Modelling the effectiveness of an isolation strategy for managing mpox outbreaks with variable infectiousness profiles.

The global outbreak of mpox in 2022 and subsequent sporadic outbreaks in 2023 highlighted the importance of nonpharmaceutical interventions such as case isolation. Individual variations in viral shedding dynamics may lead to either premature ending of isolation for infectious individuals, or unnecessarily prolonged isolation for those who are no longer infectious. Here, we developed a modeling framework to characterize heterogeneous mpox infectiousness profiles - specifically, when infected individuals cease to be infectious - based on viral load data. We examined the potential effectiveness of three different isolation rules: a symptom-based rule (the current guideline in many countries) and rules permitting individuals to stop isolating after either a fixed duration or following tests that indicate that they are no longer likely to be infectious. Our analysis suggests that the duration of viral shedding ranges from 23 to 50 days between individuals. The risk of infected individuals ending isolation too early was estimated to be 8.8% (95% CI: 6.7-10.5) after symptom clearance and 5.4% (95% CI: 4.1-6.7) after 3 weeks of isolation. While these results suggest that the current standard practice for ending isolation is effective, we found that unnecessary isolation following the infectious period could be reduced by adopting a testing-based rule.

Optimizing the timing of an end-of-outbreak declaration: Ebola virus disease in the Democratic Republic of the Congo.

Following the apparent final case in an Ebola virus disease (EVD) outbreak, the decision to declare the outbreak over must balance societal benefits of relaxing interventions against the risk of resurgence. Estimates of the end-of-outbreak probability (the probability that no future cases will occur) provide quantitative evidence that can inform the timing of an end-of-outbreak declaration. An existing modeling approach for estimating the end-of-outbreak probability requires comprehensive contact tracing data describing who infected whom to be available, but such data are often unavailable or incomplete during outbreaks. Here, we develop a Markov chain Monte Carlo-based approach that extends the previous method and does not require contact tracing data. Considering data from two EVD outbreaks in the Democratic Republic of the Congo, we find that data describing who infected whom are not required to resolve uncertainty about when to declare an outbreak over.

A Histone Methylation-MAPK Signaling Axis Drives Durable Epithelial-Mesenchymal Transition in Hypoxic Pancreatic Cancer.

The tumor microenvironment in pancreatic ductal adenocarcinoma (PDAC) plays a key role in tumor progression and response to therapy. The dense PDAC stroma causes hypovascularity, which leads to hypoxia. Here, we showed that hypoxia drives long-lasting epithelial-mesenchymal transition (EMT) in PDAC primarily through a positive-feedback histone methylation-MAPK signaling axis. Transformed cells preferentially underwent EMT in hypoxic tumor regions in multiple model systems. Hypoxia drove a cell autonomous EMT in PDAC cells, which, unlike EMT in response to growth factors, could last for weeks. Furthermore, hypoxia reduced histone demethylase KDM2A activity, suppressed PP2 family phosphatase expression, and activated MAPKs to post-translationally stabilize histone methyltransferase NSD2, leading to an H3K36me2-dependent EMT in which hypoxia-inducible factors played only a supporting role. Hypoxia-driven EMT could be antagonized in vivo by combinations of MAPK inhibitors. Collectively, these results suggest that hypoxia promotes durable EMT in PDAC by inducing a histone methylation-MAPK axis that can be effectively targeted with multidrug therapies, providing a potential strategy for overcoming chemoresistance. SIGNIFICANCE: Integrated regulation of histone methylation and MAPK signaling by the low-oxygen environment of pancreatic cancer drives long-lasting EMT that promotes chemoresistance and shortens patient survival and that can be pharmacologically inhibited. See related commentary by Wirth and Schneider, p. 1739.

Divergent transcriptomic signatures from putative mesenchymal stimuli in glioblastoma cells.

