Ex Vivo Experiments Shed Light on the Innate Immune Response from Influenza Virus.

The innate immune response is recognized as a key driver in controlling an influenza virus infection in a host. However, the mechanistic action of such innate response is not fully understood. Infection experiments on ex vivo explants from swine trachea represent an efficient alternative to animal experiments, as the explants conserved key characteristics of an organ from an animal. In the present work we compare three cellular automata models of influenza virus dynamics. The models are fitted to free virus and infected cells data from ex vivo swine trachea experiments. Our findings suggest that the presence of an immune response is necessary to explain the observed dynamics in ex vivo organ culture. Moreover, such immune response should include a refractory state for epithelial cells, and not just a reduced infection rate. Our results may shed light on how the immune system responds to an infection event.

Binding Specificity of a Novel Cyclo/Maltodextrin-Binding Protein and Its Role in the Cyclodextrin ABC Importer System from Thermoanaerobacterales.

Extracellular synthesis of functional cyclodextrins (CDs) as intermediates of starch assimilation is a convenient microbial adaptation to sequester substrates, increase the half-life of the carbon source, carry bioactive compounds, and alleviate chemical toxicity through the formation of CD-guest complexes. Bacteria encoding the four steps of the carbohydrate metabolism pathway via cyclodextrins (CM-CD) actively internalize CDs across the microbial membrane via a putative type I ATP-dependent ABC sugar importer system, MdxEFG-(X/MsmX). While the first step of the CM-CD pathway encompasses extracellular starch-active cyclomaltodextrin glucanotransferases (CGTases) to synthesize linear dextrins and CDs, it is the ABC importer system in the second step that is the critical factor in determining which molecules from the CGTase activity will be internalized by the cell. Here, structure-function relationship studies of the cyclo/maltodextrin-binding protein MdxE of the MdxEFG-MsmX importer system from Thermoanaerobacter mathranii subsp. mathranii A3 are presented. Calorimetric and fluorescence studies of recombinant MdxE using linear dextrins and CDs showed that although MdxE binds linear dextrins and CDs with high affinity, the open-to-closed conformational change is solely observed after α- and ß-CD binding, suggesting that the CM-CD pathway from Thermoanaerobacterales is exclusive for cellular internalization of these molecules. Structural analysis of MdxE coupled with docking simulations showed an overall architecture typically found in sugar-binding proteins (SBPs) that comprised two N- and C-domains linked by three small hinge regions, including the conserved aromatic triad Tyr193/Trp269/Trp378 in the C-domain and Phe87 in the N-domain involved in CD recognition and stabilization. Structural bioinformatic analysis of the entire MdxFG-MsmX importer system provided further insights into the binding, internalization, and delivery mechanisms of CDs. Hence, while the MdxE-CD complex couples to the permease subunits MdxFG to deliver the CD into the transmembrane channel, the dimerization of the cytoplasmatic promiscuous ATPase MsmX triggers active transport into the cytoplasm. This research provides the first results on a novel thermofunctional SBP and its role in the internalization of CDs in extremely thermophilic bacteria.

Mining for novel cyclomaltodextrin glucanotransferases unravels the carbohydrate metabolism pathway via cyclodextrins in Thermoanaerobacterales.

Carbohydrate metabolism via cyclodextrins (CM-CD) is an uncommon starch-converting pathway that thoroughly depends on extracellular cyclomaltodextrin glucanotransferases (CGTases) to transform the surrounding starch substrate to α-(1,4)-linked oligosaccharides and cyclodextrins (CDs). The CM-CD pathway has emerged as a convenient microbial adaptation to thrive under extreme temperatures, as CDs are functional amphipathic toroids with higher heat-resistant values than linear dextrins. Nevertheless, although the CM-CD pathway has been described in a few mesophilic bacteria and archaea, it remains obscure in extremely thermophilic prokaryotes (Topt ≥ 70 °C). Here, a new monophyletic group of CGTases with an exceptional three-domain ABC architecture was detected by (meta)genome mining of extremely thermophilic Thermoanaerobacterales living in a wide variety of hot starch-poor environments on Earth. Functional studies of a representative member, CldA, showed a maximum activity in a thermoacidophilic range (pH 4.0 and 80 °C) with remarkable product diversification that yielded a mixture of α:ß:γ-CDs (34:62:4) from soluble starch, as well as G3-G7 linear dextrins and fermentable sugars as the primary products. Together, comparative genomics and predictive functional analysis, combined with data of the functionally characterized key proteins of the gene clusters encoding CGTases, revealed the CM-CD pathway in Thermoanaerobacterales and showed that it is involved in the synthesis, transportation, degradation, and metabolic assimilation of CDs.

Employing phylogenetic tree shape statistics to resolve the underlying host population structure.

