Ultrafast Sample placement on Existing tRees (UShER) enables real-time phylogenetics for the SARS-CoV-2 pandemic

A molecular phylogeny of the Petaluridae (Odonata: Anisoptera): A 160-Million-Year-Old story of drift and extinction

Petaluridae (Odonata: Anisoptera) is a relict dragonfly family, having diverged from its sister family in the Jurassic, of eleven species that are notable among odonates (dragonflies and damselflies) for their exclusive use of fen and bog habitats, their burrowing behavior as nymphs, large body size as adults, and extended lifespans. To date, several nodes within this family remain unresolved, limiting the study of the evolution of this peculiar family. Using an anchored hybrid enrichment dataset of over 900 loci we reconstructed the species tree of Petaluridae. To estimate the temporal origin of the genera within this family, we used a set of well-vetted fossils and a relaxed molecular clock model in a divergence time estimation analysis. We estimate that Petaluridae originated in the early Cretaceous and confirm the existence of monophyletic Gondwanan and Laurasian clades within the family. Our relaxed molecular clock analysis estimated that these clades diverged from their MRCA approximately 160 mya. Extant lineages within this family were identified to have persisted from 6 (Uropetala) to 120 million years (Phenes). Our biogeographical analyses focusing on a set of key regions suggest that divergence within Petaluridae is largely correlated with continental drift, the exposure of land bridges, and the development of mountain ranges. Our results support the hypothesis that species within Petaluridae have persisted for tens of millions of years, with little fossil evidence to suggest widespread extinction in the family, despite optimal conditions for the fossilization of nymphs. Petaluridae appear to be a rare example of habitat specialists that have persisted for tens of millions of years.

First reporting of BA.1* and BA.2* recombinant SARS-CoV-2 lineage XAP from Indian wastewaters

Tracking new variants of SARS-CoV-2 is vital for managing COVID-19 spread and allocating resources. Domestic antigen testing has created surveillance gaps that make it hard to identify new viral variants. We conducted whole genome sequencing of wastewater viral genes from major and minor treatment facilities in Dehradun from March 2022 onwards. Based on our analysis, the samples that achieved higher sequencing depth and covered >90 % of the viral genome uncovered a major variant pattern resembling the XAP recombinant lineage that is reported for the first time in the City of Dehradun, Uttrakhand and is the first ever records in India as on date. This novel XAP recombinant lineage had 9, 2, 30, 1, 2, 5, 1, 1, 1 aminoacid changes (total 54 mutations) in Orf1a, Orf1b, S, E, M, N, Orf3a, Orf6 and Orf8 regions of the gene respectively that shares 49 mutations common to the ancestral lineages BA.1* and BA.2*, with 6 unique mutations. Subsequent comparison and analysis of the clinical sequence data from the region post-detection of this rare and unusual variant showed no causalities infected with the newly detected XAP lineage. These findings are indicative of future alarming situation with plausible threats of fresh spur of Omicron variant led infections in the urban community.

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.

SARS-CoV-2 Variants May Affect Saliva RT-PCR Assay Sensitivity

BACKGROUND

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) variants demonstrate predilection for different regions of the respiratory tract. While saliva-based reverse transcription-polymerase chain reaction (RT-PCR) testing is a convenient, cost-effective alternative to nasopharyngeal swabs (NPS), few studies to date have investigated whether saliva sensitivity differs across variants of concern.

METHODS

SARS-CoV-2 RT-PCR was performed on paired NPS and saliva specimens collected from individuals with acute coronavirus disease 2019 (COVID-19) symptoms or exposure to a COVID-19 household contact. Viral genome sequencing of NPS specimens and Los Angeles County surveillance data were used to determine the variant of infection. Saliva sensitivity was calculated using NPS-positive RT-PCR as the reference standard. Factors contributing to the likelihood of saliva SARS-CoV-2 RT-PCR positivity were evaluated with univariate and multivariable analyses.

RESULTS

Between June 2020 and December 2022, 548 saliva samples paired with SARS-CoV-2 positive NPS samples were tested by RT-PCR. Overall, saliva sensitivity for SARS-CoV-2 detection was 61.7% (95% CI, 57.6%-65.7%). Sensitivity was highest with Delta infection (79.6%) compared to pre-Delta (58.5%) and Omicron (61.5%) (P = 0.003 and 0.01, respectively). Saliva sensitivity was higher in symptomatic individuals across all variants compared to asymptomatic cases [pre-Delta 80.6% vs 48.3% (P < 0.001), Delta 100% vs 72.5% (P = 0.03), Omicron 78.7% vs 51.2% (P < 0.001)]. Infection with Delta, symptoms, and high NPS viral load were independently associated with 2.99-, 3.45-, and 4.0-fold higher odds of SARS-CoV-2 detection by saliva-based RT-PCR (P = 0.004, <0.001, and <0.001), respectively.