In glioblastoma, a mesenchymal phenotype is associated with especially poor patient outcomes. Various glioblastoma microenvironmental factors and therapeutic interventions are purported drivers of the mesenchymal transition, but the degree to which these cues promote the same mesenchymal transitions and the uniformity of those transitions, as defined by molecular subtyping systems, is unknown. Here, we investigate this question by analyzing publicly available patient data, surveying commonly measured transcripts for mesenchymal transitions in glioma-initiating cells (GIC), and performing next-generation RNA sequencing of GICs. Analysis of patient tumor data reveals that TGFß, TNFα, and hypoxia signaling correlate with the mesenchymal subtype more than the proneural subtype. In cultured GICs, the microenvironment-relevant growth factors TGFß and TNFα and the chemotherapeutic temozolomide promote expression of commonly measured mesenchymal transcripts. However, next-generation RNA sequencing reveals that growth factors and temozolomide broadly promote expression of both mesenchymal and proneural transcripts, in some cases with equal frequency. These results suggest that glioblastoma mesenchymal transitions do not occur as distinctly as in epithelial-derived cancers, at least as determined using common subtyping ontologies and measuring response to growth factors or chemotherapeutics. Further understanding of these issues may identify improved methods for pharmacologically targeting the mesenchymal phenotype in glioblastoma.

Isolation may select for earlier and higher peak viral load but shorter duration in SARS-CoV-2 evolution.

During the COVID-19 pandemic, human behavior change as a result of nonpharmaceutical interventions such as isolation may have induced directional selection for viral evolution. By combining previously published empirical clinical data analysis and multi-level mathematical modeling, we find that the SARS-CoV-2 variants selected for as the virus evolved from the pre-Alpha to the Delta variant had earlier and higher peak in viral load dynamics but a shorter duration of infection. Selection for increased transmissibility shapes the viral load dynamics, and the isolation measure is likely to be a driver of these evolutionary transitions. In addition, we show that a decreased incubation period and an increased proportion of asymptomatic infection are also positively selected for as SARS-CoV-2 mutated to adapt to human behavior (i.e., Omicron variants). The quantitative information and predictions we present here can guide future responses in the potential arms race between pandemic interventions and viral evolution.

Analysis of the risk and pre-emptive control of viral outbreaks accounting for within-host dynamics: SARS-CoV-2 as a case study.

In the era of living with COVID-19, the risk of localised SARS-CoV-2 outbreaks remains. Here, we develop a multiscale modelling framework for estimating the local outbreak risk for a viral disease (the probability that a major outbreak results from a single case introduced into the population), accounting for within-host viral dynamics. Compared to population-level models previously used to estimate outbreak risks, our approach enables more detailed analysis of how the risk can be mitigated through pre-emptive interventions such as antigen testing. Considering SARS-CoV-2 as a case study, we quantify the within-host dynamics using data from individuals with omicron variant infections. We demonstrate that regular antigen testing reduces, but may not eliminate, the outbreak risk, depending on characteristics of local transmission. In our baseline analysis, daily antigen testing reduces the outbreak risk by 45% compared to a scenario without antigen testing. Additionally, we show that accounting for heterogeneity in within-host dynamics between individuals affects outbreak risk estimates and assessments of the impact of antigen testing. Our results therefore highlight important factors to consider when using multiscale models to design pre-emptive interventions against SARS-CoV-2 and other viruses.

Contact-number-driven virus evolution: A multi-level modeling framework for the evolution of acute or persistent RNA virus infection.

Viruses evolve in infected host populations, and host population dynamics affect viral evolution. RNA viruses with a short duration of infection and a high peak viral load, such as SARS-CoV-2, are maintained in human populations. By contrast, RNA viruses characterized by a long infection duration and a low peak viral load (e.g., borna disease virus) can be maintained in nonhuman populations, and the process of the evolution of persistent viruses has rarely been explored. Here, using a multi-level modeling approach including both individual-level virus infection dynamics and population-scale transmission, we consider virus evolution based on the host environment, specifically, the effect of the contact history of infected hosts. We found that, with a highly dense contact history, viruses with a high virus production rate but low accuracy are likely to be optimal, resulting in a short infectious period with a high peak viral load. In contrast, with a low-density contact history, viral evolution is toward low virus production but high accuracy, resulting in long infection durations with low peak viral load. Our study sheds light on the origin of persistent viruses and why acute viral infections but not persistent virus infection tends to prevail in human society.

