Predictive evolutionary modelling for influenza virus by site-based dynamics of mutations.

Influenza virus continuously evolves to escape human adaptive immunity and generates seasonal epidemics. Therefore, influenza vaccine strains need to be updated annually for the upcoming flu season to ensure vaccine effectiveness. We develop a computational approach, beth-1, to forecast virus evolution and select representative virus for influenza vaccine. The method involves modelling site-wise mutation fitness. Informed by virus genome and population sero-positivity, we calibrate transition time of mutations and project the fitness landscape to future time, based on which beth-1 selects the optimal vaccine strain. In season-to-season prediction in historical data for the influenza A pH1N1 and H3N2 viruses, beth-1 demonstrates superior genetic matching compared to existing approaches. In prospective validations, the model shows superior or non-inferior genetic matching and neutralization against circulating virus in mice immunization experiments compared to the current vaccine. The method offers a promising and ready-to-use tool to facilitate vaccine strain selection for the influenza virus through capturing heterogeneous evolutionary dynamics over genome space-time and linking molecular variants to population immune response.

Identification of lncRNA-based regulatory mechanisms of Takifugu rubripes growth traits in fast and slow-growing family lines.

Family selection is an important method in fish aquaculture because growth is the most important economic trait. Fast-and slow-growing families of tiger puffer fish (Takifugu rubripes) have been established through family selection. The development of teleost fish is primarily controlled by the growth hormone (GH)-insulin-like growth factor 1 (IGF-1) axis that includes the hypothalamus-pituitary-liver. In this study, the molecular mechanisms underlying T. rubripes growth were analyzed by comparing transcriptomes from fast- and slow-growing families. The expressions of 214 lncRNAs were upregulated, and those of 226 were downregulated in the brain tissues of the fast-growing T. rubripes family compared to those of the slow-growing family. Differentially expressed lncRNAs centrally regulate mitogen-activated protein kinase (MAPK) and forkhead box O (FoxO) signaling pathways. Based on the results of lncRNA-gene network construction, we found that lncRNA3133.13, lncRNA23169.1, lncRNA23145.1, and lncRNA23141.3 regulated all four genes (igf1, mdm2, flt3, and cwf19l1). In addition, lncRNA7184.10 may be a negative regulator of rasgrp2 and a positive regulator of gadd45ga, foxo3b, and dusp5. These target genes are associated with the growth and development of organisms through the PI3K/AKT and MAPK/ERK pathways. Overall, transcriptomic analyses of fast- and slow-growing families of T. rubripes provided insights into the molecular mechanisms of teleost fish growth rates. Further, these analyses provide evidence for key genes related to growth regulation and the lncRNA expression regulatory network that will provide a framework for improving puffer fish germplasm resources.

C, N, and P stoichiometry for leaf litter of 62 woody species in a subtropical evergreen broadleaved forest.

To understand leaf litter stoichiometry in a subtropical evergreen broadleaved forest, we measured the contents of carbon (C), nitrogen (N) and phosphorus (P) in leaf litters of 62 main woody species in a natural forest of C. kawakamii Nature Reserve in Sanming, Fujian Province. Differences in leaf litter stoichiometry were analyzed across leaf forms (evergreen, deciduous), life forms (tree, semi-tree or shrub), and main families. Additionally, the phylogenetic signal was measured by Blomberg's K to explore the correlation between family level differentiation time and litter stoichiometry. Our results showed that the contents of C, N and P in the litter of 62 woody species were 405.97-512.16, 4.45-27.11, and 0.21-2.53 g·kg-1, respectively. C/N, C/P and N/P were 18.6-106.2, 195.9-2146.8, and 3.5-68.9, respectively. Leaf litter P content of evergreen tree species was significantly lower than that of deciduous tree species, and C/P and N/P of evergreen tree species were significantly higher than those of deciduous tree species. There was no significant difference in C, N content and C/N between the two leaf forms. There was no significant difference in litter stoichiometry among trees, semi-trees and shrubs. Effects of phylogeny on C, N content and C/N in leaf litter was significant, but not on P content, C/P and N/P. Family differentiation time was negatively correlated with leaf litter N content, and positively correlated with C/N. Leaf litter of Fagaceae had high C and N contents, C/P and N/P, and low P content and C/N, with an opposite trend for Sapidaceae. Our findings indicated that litter in subtropical forest had high C, N content and N/P, but low P content, C/N, and C/P, compared with the global scale average value. Litter of tree species in older sequence of evolutionary development had lower N content but higher C/N. There was no difference of leaf litter stoichiometry among life forms. There were significant differences in P content, C/P, and N/P between different leaf forms, with a characteristic of convergence.