CONCLUSIONS

As new variants emerge, evaluating saliva-based testing approaches may be crucial to ensure effective virus detection.

The evolutionary and transmission dynamics of HIV-1 CRF08_BC

HIV-1 CRF08_BC is a significant subtype in China, though its origin and spread remain incompletely understood. Previous studies using partial genomic data have provided insights but lack comprehensive analysis. Here, we investigate the early evolutionary and spatiotemporal dynamics of HIV-1 CRF08_BC in China and Myanmar using near-complete genome sequences. We analyzed 28 near-complete HIV-1 CRF08_BC genomes from China and Myanmar (1997-2013). Phylogenetic, molecular clock, and Bayesian discrete trait analyses were performed to infer the virus's origin, spread, and associated risk groups. Based on Bayesian time-scaled inference with the best-fitting combination of models determined by marginal likelihood estimation (MLE), we inferred the time to the most recent common ancestor (TMRCA) and evolutionary rate of HIV-1 CRF08_BC to be at 3 October 1991 (95% HPD: 22 February1989-27 November 1993) and 2.30 × 10-3 substitutions per site per year (95% HPD: 1.96 × 10-3-2.63 × 10-3), respectively. Our analysis suggests that HIV-1 CRF08_BC originated in Yunnan Province, China, among injecting drug users, and subsequently spread to other regions. This study provides valuable insights into the early dynamics of HIV-1 CRF08_BC through combined genomic and epidemiological data, which may inform effective prevention and mitigation efforts. However, the limited genomic data influenced the extent of our findings, and challenges in collecting accurate risk group information during surveillance were evident.

A comprehensive study of SARS-CoV-2 main protease (Mpro) inhibitor-resistant mutants selected in a VSV-based system

Nirmatrelvir was the first protease inhibitor specifically developed against the SARS-CoV-2 main protease (3CLpro/Mpro) and licensed for clinical use. As SARS-CoV-2 continues to spread, variants resistant to nirmatrelvir and other currently available treatments are likely to arise. This study aimed to identify and characterize mutations that confer resistance to nirmatrelvir. To safely generate Mpro resistance mutations, we passaged a previously developed, chimeric vesicular stomatitis virus (VSV-Mpro) with increasing, yet suboptimal concentrations of nirmatrelvir. Using Wuhan-1 and Omicron Mpro variants, we selected a large set of mutants. Some mutations are frequently present in GISAID, suggesting their relevance in SARS-CoV-2. The resistance phenotype of a subset of mutations was characterized against clinically available protease inhibitors (nirmatrelvir and ensitrelvir) with cell-based, biochemical and SARS-CoV-2 replicon assays. Moreover, we showed the putative molecular mechanism of resistance based on in silico molecular modelling. These findings have implications on the development of future generation Mpro inhibitors, will help to understand SARS-CoV-2 protease inhibitor resistance mechanisms and show the relevance of specific mutations, thereby informing treatment decisions.

Intrahost evolution leading to distinct lineages in the upper and lower respiratory tracts during SARS-CoV-2 prolonged infection

Accumulating evidence points to persistent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections in immunocompromised individuals as a source of novel lineages. While intrahost evolution of the virus in chronically infected patients has previously been reported, existing knowledge is primarily based on samples from the nasopharynx. In this study, we investigate the intrahost evolution and genetic diversity that accumulated during a prolonged SARS-CoV-2 infection with the Omicron BF.7 sublineage, which is estimated to have persisted for >1 year in an immunosuppressed patient. Based on the sequencing of eight samples collected at six time points, we identified 87 intrahost single-nucleotide variants, 2 indels, and a 362-bp deletion. Our analysis revealed distinct viral genotypes in the nasopharyngeal (NP), endotracheal aspirate, and bronchoalveolar lavage samples. This suggests that NP samples may not offer a comprehensive representation of the overall intrahost viral diversity. Our findings not only demonstrate that the Omicron BF.7 sublineage can further diverge from its already exceptionally mutated state but also highlight that patients chronically infected with SARS-CoV-2 can develop genetically specific viral populations across distinct anatomic compartments. This provides novel insights into the intricate nature of viral diversity and evolution dynamics in persistent infections.