Understanding how transmembrane domains regulate interactions between human BST-2 and the SARS-CoV-2 accessory protein ORF7a.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of COVID, replicates at intracellular membranes. Bone marrow stromal antigen 2 (BST-2; tetherin) is an antiviral response protein that inhibits transport of viral particles after budding within infected cells. RNA viruses such as SARS-CoV-2 use various strategies to disable BST-2, including use of transmembrane 'accessory' proteins that interfere with BST-2 oligomerization. ORF7a is a small, transmembrane protein present in SARS-CoV-2 shown previously to alter BST-2 glycosylation and function. In this study, we investigated the structural basis for BST-2 ORF7a interactions, with a particular focus on transmembrane and juxtamembrane interactions. Our results indicate that transmembrane domains play an important role in BST-2 ORF7a interactions and mutations to the transmembrane domain of BST-2 can alter these interactions, particularly single-nucleotide polymorphisms in BST-2 that result in mutations such as I28S. Using molecular dynamics simulations, we identified specific interfaces and interactions between BST-2 and ORF7a to develop a structural basis for the transmembrane interactions. Differences in glycosylation are observed for BST-2 transmembrane mutants interacting with ORF7a, consistent with the idea that transmembrane domains play a key role in their heterooligomerization. Overall, our results indicate that ORF7a transmembrane domain interactions play a key role along with extracellular and juxtamembrane domains in modulating BST-2 function.

Inference of the SARS-CoV-2 generation time using UK household data.

The distribution of the generation time (the interval between individuals becoming infected and transmitting the virus) characterises changes in the transmission risk during SARS-CoV-2 infections. Inferring the generation time distribution is essential to plan and assess public health measures. We previously developed a mechanistic approach for estimating the generation time, which provided an improved fit to data from the early months of the COVID-19 pandemic (December 2019-March 2020) compared to existing models (Hart et al., 2021). However, few estimates of the generation time exist based on data from later in the pandemic. Here, using data from a household study conducted from March to November 2020 in the UK, we provide updated estimates of the generation time. We considered both a commonly used approach in which the transmission risk is assumed to be independent of when symptoms develop, and our mechanistic model in which transmission and symptoms are linked explicitly. Assuming independent transmission and symptoms, we estimated a mean generation time (4.2 days, 95% credible interval 3.3-5.3 days) similar to previous estimates from other countries, but with a higher standard deviation (4.9 days, 3.0-8.3 days). Using our mechanistic approach, we estimated a longer mean generation time (5.9 days, 5.2-7.0 days) and a similar standard deviation (4.8 days, 4.0-6.3 days). As well as estimating the generation time using data from the entire study period, we also considered whether the generation time varied temporally. Both models suggest a shorter mean generation time in September-November 2020 compared to earlier months. Since the SARS-CoV-2 generation time appears to be changing, further data collection and analysis is necessary to continue to monitor ongoing transmission and inform future public health policy decisions.

Generation time of the alpha and delta SARS-CoV-2 variants: an epidemiological analysis.