The mechanism shaping the logistic growth of mutation proportion in epidemics at population scale.

Virus evolution is a common process of pathogen adaption to host population and environment. Frequently, a small but important fraction of virus mutations are reported to contribute to higher risks of host infection, which is one of the major determinants of infectious diseases outbreaks at population scale. The key mutations contributing to transmission advantage of a genetic variant often grow and reach fixation rapidly. Based on classic epidemiology theories of disease transmission, we proposed a mechanistic explanation of the process that between-host transmission advantage may shape the observed logistic curve of the mutation proportion in population. The logistic growth of mutation is further generalized by incorporating time-varying selective pressure to account for impacts of external factors on pathogen adaptiveness. The proposed model is implemented in real-world data of COVID-19 to capture the emerging trends and changing dynamics of the B.1.1.7 strains of SARS-CoV-2 in England. The model characterizes and establishes the underlying theoretical mechanism that shapes the logistic growth of mutation in population.

Rapid evaluation of COVID-19 vaccine effectiveness against symptomatic infection with SARS-CoV-2 variants by analysis of genetic distance.

Timely evaluation of the protective effects of Coronavirus Disease 2019 (COVID-19) vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern is urgently needed to inform pandemic control planning. Based on 78 vaccine efficacy or effectiveness (VE) data from 49 studies and 1,984,241 SARS-CoV-2 sequences collected from 31 regions, we analyzed the relationship between genetic distance (GD) of circulating viruses against the vaccine strain and VE against symptomatic infection. We found that the GD of the receptor-binding domain of the SARS-CoV-2 spike protein is highly predictive of vaccine protection and accounted for 86.3% (P = 0.038) of the VE change in a vaccine platform-based mixed-effects model and 87.9% (P = 0.006) in a manufacturer-based model. We applied the VE-GD model to predict protection mediated by existing vaccines against new genetic variants and validated the results by published real-world and clinical trial data, finding high concordance of predicted VE with observed VE. We estimated the VE against the Delta variant to be 82.8% (95% prediction interval: 68.7-96.0) using the mRNA vaccine platform, closely matching the reported VE of 83.0% from an observational study. Among the four sublineages of Omicron, the predicted VE varied between 11.9% and 33.3%, with the highest VE predicted against BA.1 and the lowest against BA.2, using the mRNA vaccine platform. The VE-GD framework enables predictions of vaccine protection in real time and offers a rapid evaluation method against novel variants that may inform vaccine deployment and public health responses.

Utility of 2-Diphenylphosphoryloxy-1,3-Dienes in Multicomponent Hetero-Diels-Alder Reactions to Construct Functionalized Six-Membered Nitrogen Heterocycles.

The utility of 2-diphenylphosphoryloxy-1,3-dienes for the construction of substituted six-membered nitrogen heterocycles is presented. These dienes undergo boron trifluoride-promoted aza-Diels-Alder reactions when reacted with imines or related species formed in situ using aldehydes and amine derivatives. The stability of the dienes allows this three-component reaction to be carried out with no special precautions to eliminate water or air. Thirty-one examples of this process are presented. The usefulness of the enol phosphate functional group is highlighted in further reactions after the cycloaddition step to generate functionalized piperidenes or pyridines.

Quantifying the effect of government interventions and virus mutations on transmission advantage during COVID-19 pandemic.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has become a major public health threat. This study aims to evaluate the effect of virus mutation activities and policy interventions on COVID-19 transmissibility in Hong Kong. METHODS: In this study, we integrated the genetic activities of multiple proteins, and quantified the effect of government interventions and mutation activities against the time-varying effective reproduction number Rt. FINDINGS: We found a significantly positive relationship between Rt and mutation activities and a significantly negative relationship between Rt and government interventions. The results showed that the mutations that contributed most to the increase of Rt were from the spike, nucleocapsid and ORF1b genes. Policy of prohibition on group gathering was estimated to have the largest impact on mitigating virus transmissibility. The model explained 63.2% of the Rt variability with the R2. CONCLUSION: Our study provided a convenient framework to estimate the effect of genetic contribution and government interventions on pathogen transmissibility. We showed that the S, N and ORF1b protein had significant contribution to the increase of transmissibility of SARS-CoV-2 in Hong Kong, while restrictions of public gathering and suspension of face-to-face class are the most effective government interventions strategies.