A program for real-time surveillance of SARS-CoV-2 genetics

The COVID-19 pandemic brought forth an urgent need for widespread genomic surveillance for rapid detection and monitoring of emerging SARS-CoV-2 variants. It necessitated design, development, and deployment of a nationwide infrastructure designed for sequestration, consolidation, and characterization of patient samples that disseminates de-identified information to public authorities in tight turnaround times. Here, we describe our development of such an infrastructure, which sequenced 594,832 high coverage SARS-CoV-2 genomes from isolates we collected in the United States (U.S.) from March 13th 2020 to July 3rd 2023. Our sequencing protocol ('Virseq') utilizes wet and dry lab procedures to generate mutation-resistant sequencing of the entire SARS-CoV-2 genome, capturing all major lineages. We also characterize 379 clinically relevant SARS-CoV-2 multi-strain co-infections and ensure robust detection of emerging lineages via simulation. The modular infrastructure, sequencing, and analysis capabilities we describe support the U.S. Centers for Disease Control and Prevention national surveillance program and serve as a model for rapid response to emerging pandemics at a national scale.

Dimensionality reduction distills complex evolutionary relationships in seasonal influenza and SARS-CoV-2

Public health researchers and practitioners commonly infer phylogenies from viral genome sequences to understand transmission dynamics and identify clusters of genetically-related samples. However, viruses that reassort or recombine violate phylogenetic assumptions and require more sophisticated methods. Even when phylogenies are appropriate, they can be unnecessary or difficult to interpret without specialty knowledge. For example, pairwise distances between sequences can be enough to identify clusters of related samples or assign new samples to existing phylogenetic clusters. In this work, we tested whether dimensionality reduction methods could capture known genetic groups within two human pathogenic viruses that cause substantial human morbidity and mortality and frequently reassort or recombine, respectively: seasonal influenza A/H3N2 and SARS-CoV-2. We applied principal component analysis (PCA), multidimensional scaling (MDS), t-distributed stochastic neighbor embedding (t-SNE), and uniform manifold approximation and projection (UMAP) to sequences with well-defined phylogenetic clades and either reassortment (H3N2) or recombination (SARS-CoV-2). For each low-dimensional embedding of sequences, we calculated the correlation between pairwise genetic and Euclidean distances in the embedding and applied a hierarchical clustering method to identify clusters in the embedding. We measured the accuracy of clusters compared to previously defined phylogenetic clades, reassortment clusters, or recombinant lineages. We found that MDS embeddings accurately represented pairwise genetic distances including the intermediate placement of recombinant SARS-CoV-2 lineages between parental lineages. Clusters from t-SNE embeddings accurately recapitulated known phylogenetic clades, H3N2 reassortment groups, and SARS-CoV-2 recombinant lineages. We show that simple statistical methods without a biological model can accurately represent known genetic relationships for relevant human pathogenic viruses. Our open source implementation of these methods for analysis of viral genome sequences can be easily applied when phylogenetic methods are either unnecessary or inappropriate.

High-resolution epidemiological landscape from ~290,000 SARS-CoV-2 genomes from Denmark

Vast amounts of pathogen genomic, demographic and spatial data are transforming our understanding of SARS-CoV-2 emergence and spread. We examined the drivers of molecular evolution and spread of 291,791 SARS-CoV-2 genomes from Denmark in 2021. With a sequencing rate consistently exceeding 60%, and up to 80% of PCR-positive samples between March and November, the viral genome set is broadly whole-epidemic representative. We identify a consistent rise in viral diversity over time, with notable spikes upon the importation of novel variants (e.g., Delta and Omicron). By linking genomic data with rich individual-level demographic data from national registers, we find that individuals aged  < 15 and  > 75 years had a lower contribution to molecular change (i.e., branch lengths) compared to other age groups, but similar molecular evolutionary rates, suggesting a lower likelihood of introducing novel variants. Similarly, we find greater molecular change among vaccinated individuals, suggestive of immune evasion. We also observe evidence of transmission in rural areas to follow predictable diffusion processes. Conversely, urban areas are expectedly more complex due to their high mobility, emphasising the role of population structure in driving virus spread. Our analyses highlight the added value of integrating genomic data with detailed demographic and spatial information, particularly in the absence of structured infection surveys.

