Population impact of SARS-CoV-2 variants with enhanced transmissibility and/or partial immune escape.

SARS-CoV-2 variants of concern exhibit varying degrees of transmissibility and, in some cases, escape from acquired immunity. Much effort has been devoted to measuring these phenotypes, but understanding their impact on the course of the pandemic-especially that of immune escape-has remained a challenge. Here, we use a mathematical model to simulate the dynamics of wild-type and variant strains of SARS-CoV-2 in the context of vaccine rollout and nonpharmaceutical interventions. We show that variants with enhanced transmissibility frequently increase epidemic severity, whereas those with partial immune escape either fail to spread widely or primarily cause reinfections and breakthrough infections. However, when these phenotypes are combined, a variant can continue spreading even as immunity builds up in the population, limiting the impact of vaccination and exacerbating the epidemic. These findings help explain the trajectories of past and present SARS-CoV-2 variants and may inform variant assessment and response in the future.

Lineage tracing reveals B cell antibody class switching is stochastic, cell-autonomous, and tuneable.

To optimize immunity to pathogens, B lymphocytes generate plasma cells with functionally diverse antibody isotypes. By lineage tracing single cells within differentiating B cell clones, we identified the heritability of discrete fate controlling mechanisms to inform a general mathematical model of B cell fate regulation. Founder cells highly influenced clonal plasma-cell fate, whereas class switch recombination (CSR) was variegated within clones. In turn, these CSR patterns resulted from independent all-or-none expression of both activation-induced cytidine deaminase (AID) and IgH germline transcription (GLT), with the latter being randomly re-expressed after each cell division. A stochastic model premised on these molecular transition rules accurately predicted antibody switching outcomes under varied conditions in vitro and during an immune response in vivo. Thus, the generation of functionally diverse antibody types follows rules of autonomous cellular programming that can be adapted and modeled for the rational control of antibody classes for potential therapeutic benefit.

The Clock and Wavefront Self-Organizing model recreates the dynamics of mouse somitogenesis in vivo and in vitro.

During mouse development, presomitic mesoderm cells synchronize Wnt and Notch oscillations, creating sequential phase waves that pattern somites. Traditional somitogenesis models attribute phase waves to a global modulation of the oscillation frequency. However, increasing evidence suggests that they could arise in a self-organizing manner. Here, we introduce the Sevilletor, a novel reaction-diffusion system that serves as a framework to compare different somitogenesis patterning hypotheses. Using this framework, we propose the Clock and Wavefront Self-Organizing model that considers an excitable self-organizing region where phase waves form independent of global frequency gradients. The model recapitulates the change in relative phase of Wnt and Notch observed during mouse somitogenesis and provides a theoretical basis for understanding the excitability of mouse presomitic mesoderm cells in vitro.

Engineering tertiary chirality in helical biopolymers.

Tertiary chirality describes the handedness of supramolecular assemblies and relies not only on the primary and secondary structures of the building blocks but also on topological driving forces that have been sparsely characterized. Helical biopolymers, especially DNA, have been extensively investigated as they possess intrinsic chirality that determines the optical, mechanical, and physical properties of the ensuing material. Here, we employ the DNA tensegrity triangle as a model system to locate the tipping points in chirality inversion at the tertiary level by X-ray diffraction. We engineer tensegrity triangle crystals with incremental rotational steps between immobile junctions from 3 to 28 base pairs (bp). We construct a mathematical model that accurately predicts and explains the molecular configurations in both this work and previous studies. Our design framework is extendable to other supramolecular assemblies of helical biopolymers and can be used in the design of chiral nanomaterials, optically active molecules, and mesoporous frameworks, all of which are of interest to physical, biological, and chemical nanoscience.

Deciphering the underlying mechanisms of the pharyngeal pumping motions in Caenorhabditis elegans.

