The origins and potential future of SARS-CoV-2 variants of concern in the evolving COVID-19 pandemic

SARS-CoV-2 epidemiology, kinetics, and evolution: A narrative review

Since winter 2019, SARS-CoV-2 has emerged, spread, and evolved all around the globe. We explore 4 y of evolutionary epidemiology of this virus, ranging from the applied public health challenges to the more conceptual evolutionary biology perspectives. Through this review, we first present the spread and lethality of the infections it causes, starting from its emergence in Wuhan (China) from the initial epidemics all around the world, compare the virus to other betacoronaviruses, focus on its airborne transmission, compare containment strategies ("zero-COVID" vs. "herd immunity"), explain its phylogeographical tracking, underline the importance of natural selection on the epidemics, mention its within-host population dynamics. Finally, we discuss how the pandemic has transformed (or should transform) the surveillance and prevention of viral respiratory infections and identify perspectives for the research on epidemiology of COVID-19.

Recent SARS-CoV-2 evolution trajectories indicate the emergence of Omicron's several subvariants and the current rise of KP.3.1.1 and XEC

The recent COVID-19 pandemic is one of the quickest-evolving pandemics in the world history. Therefore, the evolution of SARS-CoV-2 needs to be tracked consistently. Various VOIs, VOCs, and recent subvariants of Omicron have emerged from the dynamically evolving SARS-CoV-2. Various offspring of the Omicron subvariants have emerged since its origin, including lineages such as BA, BQ, and XBB, as well as more recent subvariants like BA.2.86, JN.1, JN.11.1, KP.3, KP.3.1.1, and XEC. The study evaluated the overall and one year evolutionary patterns, genome diversity, divergence event, transmission and geographical distributions, circulating frequency, entropy diversity, mutational diversity, risk mutations in S-protein and mutational fitness of the subvariants. The study estimated the substitution rate of all variants and subvariants of SARS-CoV-2 since its origin (32.001 × 10-4 subs/year). The geographical distributions of the recent KP.3.1.1 and XEC subvariant indicated its distribution in North America, South America, Europe, and Southeast Asia. Simultaneously, genome mutational landscapes were noted, including Spike and RBD mutations. We found that JN.1, JN.1.11.1, KP.3, KP.3.1.1 and XEC subvariants have gained the highest mutational fitness in Europe and North America. Our study indicates that the rapid evolution and highest frequency of mutational fitness have created a variety of subvariants from Omicron. It also indicates a shift from waves to mini-waves. Finally, our possible explanation is that mutation-driven divergent evolution contributes to the emergence of recent subvariants.

Mutant emergence timing and population immunisation status impact epidemiological dynamics

A key question in evolutionary epidemiology is to determine differences in the conditions that may allow some mutant strains to spread in a population where a resident strain is already circulating. Evolutionary invasion analyses assume that the immunity is long-lasting for previously infected individuals making it difficult to study traits such as immune escape. We relax this last assumption and allow the environment faced by the mutant to fluctuate outside of any epidemiological equilibrium. We introduce an original two-strains non-Markovian model that accounts for realistic immunity waning and cross-immunity, inspired by the case of SARS-CoV-2 variants. We show that mutants with increased contagiousness or with some immune escape abilities are more likely to invade the population. We also show that the timing of the introduction of mutant strain in the population is key because it is associated with the population's immunisation status. Our results underline the importance of immune waning and non-equilibrium dynamics on infectious disease evolution.

Exploring the therapeutic potential of Thymus vulgaris ethanol extract: a computational screening for antimicrobial compounds against COVID-19 induced mucormycosis

COVID-19-associated mucormycosis (CAM) has emerged as a concerning complication during the COVID-19 pandemic. In this study, we explored the potential of phytochemicals and flavonoids identified through High-Performance Liquid Chromatography (HPLC) analysis of Thymus vulgaris plant extract against key proteins of CAM, namely heat shock protein A5 (GPR78) and epidermal growth factor receptor (EGFR). HPLC analysis revealed the presence of bioactive compounds, including chlorogenic acid, cinamic acid, quercetin, coumaric acid, gallic acid, and syringic acid. To assess their efficacy against CAM, computational analyses were performed, including molecular docking analysis, pharmacophore characterization, ADME and molecular dynamics simulations. The results demonstrated that chlorogenic acid exhibited strong binding affinity against EGFR with a docking score of -7.6 kcal/mol, while quercetin showed favorable binding affinity against HSP A5 (GPR78) with a docking score of -10.1 kcal/mol. Both chlorogenic acid and quercetin displayed promising ADME properties, indicating their potential as drug candidates. Nevertheless, it was observed that chlorogenic acid did not adhere to Lipinski's rule, and its gastrointestinal (GI) absorption was relatively low when compared to quercetin. Unlike chlorogenic acid, quercetin does conform to Lipinski's rule and showed high GI absorption. Moreover, pharmacophore characterization of both drug candidates revealed a substantial number of binding sites, suggesting the likelihood of stable bond formation. Normal mode analysis revealed higher eigenvalues for the quercetin-HSPA5 complex compared to the chlorogenic acid-EGFR complex, indicating greater structural rigidity and stability. Overall, our findings highlight the potential of chlorogenic acid and quercetin as promising drug candidates against CAM. Furthermore, in-vitro and in-vivo studies are needed to validate their efficacy and safety for clinical use in treating mucormycosis associated with COVID-19. These findings may offer valuable insights into the development of novel therapeutic options to combat this challenging co-infectious disease.

Identifying the panorama of potential pandemic pathogens and their key characteristics: a systematic scoping review

The globe has recently seen several terrifying pandemics and outbreaks, underlining the ongoing danger presented by infectious microorganisms. This literature review aims to explore the wide range of infections that have the potential to lead to pandemics in the present and the future and pave the way to the conception of epidemic early warning systems. A systematic review was carried out to identify and compile data on infectious agents known to cause pandemics and those that pose future concerns. One hundred and fifteen articles were included in the review. They provided insights on 25 pathogens that could start or contribute to creating pandemic situations. Diagnostic procedures, clinical symptoms, and infection transmission routes were analyzed for each of these pathogens. Each infectious agent's potential is discussed, shedding light on the crucial aspects that render them potential threats to the future. This literature review provides insights for policymakers, healthcare professionals, and researchers in their quest to identify potential pandemic pathogens, and in their efforts to enhance pandemic preparedness through building early warning systems for continuous epidemiological monitoring.

Diagnostic performance of allele-specific RT-qPCR and genomic sequencing in wastewater-based surveillance of SARS-CoV-2

Clinical genomic surveillance is regarded as the gold standard for monitoring SARS-CoV-2 variants globally. However, as the pandemic wanes, reduced testing poses a risk to effectively tracking the trajectory of these variants within populations. Wastewater-based genomic surveillance that estimates variant frequency based on its defining set of alleles derived from clinical genomic surveillance has been successfully implemented. This method has its challenges, and allele-specific (AS) RT-qPCR or RT-dPCR may instead be used as a complementary method for estimating variant prevalence. Demonstrating equivalent performance of these methods is a prerequisite for their continued application in current and future pandemics. Here, we compared single-allele frequency using AS-RT-qPCR, to single-allele or haplotype frequency estimations derived from amplicon-based sequencing to estimate variant prevalence in wastewater during emergent and prevalent periods of Delta, Omicron, and two sub-lineages of Omicron. We found that all three methods of frequency estimation were concordant and contained sufficient information to describe the trajectory of variant prevalence. We further confirmed the accuracy of these methods by quantifying the diagnostic performance through Youden's index. The Youden's index of AS-RT-qPCR was reduced during the low prevalence period of a particular variant while the same allele in sequencing was negatively influenced due to insufficient read depth. Youden's index of haplotype-based calls was negatively influenced when alleles were common between variants. Coupling AS-RT-qPCR with sequencing can overcome the shortcomings of either platform and provide a comprehensive picture to the stakeholders for public health responses.