mtDNA "nomenclutter" and its consequences on the interpretation of genetic data

Population-based studies of human mitochondrial genetic diversity often require the classification of mitochondrial DNA (mtDNA) haplotypes into more than 5400 described haplogroups, and further grouping those into hierarchically higher haplogroups. Such secondary haplogroup groupings (e.g., "macro-haplogroups") vary across studies, as they depend on the sample quality, technical factors of haplogroup calling, the aims of the study, and the researchers' understanding of the mtDNA haplogroup nomenclature. Retention of historical nomenclature coupled with a growing number of newly described mtDNA lineages results in increasingly complex and inconsistent nomenclature that does not reflect phylogeny well. This "clutter" leaves room for grouping errors and inconsistencies across scientific publications, especially when the haplogroup names are used as a proxy for secondary groupings, and represents a source for scientific misinterpretation. Here we explore the effects of phylogenetically insensitive secondary mtDNA haplogroup groupings, and the lack of standardized secondary haplogroup groupings on downstream analyses and interpretation of genetic data. We demonstrate that frequency-based analyses produce inconsistent results when different secondary mtDNA groupings are applied, and thus allow for vastly different interpretations of the same genetic data. The lack of guidelines and recommendations on how to choose appropriate secondary haplogroup groupings presents an issue for the interpretation of results, as well as their comparison and reproducibility across studies. To reduce biases originating from arbitrarily defined secondary nomenclature-based groupings, we suggest that future updates of mtDNA phylogenies aimed for the use in mtDNA haplogroup nomenclature should also provide well-defined and standardized sets of phylogenetically meaningful algorithm-based secondary haplogroup groupings such as "macro-haplogroups", "meso-haplogroups", and "micro-haplogroups". Ideally, each of the secondary haplogroup grouping levels should be informative about different human population history events. Those phylogenetically informative levels of haplogroup groupings can be easily defined using TreeCluster, and then implemented into haplogroup callers such as HaploGrep3. This would foster reproducibility across studies, provide a grouping standard for population-based studies, and reduce errors associated with haplogroup nomenclatures in future studies.

Wastewater multiplex PCR amplicon sequencing revealed community transmission of SARS-CoV-2 lineages during the outbreak of infection in Chinese Mainland

During the COVID-19, wastewater-based epidemiology (WBE) has become a powerful epidemic surveillance tool widely used worldwide. However, the development and application of this technology in Chinese Mainland are relatively lagging. Herein, we for the first time monitored the community circulation of SARS-CoV-2 lineages using WBE methods in Chinese Mainland. During the peak period of infection outbreak at the end of 2022, six precious sewage samples were collected from the manhole in the student dormitory area on Wangjiang Campus of Sichuan University. RT-qPCR revealed that the six sewage samples were all positive for SARS-CoV-2 RNA. Multiplex PCR amplicon sequencing of the sewage samples reflected the local transmission of SARS-CoV-2 variants. The results of two deconvolution methods indicate that the main virus lineages have clear evolutionary genetic correlations. Furthermore, the sampling time is consistent with the timeline of concern for these virus lineages, as well as the timeline of uploading the nucleic acid sequences from the corresponding lineages in Sichuan to the database. These results demonstrate the reliability of the sewage sequencing results. Multiplex PCR amplicon sequencing is by far the most powerful analytical tool of WBE, enabling quantitative detection of virus lineages transmission and evolution at the community level.

Standardized Phylogenetic Classification of Human Respiratory Syncytial Virus below the Subgroup Level

A globally implemented unified phylogenetic classification for human respiratory syncytial virus (HRSV) below the subgroup level remains elusive. We formulated global consensus of HRSV classification on the basis of the challenges and limitations of our previous proposals and the future of genomic surveillance. From a high-quality curated dataset of 1,480 HRSV-A and 1,385 HRSV-B genomes submitted to GenBank and GISAID (https://www.gisaid.org) public sequence databases through March 2023, we categorized HRSV-A/B sequences into lineages based on phylogenetic clades and amino acid markers. We defined 24 lineages within HRSV-A and 16 within HRSV-B and provided guidelines for defining prospective lineages. Our classification demonstrated robustness in its applicability to both complete and partial genomes. We envision that this unified HRSV classification proposal will strengthen HRSV molecular epidemiology on a global scale.

Widespread exposure to SARS-CoV-2 in wildlife communities

Pervasive SARS-CoV-2 infections in humans have led to multiple transmission events to animals. While SARS-CoV-2 has a potential broad wildlife host range, most documented infections have been in captive animals and a single wildlife species, the white-tailed deer. The full extent of SARS-CoV-2 exposure among wildlife communities and the factors that influence wildlife transmission risk remain unknown. We sampled 23 species of wildlife for SARS-CoV-2 and examined the effects of urbanization and human use on seropositivity. Here, we document positive detections of SARS-CoV-2 RNA in six species, including the deer mouse, Virginia opossum, raccoon, groundhog, Eastern cottontail, and Eastern red bat between May 2022-September 2023 across Virginia and Washington, D.C., USA. In addition, we found that sites with high human activity had three times higher seroprevalence than low human-use areas. We obtained SARS-CoV-2 genomic sequences from nine individuals of six species which were assigned to seven Pango lineages of the Omicron variant. The close match to variants circulating in humans at the time suggests at least seven recent human-to-animal transmission events. Our data support that exposure to SARS-CoV-2 has been widespread in wildlife communities and suggests that areas with high human activity may serve as points of contact for cross-species transmission.

