Seasonal effects decouple SARS-CoV-2 haplotypes worldwide.

Background: Variants of concern (VOCs) have been replacing each other during the still rampant COVID-19 pandemic. As a result, SARS-CoV-2 populations have evolved increasingly intricate constellations of mutations that often enhance transmissibility, disease severity, and other epidemiological characteristics. The origin and evolution of these constellations remain puzzling. Methods: Here we study the evolution of VOCs at the proteome level by analyzing about 12 million genomic sequences retrieved from GISAID on July 23, 2022. A total 183,276 mutations were identified and filtered with a relevancy heuristic. The prevalence of haplotypes and free-standing mutations was then tracked monthly in various latitude corridors of the world. Results: A chronology of 22 haplotypes defined three phases driven by protein flexibility-rigidity, environmental sensing, and immune escape. A network of haplotypes illustrated the recruitment and coalescence of mutations into major VOC constellations and seasonal effects of decoupling and loss. Protein interaction networks mediated by haplotypes predicted communications impacting the structure and function of proteins, showing the increasingly central role of molecular interactions involving the spike (S), nucleocapsid (N), and membrane (M) proteins. Haplotype markers either affected fusogenic regions while spreading along the sequence of the S-protein or clustered around binding domains. Modeling of protein structure with AlphaFold2 showed that VOC Omicron and one of its haplotypes were major contributors to the distortion of the M-protein endodomain, which behaves as a receptor of other structural proteins during virion assembly. Remarkably, VOC constellations acted cooperatively to balance the more extreme effects of individual haplotypes. Conclusions: Our study uncovers seasonal patterns of emergence and diversification occurring amid a highly dynamic evolutionary landscape of bursts and waves. The mapping of genetically-linked mutations to structures that sense environmental change with powerful ab initio modeling tools demonstrates the potential of deep-learning for COVID-19 predictive intelligence and therapeutic intervention.

The emergence of SARS-CoV-2 variants of concern in Australia by haplotype coalescence reveals a continental link to COVID-19 seasonality.

SARS-CoV-2 continues to evolve, even after implementation of public-wide vaccination, as can be observed by an increasing number of mutations over time. Compared to responses by the United States and European countries, the disease mitigation strategies employed by the Australian government have been swift and effective. This provides a unique opportunity to study the emergence of variants of concern (VOCs) at many latitude levels in a country that has been able to control infection for the majority of the pandemic. In the present study, we explored the occurrence and accumulation of major mutations typical of VOCs in different regions of Australia and the effects that latitude has on the establishment of VOC-induced disease. We also studied the constellation of mutations characteristic of VOCs to determine if the mutation sets acted as haplotypes. Our goal was to explore processes behind the emergence of VOCs as the viral disease progresses towards becoming endemic. Most reported COVID-19 cases were in largest cities located within a -30°S to - 50°S latitude corridor previously identified to be associated with seasonal behavior. Accumulation plots of individual amino acid variants of major VOCs showed that the first major haplotypes reported worldwide were also present in Australia. A classification of accumulation plots revealed the existence of 18 additional haplotypes associated with VOCs alpha, delta and omicron. Core mutant constellations for these VOCs and curve overlaps for variants in each set of haplotypes demonstrated significant decoupling patterns, suggesting processes of emergence. Finally, construction of a "haplotype network" that describes the viral population landscape of Australia throughout the COVID-19 pandemic revealed significant and unanticipated seasonal patterns of emergence and diversification. These results provide a unique window into our evolutionary understanding of a human pathogen of great significance. They may guide future research into mitigation and prediction strategies for future VOCs.

The seasonal behaviour of COVID-19 and its galectin-like culprit of the viral spike.