Sensing of SARS-CoV-2-infected cells by plasmacytoid dendritic cells is modulated via an interplay between CD54/ICAM-1 and CD11a/LFA-1 αL integrin

SARS-CoV-2 infection induces interferon (IFN) response by plasmacytoid dendritic cells (pDCs), but the underlying mechanisms are poorly defined. Here, we show that the bulk of the IFN-I release comes from pDC sensing of infected cells and not cell-free virions. Physical contact (or conjugates) between pDCs and infected cells is mediated through CD54-CD11a engagement, and such conjugate formation is required for efficient IFN-I production. Interestingly, CD11a is inducible on infected epithelial cells when they are co-cultured with PBMCs, thus allowing for potentially bidirectional cross-talks between CD54 and CD11a, which further amplify the sensing. SARS-CoV-2 variants of concern (VOCs) are sensed less efficiently than the Wuhan ancestral strain (LSPQ1), but the mechanisms driving the defect are different among the VOCs. CD11a induction on infected cells is correlated with their ability to form cell conjugates with pDCs. Impaired sensing of the Alpha variant is linked to reduced CD11a induction on infected cells and to fewer conjugates formed with pDCs. Collectively, our findings provide new insights into how SARS-CoV-2-infected cells are sensed by pDCs and reveal that this process is targeted by some VOCs to limit IFN-I production.

IMPORTANCE

Type I interferons (IFN-I) represent an important component of the host's innate defense against initial SARS-CoV-2 infections. Plasmacytoid dendritic cells (pDCs) produce large quantities of IFN-I upon recognition of viral particles or infected cells. This study shows that pDCs sense infected cells more efficiently than viral particles, leading to a higher production of IFN-I. Physical contact between a pDC and an infected cell is critical to this process; the interaction is mediated via CD11a and ICAM-1 complex and potentially is bidirectional. SARS-CoV-2 variants of concern (VOCs) have evolved to limit the IFN response through different mechanisms. For the Alpha variant, reduced level of CD11a on infected cells is linked to less contact with pDCs and decreased IFN-I release. Overall, our study characterizes some of the early steps involved in pDC-mediated response against SARS-CoV-2 infection and shows that these processes are targeted by VOCs to likely limit IFN-I response and enhance viral spread.

Subphenotypes of Long COVID and the clinical applications of probiotics

As the number of infections and deaths attributable to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection continues to rise, it is now becoming apparent that the health impacts of the Coronavirus disease (COVID-19) may not be limited to infection and the subsequent resolution of symptoms. Reports have shown that patients with SARS-CoV-2 infection may experience multiple symptoms across different organ systems that are associated with adverse health outcomes and develop new cardiac, renal, respiratory, musculoskeletal, and nervous conditions, a condition known as Long COVID or the post-acute sequelae of COVID-19 (PASC). This review provides insights into distinct subphenotypes of Long COVID and identifies microbiota dysbiosis as a common theme and crucial target for future therapies. Another important finding is that Long COVID is associated with prolonged and increased inflammation, potentially attributable to immune system dysfunction. A promising solution lies in the potential of probiotics to mitigate Long COVID symptoms by restoring gut microbiota balance and modulating the immune response. By evaluating the current clinical development landscape of the use of probiotics to treat Long COVID symptoms, this paper provides recommendations for future research by stressing the need to understand the modulation of bacterium strains followed by probiotic therapy to understand the association of microbiota dysbiosis with Long COVID symptoms. This will facilitate the development of effective probiotic formulations that could serve as reliable therapies against Long COVID.

Evolutionary Mechanisms of the Emergence of the Variants of Concern of SARS-CoV-2

The evolutionary origin of the variants of concern (VOCs) of SARS-CoV-2, characterized by a large number of new substitutions and strong changes in virulence and transmission rate, is intensely debated. The leading explanation in the literature is a chronic infection in immunocompromised individuals, where the virus evolves before returning into the main population. The present article reviews less-investigated hypotheses of VOC emergence with transmission between acutely infected hosts, with a focus on the mathematical models of stochastic evolution that have proved to be useful for other viruses, such as HIV and influenza virus. The central message is that understanding the acting factors of VOC evolution requires the framework of stochastic multi-locus evolution models, and that alternative hypotheses can be effectively verified by fitting results of computer simulation to empirical data.

Paying attention to the SARS-CoV-2 dialect : a deep neural network approach to predicting novel protein mutations

Predicting novel mutations has long-lasting impacts on life science research. Traditionally, this problem is addressed through wet-lab experiments, which are often expensive and time consuming. The recent advancement in neural language models has provided stunning results in modeling and deciphering sequences. In this paper, we propose a Deep Novel Mutation Search (DNMS) method, using deep neural networks, to model protein sequence for mutation prediction. We use SARS-CoV-2 spike protein as the target and use a protein language model to predict novel mutations. Different from existing research which is often limited to mutating the reference sequence for prediction, we propose a parent-child mutation prediction paradigm where a parent sequence is modeled for mutation prediction. Because mutations introduce changing context to the underlying sequence, DNMS models three aspects of the protein sequences: semantic changes, grammatical changes, and attention changes, each modeling protein sequence aspects from shifting of semantics, grammar coherence, and amino-acid interactions in latent space. A ranking approach is proposed to combine all three aspects to capture mutations demonstrating evolving traits, in accordance with real-world SARS-CoV-2 spike protein sequence evolution. DNMS can be adopted for an early warning variant detection system, creating public health awareness of future SARS-CoV-2 mutations.

A Bibliometric Analysis on Multi-epitope Vaccine Development Against SARS-CoV-2: Current Status, Development, and Future Directions

The etiological agent for the coronavirus disease 2019 (COVID-19), the SARS-CoV-2, caused a global pandemic. Although mRNA, viral-vectored, DNA, and recombinant protein vaccine candidates were effective against the SARS-CoV-2 Wuhan strain, the emergence of SARS-CoV-2 variants of concern (VOCs) reduced the protective efficacies of these vaccines. This necessitates the need for effective and accelerated vaccine development against mutated VOCs. The development of multi-epitope vaccines against SARS-CoV-2 based on in silico identification of highly conserved and immunogenic epitopes is a promising strategy for future SARS-CoV-2 vaccine development. Considering the evolving landscape of the COVID-19 pandemic, we have conducted a bibliometric analysis to consolidate current findings and research trends in multi-epitope vaccine development to provide insights for future vaccine development strategies. Analysis of 102 publications on multi-epitope vaccine development against SARS-CoV-2 revealed significant growth and global collaboration, with India leading in the number of publications, along with an identification of the most prolific authors. Key journals included the Journal of Biomolecular Structure and Dynamics, while top collaborations involved Pakistan-China and India-USA. Keyword analysis showed a prominent focus on immunoinformatics, epitope prediction, and spike glycoprotein. Advances in immunoinformatics, including AI-driven epitope prediction, offer promising avenues for the development of safe and effective multi-epitope vaccines. Immunogenicity may be further improved through nanoparticle-based systems or the use of adjuvants along with real-time genomic surveillance to tailor vaccines against emerging variants.

How fingers affect folding of a thumb: Inter-subdomain cooperation in the folding of SARS-CoV-2 RdRp protein

The RNA-dependent RNA polymerase (RdRp) of SARS-CoV-2 is a critical enzyme essential for the virus's replication and transcription, making it a key therapeutic target. The RdRp protein exhibits a characteristic cupped right-hand shaped structure with two vital subdomains: the fingers and the thumb. Despite being distinct, biophysical experiments suggest that these subdomains cooperate to facilitate RNA accommodation, ensuring RdRp functionality. To investigate the structure-based mechanisms underlying the fingers-thumb interaction in both apo and RNA-bound RdRp, we constructed a coarse-grained structure-based model based on recent cryo-electron microscopy data. The simulations reveal frequent open-to-closed conformational transitions in apo RdRp, akin to a breathing-like motion. These conformational changes are regulated by the fingers-thumb association and the folding dynamics of the thumb subdomain. The thumb adopts a stable fold only when tethered by the fingers-thumb interface; when these subdomains are disconnected, the thumb transitions into an open state. A significant number of open-to-closed transition events were analyzed to generate a transition contact probability map, which highlights a few specific residues at the thumb-fingers interface, distant from the RNA accommodation sites, as essential for inducing the thumb's folding process. Given that thumb subdomain folding is critical for RNA binding and viral replication, the study proposes that these interfacial residues may function as remote regulatory switches and could be targeted for the development of allosteric drugs against SARS-CoV-2 and similar RNA viruses.

An environmental scan of messages promoting compliance behaviour for a medical directive in COVID-19

Compliance with COVID-19 preventive behaviours together with the urgency to contain the virus underscored the need for rapid yet effective public health massaging. While messages aimed to inform and protect the public, the evolving situation often precluded the use of theoretically-based and empirically-informed approaches. This study aimed to analyse the presence and prevalence of belief-based constructs and strategies known to foster behaviour change embedded within Australian Government communications regarding compliance with QR code check-in behaviour during the COVID-19 pandemic using the Theory of Planned Behaviour as a guiding framework. Six belief codes and five behaviour change techniques were identified in 17 communication messages. Findings highlight the use of potentially effective strategies in the messages to change behaviour; for example, drawing on attitudinal and self-efficacy beliefs. Yet, results identified gaps, such as a lack of strategies to highlight normative influences and build habits that can inform future messaging and pandemic preparedness.