Emergence of Recombinant SARS-CoV-2 Variants in California from 2020 to 2022

The detection, characterization, and monitoring of SARS-CoV-2 recombinant variants constitute a challenge for public health authorities worldwide. Recombinant variants, composed of two or more SARS-CoV-2 lineages, often have unknown impacts on transmission, immune escape, and virulence in the early stages of emergence. We examined 4213 SARS-CoV-2 recombinant SARS-CoV-2 genomes collected between 2020 and 2022 in California to describe regional and statewide trends in prevalence. Many of these recombinant genomes, such as those belonging to the XZ lineage or novel recombinant lineages, likely originated within the state of California. We discuss the challenges and limitations surrounding Pango lineage assignments, the use of publicly available sequence data, and adequate sample sizes for epidemiologic analyses. Although these challenges will continue as SARS-CoV-2 sequencing volumes decrease globally, this study enhances our understanding of SARS-CoV-2 recombinant genomes to date while providing a foundation for future insights into emerging recombinant lineages.

F1ALA: ultrafast and memory-efficient ancestral lineage annotation applied to the huge SARS-CoV-2 phylogeny

The unprecedentedly large size of the global SARS-CoV-2 phylogeny makes any computation on the tree difficult. Lineage identification (e.g. the PANGO nomenclature for SARS-CoV-2) and assignment are key to track the virus evolution. It requires annotating clade roots of lineages to unlabeled ancestral nodes in a phylogenetic tree. Then the lineage labels of descendant samples under these clade roots can be inferred to be the corresponding lineages. This is the ancestral lineage annotation problem, and matUtils (a package in pUShER) and PastML are commonly used methods. However, their computational tractability is a challenge and their accuracy needs further exploration in huge SARS-CoV-2 phylogenies. We have developed an efficient and accurate method, called "F1ALA", that utilizes the F1-score to evaluate the confidence with which a specific ancestral node can be annotated as the clade root of a lineage, given the lineage labels of a set of taxa in a rooted tree. Compared to these methods, F1ALA achieved roughly an order of magnitude faster yet with ∼12% of their memory usage when annotating 2277 PANGO lineages in a phylogeny of 5.26 million taxa. F1ALA allows real-time lineage tracking to be performed on a laptop computer. F1ALA outperformed matUtils (pUShER) with statistical significance, and had comparable accuracy to PastML in tests on empirical and simulated data. F1ALA enables a tree refinement by pruning taxa with inconsistent labels to their closest annotation nodes and re-inserting them back to the pruned tree to improve a SARS-CoV-2 phylogeny with both higher log-likelihood and lower parsimony score. Given the ultrafast speed and high accuracy, we anticipated that F1ALA will also be useful for large phylogenies of other viruses. Codes and benchmark datasets are publicly available at https://github.com/id-bioinfo/F1ALA.

Phylogenetic signatures reveal multilevel selection and fitness costs in SARS-CoV-2

Background

Large-scale sequencing of SARS-CoV-2 has enabled the study of viral evolution during the COVID-19 pandemic. Some viral mutations may be advantageous to viral replication within hosts but detrimental to transmission, thus carrying a transient fitness advantage. By affecting the number of descendants, persistence times and growth rates of associated clades, these mutations generate localised imbalance in phylogenies. Quantifying these features in closely-related clades with and without recurring mutations can elucidate the tradeoffs between within-host replication and between-host transmission.

Methods

We implemented a novel phylogenetic clustering algorithm ( mlscluster, https://github.com/mrc-ide/mlscluster) to systematically explore time-scaled phylogenies for mutations under transient/multilevel selection. We applied this method to a SARS-CoV-2 time-calibrated phylogeny with >1.2 million sequences from England, and characterised these recurrent mutations that may influence transmission fitness across PANGO-lineages and genomic regions using Poisson regressions and summary statistics.

Results

We found no major differences across two epidemic stages (before and after Omicron), PANGO-lineages, and genomic regions. However, spike, nucleocapsid, and ORF3a were proportionally more enriched for transmission fitness polymorphisms (TFP)-homoplasies than other proteins. We provide a catalog of SARS-CoV-2 sites under multilevel selection, which can guide experimental investigations within and beyond the spike protein.