Comparison of Imine- and Phosphinimine-Supported Indium Complexes: Tuning the Reactivity for the Sequential and Simultaneous Copolymerization of Lactide and ε-Caprolactone.

Imine- and phosphinimine-supported indium complexes were used as catalysts in the polymerization of racemic lactide and ε-caprolactone as well as their copolymerization by the sequential and simultaneous addition of monomers. Tuning the electronics and sterics of the indium centers by either (i) changing the nature of the nitrogen donors and (ii) coordinating a hemilabile side group had a significant effect on the reactivity of the complexes, their stability, and their control in the synthesis of block copolymers. Specifically, the imine-supported complex (5) showed the highest activity in the homo- and copolymerization of the cyclic esters, in contrast to the phosphinimine-supported complex (7), which was significantly slower and less stable. The presence of morpholine and thiomorpholine hemilabile side groups either reduced the activity or prevented the formation of alkoxide complexes.

Temporal Patterns in the Evolutionary Genetic Distance of SARS-CoV-2 during the COVID-19 Pandemic.

During coronavirus disease 2019 (COVID-19) pandemic, the genetic mutations of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) occurred frequently. Some mutations in the spike protein are considered to promote transmissibility of the virus, while the mutation patterns in other proteins are less studied and may also be important in understanding the characteristics of SARS-CoV-2. We used the sequencing data of SARS-CoV-2 strains in California to investigate the time-varying patterns of the evolutionary genetic distance. The accumulative genetic distances were quantified across different time periods and in different viral proteins. The increasing trends of genetic distance were observed in spike protein (S protein), the RNA-dependent RNA polymerase (RdRp) region and nonstructural protein 3 (nsp3) of open reading frame 1 (ORF1), and nucleocapsid protein (N protein). The genetic distances in ORF3a, ORF8, and nsp2 of ORF1 started to diverge from their original variants after September 2020. By contrast, mutations in other proteins appeared transiently, and no evident increasing trend was observed in the genetic distance to the original variants. This study presents distinct patterns of the SARS-CoV-2 mutations across multiple proteins from the aspect of genetic distance. Future investigation shall be conducted to study the effects of accumulative mutations on epidemics characteristics.

Differences in the case fatality risks associated with SARS-CoV-2 Delta and non-Delta variants in relation to vaccine coverage: An early ecological study in the United Kingdom.

The circulation of SARS-CoV-2 Delta (i.e., B.1.617.2) variants challenges the pandemic control. Our analysis showed that in the United Kingdom (UK), the reported case fatality ratio (CFR) decreased from May to July 2021 for non-Delta variant, whereas the decreasing trends of the CFR of Delta variant appeared weak and insignificant. The association between vaccine coverage and CFR might be stratified by different circulating variants. Due to the limitation of ecological study design, the interpretation of our results should be treated with caution.

Characterization of key amino acid substitutions and dynamics of the influenza virus H3N2 hemagglutinin.

The annual epidemics of seasonal influenza is partly attributed to the continued virus evolution. It is challenging to evaluate the effect of influenza virus mutations on evading population immunity. In this study, we introduce a novel statistical and computational approach to measure the dynamic molecular determinants underlying epidemics using effective mutations (EMs), and account for the time of waning mutation advantage against herd immunity by measuring the effective mutation periods (EMPs). Extensive analysis is performed on the sequencing and epidemiology data of H3N2 epidemics in ten regions from season to season. We systematically identified 46 EMs in the hemagglutinin (HA) gene, in which the majority were antigenic sites. Eight EMs were located in immunosubdominant stalk domain, an important target for developing broadly reactive antibodies. The EMs might provide timely information on key substitutions for influenza vaccines antigen design. The EMP suggested that major genetic variants of H3N2 circulated in Southeast Asia for an average duration of 4.5 years (SD 2.4) compared to a significantly shorter 2.0 years (SD 1.0) in temperate regions. The proposed method bridges population epidemics and molecular characteristics of infectious diseases, and would find broad applications in various pathogens mutation estimations.

Real-time quantification of the transmission advantage associated with a single mutation in pathogen genomes: a case study on the D614G substitution of SARS-CoV-2.