The pharynx of the nematode Caenorhabditis elegans is a neuromuscular organ that exhibits typical pumping motions, which result in the intake of food particles from the environment. In-depth inspection reveals slightly different dynamics at the various pharyngeal areas, rather than synchronous pumping motions of the whole organ, which are important for its effective functioning. While the different pumping dynamics are well characterized, the underlying mechanisms that generate them are not known. In this study, the C. elegans pharynx was modeled in a bottom-up fashion, including all of the underlying biological processes that lead to, and including, its end function, food intake. The mathematical modeling of all processes allowed performing comprehensive, quantitative analyses of the system as a whole. Our analyses provided detailed explanations for the various pumping dynamics generated at the different pharyngeal areas; a fine-resolution description of muscle dynamics, both between and within different pharyngeal areas; a quantitative assessment of the values of many parameters of the system that are unavailable in the literature; and support for a functional role of the marginal cells, which are currently assumed to mainly have a structural role in the pharynx. In addition, our model predicted that in tiny organisms such as C. elegans, the generation of long-lasting action potentials must involve ions other than calcium. Our study exemplifies the power of mathematical models, which allow a more accurate, higher-resolution inspection of the studied system, and an easier and faster execution of in silico experiments than feasible in the lab.

Mechanics reveals the role of peristome geometry in prey capture in carnivorous pitcher plants (Nepenthes).

Carnivorous pitcher plants (Nepenthes) are a striking example of a natural pitfall trap. The trap's slippery rim, or peristome, plays a critical role in insect capture via an aquaplaning mechanism that is well documented. While the peristome has received significant research attention, the conspicuous variation in peristome geometry across the genus remains unexplored. We examined the mechanics of prey capture using Nepenthes pitcher plants with divergent peristome geometries. Inspired by living material, we developed a mathematical model that links the peristomes' three-dimensional geometries to the physics of prey capture under the laws of Newtonian mechanics. Linking form and function enables us to test hypotheses related to the function of features such as shape and ornamentation, orientation in a gravitational field, and the presence of "teeth," while analysis of the energetic costs and gains of a given geometry provides a means of inferring potential evolutionary pathways. In a separate modeling approach, we show how prey size may correlate with peristome dimensions for optimal capture. Our modeling framework provides a physical platform to understand how divergence in peristome morphology may have evolved in the genus Nepenthes in response to shifts in prey diversity, availability, and size.

Plants sum and subtract stimuli over different timescales.

Mounting evidence suggests that plants engage complex computational processes to quantify and integrate sensory information over time, enabling remarkable adaptive growth strategies. However, quantitative understanding of these computational processes is limited. We report experiments probing the dependence of gravitropic responses of wheat coleoptiles on previous stimuli. First, building on a mathematical model that identifies this dependence as a form of memory, or a filter, we use experimental observations to reveal the mathematical principles of how coleoptiles integrate multiple stimuli over time. Next, we perform two-stimulus experiments, informed by model predictions, to reveal fundamental computational processes. We quantitatively show that coleoptiles respond not only to sums but also to differences between stimuli over different timescales, constituting evidence that plants can compare stimuli-crucial for search and regulation processes. These timescales also coincide with oscillations observed in gravitropic responses of wheat coleoptiles, suggesting shoots may combine memory and movement in order to enhance posture control and sensing capabilities.

Noncoding RNAs in Polycomb and Trithorax Regulation: A Quantitative Perspective.

The question of how noncoding RNAs are involved in Polycomb group (PcG) and Trithorax group (TrxG) regulation has been on an extraordinary journey over the last three decades. Favored models have risen and fallen, and healthy debates have swept back and forth. The field has recently reached a critical mass of compelling data that throws light on several previously unresolved issues. The time is ripe for a fruitful combination of these findings with two other long-running avenues of research, namely the biochemical properties of the PcG/TrxG system and the application of theoretical mathematical models toward an understanding of the system's regulatory properties. I propose that integrating our current knowledge of noncoding RNA into a quantitative biochemical and theoretical framework for PcG and TrxG regulation has the potential to reconcile several apparently conflicting models and identifies fascinating questions for future research.

Proteome characterization of liver-kidney comorbidity after microbial sepsis.