Conclusions

This study provides empirical evidence for the existence of important tradeoffs between within-host replication and between-host transmission shaping the fitness landscape of SARS-CoV-2. This method may be used as a fast and scalable means to shortlist large sequence databases for sites under putative multilevel selection which may warrant subsequent confirmatory analyses and experimental confirmation.

Discovery of disease-adapted bacterial lineages in inflammatory bowel diseases

Gut bacteria are implicated in inflammatory bowel disease (IBD), but the strains driving these associations are unknown. Large-scale studies of microbiome evolution could reveal the imprint of disease on gut bacteria, thus pinpointing the strains and genes that may underlie inflammation. Here, we use stool metagenomes of thousands of IBD patients and healthy controls to reconstruct 140,000 strain genotypes, revealing hundreds of lineages enriched in IBD. We demonstrate that these strains are ancient, taxonomically diverse, and ubiquitous in humans. Moreover, disease-associated strains outcompete their healthy counterparts during inflammation, implying long-term adaptation to disease. Strain genetic differences map onto known axes of inflammation, including oxidative stress, nutrient biosynthesis, and immune evasion. Lastly, the loss of health-associated strains of Eggerthella lenta was predictive of fecal calprotectin, a biomarker of disease severity. Our work identifies reservoirs of strain diversity that may impact inflammatory disease and can be extended to other microbiome-associated diseases.

CMAPLE: Efficient Phylogenetic Inference in the Pandemic Era

We have recently introduced MAPLE (MAximum Parsimonious Likelihood Estimation), a new pandemic-scale phylogenetic inference method exclusively designed for genomic epidemiology. In response to the need for enhancing MAPLE's performance and scalability, here we present two key components: (i) CMAPLE software, a highly optimized C++ reimplementation of MAPLE with many new features and advancements, and (ii) CMAPLE library, a suite of application programming interfaces to facilitate the integration of the CMAPLE algorithm into existing phylogenetic inference packages. Notably, we have successfully integrated CMAPLE into the widely used IQ-TREE 2 software, enabling its rapid adoption in the scientific community. These advancements serve as a vital step toward better preparedness for future pandemics, offering researchers powerful tools for large-scale pathogen genomic analysis.

Please Mind the Gap: Indel-Aware Parsimony for Fast and Accurate Ancestral Sequence Reconstruction and Multiple Sequence Alignment Including Long Indels

Despite having important biological implications, insertion, and deletion (indel) events are often disregarded or mishandled during phylogenetic inference. In multiple sequence alignment, indels are represented as gaps and are estimated without considering the distinct evolutionary history of insertions and deletions. Consequently, indels are usually excluded from subsequent inference steps, such as ancestral sequence reconstruction and phylogenetic tree search. Here, we introduce indel-aware parsimony (indelMaP), a novel way to treat gaps under the parsimony criterion by considering insertions and deletions as separate evolutionary events and accounting for long indels. By identifying the precise location of an evolutionary event on the tree, we can separate overlapping indel events and use affine gap penalties for long indel modeling. Our indel-aware approach harnesses the phylogenetic signal from indels, including them into all inference stages. Validation and comparison to state-of-the-art inference tools on simulated data show that indelMaP is most suitable for densely sampled datasets with closely to moderately related sequences, where it can reach alignment quality comparable to probabilistic methods and accurately infer ancestral sequences, including indel patterns. Due to its remarkable speed, our method is well suited for epidemiological datasets, eliminating the need for downsampling and enabling the exploitation of the additional information provided by dense taxonomic sampling. Moreover, indelMaP offers new insights into the indel patterns of biologically significant sequences and advances our understanding of genetic variability by considering gaps as crucial evolutionary signals rather than mere artefacts.

Distinct SARS-CoV-2 populational immune backgrounds tolerate divergent RBD evolutionary preferences

Immune evasion is a pivotal force shaping the evolution of viruses. Nonetheless, the extent to which virus evolution varies among populations with diverse immune backgrounds remains an unsolved mystery. Prior to the widespread SARS-CoV-2 infections in December 2022 and January 2023, the Chinese population possessed a markedly distinct (less potent) immune background due to its low infection rate, compared to countries experiencing multiple infection waves, presenting an unprecedented opportunity to investigate how the virus has evolved under different immune contexts. We compared the mutation spectrum and functional potential of the newly derived mutations that occurred in BA.5.2.48, BF.7.14 and BA.5.2.49-variants prevalent in China-with their counterparts in other countries. We found that the emerging mutations in the receptor-binding-domain region in these lineages were more widely dispersed and evenly distributed across different epitopes. These mutations led to a higher angiotensin-converting enzyme 2 (ACE2) binding affinity and reduced potential for immune evasion compared to their counterparts in other countries. These findings suggest a milder immune pressure and less evident immune imprinting within the Chinese population. Despite the emergence of numerous immune-evading variants in China, none of them outcompeted the original strain until the arrival of the XBB variant, which had stronger immune evasion and subsequently outcompeted all circulating variants. Our findings demonstrated that the continuously changing immune background led to varying evolutionary pressures on SARS-CoV-2. Thus, in addition to viral genome surveillance, immune background surveillance is also imperative for predicting forthcoming mutations and understanding how these variants spread in the population.