BACKGROUND: Host population structure is a key determinant of pathogen and infectious disease transmission patterns. Pathogen phylogenetic trees are useful tools to reveal the population structure underlying an epidemic. Determining whether a population is structured or not is useful in informing the type of phylogenetic methods to be used in a given study. We employ tree statistics derived from phylogenetic trees and machine learning classification techniques to reveal an underlying population structure. RESULTS: In this paper, we simulate phylogenetic trees from both structured and non-structured host populations. We compute eight statistics for the simulated trees, which are: the number of cherries; Sackin, Colless and total cophenetic indices; ladder length; maximum depth; maximum width, and width-to-depth ratio. Based on the estimated tree statistics, we classify the simulated trees as from either a non-structured or a structured population using the decision tree (DT), K-nearest neighbor (KNN) and support vector machine (SVM). We incorporate the basic reproductive number ([Formula: see text]) in our tree simulation procedure. Sensitivity analysis is done to investigate whether the classifiers are robust to different choice of model parameters and to size of trees. Cross-validated results for area under the curve (AUC) for receiver operating characteristic (ROC) curves yield mean values of over 0.9 for most of the classification models. CONCLUSIONS: Our classification procedure distinguishes well between trees from structured and non-structured populations using the classifiers, the two-sample Kolmogorov-Smirnov, Cucconi and Podgor-Gastwirth tests and the box plots. SVM models were more robust to changes in model parameters and tree size compared to KNN and DT classifiers. Our classification procedure was applied to real -world data and the structured population was revealed with high accuracy of [Formula: see text] using SVM-polynomial classifier.

Transmission dynamics and forecasts of the COVID-19 pandemic in Mexico, March-December 2020.

Mexico has experienced one of the highest COVID-19 mortality rates in the world. A delayed implementation of social distancing interventions in late March 2020 and a phased reopening of the country in June 2020 has facilitated sustained disease transmission in the region. In this study we systematically generate and compare 30-day ahead forecasts using previously validated growth models based on mortality trends from the Institute for Health Metrics and Evaluation for Mexico and Mexico City in near real-time. Moreover, we estimate reproduction numbers for SARS-CoV-2 based on the methods that rely on genomic data as well as case incidence data. Subsequently, functional data analysis techniques are utilized to analyze the shapes of COVID-19 growth rate curves at the state level to characterize the spatiotemporal transmission patterns of SARS-CoV-2. The early estimates of the reproduction number for Mexico were estimated between Rt ~1.1-1.3 from the genomic and case incidence data. Moreover, the mean estimate of Rt has fluctuated around ~1.0 from late July till end of September 2020. The spatial analysis characterizes the state-level dynamics of COVID-19 into four groups with distinct epidemic trajectories based on epidemic growth rates. Our results show that the sequential mortality forecasts from the GLM and Richards model predict a downward trend in the number of deaths for all thirteen forecast periods for Mexico and Mexico City. However, the sub-epidemic and IHME models perform better predicting a more realistic stable trajectory of COVID-19 mortality trends for the last three forecast periods (09/21-10/21, 09/28-10/27, 09/28-10/27) for Mexico and Mexico City. Our findings indicate that phenomenological models are useful tools for short-term epidemic forecasting albeit forecasts need to be interpreted with caution given the dynamic implementation and lifting of social distancing measures.

Nested model reveals potential amplification of an HIV epidemic due to drug resistance.

The use of antiretroviral therapy (ART) is the most efficient measure in controlling the HIV epidemic. However, emergence of drug-resistant strains can reduce the potential benefits of ART. The viral dynamics of drug-sensitive and drug-resistant strains at the individual level may play a crucial role in the emergence and spread of drug resistance in a population. We investigate the effect of the viral dynamics within an infected individual on the epidemiological dynamics of HIV using a nested model that links both dynamical levels. A time-dependent between-host transmission rate that receives feedback from a model of two-strain virus dynamics within a host is incorporated into an epidemiological model of HIV. We analyze the resulting dynamics of the model and identify model parameters such as time when ART is initiated, fraction of cases treated, and the probability that a patient develops drug resistance, as having the greatest impact on total infection and prevalence of drug resistance. Importantly, for small values of the risk of a patient developing drug resistance, increasing the fraction of cases treated can increase the cumulative number of infected individuals. Such a pattern is the result of the balance between not treating a patient and having future cases still sensitive to treatment, and treating the patient and increasing the chances for future (untreatable) drug-resistant infections. The current modeling framework incorporates important aspects of virus dynamics within a host into an epidemic model. This approach provides useful insights on the drug resistance dynamics of an epidemic of HIV, which may assist in identifying an optimal use of ART.

The role of backward mutations on the within-host dynamics of HIV-1.