BACKGROUND: In May, 2021, the delta (B.1.617.2) SARS-CoV-2 variant became dominant in the UK, superseded by the omicron (B.1.1.529) variant in December, 2021. The delta variant is associated with increased transmissibility compared with the alpha variant, which was the dominant variant in the UK between December, 2020, and May, 2021. To understand transmission and the effectiveness of interventions, we aimed to investigate whether the delta variant generation time (the interval between infections in infector-infectee pairs) is shorter-ie, transmissions are happening more quickly-than that of the alpha variant. METHODS: In this epidemiological analysis, we analysed transmission data from an ongoing UK Health Security Agency (UKHSA) prospective household study. Households were recruited to the study after an index case had a positive PCR test and genomic sequencing was used to determine the variant responsible. By fitting a mathematical transmission model to the data, we estimated the intrinsic generation time (which assumes a constant supply of susceptible individuals throughout infection) and the household generation time (which reflects realised transmission in the study households, accounting for susceptible depletion) for the alpha and delta variants. FINDINGS: Between February and August, 2021, 227 households consisting of 559 participants were recruited to the UKHSA study. The alpha variant was detected or assumed to be responsible for infections in 131 households (243 infections in 334 participants) recruited in February-May, and the delta variant in 96 households (174 infections in 225 participants) in May-August. The mean intrinsic generation time was shorter for the delta variant (4·7 days, 95% credible interval [CI] 4·1-5·6) than the alpha variant (5·5 days, 4·7-6·5), with 92% posterior probability. The mean household generation time was 28% (95% CI 0-48%) shorter for the delta variant (3·2 days, 95% CI 2·5-4·2) than the alpha variant (4·5 days, 3·7-5·4), with 97·5% posterior probability. INTERPRETATION: The delta variant transmits more quickly in households than the alpha variant, which can be attributed to faster depletion of susceptible individuals in households and a possible decrease in the intrinsic generation time. Interventions such as contact tracing, testing, and isolation might be less effective if transmission of the virus occurs quickly. FUNDING: National Institute for Health Research, UK Health Security Agency, Engineering and Physical Sciences Research Council, and UK Research and Innovation.

High infectiousness immediately before COVID-19 symptom onset highlights the importance of continued contact tracing.

Background: Understanding changes in infectiousness during SARS-COV-2 infections is critical to assess the effectiveness of public health measures such as contact tracing. Methods: Here, we develop a novel mechanistic approach to infer the infectiousness profile of SARS-COV-2-infected individuals using data from known infector-infectee pairs. We compare estimates of key epidemiological quantities generated using our mechanistic method with analogous estimates generated using previous approaches. Results: The mechanistic method provides an improved fit to data from SARS-CoV-2 infector-infectee pairs compared to commonly used approaches. Our best-fitting model indicates a high proportion of presymptomatic transmissions, with many transmissions occurring shortly before the infector develops symptoms. Conclusions: High infectiousness immediately prior to symptom onset highlights the importance of continued contact tracing until effective vaccines have been distributed widely, even if contacts from a short time window before symptom onset alone are traced. Funding: Engineering and Physical Sciences Research Council (EPSRC).

The risk of a person with COVID-19 spreading the SARS-CoV-2 virus that causes it to others varies over the course of their infection. Transmission depends both on how much virus is in the infected person's airway and their behaviors, such as whether they wear a mask and how many people they have contact with. Learning more about when people are most infectious would help public health officials stop the spread of the virus. For example, officials can then introduce policies that ensure that people are isolated when they are most infectious. The majority of studies assessing when people with COVID-19 are most infectious so far have assumed that transmission is not linked to when symptoms appear. But that may not be true. After people develop symptoms, they may be more likely to stay home, avoid others, or take other measures that prevent transmission. Using computer modeling and data from previous studies of individuals who infected others with SARS-CoV-2, Hart et al. show that about 65% of virus transmission occurs before symptoms develop. In fact, the computational experiments show the risk of transmission is highest immediately before symptoms develop. This highlights the importance of identifying people exposed to someone infected with the virus and isolating potential recipients before they develop symptoms. This information may help public health officials develop more effective strategies to prevent the spread of SARS-CoV-2. It may also help scientists develop more accurate models to predict the spread of the virus. However, the computational experiments used data on infections early in the pandemic that may not reflect the current situation. Changes in public health policy, the behavior of individuals and the appearance of new strains of SARS-CoV-2, all affect the timing of transmission. As more recent data become available, Hart et al. plan to explore how characteristics of transmission have changed as the pandemic has progressed.