Sepsis is a life-threatening condition that occurs when the body responds to an infection but subsequently triggers widespread inflammation and impaired blood flow. These pathologic responses can rapidly cause multiple organ dysfunction or failure either one by one or simultaneously. The fundamental common mechanisms involved in sepsis-induced multiple organ dysfunction remain unclear. Here, employing quantitative global and phosphoproteomics, we examine the liver's temporal proteome and phosphoproteome changes after moderate sepsis induced by cecum ligation and puncture. In total, 4593 global proteins and 1186 phosphoproteins according to 3275 phosphosites were identified. To characterize the liver-kidney comorbidity after sepsis, we developed a mathematical model and performed cross-analyses of liver and kidney proteome data obtained from the same set of mice. Beyond immune response, we showed the commonly disturbed pathways and key regulators of the liver-kidney comorbidity are linked to energy metabolism and consumption. Our data provide open resources to understand the communication between the liver and kidney as they work to fight infection and maintain homeostasis.

Pandemic fatigue impedes mitigation of COVID-19 in Hong Kong.

Hong Kong has implemented stringent public health and social measures (PHSMs) to curb each of the four COVID-19 epidemic waves since January 2020. The third wave between July and September 2020 was brought under control within 2 m, while the fourth wave starting from the end of October 2020 has taken longer to bring under control and lasted at least 5 mo. Here, we report the pandemic fatigue as one of the potential reasons for the reduced impact of PHSMs on transmission in the fourth wave. We contacted either 500 or 1,000 local residents through weekly random-digit dialing of landlines and mobile telephones from May 2020 to February 2021. We analyze the epidemiological impact of pandemic fatigue by using the large and detailed cross-sectional telephone surveys to quantify risk perception and self-reported protective behaviors and mathematical models to incorporate population protective behaviors. Our retrospective prediction suggests that an increase of 100 daily new reported cases would lead to 6.60% (95% CI: 4.03, 9.17) more people worrying about being infected, increase 3.77% (95% CI: 2.46, 5.09) more people to avoid social gatherings, and reduce the weekly mean reproduction number by 0.32 (95% CI: 0.20, 0.44). Accordingly, the fourth wave would have been 14% (95% CI%: -53%, 81%) smaller if not for pandemic fatigue. This indicates the important role of mitigating pandemic fatigue in maintaining population protective behaviors for controlling COVID-19.

Simulating respiratory disease transmission within and between classrooms to assess pandemic management strategies at schools.

The global spread of coronavirus disease 2019 (COVID-19) has emphasized the need for evidence-based strategies for the safe operation of schools during pandemics that balance infection risk with the society's responsibility of allowing children to attend school. Due to limited empirical data, existing analyses assessing school-based interventions in pandemic situations often impose strong assumptions, for example, on the relationship between class size and transmission risk, which could bias the estimated effect of interventions, such as split classes and staggered attendance. To fill this gap in school outbreak studies, we parameterized an individual-based model that accounts for heterogeneous contact rates within and between classes and grades to a multischool outbreak data of influenza. We then simulated school outbreaks of respiratory infectious diseases of ongoing threat (i.e., COVID-19) and potential threat (i.e., pandemic influenza) under a variety of interventions (changing class structures, symptom screening, regular testing, cohorting, and responsive class closures). Our results suggest that interventions changing class structures (e.g., reduced class sizes) may not be effective in reducing the risk of major school outbreaks upon introduction of a case and that other precautionary measures (e.g., screening and isolation) need to be employed. Class-level closures in response to detection of a case were also suggested to be effective in reducing the size of an outbreak.

The effectiveness of COVID-19 testing and contact tracing in a US city.