The quality of life for patients infected with human immunodeficiency virus (HIV-1) has been positively impacted by the use of antiretroviral therapy (ART). However, the benefits of ART are usually halted by the emergence of drug resistance. Drug-resistant strains arise from virus mutations, as HIV-1 reverse transcription is prone to errors, with mutations normally carrying fitness costs to the virus. When ART is interrupted, the wild-type drug-sensitive strain rapidly out-competes the resistant strain, as the former strain is fitter than the latter in the absence of ART. One mechanism for sustaining the sensitive strain during ART is given by the virus mutating from resistant to sensitive strains, which is referred to as backward mutation. This is important during periods of treatment interruptions as prior existence of the sensitive strain would lead to replacement of the resistant strain. In order to assess the role of backward mutations in the dynamics of HIV-1 within an infected host, we analyze a mathematical model of two interacting virus strains in either absence or presence of ART. We study the effect of backward mutations on the definition of the basic reproductive number, and the value and stability of equilibrium points. The analysis of the model shows that, thanks to both forward and backward mutations, sensitive and resistant strains co-exist. In addition, conditions for the dominance of a viral strain with or without ART are provided. For this model, backward mutations are shown to be necessary for the persistence of the sensitive strain during ART.

Quantifying transmission of highly pathogenic and low pathogenicity H7N1 avian influenza in turkeys.

Outbreaks of avian influenza in poultry can be devastating, yet many of the basic epidemiological parameters have not been accurately characterised. In 1999-2000 in Northern Italy, outbreaks of H7N1 low pathogenicity avian influenza virus (LPAI) were followed by the emergence of H7N1 highly pathogenic avian influenza virus (HPAI). This study investigates the transmission dynamics in turkeys of representative HPAI and LPAI H7N1 virus strains from this outbreak in an experimental setting, allowing direct comparison of the two strains. The fitted transmission rates for the two strains are similar: 2.04 (1.5-2.7) per day for HPAI, 2.01 (1.6-2.5) per day for LPAI. However, the mean infectious period is far shorter for HPAI (1.47 (1.3-1.7) days) than for LPAI (7.65 (7.0-8.3) days), due to the rapid death of infected turkeys. Hence the basic reproductive ratio, [Formula: see text] is significantly lower for HPAI (3.01 (2.2-4.0)) than for LPAI (15.3 (11.8-19.7)). The comparison of transmission rates and [Formula: see text] are critically important in relation to understanding how HPAI might emerge from LPAI. Two competing hypotheses for how transmission rates vary with population size are tested by fitting competing models to experiments with differing numbers of turkeys. A model with frequency-dependent transmission gives a significantly better fit to experimental data than density-dependent transmission. This has important implications for extrapolating experimental results from relatively small numbers of birds to the commercial poultry flock size, and for how control, including vaccination, might scale with flock size.

Dynamics of influenza virus infection and pathology.

A key question in pandemic influenza is the relative roles of innate immunity and target cell depletion in limiting primary infection and modulating pathology. Here, we model these interactions using detailed data from equine influenza virus infection, combining viral and immune (type I interferon) kinetics with estimates of cell depletion. The resulting dynamics indicate a powerful role for innate immunity in controlling the rapid peak in virus shedding. As a corollary, cells are much less depleted than suggested by a model of human influenza based only on virus-shedding data. We then explore how differences in the influence of viral proteins on interferon kinetics can account for the observed spectrum of virus shedding, immune response, and influenza pathology. In particular, induction of high levels of interferon ("cytokine storms"), coupled with evasion of its effects, could lead to severe pathology, as hypothesized for some fatal cases of influenza.

Confined animal feeding operations as amplifiers of influenza.

Influenza pandemics occur when a novel influenza strain, often of animal origin, becomes transmissible between humans. Domestic animal species such as poultry or swine in confined animal feeding operations (CAFOs) could serve as local amplifiers for such a new strain of influenza. A mathematical model is used to examine the transmission dynamics of a new influenza virus among three sequentially linked populations: the CAFO species, the CAFO workers (the bridging population), and the rest of the local human population. Using parameters based on swine data, simulations showed that when CAFO workers comprised 15-45% of the community, human influenza cases increased by 42-86%. Successful vaccination of at least 50% of CAFO workers cancelled the amplification. A human influenza epidemic due to a new virus could be locally amplified by the presence of confined animal feeding operations in the community. Thus vaccination of CAFO workers would be an effective use of a pandemic vaccine.

Competing species models with an infectious disease.

The frequency-dependent (standard) form of the incidence is used for the transmission dynamics of an infectious disease in a competing species model. In the global analysis of the SIS model with the birth rate independent of the population size, a modified reproduction number R(1) determines the asymptotic behavior, so that the disease dies out if R(1) 1. Because the disease-reduced reproduction and disease-related death rates are often different in two competing species, a shared disease can change the outcome of the competition. Models of SIR and SIRS type are also considered. A key result in all of these models with the frequency-dependent incidence is that the disease must either die out in both species or remain endemic in both species.