Key questions for modelling COVID-19 exit strategies.

Combinations of intense non-pharmaceutical interventions (lockdowns) were introduced worldwide to reduce SARS-CoV-2 transmission. Many governments have begun to implement exit strategies that relax restrictions while attempting to control the risk of a surge in cases. Mathematical modelling has played a central role in guiding interventions, but the challenge of designing optimal exit strategies in the face of ongoing transmission is unprecedented. Here, we report discussions from the Isaac Newton Institute 'Models for an exit strategy' workshop (11-15 May 2020). A diverse community of modellers who are providing evidence to governments worldwide were asked to identify the main questions that, if answered, would allow for more accurate predictions of the effects of different exit strategies. Based on these questions, we propose a roadmap to facilitate the development of reliable models to guide exit strategies. This roadmap requires a global collaborative effort from the scientific community and policymakers, and has three parts: (i) improve estimation of key epidemiological parameters; (ii) understand sources of heterogeneity in populations; and (iii) focus on requirements for data collection, particularly in low-to-middle-income countries. This will provide important information for planning exit strategies that balance socio-economic benefits with public health.

Comparative Biochemical and Structural Analysis of Novel Cellulose Binding Proteins (Tapirins) from Extremely Thermophilic Caldicellulosiruptor Species.

Genomes of extremely thermophilic Caldicellulosiruptor species encode novel cellulose binding proteins, called tapirins, located proximate to the type IV pilus locus. The C-terminal domain of Caldicellulosiruptor kronotskyensis tapirin 0844 (Calkro_0844) is structurally unique and has a cellulose binding affinity akin to that seen with family 3 carbohydrate binding modules (CBM3s). Here, full-length and C-terminal versions of tapirins from Caldicellulosiruptor bescii (Athe_1870), Caldicellulosiruptor hydrothermalis (Calhy_0908), Caldicellulosiruptor kristjanssonii (Calkr_0826), and Caldicellulosiruptor naganoensis (NA10_0869) were produced recombinantly in Escherichia coli and compared to Calkro_0844. All five tapirins bound to microcrystalline cellulose, switchgrass, poplar, and filter paper but not to xylan. Densitometry analysis of bound protein fractions visualized by SDS-PAGE revealed that Calhy_0908 and Calkr_0826 (from weakly cellulolytic species) associated with the cellulose substrates to a greater extent than Athe_1870, Calkro_0844, and NA10_0869 (from strongly cellulolytic species). Perhaps this relates to their specific needs to capture glucans released from lignocellulose by cellulases produced in Caldicellulosiruptor communities. Calkro_0844 and NA10_0869 share a higher degree of amino acid sequence identity (>80% identity) with each other than either does with Athe_1870 (∼50%). The levels of amino acid sequence identity of Calhy_0908 and Calkr_0826 to Calkro_0844 were only 16% and 36%, respectively, although the three-dimensional structures of their C-terminal binding regions were closely related. Unlike the parent strain, C. bescii mutants lacking the tapirin genes did not bind to cellulose following short-term incubation, suggesting a role in cell association with plant biomass. Given the scarcity of carbohydrates in neutral terrestrial hot springs, tapirins likely help scavenge carbohydrates from lignocellulose to support growth and survival of Caldicellulosiruptor species.IMPORTANCE The mechanisms by which microorganisms attach to and degrade lignocellulose are important to understand if effective approaches for conversion of plant biomass into fuels and chemicals are to be developed. Caldicellulosiruptor species grow on carbohydrates from lignocellulose at elevated temperatures and have biotechnological significance for that reason. Novel cellulose binding proteins, called tapirins, are involved in the way that Caldicellulosiruptor species interact with microcrystalline cellulose, and additional information about the diversity of these proteins across the genus, including binding affinity and three-dimensional structural comparisons, is provided here.