BACKGROUND: The COVID-19 pandemic poses serious threats to global health, and the emerging mutation in SARS-CoV-2 genomes, e.g., the D614G substitution, is one of the major challenges of disease control. Characterizing the role of the mutation activities is of importance to understand how the evolution of pathogen shapes the epidemiological outcomes at population scale. METHODS: We developed a statistical framework to reconstruct variant-specific reproduction numbers and estimate transmission advantage associated with the mutation activities marked by single substitution empirically. Using likelihood-based approach, the model is exemplified with the COVID-19 surveillance data from January 1 to June 30, 2020 in California, USA. We explore the potential of this framework to generate early warning signals for detecting transmission advantage on a real-time basis. RESULTS: The modelling framework in this study links together the mutation activity at molecular scale and COVID-19 transmissibility at population scale. We find a significant transmission advantage of COVID-19 associated with the D614G substitution, which increases the infectivity by 54% (95%CI: 36, 72). For the early alarming potentials, the analytical framework is demonstrated to detect this transmission advantage, before the mutation reaches dominance, on a real-time basis. CONCLUSIONS: We reported an evidence of transmission advantage associated with D614G substitution, and highlighted the real-time estimating potentials of modelling framework.

A Bayesian method for synthesizing multiple diagnostic outcomes of COVID-19 tests.

The novel coronavirus disease 2019 (COVID-19) has spread worldwide and threatened human life. Diagnosis is crucial to contain the spread of SARS-CoV-2 infections and save lives. Diagnostic tests for COVID-19 have varying sensitivity and specificity, and the false-negative results would have substantial consequences to patient treatment and pandemic control. To detect all suspected infections, multiple testing is widely used. However, it may be challenging to build an assertion when the testing results are inconsistent. Considering the situation where there is more than one diagnostic outcome for each subject, we proposed a Bayesian probabilistic framework based on the sensitivity and specificity of each diagnostic method to synthesize a posterior probability of being infected by SARS-CoV-2. We demonstrated that the synthesized posterior outcome outperformed each individual testing outcome. A user-friendly web application was developed to implement our analytic framework with free access via http://www2.ccrb.cuhk.edu.hk/statgene/COVID_19/. The web application enables the real-time display of the integrated outcome incorporating two or more tests and calculated based on Bayesian posterior probability. A simulation-based assessment demonstrated higher accuracy and precision of the Bayesian probabilistic model compared with a single-test outcome. The online tool developed in this study can assist physicians in making clinical evaluations by effectively integrating multiple COVID-19 tests.

iNOS Promotes the Development of Osteosarcoma via Wnt/ß-Catenin Pathway.

Inducible nitric oxide synthase (iNOS), accompanied with protumor and antitumor activity, has been studied in multiple cancers. However, the role of iNOS expression in osteosarcoma (OS) is far from being fully understood. In present work, iNOS levels were detected in OS tissues and cell lines. Colony formation assay, Transwell assay, and fow cytometer were used to assess proliferation, migration, invasion, and apoptosis abilities in vitro after iNOS inhibition. Western blotting determined the expressions of iNOS, MMP2, MMP9, C-MYC, Ki67, PCNA, and ß-catenin. Mice transfected with OS cells were to evaluate tumor formation. IHC assay was to evaluate the expressions of iNOS and ß-catenin in mice. The results showed that iNOS was upregulated in both OS tissues and cells compared with that in matched normal tissues or cells. And we found that proliferation, migration, and invasion numbers of OS cells were decreased, and apoptosis numbers of OS cells were increased after iNOS inhibition. MMP2, MMP9, C-MYC, Ki67, and PCNA levels were also reduced in OS cells treated with iNOS inhibition. Else, iNOS inhibition would suppress ß-catenin expression in OS cells to regulate MMP2, MMP9, C-MYC, Ki67, and PCNA expressions. In addition, tumor formation, iNOS expression, and ß-catenin expression were inhibited in mice transplanted with iNOS knockout OS cells. These results indicated that iNOS might be a potential therapeutic target for OS.

Differential Influence of Age on the Relationship between Genetic Mismatch and A(H1N1)pdm09 Vaccine Effectiveness.

Assessment of influenza vaccine effectiveness (VE) and identification of relevant influencing factors are the current priorities for optimizing vaccines to reduce the impacts of influenza. To date, how the difference between epidemic strains and vaccine strains at genetic scale affects age-specific vaccine performance remains ambiguous. This study investigated the association between genetic mismatch on hemagglutinin and neuraminidase genes and A(H1N1)pdm09 VE in different age groups with a novel computational approach. We found significant linear relationships between VE and genetic mismatch in children, young adults, and middle-aged adults. In the children's group, each 3-key amino acid mutation was associated with an average of 10% decrease in vaccine effectiveness in a given epidemic season, and genetic mismatch exerted no influence on VE for the elderly group. We demonstrated that present vaccines were most effective for children, while protection for the elderly was reduced and indifferent to vaccine component updates. Modeling such relationships is practical to inform timely evaluation of VE in different groups of populations during mass vaccination and may inform age-specific vaccination regimens.