Seasonal behaviour is an attribute of many viral diseases. Like other 'winter' RNA viruses, infections caused by the causative agent of COVID-19, SARS-CoV-2, appear to exhibit significant seasonal changes. Here we discuss the seasonal behaviour of COVID-19, emerging viral phenotypes, viral evolution, and how the mutational landscape of the virus affects the seasonal attributes of the disease. We propose that the multiple seasonal drivers behind infectious disease spread (and the spread of COVID-19 specifically) are in 'trade-off' relationships and can be better described within a framework of a 'triangle of viral persistence' modulated by the environment, physiology, and behaviour. This 'trade-off' exists as one trait cannot increase without a decrease in another. We also propose that molecular components of the virus can act as sensors of environment and physiology, and could represent molecular culprits of seasonality. We searched for flexible protein structures capable of being modulated by the environment and identified a galectin-like fold within the N-terminal domain of the spike protein of SARS-CoV-2 as a potential candidate. Tracking the prevalence of mutations in this structure resulted in the identification of a hemisphere-dependent seasonal pattern driven by mutational bursts. We propose that the galectin-like structure is a frequent target of mutations because it helps the virus evade or modulate the physiological responses of the host to further its spread and survival. The flexible regions of the N-terminal domain should now become a focus for mitigation through vaccines and therapeutics and for prediction and informed public health decision making.

Identifying False Human Papillomavirus (HPV) Vaccine Information and Corresponding Risk Perceptions From Twitter: Advanced Predictive Models.

BACKGROUND: The vaccination uptake rates of the human papillomavirus (HPV) vaccine remain low despite the fact that the effectiveness of HPV vaccines has been established for more than a decade. Vaccine hesitancy is in part due to false information about HPV vaccines on social media. Combating false HPV vaccine information is a reasonable step to addressing vaccine hesitancy. OBJECTIVE: Given the substantial harm of false HPV vaccine information, there is an urgent need to identify false social media messages before it goes viral. The goal of the study is to develop a systematic and generalizable approach to identifying false HPV vaccine information on social media. METHODS: This study used machine learning and natural language processing to develop a series of classification models and causality mining methods to identify and examine true and false HPV vaccine-related information on Twitter. RESULTS: We found that the convolutional neural network model outperformed all other models in identifying tweets containing false HPV vaccine-related information (F score=91.95). We also developed completely unsupervised causality mining models to identify HPV vaccine candidate effects for capturing risk perceptions of HPV vaccines. Furthermore, we found that false information contained mostly loss-framed messages focusing on the potential risk of vaccines covering a variety of topics using more diverse vocabulary, while true information contained both gain- and loss-framed messages focusing on the effectiveness of vaccines covering fewer topics using relatively limited vocabulary. CONCLUSIONS: Our research demonstrated the feasibility and effectiveness of using predictive models to identify false HPV vaccine information and its risk perceptions on social media.

New Pathways of Mutational Change in SARS-CoV-2 Proteomes Involve Regions of Intrinsic Disorder Important for Virus Replication and Release.

The massive worldwide spread of the SARS-CoV-2 virus is fueling the COVID-19 pandemic. Since the first whole-genome sequence was published in January 2020, a growing database of tens of thousands of viral genomes has been constructed. This offers opportunities to study pathways of molecular change in the expanding viral population that can help identify molecular culprits of virulence and virus spread. Here we investigate the genomic accumulation of mutations at various time points of the early pandemic to identify changes in mutationally highly active genomic regions that are occurring worldwide. We used the Wuhan NC_045512.2 sequence as a reference and sampled 15 342 indexed sequences from GISAID, translating them into proteins and grouping them by month of deposition. The per-position amino acid frequencies and Shannon entropies of the coding sequences were calculated for each month, and a map of intrinsic disorder regions and binding sites was generated. The analysis revealed dominant variants, most of which were located in loop regions and on the surface of the proteins. Mutation entropy decreased between March and April of 2020 after steady increases at several sites, including the D614G mutation site of the spike (S) protein that was previously found associated with higher case fatality rates and at sites of the NSP12 polymerase and the NSP13 helicase proteins. Notable expanding mutations include R203K and G204R of the nucleocapsid (N) protein inter-domain linker region and G251V of the viroporin encoded by ORF3a between March and April. The regions spanning these mutations exhibited significant intrinsic disorder, which was enhanced and decreased by the N-protein and viroporin 3a protein mutations, respectively. These results predict an ongoing mutational shift from the spike and replication complex to other regions, especially to encoded molecules known to represent major ß-interferon antagonists. The study provides valuable information for therapeutics and vaccine design, as well as insight into mutation tendencies that could facilitate preventive control.