"The COVID-19 pandemic in BRICS: Milestones, interventions, and molecular epidemiology"

Brazil, Russia, India, China, and South Africa (BRICS) are a group of developing countries with shared economic, healthcare, and scientific interests. These countries navigate multiple syndemics, and the COVID-19 pandemic placed severe strain on already burdened BRICS' healthcare systems, hampering effective pandemic interventions. Genomic surveillance and molecular epidemiology remain indispensable tools for facilitating informed pandemic intervention. To evaluate the combined manner in which the pandemic unfolded in BRICS countries, we reviewed the BRICS pandemic epidemiological and genomic milestones, which included the first reported cases and deaths, and pharmaceutical and non-pharmaceutical interventions implemented in these countries. To assess the development of genomic surveillance capacity and efficiency over the pandemic, we analyzed the turnaround time from sample collection to data availability and the technologies used for genomic analysis. This data provided information on the laboratory capacities that enable the detection of emerging SARS-CoV-2 variants and highlight their potential for monitoring other pathogens in ongoing public health efforts. Our analyses indicated that BRICS suffered >105.6M COVID-19 infections, resulting in >1.7M deaths. BRICS countries detected intricate genetic combinations of SARS-CoV-2 variants that fueled country-specific pandemic waves. BRICS' genomic surveillance programs enabled the identification and characterization of the majority of globally circulating Variants of Concern (VOCs) and their descending lineages. Pandemic intervention strategies first implemented by BRICS countries included non-pharmaceutical interventions during the onset of the pandemic, such as nationwide lockdowns, quarantine procedures, the establishment of fever clinics, and mask mandates- which were emulated internationally. Vaccination rollout strategies complemented this, some representing the first of their kind. Improvements in BRICS sequencing and data generation turnaround time facilitated quicker detection of circulating and emerging variants, supported by investments in sequencing and bioinformatic infrastructure. Intra-BRICS cooperation contributed to the ongoing intervention in COVID-19 and other pandemics, enhancing collective capabilities in addressing these health challenges. The data generated continues to inform BRICS-centric pandemic intervention strategies and influences global health matters. The increased laboratory and bioinformatic capacity post-COVID-19 will support the detection of emerging pathogens.

Circulatory microRNAs as potential biomarkers for different aspects of COVID-19

The coronavirus disease of 2019 (COVID-19), caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), can alter the expression levels of host microRNAs (miRNAs). Increasing evidence suggests that circulating miRNAs can potentially play an important role in the diagnosis and prognosis of respiratory infectious diseases, especially COVID-19, and might serve as sensitive indicators of disease before the emergence of clinical symptoms. Here, we review the potential of circulatory microRNAs as novel biomarkers for different aspects of COVID-19. Recent studies have suggested that they can be useful not only for COVID-19 prognosis but also for prediction of disease severity and mortality among intensive care unit (ICU) and ward patients. Moreover, extracellular vesicle (EV) miRNAs can be associated with antibody titer after COVID-19 vaccination. This review provides an overview of miRNA-based biomarkers.

Assessing COVID-19 Vaccine Effectiveness and Risk Factors for Severe Outcomes through Machine Learning Techniques: A Real-World Data Study in Andalusia, Spain

BACKGROUND

COVID-19 vaccination has become a pivotal global strategy in managing the pandemic. Despite COVID-19 no longer being classified as a Public Health Emergency of International Concern, the virus continues affecting people worldwide. This study aimed to evaluate risk factors and vaccine effectiveness on COVID-19-related hospital admissions, intensive care unit (ICU) admission and mortality within the Andalusian population throughout the pandemic.

METHODS

From March 2020 to April 2022, 671,229 individuals, out of 9,283,485 with electronic health records in Andalusia, experienced SARS-CoV-2 infection and were included in the analysis. Data on demographics, medical history, vaccine administration, and hospitalization records were collected. Associations between medical history, COVID-19 vaccines, and COVID-19 outcomes were assessed.

RESULTS

Our study identified 48,196 hospital admissions, 5,057 ICU admissions, and 11,289 deaths linked to COVID-19. Age, male sex, and chronic diseases were identified as risk factors, while the COVID-19 vaccine demonstrated protective effects, although with reduced effectiveness during the omicron variant period. However, the risk for these outcomes increased over time after receiving the last vaccine dose, particularly after six months, especially among those aged 60 or older.

CONCLUSION

The global health challenge of COVID-19 persists, marked by emerging variants with higher virulence and severity, particularly among the unvaccinated and those beyond six months post-vaccination, especially those aged 60 and above. These findings highlight the need for robust surveillance systems targeting new variants and administering booster doses, particularly for individuals aged 60 or older with underlying health conditions, to mitigate the global burden of COVID-19.

The surfactant protein B polymorphisms (rs7316 and rs1130866) and their correlation with disease progression of COVID-19

BACKGROUND

It is critical to examine the pathogenic pathways in coronavirus disease 2019 (COVID-19) that resulted in the development of severe lung injury. Surfactant protein B (SFTPB) is a vital component for sustaining life and serves pivotal functions in the host's defensive mechanisms and alveolar surface tension reduction. Our study aimed to determine the effect of SFTPB rs7316 and rs1130866 variants on the course of disease in COVID-19 patients.

METHODS

The study cohort comprised 3,184 individuals diagnosed with COVID-19. We employed the RFLP approach to determine the variations of the SFTPB genes.

RESULTS

SFTPB rs7316 did not exhibit a statistically significant correlation with COVID-19 mortality across different inheritance models. But, after making more changes for SARS-CoV-2 variants, it was found that there was a strong link between the TT and TC genotypes of SFTPB rs7316 and death rates, especially for the Delta variant. Furthermore, our study's findings indicate a significant association between the SFTPB rs1130866 G allele and an elevated risk of mortality in COVID-19 across all variants of SARS-CoV-2.

CONCLUSIONS

The use of the SFTPB rs1130866 marker has the potential to facilitate the prediction of COVID-19 severity. On the other hand, for SFTPB rs7316, this kind of prediction seems to depend on the particular SARS-CoV-2 variants.

Modelling on COVID-19 control with double and booster-dose vaccination

COVID-19 vaccines have been illustrated to lessen the growth of sickness caused by the virus effectively. In any case, inoculation has consistently been controversial, with differing opinions and viewpoints. This has compelled some individuals to decide against receiving the vaccine. These divergent viewpoints have had a trivial impact on the epidemic's dynamics and the disease's development. In response to vaccinated individuals still falling ill, many countries have implemented booster vaccines to protect further. In this specific investigation, a mathematical model composed of seven compartments is employed to examine the effectiveness of a booster dose in preventing and treating the transmission of COVID-19. The principles of mathematics are employed to analyse and investigate the dynamics of the disease. Using a qualitative prototype analysis, we acquired valuable insights into its effectiveness. One essential aspect is the basic reproduction number, a critical determinant of the disease's spread. This calculation is determined by studying the system's equilibrium and evaluating its stability. Furthermore, we examined the balance from a local and global viewpoint, considering the possibility of bifurcation and the model's reproductive number sensitivity index. Through numerical simulations, we have visually illustrated the analytical findings outlined in this research paper and presented a thorough examination of the efficacy of booster shots as a preventive and therapeutic measure in the spread dynamics of COVID-19.

Wearable threads for monitoring sanitizer quality using dye displacement assay

This study employs zero-cost (≈0.01 $) and durable thread-based devices to evaluate the quality of simulated and commercial sanitizer samples through dye displacement assay (DDA). A diverse range of sanitizer compositions, including ethanol concentrations of 55%, 75%, and 95% (v/v), were analysed. This investigation encompasses an assessment of the marker type (Doms and Hauser brands) on the migration distance of the dye region marked on thread devices. Our results demonstrate a proportional increase in the migration distance of the dye with increasing ethanol concentrations due to a decrease in the coefficient of viscosity and solvation power of ethanol on dye molecules. Additionally, a field trial for the thorough assessment of commercial sanitizer quality using thread-based devices further underscores the efficacy of this methodology. A calibration plot for a braided thread with Doms marker dye provides a reliable means to quantitatively assess the ethanol content in different commercial sanitizer compositions. Our findings collectively highlight the significance of this innovative method as a valuable tool for quality control and assessment for public health and hygiene as well as for preparing us for another pandemic.