Although testing, contact tracing, and case isolation programs can mitigate COVID-19 transmission and allow the relaxation of social distancing measures, few countries worldwide have succeeded in scaling such efforts to levels that suppress spread. The efficacy of test-trace-isolate likely depends on the speed and extent of follow-up and the prevalence of SARS-CoV-2 in the community. Here, we use a granular model of COVID-19 transmission to estimate the public health impacts of test-trace-isolate programs across a range of programmatic and epidemiological scenarios, based on testing and contact tracing data collected on a university campus and surrounding community in Austin, TX, between October 1, 2020, and January 1, 2021. The median time between specimen collection from a symptomatic case and quarantine of a traced contact was 2 days (interquartile range [IQR]: 2 to 3) on campus and 5 days (IQR: 3 to 8) in the community. Assuming a reproduction number of 1.2, we found that detection of 40% of all symptomatic cases followed by isolation is expected to avert 39% (IQR: 30% to 45%) of COVID-19 cases. Contact tracing is expected to increase the cases averted to 53% (IQR: 42% to 58%) or 40% (32% to 47%), assuming the 2- and 5-day delays estimated on campus and in the community, respectively. In a tracing-accelerated scenario, in which 75% of contacts are notified the day after specimen collection, cases averted increase to 68% (IQR: 55% to 72%). An accelerated contact tracing program leveraging rapid testing and electronic reporting of test results can significantly curtail local COVID-19 transmission.

Modeling suggests gene editing combined with vaccination could eliminate a persistent disease in livestock.

Recent breakthroughs in gene-editing technologies that can render individual animals fully resistant to infections may offer unprecedented opportunities for controlling future epidemics in farm animals. Yet, their potential for reducing disease spread is poorly understood as the necessary theoretical framework for estimating epidemiological effects arising from gene-editing applications is currently lacking. Here, we develop semistochastic modeling approaches to investigate how the adoption of gene editing may affect infectious disease prevalence in farmed animal populations and the prospects and time scale for disease elimination. We apply our models to the porcine reproductive and respiratory syndrome (PRRS), one of the most persistent global livestock diseases to date. Whereas extensive control efforts have shown limited success, recent production of gene-edited pigs that are fully resistant to the PRRS virus have raised expectations for eliminating this deadly disease. Our models predict that disease elimination on a national scale would be difficult to achieve if gene editing was used as the only disease control. However, from a purely epidemiological perspective, disease elimination may be achievable within 3 to 6 y, if gene editing were complemented with widespread and sufficiently effective vaccination. Besides strategic distribution of genetically resistant animals, several other key determinants underpinning the epidemiological impact of gene editing were identified.

The Potential Benefits of Delaying Seasonal Influenza Vaccine Selections for the Northern Hemisphere: A Retrospective Modeling Study in the United States.

BACKGROUND: Antigenic similarity between vaccine viruses and circulating viruses is crucial for achieving high vaccine effectiveness against seasonal influenza. New non-egg-based vaccine production technologies could revise current vaccine formulation schedules. We aim to assess the potential benefit of delaying seasonal influenza vaccine virus selection decisions. METHODS: We identified seasons where season-dominant viruses presented increasing prevalence after vaccine formulation had been decided in February for the Northern Hemisphere, contributing to their antigenic discrepancy with vaccine viruses. Using a SEIR (susceptible-exposed-infectious-recovered) model of seasonal influenza in the United States, we evaluated the impact of updating vaccine decisions with more antigenically similar vaccine viruses on the influenza burden in the United States. RESULTS: In 2014-2015 and 2019-2020, the season-dominant A(H3N2) subclade and B/Victoria clade, respectively, presented increasing prevalence after vaccine decisions were already made for the Northern Hemisphere. Our model showed that the updated A(H3N2) vaccine could have averted 5000-65 000 influenza hospitalizations in the United States in 2014-2015, whereas updating the B/Victoria vaccine component did not substantially change influenza burden in the 2019-2020 season. CONCLUSIONS: With rapid vaccine production, revising current timelines for vaccine selection could result in substantial epidemiological benefits, particularly when additional data could help improve the antigenic match between vaccine and circulating viruses.

The Fading of the Mpox Outbreak Among Men Who Have Sex With Men: A Mathematical Modelling Study.