Scaling DEPP phylogenetic placement to ultra-large reference trees: a tree-aware ensemble approach

MOTIVATION

Phylogenetic placement of a query sequence on a backbone tree is increasingly used across biomedical sciences to identify the content of a sample from its DNA content. The accuracy of such analyses depends on the density of the backbone tree, making it crucial that placement methods scale to very large trees. Moreover, a new paradigm has been recently proposed to place sequences on the species tree using single-gene data. The goal is to better characterize the samples and to enable combined analyses of marker-gene (e.g., 16S rRNA gene amplicon) and genome-wide data. The recent method DEPP enables performing such analyses using metric learning. However, metric learning is hampered by a need to compute and save a quadratically growing matrix of pairwise distances during training. Thus, the training phase of DEPP does not scale to more than roughly 10 000 backbone species, a problem that we faced when trying to use our recently released Greengenes2 (GG2) reference tree containing 331 270 species.

RESULTS

This paper explores divide-and-conquer for training ensembles of DEPP models, culminating in a method called C-DEPP. While divide-and-conquer has been extensively used in phylogenetics, applying divide-and-conquer to data-hungry machine-learning methods needs nuance. C-DEPP uses carefully crafted techniques to enable quasi-linear scaling while maintaining accuracy. C-DEPP enables placing 20 million 16S fragments on the GG2 reference tree in 41 h of computation.

AVAILABILITY AND IMPLEMENTATION

The dataset and C-DEPP software are freely available at https://github.com/yueyujiang/dataset_cdepp/.

The role of socio-economic disparities in the relative success and persistence of SARS-CoV-2 variants in New York City in early 2021

Socio-economic disparities were associated with disproportionate viral incidence between neighborhoods of New York City (NYC) during the first wave of SARS-CoV-2. We investigated how these disparities affected the co-circulation of SARS-CoV-2 variants during the second wave in NYC. We tested for correlation between the prevalence, in late 2020/early 2021, of Alpha, Iota, Iota with E484K mutation (Iota-E484K), and B.1-like genomes and pre-existing immunity (seropositivity) in NYC neighborhoods. In the context of varying seroprevalence we described socio-economic profiles of neighborhoods and performed migration and lineage persistence analyses using a Bayesian phylogeographical framework. Seropositivity was greater in areas with high poverty and a larger proportion of Black and Hispanic or Latino residents. Seropositivity was positively correlated with the proportion of Iota-E484K and Iota genomes, and negatively correlated with the proportion of Alpha and B.1-like genomes. The proportion of persisting Alpha lineages declined over time in locations with high seroprevalence, whereas the proportion of persisting Iota-E484K lineages remained the same in high seroprevalence areas. During the second wave, the geographic variation of standing immunity, due to disproportionate disease burden during the first wave of SARS-CoV-2 in NYC, allowed for the immune evasive Iota-E484K variant, but not the more transmissible Alpha variant, to circulate in locations with high pre-existing immunity.

Accurate, scalable, and fully automated inference of species trees from raw genome assemblies using ROADIES

Inference of species trees plays a crucial role in advancing our understanding of evolutionary relationships and has immense significance for diverse biological and medical applications. Extensive genome sequencing efforts are currently in progress across a broad spectrum of life forms, holding the potential to unravel the intricate branching patterns within the tree of life. However, estimating species trees starting from raw genome sequences is quite challenging, and the current cutting-edge methodologies require a series of error-prone steps that are neither entirely automated nor standardized. In this paper, we present ROADIES, a novel pipeline for species tree inference from raw genome assemblies that is fully automated, easy to use, scalable, free from reference bias, and provides flexibility to adjust the tradeoff between accuracy and runtime. The ROADIES pipeline eliminates the need to align whole genomes, choose a single reference species, or pre-select loci such as functional genes found using cumbersome annotation steps. Moreover, it leverages recent advances in phylogenetic inference to allow multi-copy genes, eliminating the need to detect orthology. Using the genomic datasets released from large-scale sequencing consortia across three diverse life forms (placental mammals, pomace flies, and birds), we show that ROADIES infers species trees that are comparable in quality with the state-of-the-art approaches but in a fraction of the time. By incorporating optimal approaches and automating all steps from assembled genomes to species and gene trees, ROADIES is poised to improve the accuracy, scalability, and reproducibility of phylogenomic analyses.