In-silico evaluation of the T-cell based immune response against SARS-CoV-2 omicron variants

During of COVID-19 pandemic, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has continuously evolved, resulting in the emergence of several new variants of concerns (VOCs) with numerous mutations. These VOCs dominate in various regions due to increased transmissibility and antibody evasion, potentially reducing vaccine effectiveness. Nonetheless, it remains uncertain whether the recent SARS-CoV-2 VOCs have the ability to circumvent the T cell immunity elicited by either COVID-19 vaccination or natural infection. To address this, we conducted in-silico analysis to examine the impact of VOC-specific mutations at the epitope level and T cell cross-reactivity with the ancestral SARS-CoV-2. According to the in-silico investigation, T cell responses triggered by immunization or prior infections still recognize the variants in spite of mutations. These variants are expected to either maintain their dominant epitope HLA patterns or bind with new HLAs, unlike the epitopes of the ancestral strain. Our findings indicate that a significant proportion of immuno-dominant CD8 + and CD4 + epitopes are conserved across all the variants, implying that existing vaccines might maintain efficacy against new variations. However, further in-vitro and in-vivo studies are needed to validate the in-silico results and fully elucidate immune sensitivities to VOCs.

A conceptual health state diagram for modelling the transmission of a (re)emerging infectious respiratory disease in a human population

Mathematical modelling of (re)emerging infectious respiratory diseases among humans poses multiple challenges for modellers, which can arise as a result of limited data and surveillance, uncertainty in the natural history of the disease, as well as public health and individual responses to outbreaks. Here, we propose a COVID-19-inspired health state diagram (HSD) to serve as a foundational framework for conceptualising the modelling process for (re)emerging respiratory diseases, and public health responses, in the early stages of their emergence. The HSD aims to serve as a starting point for reflection on the structure and parameterisation of a transmission model to assess the impact of the (re)emerging disease and the capacity of public health interventions to control transmission. We also explore the adaptability of the HSD to different (re)emerging diseases using the characteristics of three respiratory diseases of historical public health importance. We outline key questions to contemplate when applying and adapting this HSD to (re)emerging infectious diseases and provide reflections on adapting the framework for public health-related interventions.

Genomic epidemiology and evolutionary dynamics of the Omicron variant of SARS-CoV-2 during the fifth wave of COVID-19 in Pakistan

Introduction

The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has posed extraordinary challenges to global health systems and economies. The virus's rapid evolution has resulted in several variants of concern (VOCs), including the highly transmissible Omicron variant, characterized by extensive mutations. In this study, we investigated the genetic diversity, population differentiation, and evolutionary dynamics of the Omicron VOC during the fifth wave of COVID-19 in Pakistan.

Methods

A total of 954 Omicron genomes sequenced during the fifth wave of COVID-19 in Pakistan were analyzed. A Bayesian framework was employed for phylogenetic reconstructions, molecular dating, and population dynamics analysis.

Results

Using a population genomics approach, we analyzed Pakistani Omicron samples, revealing low within-population genetic diversity and significant structural variation in the spike (S) protein. Phylogenetic analysis showed that the Omicron variant in Pakistan originated from two distinct lineages, BA.1 and BA.2, which were introduced from South Africa, Thailand, Spain, and Belgium. Omicron-specific mutations, including those in the receptor-binding domain, were identified. The estimated molecular evolutionary rate was 2.562E-3 mutations per site per year (95% HPD interval: 8.8067E-4 to 4.1462E-3). Bayesian skyline plot analysis indicated a significant population expansion at the end of 2021, coinciding with the global Omicron outbreak. Comparative analysis with other VOCs showed Omicron as a highly divergent, monophyletic group, suggesting a unique evolutionary pathway.

Conclusions

This study provides a comprehensive overview of Omicron's genetic diversity, genomic epidemiology, and evolutionary dynamics in Pakistan, emphasizing the need for global collaboration in monitoring variants and enhancing pandemic preparedness.

How Immunocompromised Hosts Were Left Behind in the Quest to Control the COVID-19 Pandemic

The immunocompromised population was disproportionately affected by the severe acute respiratory syndrome coronavirus 2 pandemic. However, these individuals were largely excluded from clinical trials of vaccines, monoclonal antibodies, and small molecule antivirals. Although the community of scientists, clinical researchers, and funding agencies have proven that these therapeutics can be made and tested in record time, extending this progress to vulnerable and medically complex individuals from the start has been a missed opportunity. Here, we advocate that it is paramount to plan for future pandemics by investing in specific clinical trial infrastructure for the immunocompromised population to be prepared when the need arises.

Molecular Characterization and Genomic Surveillance of SARS-CoV-2 Lineages in Central India

Since the first reported case of COVID-19 in December 2019, several SARS-CoV-2 variants have evolved, and some of them have shown higher transmissibility, becoming the prevalent strains. Genomic epidemiological investigations into strains from different time points, including the early stages of the pandemic, are very crucial for understanding the evolution and transmission patterns. Using whole-genome sequences, our study describes the early landscape of SARS-CoV-2 variants in central India retrospectively (including the first known occurrence of SARS-CoV-2 in Madhya Pradesh). We performed amplicon-based whole-genome sequencing of randomly selected SARS-CoV-2 isolates (n = 38) collected between 2020 and 2022 at state level VRDL, ICMR-NIRTH, Jabalpur, from 11899 RT-qPCR-positive samples. We observed the presence of five lineages, namely B.1, B.1.1, B.1.36.8, B.1.195, and B.6, in 19 genomes from the first wave cases and variants of concern (VOCs) lineages, i.e., B.1.617.2 (Delta) and BA.2.10 (Omicron) in the second wave cases. There was a shift in mutational pattern in the spike protein coding region of SRAS-CoV-2 strains from the second wave in contrast to the first wave. In the first wave of infections, we observed variations in the ORF1Ab region, and with the emergence of Delta lineages, the D614G mutation associated with an increase in infectivity became a prominent change. We have identified five immune escape variants in the S gene, P681R, P681H, L452R, Q57H, and N501Y, in the isolates collected during the second wave. Furthermore, these genomes were compared with 2160 complete genome sequences reported from central India that encompass 109 different SARS-CoV-2 lineages. Among them, VOC lineages Delta (28.93%) and Omicron (56.11%) were circulating predominantly in this region. This study provides useful insights into the genetic diversity of SARS-CoV-2 strains over the initial course of the COVID-19 pandemic in central India.

Higher Frequency of SARS-CoV-2 RNA Shedding by Cats than Dogs in Households with Owners Recently Diagnosed with COVID-19

Studies have demonstrated the susceptibility of companion animals to natural infection with SARS-CoV-2. Using quantitative reverse transcription polymerase chain reaction and sequencing analyses, this study investigated SARS-CoV-2 RNA excretion in pets in households with infected owners. Oropharyngeal and rectal swabs were collected from dogs and cats in Parana, Southern Brazil, between October 2020 and April 2021. Viral RNA was detected in 25% of cats and 0.98% of dog oropharyngeal swabs; however, systemic, respiratory, and gastrointestinal signs were absent. Complete viral genomes belonged to the Gamma lineage. Phylogenetic analyses indicated that pet samples were probably derived from human-positive cases in Parana. Viral excretion in the oropharynx was more frequent in cats than in dogs. Mutations in the S protein characteristic of Gamma strains were present in all sequenced SARS-CoV-2 strains. The receptor-binding domain of these Brazilian strains did not show any additional mutations not reported in the Gamma strains. Mutations in NSP6, NSP12, and N proteins previously mapped to strains that infect deer or minks were detected. This study highlights the importance of actively monitoring the SARS-CoV-2 strains that infect pets with continued viral exposure. Monitoring genetic changes is crucial because new variants adapted to animals may pose human health risks.

Symptomatology and IgG Levels before and after SARS-CoV-2 Omicron Breakthrough Infections in Vaccinated Individuals

(1) Background: After the COVID-19 pandemic, there is concern regarding the immunity of the population to SARS-CoV-2 variants, particularly the Omicron variant and its sub-lineages. (2) Methods: The study involved analyzing the immune response and symptomatology of 27 vaccinated individuals who were subsequently infected by Omicron sub-lineages. Blood samples were collected for serological analysis, including the detection of IgG antibodies reactive to the Nucleocapsid (N) and Spike (S) antigens of SARS-CoV-2. Additionally, participants were interviewed to assess the intensity of symptoms during the infection. (3) Results: Despite the high levels of anti-Spike IgG observed after vaccination, all participants were infected by Omicron sub-lineages. The most common symptoms reported by participants were fever or chills, sore throat, and cough. The levels of anti-Spike IgG found prior to infection did not correlate with symptom intensity post-infection. However, it was observed that high post-infection anti-Nucleocapsid IgG levels correlated with mild symptoms during the course of the disease, suggesting a potential role for anti-N antibodies in symptom intensity. (4) Conclusions: In line with previous studies, the high levels of IgG anti-Spike resulting from vaccination did not provide complete protection against infection by the Omicron variant. Additionally, our data suggest that anti-Nucleocapsid IgG titers are negatively correlated with the intensity of the symptoms during mild infections.