BACKGROUND: In the Netherlands, the number of mpox cases started declining before mpox vaccination was initiated. Most cases were men who have sex with men (MSM). We investigated whether the decline in mpox could be attributed to infection-induced immunity or behavioral adaptations. METHODS: We developed a transmission model and accounted for possible behavioral adaptations: fewer casual partners and shorter time until MSM with mpox refrain from sexual contacts. RESULTS: Without behavioral adaptations, the peak in modelled cases matched observations, but the decline was less steep than observed. With behavioral adaptations in the model, we found a decline of 16%-18% in numbers of casual partners in June and 13%-22% in July 2022. Model results showed a halving of the time before refraining from sex. When mpox vaccination started, 57% of MSM with very high sexual activity in the model had been infected. Model scenarios revealed that the outbreak could have waned by November 2022 even without vaccination. CONCLUSIONS: The limited duration of the mpox outbreak in the Netherlands can be ascribed primarily to infection-induced immunity among MSM with high sexual activity levels. The decline was accelerated by behavioral adaptations. Immunity among those most sexually active is essential to impede mpox resurgence.

Characteristics of the Sexual Networks of Men Who Have Sex With Men in Montréal, Toronto, and Vancouver: Insights from Canada's 2022 Mpox Outbreak.

BACKGROUND: The 2022-2023 global mpox outbreak disproportionately affected gay, bisexual, and other men who have sex with men (GBM). We investigated differences in GBM's sexual partner distributions across Canada's 3 largest cities and over time, and how they shaped transmission. METHODS: The Engage Cohort Study (2017-2023) recruited GBM via respondent-driven sampling in Montréal, Toronto, and Vancouver (n = 2449). We compared reported sexual partner distributions across cities and periods: before COVID-19 (2017-2019), pandemic (2020-2021), and after lifting of restrictions (2021-2023). We used Bayesian regression and poststratification to model partner distributions. We estimated mpox's basic reproduction number (R0) using a risk-stratified compartmental model. RESULTS: Pre-COVID-19 pandemic distributions were comparable: fitted average partners (past 6 months) were 10.4 (95% credible interval: 9.4-11.5) in Montréal, 13.1 (11.3-15.1) in Toronto, and 10.7 (9.5-12.1) in Vancouver. Sexual activity decreased during the pandemic and increased after lifting of restrictions, but remained below prepandemic levels. Based on reported cases, we estimated R0 of 2.4 to 2.7 and similar cumulative incidences (0.7%-0.9%) across cities. CONCLUSIONS: Similar sexual partner distributions may explain comparable R0 and cumulative incidence across cities. With potential for further recovery in sexual activity, mpox vaccination and surveillance strategies should be maintained.

Renal handling of albumin in rats with early stage diabetes: A theoretical analysis.

In early diabetic nephropathy (DN), recent studies have shown that albuminuria stems mostly from alterations in tubular function rather than from glomerular damage. Several factors in DN, including hyperfiltration, hypertrophy and reduced abundance of the albumin receptors megalin and cubilin, affect albumin endocytosis in the proximal tubule (PT). To assess their respective contribution, we developed a model of albumin handling in the rat PT that couples the transport of albumin to that of water and solutes. Our simulations suggest that, under basal conditions, ∼75% of albumin is retrieved in the S1 segment. The model predicts negligible uptake in S3, as observed experimentally. It also accurately predicts the impact of acute hyperglycaemia on urinary albumin excretion. Simulations reproduce observed increases in albumin excretion in early DN by considering the combined effects of increased glomerular filtration rate (GFR), osmotic diuresis, hypertrophy, and megalin and cubilin downregulation, without stipulating changes in glomerular permselectivity. The results indicate that in isolation, glucose-elicited osmotic diuresis and glucose transporter upregulation raise albumin excretion only slightly. Enlargement of PT diameter not only augments uptake via surface area expansion, but also reduces fluid velocity and thus shear stress-induced stimulation of endocytosis. Overall, our model predicts that downregulation of megalin and cubilin and hyperfiltration both contribute significantly to increasing albumin excretion in rats with early-stage diabetes. The results also suggest that acute sodium-glucose cotransporter 2 inhibition lowers albumin excretion only if GFR decreases sufficiently, and that angiotensin II receptor blockers mitigate urinary albumin loss in early DN in large part by upregulating albumin receptor abundance. KEY POINTS: The urinary excretion of albumin is increased in early diabetic nephropathy (DN). It is difficult to experimentally disentangle the multiple factors that affect the renal handling of albumin in DN. We developed a mathematical model of albumin transport in the rat proximal tubule (PT) to examine the impact of elevated plasma glucose, hyperfiltration, PT hypertrophy and reduced abundance of albumin receptors on albumin uptake and excretion in DN. Our model predicts that glucose-elicited osmotic diuresis per se raises albumin excretion only slightly. Conversely, increases in PT diameter and length favour reduced albumin excretion. Our results suggest that downregulation of the receptors megalin and cubilin in PT cells and hyperfiltration both contribute significantly to increasing albumin excretion in DN. The model helps to better understand the mechanisms underlying urinary loss of albumin in early-stage diabetes, and the impact of specific treatments thereupon.