Inferring the Association between the Risk of COVID-19 Case Fatality and N501Y Substitution in SARS-CoV-2.

As COVID-19 is posing a serious threat to global health, the emerging mutation in SARS-CoV-2 genomes, for example, N501Y substitution, is one of the major challenges against control of the pandemic. Characterizing the relationship between mutation activities and the risk of severe clinical outcomes is of public health importance for informing the healthcare decision-making process. Using a likelihood-based approach, we developed a statistical framework to reconstruct a time-varying and variant-specific case fatality ratio (CFR), and to estimate changes in CFR associated with a single mutation empirically. For illustration, the statistical framework is implemented to the COVID-19 surveillance data in the United Kingdom (UK). The reconstructed instantaneous CFR gradually increased from 1.0% in September to 2.2% in November 2020 and stabilized at this level thereafter, which monitors the mortality risk of COVID-19 on a real-time basis. We identified a link between the SARS-CoV-2 mutation activity at molecular scale and COVID-19 mortality risk at population scale, and found that the 501Y variants may slightly but not significantly increase 18% of fatality risk than the preceding 501N variants. We found no statistically significant evidence of change in COVID-19 mortality risk associated with 501Y variants, and highlighted the real-time estimating potentials of the modelling framework.

Modelling the association between COVID-19 transmissibility and D614G substitution in SARS-CoV-2 spike protein: using the surveillance data in California as an example.

BACKGROUND: The COVID-19 pandemic poses a serious threat to global health, and pathogenic mutations are a major challenge to disease control. We developed a statistical framework to explore the association between molecular-level mutation activity of SARS-CoV-2 and population-level disease transmissibility of COVID-19. METHODS: We estimated the instantaneous transmissibility of COVID-19 by using the time-varying reproduction number (Rt). The mutation activity in SARS-CoV-2 is quantified empirically depending on (i) the prevalence of emerged amino acid substitutions and (ii) the frequency of these substitutions in the whole sequence. Using the likelihood-based approach, a statistical framework is developed to examine the association between mutation activity and Rt. We adopted the COVID-19 surveillance data in California as an example for demonstration. RESULTS: We found a significant positive association between population-level COVID-19 transmissibility and the D614G substitution on the SARS-CoV-2 spike protein. We estimate that a per 0.01 increase in the prevalence of glycine (G) on codon 614 is positively associated with a 0.49% (95% CI: 0.39 to 0.59) increase in Rt, which explains 61% of the Rt variation after accounting for the control measures. We remark that the modeling framework can be extended to study other infectious pathogens. CONCLUSIONS: Our findings show a link between the molecular-level mutation activity of SARS-CoV-2 and population-level transmission of COVID-19 to provide further evidence for a positive association between the D614G substitution and Rt. Future studies exploring the mechanism between SARS-CoV-2 mutations and COVID-19 infectivity are warranted.

Protective effects of Bacillus subtilis ASAG 216 on growth performance, antioxidant capacity, gut microbiota and tissues residues of weaned piglets fed deoxynivalenol contaminated diets.

Deoxynivalenol (DON) poses a serious health threat to animals and humans consuming DON-contaminated food and feed. Biological means of detoxification of DON are considered as one of the effective strategies. The aim of the work was to study ameliorative effects of Bacillus subtilis ASAG 216 on DON-induced toxicosis in piglets. A decrease in average daily gain and average daily feed intake was observed in piglets fed DON-contaminated feed. In addition, DON exposure increased the serum concentrations of aspartate aminotransferase, immunoglobulin A, diamine oxidase, endotoxin, and peptide YY. Moreover, DON exposure caused oxidative stress in the serum, liver and jejunum, induced intestinal inflammation, impaired the intestinal barrier, and disturbed the gut microbiota homeostasis. Supplementation of B. subtilis ASAG 216 effectively attenuated the aforementioned effects of DON on piglets. Moreover, DON and de-epoxy-DON (DOM-1) in the serum, liver and kidney were significantly decreased when B. subtilis ASAG 216 was added to DON-contaminated diet. Our results imply that B. subtilis ASAG 216 can protect against DON-induced toxicosis in piglets, and thus this strain has a potential to be used as an animal feed ingredient to counteract harmful effects of DON in animals.