Mathematical modelling for pandemic preparedness in Canada: Learning from COVID-19

Background

The COVID-19 pandemic underlined the need for pandemic planning but also brought into focus the use of mathematical modelling to support public health decisions. The types of models needed (compartment, agent-based, importation) are described. Best practices regarding biological realism (including the need for multidisciplinary expert advisors to modellers), model complexity, consideration of uncertainty and communications to decision-makers and the public are outlined.

Methods

A narrative review was developed from the experiences of COVID-19 by members of the Public Health Agency of Canada External Modelling Network for Infectious Diseases (PHAC EMN-ID), a national community of practice on mathematical modelling of infectious diseases for public health.

Results

Modelling can best support pandemic preparedness in two ways: 1) by modelling to support decisions on resource needs for likely future pandemics by estimating numbers of infections, hospitalized cases and cases needing intensive care, associated with epidemics of "hypothetical-yet-plausible" pandemic pathogens in Canada; and 2) by having ready-to-go modelling methods that can be readily adapted to the features of an emerging pandemic pathogen and used for long-range forecasting of the epidemic in Canada, as well as to explore scenarios to support public health decisions on the use of interventions.

Conclusion

There is a need for modelling expertise within public health organizations in Canada, linked to modellers in academia in a community of practice, within which relationships built outside of times of crisis can be applied to enhance modelling during public health emergencies. Key challenges to modelling for pandemic preparedness include the availability of linked public health, hospital and genomic data in Canada.

The Canadian VirusSeq Data Portal and Duotang: open resources for SARS-CoV-2 viral sequences and genomic epidemiology

The COVID-19 pandemic led to a large global effort to sequence SARS-CoV-2 genomes from patient samples to track viral evolution and inform the public health response. Millions of SARS-CoV-2 genome sequences have been deposited in global public repositories. The Canadian COVID-19 Genomics Network (CanCOGeN - VirusSeq), a consortium tasked with coordinating expanded sequencing of SARS-CoV-2 genomes across Canada early in the pandemic, created the Canadian VirusSeq Data Portal, with associated data pipelines and procedures, to support these efforts. The goal of VirusSeq was to allow open access to Canadian SARS-CoV-2 genomic sequences and enhanced, standardized contextual data that were unavailable in other repositories and that meet FAIR standards (Findable, Accessible, Interoperable and Reusable). In addition, the portal data submission pipeline contains data quality checking procedures and appropriate acknowledgement of data generators that encourages collaboration. From inception to execution, the portal was developed with a conscientious focus on strong data governance principles and practices. Extensive efforts ensured a commitment to Canadian privacy laws, data security standards, and organizational processes. This portal has been coupled with other resources, such as Viral AI, and was further leveraged by the Coronavirus Variants Rapid Response Network (CoVaRR-Net) to produce a suite of continually updated analytical tools and notebooks. Here we highlight this portal (https://virusseq-dataportal.ca/), including its contextual data not available elsewhere, and the Duotang (https://covarr-net.github.io/duotang/duotang.html), a web platform that presents key genomic epidemiology and modelling analyses on circulating and emerging SARS-CoV-2 variants in Canada. Duotang presents dynamic changes in variant composition of SARS-CoV-2 in Canada and by province, estimates variant growth, and displays complementary interactive visualizations, with a text overview of the current situation. The VirusSeq Data Portal and Duotang resources, alongside additional analyses and resources computed from the portal (COVID-MVP, CoVizu), are all open source and freely available. Together, they provide an updated picture of SARS-CoV-2 evolution to spur scientific discussions, inform public discourse, and support communication with and within public health authorities. They also serve as a framework for other jurisdictions interested in open, collaborative sequence data sharing and analyses.

Forecasting dominance of SARS-CoV-2 lineages by anomaly detection using deep AutoEncoders

The coronavirus disease of 2019 (COVID-19) pandemic is characterized by sequential emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants, lineages, and sublineages, outcompeting previously circulating ones because of, among other factors, increased transmissibility and immune escape. We propose DeepAutoCoV, an unsupervised deep learning anomaly detection system to predict future dominant lineages (FDLs). We define FDLs as viral (sub)lineages that will constitute more than 10% of all the viral sequences added to the GISAID database on a given week. DeepAutoCoV is trained and validated by assembling global and country-specific data sets from over 16 million Spike protein sequences sampled over a period of about 4 years. DeepAutoCoV successfully flags FDLs at very low frequencies (0.01% - 3%), with median lead times of 4-17 weeks, and predicts FDLs ~5 and ~25 times better than a baseline approach For example, the B.1.617.2 vaccine reference strain was flagged as FDL when its frequency was only 0.01%, more than a year before it was considered for an updated COVID-19 vaccine. Furthermore, DeepAutoCoV outputs interpretable results by pinpointing specific mutations potentially linked to increased fitness, and may provide significant insights for the optimization of public health pre-emptive intervention strategies.

COVID-19 mutations: An overview

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) belongs to the genus Beta coronavirus and the family of Coronaviridae. It is a positive-sense, non-segmented single-strand RNA virus. Four common types of human coronaviruses circulate globally, particularly in the fall and winter seasons. They are responsible for 10%-30% of all mild upper respiratory tract infections in adults. These are 229E, NL63 of the Alfacoronaviridae family, OC43, and HKU1 of the Betacoronaviridae family. However, there are three highly pathogenic human coronaviruses: SARS-CoV-2, Middle East respiratory syndrome coronavirus, and the latest pandemic caused by the SARS-CoV-2 infection. All viruses, including SARS-CoV-2, have the inherent tendency to evolve. SARS-CoV-2 is still evolving in humans. Additionally, due to the development of herd immunity, prior infection, use of medication, vaccination, and antibodies, the viruses are facing immune pressure. During the replication process and due to immune pressure, the virus may undergo mutations. Several SARS-CoV-2 variants, including the variants of concern (VOCs), such as B.1.1.7 (Alpha), B.1.351 (Beta), B.1.617/B.1.617.2 (Delta), P.1 (Gamma), and B.1.1.529 (Omicron) have been reported from various parts of the world. These VOCs contain several important mutations; some of them are on the spike proteins. These mutations may lead to enhanced infectivity, transmissibility, and decreased neutralization efficacy by monoclonal antibodies, convalescent sera, or vaccines. Mutations may also lead to a failure of detection by molecular diagnostic tests, leading to a delayed diagnosis, increased community spread, and delayed treatment. We searched PubMed, EMBASE, Covariant, the Stanford variant Database, and the CINAHL from December 2019 to February 2023 using the following search terms: VOC, SARS-CoV-2, Omicron, mutations in SARS-CoV-2, etc. This review discusses the various mutations and their impact on infectivity, transmissibility, and neutralization efficacy.

SARS-CoV-2 Variants from Long-Term, Persistently Infected Immunocompromised Patients Have Altered Syncytia Formation, Temperature-Dependent Replication, and Serum Neutralizing Antibody Escape

SARS-CoV-2 infection of immunocompromised individuals often leads to prolonged detection of viral RNA and infectious virus in nasal specimens, presumably due to the lack of induction of an appropriate adaptive immune response. Mutations identified in virus sequences obtained from persistently infected patients bear signatures of immune evasion and have some overlap with sequences present in variants of concern. We characterized virus isolates obtained greater than 100 days after the initial COVID-19 diagnosis from two COVID-19 patients undergoing immunosuppressive cancer therapy, wand compared them to an isolate from the start of the infection. Isolates from an individual who never mounted an antibody response specific to SARS-CoV-2 despite the administration of convalescent plasma showed slight reductions in plaque size and some showed temperature-dependent replication attenuation on human nasal epithelial cell culture compared to the virus that initiated infection. An isolate from another patient-who did mount a SARS-CoV-2 IgM response-showed temperature-dependent changes in plaque size as well as increased syncytia formation and escape from serum-neutralizing antibodies. Our results indicate that not all virus isolates from immunocompromised COVID-19 patients display clear signs of phenotypic change, but increased attention should be paid to monitoring virus evolution in this patient population.