The evolution of chemodiversity in plants-From verbal to quantitative models.

Plants harbour a great chemodiversity, that is diversity of specialised metabolites (SMs), at different scales. For instance, individuals can produce a large number of SMs, and populations can differ in their metabolite composition. Given the ecological and economic importance of plant chemodiversity, it is important to understand how it arises and is maintained over evolutionary time. For other dimensions of biodiversity, that is species diversity and genetic diversity, quantitative models play an important role in addressing such questions. Here, we provide a synthesis of existing hypotheses and quantitative models, that is mathematical models and computer simulations, for the evolution of plant chemodiversity. We describe each model's ingredients, that is the biological processes that shape chemodiversity, the scales it considers and whether it has been formalized as a quantitative model. Although we identify several quantitative models, not all are dynamic and many influential models have remained verbal. To fill these gaps, we outline our vision for the future of chemodiversity modelling. We identify quantitative models used for genetic variation that may be adapted for chemodiversity, and we present a flexible framework for the creation of individual-based models that address different scales of chemodiversity and combine different ingredients that bring this chemodiversity about.

Public Health Impact of Paxlovid as Treatment for COVID-19, United States.

We evaluated the population-level benefits of expanding treatment with the antiviral drug Paxlovid (nirmatrelvir/ritonavir) in the United States for SARS-CoV-2 Omicron variant infections. Using a multiscale mathematical model, we found that treating 20% of symptomatic case-patients with Paxlovid over a period of 300 days beginning in January 2022 resulted in life and cost savings. In a low-transmission scenario (effective reproduction number of 1.2), this approach could avert 0.28 million (95% CI 0.03-0.59 million) hospitalizations and save US $56.95 billion (95% CI US $2.62-$122.63 billion). In a higher transmission scenario (effective reproduction number of 3), the benefits increase, potentially preventing 0.85 million (95% CI 0.36-1.38 million) hospitalizations and saving US $170.17 billion (95% CI US $60.49-$286.14 billion). Our findings suggest that timely and widespread use of Paxlovid could be an effective and economical approach to mitigate the effects of COVID-19.

Human-mouse comparison of the multistage nature of radiation carcinogenesis in a mathematical model.

Mouse models are vital for assessing risk from environmental carcinogens, including ionizing radiation, yet the interspecies difference in the dose response precludes direct application of experimental evidence to humans. Herein, we take a mathematical approach to delineate the mechanism underlying the human-mouse difference in radiation-related cancer risk. We used a multistage carcinogenesis model assuming a mutational action of radiation to analyze previous data on cancer mortality in the Japanese atomic bomb survivors and in lifespan mouse experiments. Theoretically, the model predicted that exposure will chronologically shift the age-related increase in cancer risk forward by a period corresponding to the time in which the spontaneous mutational process generates the same mutational burden as that the exposure generates. This model appropriately fitted both human and mouse data and suggested a linear dose response for the time shift. The effect per dose decreased with increasing age at exposure similarly between humans and mice on a per-lifespan basis (0.72- and 0.71-fold, respectively, for every tenth lifetime). The time shift per dose was larger by two orders of magnitude in humans (7.8 and 0.046 years per Gy for humans and mice, respectively, when exposed at ~35% of their lifetime). The difference was mostly explained by the two orders of magnitude difference in spontaneous somatic mutation rates between the species plus the species-independent radiation-induced mutation rate. Thus, the findings delineate the mechanism underlying the interspecies difference in radiation-associated cancer mortality and may lead to the use of experimental evidence for risk prediction in humans.