordia, cotyledon tips, and provascular tissues. However, in the embryo hypophysis and root meristem, when auxin exceeds critical levels, ICR1 is rapidly destabilized by an SCF(TIR1/AFB) [SKP, Cullin, F-box (transport inhibitor response 1/auxin signaling F-box protein)]-dependent auxin signaling mechanism. Furthermore, ectopic expression of ICR1 in the embryo hypophysis resulted in reduction of auxin accumulation and concomitant root growth arrest. ICR1 disappeared during root regeneration and lateral root initiation concomitantly with the formation of a local auxin maximum in response to external auxin treatments and transiently after gravitropic stimulation. Destabilization of ICR1 was impaired after inhibition of auxin transport and signaling, proteasome function, and protein synthesis. A mathematical model based on these findings shows that an in vivo-like auxin distribution, rootward auxin flux, and shootward reflux can be simulated without assuming preexisting tissue polarity. Our experimental results and mathematical modeling indicate that regulation of auxin distribution is tightly associated with auxin-dependent ICR1 levels.