Persistent COVID-19 Infection in an Immunocompromised Host: A Case Report

This case report highlights the prolonged SARS-CoV-2 reverse transcriptase polymerase chain reaction positivity in a 32-year-old immunocompromised male with a history of kidney transplants and chronic kidney disease. The whole genome sequencing of nasopharyngeal samples for SARS-CoV-2 collected 12 days apart showed the presence of the BA.1.1 Omicron variant. It revealed evidence of intra-host viral evolution, showing the development and loss of specific mutations over time. This report emphasizes the need for continuous monitoring strategies for immunocompromised patients, as they may serve as reservoirs for viral evolution and potentially give rise to immune escape variants.

Data-driven mathematical modeling approaches for COVID-19: A survey

In this review, we successively present the methods for phenomenological modeling of the evolution of reported and unreported cases of COVID-19, both in the exponential phase of growth and then in a complete epidemic wave. After the case of an isolated wave, we present the modeling of several successive waves separated by endemic stationary periods. Then, we treat the case of multi-compartmental models without or with age structure. Eventually, we review the literature, based on 260 articles selected in 11 sections, ranging from the medical survey of hospital cases to forecasting the dynamics of new cases in the general population. This review favors the phenomenological approach over the mechanistic approach in the choice of references and provides simulations of the evolution of the number of observed cases of COVID-19 for 10 states (California, China, France, India, Israel, Japan, New York, Peru, Spain and United Kingdom).

A dynamic ensemble model for short-term forecasting in pandemic situations

During the COVID-19 pandemic, many hospitals reached their capacity limits and could no longer guarantee treatment of all patients. At the same time, governments endeavored to take sensible measures to stop the spread of the virus while at the same time trying to keep the economy afloat. Many models extrapolating confirmed cases and hospitalization rate over short periods of time have been proposed, including several ones coming from the field of machine learning. However, the highly dynamic nature of the pandemic with rapidly introduced interventions and new circulating variants imposed non-trivial challenges for the generalizability of such models. In the context of this paper, we propose the use of ensemble models, which are allowed to change in their composition or weighting of base models over time and could thus better adapt to highly dynamic pandemic or epidemic situations. In that regard, we also explored the use of secondary metadata-Google searches-to inform the ensemble model. We tested our approach using surveillance data from COVID-19, Influenza, and hospital syndromic surveillance of severe acute respiratory infections (SARI). In general, we found ensembles to be more robust than the individual models. Altogether we see our work as a contribution to enhance the preparedness for future pandemic situations.

Excess burden of respiratory and abdominal conditions following COVID-19 infections during the ancestral and Delta variant periods in the United States: An EHR-based cohort study from the RECOVER program

IMPORTANCE

The frequency and characteristics of post-acute sequelae of SARS-CoV-2 infection (PASC) may vary by SARS-CoV-2 variant.

OBJECTIVE

To characterize PASC-related conditions among individuals likely infected by the ancestral strain in 2020 and individuals likely infected by the Delta variant in 2021.

DESIGN

Retrospective cohort study of electronic medical record data for approximately 27 million patients from March 1, 2020-November 30, 2021.

SETTING

Healthcare facilities in New York and Florida.

PARTICIPANTS

Patients who were at least 20 years old and had diagnosis codes that included at least one SARS-CoV-2 viral test during the study period.

EXPOSURE

Laboratory-confirmed COVID-19 infection, classified by the most common variant prevalent in those regions at the time.

MAIN OUTCOME(S) AND MEASURE(S)

Relative risk (estimated by adjusted hazard ratio [aHR]) and absolute risk difference (estimated by adjusted excess burden) of new conditions, defined as new documentation of symptoms or diagnoses, in persons between 31-180 days after a positive COVID-19 test compared to persons without a COVID-19 test or diagnosis during the 31-180 days after the last negative test.

RESULTS

We analyzed data from 560,752 patients. The median age was 57 years; 60.3% were female, 20.0% non-Hispanic Black, and 19.6% Hispanic. During the study period, 57,616 patients had a positive SARS-CoV-2 test; 503,136 did not. For infections during the ancestral strain period, pulmonary fibrosis, edema (excess fluid), and inflammation had the largest aHR, comparing those with a positive test to those without a COVID-19 test or diagnosis (aHR 2.32 [95% CI 2.09 2.57]), and dyspnea (shortness of breath) carried the largest excess burden (47.6 more cases per 1,000 persons). For infections during the Delta period, pulmonary embolism had the largest aHR comparing those with a positive test to a negative test (aHR 2.18 [95% CI 1.57, 3.01]), and abdominal pain carried the largest excess burden (85.3 more cases per 1,000 persons).

CONCLUSIONS AND RELEVANCE

We documented a substantial relative risk of pulmonary embolism and a large absolute risk difference of abdomen-related symptoms after SARS-CoV-2 infection during the Delta variant period. As new SARS-CoV-2 variants emerge, researchers and clinicians should monitor patients for changing symptoms and conditions that develop after infection.

The Designed Ankyrin Repeat Protein Antiviral Ensovibep for Nonhospitalized Patients With Coronavirus Disease 2019: Results From EMPATHY, a Randomized, Placebo-Controlled Phase 2 Study

Background

The coronavirus disease 2019 (COVID-19) pandemic was characterized by rapid evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants, affecting viral transmissibility, virulence, and response to vaccines/therapeutics. EMPATHY (NCT04828161), a phase 2 study, investigated the safety/efficacy of ensovibep, a multispecific designed ankyrin repeat protein (DARPin) with multivariant in vitro activity, in ambulatory patients with mild to moderate COVID-19.

Methods

Nonhospitalized, symptomatic patients (N = 407) with COVID-19 were randomized to receive single-dose intravenous ensovibep (75, 225, or 600 mg) or placebo and followed until day 91. The primary endpoint was time-weighted change from baseline in log10 SARS-CoV-2 viral load through day 8. Secondary endpoints included proportion of patients with COVID-19-related hospitalizations, emergency room (ER) visits, and/or all-cause mortality to day 29; time to sustained clinical recovery to day 29; and safety to day 91.

Results

Ensovibep showed superiority versus placebo in reducing log10 SARS-CoV-2 viral load; treatment differences versus placebo in time-weighted change from baseline were -0.42 (P = .002), -0.33 (P = .014), and -0.59 (P < .001) for 75, 225, and 600 mg, respectively. Ensovibep-treated patients had fewer COVID-19-related hospitalizations, ER visits, and all-cause mortality (relative risk reduction: 78% [95% confidence interval, 16%-95%]) and a shorter median time to sustained clinical recovery than placebo. Treatment-emergent adverse events occurred in 44.3% versus 54.0% of patients in the ensovibep and placebo arms; grade 3 events were consistent with COVID-19 morbidity. Two deaths were reported with placebo and none with ensovibep.

Conclusions

All 3 doses of ensovibep showed antiviral efficacy and clinical benefits versus placebo and an acceptable safety profile in nonhospitalized patients with COVID-19.

A link between evolution and society fostering the UN sustainable development goals

Given the multitude of challenges Earth is facing, sustainability science is of key importance to our continued existence. Evolution is the fundamental biological process underlying the origin of all biodiversity. This phylogenetic diversity fosters the resilience of ecosystems to environmental change, and provides numerous resources to society, and options for the future. Genetic diversity within species is also key to the ability of populations to evolve and adapt to environmental change. Yet, the value of evolutionary processes and the consequences of their impairment have not generally been considered in sustainability research. We argue that biological evolution is important for sustainability and that the concepts, theory, data, and methodological approaches used in evolutionary biology can, in crucial ways, contribute to achieving the UN Sustainable Development Goals (SDGs). We discuss how evolutionary principles are relevant to understanding, maintaining, and improving Nature Contributions to People (NCP) and how they contribute to the SDGs. We highlight specific applications of evolution, evolutionary theory, and evolutionary biology's diverse toolbox, grouped into four major routes through which evolution and evolutionary insights can impact sustainability. We argue that information on both within-species evolutionary potential and among-species phylogenetic diversity is necessary to predict population, community, and ecosystem responses to global change and to make informed decisions on sustainable production, health, and well-being. We provide examples of how evolutionary insights and the tools developed by evolutionary biology can not only inspire and enhance progress on the trajectory to sustainability, but also highlight some obstacles that hitherto seem to have impeded an efficient uptake of evolutionary insights in sustainability research and actions to sustain SDGs. We call for enhanced collaboration between sustainability science and evolutionary biology to understand how integrating these disciplines can help achieve the sustainable future envisioned by the UN SDGs.

Endemic does not mean constant as SARS-CoV-2 continues to evolve

COVID-19 has become endemic, with dynamics that reflect the waning of immunity and re-exposure, by contrast to the epidemic phase driven by exposure in immunologically naïve populations. Endemic does not, however, mean constant. Further evolution of SARS-CoV-2, as well as changes in behavior and public health policy, continue to play a major role in the endemic load of disease and mortality. In this article, we analyze evolutionary models to explore the impact that a newly arising variant can have on the short-term and longer-term endemic load, characterizing how these impacts depend on the transmission and immunological properties of the variants. We describe how evolutionary changes in the virus will increase the endemic load most for a persistently immune-escape variant, by an intermediate amount for a more transmissible variant, and least for a transiently immune-escape variant. Balancing the tendency for evolution to favor variants that increase the endemic load, we explore the impact of vaccination strategies and non-pharmaceutical interventions that can counter these increases in the impact of disease. We end with some open questions about the future of COVID-19 as an endemic disease.

The Canadian VirusSeq Data Portal & Duotang: open resources for SARS-CoV-2 viral sequences and genomic epidemiology

The COVID-19 pandemic led to a large global effort to sequence SARS-CoV-2 genomes from patient samples to track viral evolution and inform public health response. Millions of SARS-CoV-2 genome sequences have been deposited in global public repositories. The Canadian COVID-19 Genomics Network (CanCOGeN - VirusSeq), a consortium tasked with coordinating expanded sequencing of SARS-CoV-2 genomes across Canada early in the pandemic, created the Canadian VirusSeq Data Portal, with associated data pipelines and procedures, to support these efforts. The goal of VirusSeq was to allow open access to Canadian SARS-CoV-2 genomic sequences and enhanced, standardized contextual data that were unavailable in other repositories and that meet FAIR standards (Findable, Accessible, Interoperable and Reusable). In addition, the Portal data submission pipeline contains data quality checking procedures and appropriate acknowledgement of data generators that encourages collaboration. From inception to execution, the portal was developed with a conscientious focus on strong data governance principles and practices. Extensive efforts ensured a commitment to Canadian privacy laws, data security standards, and organizational processes. This Portal has been coupled with other resources like Viral AI and was further leveraged by the Coronavirus Variants Rapid Response Network (CoVaRR-Net) to produce a suite of continually updated analytical tools and notebooks. Here we highlight this Portal, including its contextual data not available elsewhere, and the 'Duotang', a web platform that presents key genomic epidemiology and modeling analyses on circulating and emerging SARS-CoV-2 variants in Canada. Duotang presents dynamic changes in variant composition of SARS-CoV-2 in Canada and by province, estimates variant growth, and displays complementary interactive visualizations, with a text overview of the current situation. The VirusSeq Data Portal and Duotang resources, alongside additional analyses and resources computed from the Portal (COVID-MVP, CoVizu), are all open-source and freely available. Together, they provide an updated picture of SARS-CoV-2 evolution to spur scientific discussions, inform public discourse, and support communication with and within public health authorities. They also serve as a framework for other jurisdictions interested in open, collaborative sequence data sharing and analyses.

Detection of SARS-CoV-2 B.1.1.529 (Omicron) variant by SYBR Green-based RT-qPCR

The coronavirus disease 2019 (COVID-19) pandemic is unceasingly spreading across the globe, and recently a highly transmissible Omicron SARS-CoV-2 variant (B.1.1.529) has been discovered in South Africa and Botswana. Rapid identification of this variant is essential for pandemic assessment and containment. However, variant identification is mainly being performed using expensive and time-consuming genomic sequencing. In this study, we propose an alternative RT-qPCR approach for the detection of the Omicron BA.1 variant using a low-cost and rapid SYBR Green method. We have designed specific primers to confirm the deletion mutations in the spike (S Δ143-145) and the nucleocapsid (N Δ31-33) which are characteristics of this variant. For the evaluation, we used 120 clinical samples from patients with PCR-confirmed SARS-CoV-2 infections, and displaying an S-gene target failure (SGTF) when using TaqPath COVID-19 kit (Thermo Fisher Scientific, Waltham, USA) that included the ORF1ab, S, and N gene targets. Our results showed that all the 120 samples harbored S Δ143-145 and N Δ31-33, which was further confirmed by whole-genome sequencing of 10 samples, thereby validating our SYBR Green-based protocol. This protocol can be easily implemented to rapidly confirm the diagnosis of the Omicron BA.1 variant in COVID-19 patients and prevent its spread among populations, especially in countries with high prevalence of SGTF profile.

Positive selection underlies repeated knockout of ORF8 in SARS-CoV-2 evolution

Knockout of the ORF8 protein has repeatedly spread through the global viral population during SARS-CoV-2 evolution. Here we use both regional and global pathogen sequencing to explore the selection pressures underlying its loss. In Washington State, we identified transmission clusters with ORF8 knockout throughout SARS-CoV-2 evolution, not just on novel, high fitness viral backbones. Indeed, ORF8 is truncated more frequently and knockouts circulate for longer than for any other gene. Using a global phylogeny, we find evidence of positive selection to explain this phenomenon: nonsense mutations resulting in shortened protein products occur more frequently and are associated with faster clade growth rates than synonymous mutations in ORF8. Loss of ORF8 is also associated with reduced clinical severity, highlighting the diverse clinical impacts of SARS-CoV-2 evolution.

Lineage frequency time series reveal elevated levels of genetic drift in SARS-CoV-2 transmission in England

Genetic drift in infectious disease transmission results from randomness of transmission and host recovery or death. The strength of genetic drift for SARS-CoV-2 transmission is expected to be high due to high levels of superspreading, and this is expected to substantially impact disease epidemiology and evolution. However, we don't yet have an understanding of how genetic drift changes over time or across locations. Furthermore, noise that results from data collection can potentially confound estimates of genetic drift. To address this challenge, we develop and validate a method to jointly infer genetic drift and measurement noise from time-series lineage frequency data. Our method is highly scalable to increasingly large genomic datasets, which overcomes a limitation in commonly used phylogenetic methods. We apply this method to over 490,000 SARS-CoV-2 genomic sequences from England collected between March 2020 and December 2021 by the COVID-19 Genomics UK (COG-UK) consortium and separately infer the strength of genetic drift for pre-B.1.177, B.1.177, Alpha, and Delta. We find that even after correcting for measurement noise, the strength of genetic drift is consistently, throughout time, higher than that expected from the observed number of COVID-19 positive individuals in England by 1 to 3 orders of magnitude, which cannot be explained by literature values of superspreading. Our estimates of genetic drift suggest low and time-varying establishment probabilities for new mutations, inform the parametrization of SARS-CoV-2 evolutionary models, and motivate future studies of the potential mechanisms for increased stochasticity in this system.

Flow Cytometry-Based Measurement of Antibodies Specific for Cell Surface-Expressed Folded SARS-CoV-2 Receptor-Binding Domains

BACKGROUND

COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has now become endemic and is currently one of the important respiratory virus infections regularly affecting mankind. The assessment of immunity against SARS-CoV-2 and its variants is important for guiding active and passive immunization and SARS-CoV-2-specific treatment strategies.

METHODS

We here devised a novel flow cytometry-based diagnostic platform for the assessment of immunity against cell-bound virus antigens. This platform is based on a collection of HEK-293T cell lines which, as exemplified in our study, stably express the receptor-binding domains (RBDs) of the SARS-CoV-2 S-proteins of eight major SARS-CoV-2 variants, ranging from Wuhan-Hu-1 to Omicron.

RESULTS

RBD-expressing cell lines stably display comparable levels of RBD on the surface of HEK-293T cells, as shown with anti-FLAG-tag antibodies directed against a N-terminally introduced 3x-FLAG sequence while the functionality of RBD was proven by ACE2 binding. We exemplify the usefulness and specificity of the cell-based test by direct binding of IgG and IgA antibodies of SARS-CoV-2-exposed and/or vaccinated individuals in which the assay shows a wide linear performance range both at very low and very high serum antibody concentrations. In another application, i.e., antibody adsorption studies, the test proved to be a powerful tool for measuring the ratios of individual variant-specific antibodies.

CONCLUSION

We have established a toolbox for measuring SARS-CoV-2-specific immunity against cell-bound virus antigens, which may be considered as an important addition to the armamentarium of SARS-CoV-2-specific diagnostic tests, allowing flexible and quick adaptation to new variants of concern.

SARS-CoV-2 Omicron Spike shows strong binding affinity and favourable interaction landscape with the TLR4/MD2 compared to other variants

Emergences of SARS-CoV-2 variants have made the pandemic more critical. Toll-like receptor 4 (TLR4) recognizes the molecular patterns of pathogens and activates the production of proinflammatory cytokines to restrain the infection. We have identified a molecular basis of interaction between the Spike and TLR4 of SARS-CoV-2 and its present and past VOCs (variant- of concern) through in silico analysis. The interaction of wild type Spike with TLR4 showed 15 number hydrogen bonds formation. Similarly, the Alpha variants' Spike with the TLR4 has illustrated that 14 hydrogen bonds participated in the interaction. However, the Delta Spike and TLR4 interaction interface showed that 17 hydrogen bonds were formed during the interaction. Furthermore, Omicron S-glycoprotein and TLR4 interaction interface was depicted (interaction score: -170.3), and 16 hydrogen bonds were found to have been formed in the interaction. Omicron S-glycoprotein shows stronger binding affinity with the TLR4 than wild type, Alpha, and Delta variants. Similarly, the Alpha Spike shows higher binding affinity with TLR4 than the wild type and Delta variant. Now, it is an open question of the molecular basis of the interaction of Spike and TLR4 and the activated downstream signaling events of TLR4 for SARS-CoV-2 and its variants.

Boosting Vaccine Response in Autoimmune Rheumatic Disease Patients With Inadequate Seroconversion: An Analysis of the Immunogenicity of Vector-Based and Inactivated Vaccines

BACKGROUND

An additional dose of COVID-19 vaccine is being offered to vaccinated people, especially those immunocompromised. The most widely available vaccines in India are the adenoviral vector-based AZD1222 (ChAdOx1 nCoV-19) and the heat-inactivated (BBV152). This study investigated the efficacy of both vaccines in patients with autoimmune rheumatic diseases (AIRD).

OBJECTIVES

 To compare final anti-SARS-CoV-2 antibody titers, neutralization of pseudovirions by these antibodies, and T cell responses between patients of AIRD who had received the third dose of AZD1222 and BBV152 vaccines.

METHODS

Patients with stable AIRD who had completed two doses of COVID-19 vaccination but had a suboptimal response (anti-receptor binding domain (RBD) antibody<212) were randomized (1:1) to receive either AZD1222 or BBV152 as a booster dose. Patients with previous hybrid immunity or those who developed COVID-19 during the trial were excluded. Antibody titers, neutralization of Wuhan and Omicron pseudovirions, and interferon release by T cells (enzyme-linked immunosorbent spot (ELISpot)) in response to the Spike antigen were measured four weeks after this booster dose.

RESULTS

146 were screened, 91 were randomized, and 67 were analyzed per protocol. The third dose improved antibody titers (p<0.001), neutralization of the Wuhan strain (p<0.001), and T cell interferon release (p<0.001) but not neutralization of the Omicron strain (p=0.24). Antibody titers were higher (p<0.005) after ADZ1222 boost (2,414 IU (interquartile range (IQR): 330-10,315)) than BBV1222 (347.7 IU (0.4-973)). Neutralization of the Wuhan stain was better (AZD1222: 76.6%(23.0-95.45) versus BBV152 (32.7% (0-78.9), p=0.03 by ANCOVA). Neutralization of Omicron (0 (0-28.4) vs 0 (0-4.8)) and T cell interferon release (57.0 IU (23.5-95) vs 50.5 IU (13.2-139)) were similar.

CONCLUSION

The third dose improved all parameters of immunogenicity in AIRD patients with previous inadequate responses except Omicron neutralization. The vector-based vaccine exhibits notable efficacy, particularly in antibody titers and neutralizing the Wuhan strain.

TRIAL REGISTRATION

CTRI/2021/12/038928.

Spike S2 Subunit: Possible Target for Detecting Novel SARS-CoV-2 Variants with Multiple Mutations

Novel SARS-CoV-2 variants have multiple mutations that may impact molecular diagnostics. The markedly conserved S2 subunit may be utilized to detect new variants. A comparison of 694 specimens (2019-2022) in Thailand using a commercial RT-PCR kit and the kit in combination with S2 primers and a probe was performed. Delayed amplification in ORF1ab was detected in one BA.4 omicron, whereas no amplification problem was encountered in the S2 target. There were no statistically significant differences in mean Ct value between the target genes (E, N, ORF1ab, and S2) and no significant differences in mean Ct value between the reagents. Furthermore, 230,821 nucleotide sequences submitted by 20 representative counties in each region (Jan-Oct 2022) have been checked for mutations in S2 primers and probe using PrimerChecker; there is a very low chance of encountering performance problems. The S2 primers and probe are still bound to the top five currently circulating variants in all countries and Thailand without mismatch recognition (Jun-Nov 2023). This study shows the possible benefits of detecting S2 in combination with simultaneously detecting three genes in a kit without affecting the Ct value of each target. The S2 subunit may be a promising target for the detection of SARS-CoV-2 variants with multiple mutations.

Severe acute respiratory syndrome Coronavirus-2 GenoAnalyzer and mutagenic anomaly detector using FCMFI and NSCE

In order to deepen our understanding of the virus and help guide the creation of efficient therapies, this study uses artificial intelligence tools to thoroughly explore the genetic sequences of the SARS-CoV-2 virus. The process starts by using the Fuzzy Closure Miner for Frequent Itemsets (FCMFI) on a large corpus of SARS-CoV-2 genomic sequences to reveal hidden patterns, including nucleotides base sequences, repeating motifs, and corresponding interchanges. Then, using the Nucleotide Sequence Comprehension Engine (NSCE) technique, we were able to precisely define the genomic areas for mutation analysis. Structured and unstructured proteins are both strongly impacted by virus mutations, with spike proteins that are linked to the severity of COVID-19 pneumonia being particularly affected. Notably, the Mutagenic Anomaly Detector shows a 65 % efficiency boost in computing genome mutation rates compared to conventional point mutation analysis, while GenoAnalyzer offers a remarkable 93.33 % improvement over existing approaches in recognizing common genomic sequence patterns. These results highlight the potential of FCMFI to reveal complex genomic patterns and significant insights in COVID-19 genetic sequences when combined with mutation analysis. The Mutagenic Anomaly Detector and GenoAnalyzer show promise for revealing hidden genomic patterns and precisely estimating the SARS-CoV-2 mutation rate.

Prevalence of persistent SARS-CoV-2 in a large community surveillance study

Persistent SARS-CoV-2 infections may act as viral reservoirs that could seed future outbreaks1-5, give rise to highly divergent lineages6-8 and contribute to cases with post-acute COVID-19 sequelae (long COVID)9,10. However, the population prevalence of persistent infections, their viral load kinetics and evolutionary dynamics over the course of infections remain largely unknown. Here, using viral sequence data collected as part of a national infection survey, we identified 381 individuals with SARS-CoV-2 RNA at high titre persisting for at least 30 days, of which 54 had viral RNA persisting at least 60 days. We refer to these as 'persistent infections' as available evidence suggests that they represent ongoing viral replication, although the persistence of non-replicating RNA cannot be ruled out in all. Individuals with persistent infection had more than 50% higher odds of self-reporting long COVID than individuals with non-persistent infection. We estimate that 0.1-0.5% of infections may become persistent with typically rebounding high viral loads and last for at least 60 days. In some individuals, we identified many viral amino acid substitutions, indicating periods of strong positive selection, whereas others had no consensus change in the sequences for prolonged periods, consistent with weak selection. Substitutions included mutations that are lineage defining for SARS-CoV-2 variants, at target sites for monoclonal antibodies and/or are commonly found in immunocompromised people11-14. This work has profound implications for understanding and characterizing SARS-CoV-2 infection, epidemiology and evolution.

A framework for automated scalable designation of viral pathogen lineages from genomic data

Pathogen lineage nomenclature systems are a key component of effective communication and collaboration for researchers and public health workers. Since February 2021, the Pango dynamic lineage nomenclature for SARS-CoV-2 has been sustained by crowdsourced lineage proposals as new isolates were sequenced. This approach is vulnerable to time-critical delays as well as regional and personal bias. Here we developed a simple heuristic approach for dividing phylogenetic trees into lineages, including the prioritization of key mutations or genes. Our implementation is efficient on extremely large phylogenetic trees consisting of millions of sequences and produces similar results to existing manually curated lineage designations when applied to SARS-CoV-2 and other viruses including chikungunya virus, Venezuelan equine encephalitis virus complex and Zika virus. This method offers a simple, automated and consistent approach to pathogen nomenclature that can assist researchers in developing and maintaining phylogeny-based classifications in the face of ever-increasing genomic datasets.