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.

Precision epidemiology at the nexus of mathematics and nanotechnology: Unraveling the dance of viral dynamics

The intersection of mathematical modeling, nanotechnology, and epidemiology marks a paradigm shift in our battle against infectious diseases, aligning with the focus of the journal on the regulation, expression, function, and evolution of genes in diverse biological contexts. This exploration navigates the intricate dance of viral transmission dynamics, highlighting mathematical models as dual tools of insight and precision instruments, a theme relevant to the diverse sections of Gene. In the context of virology, ethical considerations loom large, necessitating robust frameworks to protect individual rights, an aspect essential in infectious disease research. Global collaboration emerges as a critical pillar in our response to emerging infectious diseases, fortified by the predictive prowess of mathematical models enriched by nanotechnology. The synergy of interdisciplinary collaboration, training the next generation to bridge mathematical rigor, biology, and epidemiology, promises accelerated discoveries and robust models that account for real-world complexities, fostering innovation and exploration in the field. In this intricate review, mathematical modeling in viral transmission dynamics and epidemiology serves as a guiding beacon, illuminating the path toward precision interventions, global preparedness, and the collective endeavor to safeguard human health, resonating with the aim of advancing knowledge in gene regulation and expression.

Concepts and methods for predicting viral evolution

The seasonal human influenza virus undergoes rapid evolution, leading to significant changes in circulating viral strains from year to year. These changes are typically driven by adaptive mutations, particularly in the antigenic epitopes, the regions of the viral surface protein haemagglutinin targeted by human antibodies. Here we describe a consistent set of methods for data-driven predictive analysis of viral evolution. Our pipeline integrates four types of data: (1) sequence data of viral isolates collected on a worldwide scale, (2) epidemiological data on incidences, (3) antigenic characterization of circulating viruses, and (4) intrinsic viral phenotypes. From the combined analysis of these data, we obtain estimates of relative fitness for circulating strains and predictions of clade frequencies for periods of up to one year. Furthermore, we obtain comparative estimates of protection against future viral populations for candidate vaccine strains, providing a basis for pre-emptive vaccine strain selection. Continuously updated predictions obtained from the prediction pipeline for influenza and SARS-CoV-2 are available on the website previr.app .

Concepts and methods for predicting viral evolution

The seasonal human influenza virus undergoes rapid evolution, leading to significant changes in circulating viral strains from year to year. These changes are typically driven by adaptive mutations, particularly in the antigenic epitopes, the regions of the viral surface protein haemagglutinin targeted by human antibodies. Here we describe a consistent set of methods for data-driven predictive analysis of viral evolution. Our pipeline integrates four types of data: (1) sequence data of viral isolates collected on a worldwide scale, (2) epidemiological data on incidences, (3) antigenic characterization of circulating viruses, and (4) intrinsic viral phenotypes. From the combined analysis of these data, we obtain estimates of relative fitness for circulating strains and predictions of clade frequencies for periods of up to one year. Furthermore, we obtain comparative estimates of protection against future viral populations for candidate vaccine strains, providing a basis for pre-emptive vaccine strain selection. Continuously updated predictions obtained from the prediction pipeline for influenza and SARS-CoV-2 are available on the website previr.app.

Effect of molnupiravir on SARS-CoV-2 evolution in immunocompromised patients: a retrospective observational study

INTRODUCTION

Continued SARS-CoV-2 infection among immunocompromised individuals is likely to play a role in generating genomic diversity and the emergence of novel variants. Antiviral treatments such as molnupiravir are used to mitigate severe COVID-19 outcomes, but the extended effects of these drugs on viral evolution in patients with chronic infections remain uncertain. This study investigates how molnupiravir affects SARS-CoV-2 evolution in immunocompromised patients with prolonged infections.

METHODS

The study included five immunocompromised patients treated with molnupiravir and four patients not treated with molnupiravir (two immunocompromised and two non-immunocompromised). We selected patients who had been infected by similar SARS-CoV-2 variants and with high-quality genomes across timepoints to allow comparison between groups. Throat and nasopharyngeal samples were collected in patients up to 44 days post treatment and were sequenced using tiled amplicon sequencing followed by variant calling. The UShER pipeline and University of California Santa Cruz genome viewer provided insights into the global context of variants. Treated and untreated patients were compared, and mutation profiles were visualised to understand the impact of molnupiravir on viral evolution.

FINDINGS

Patients treated with molnupiravir showed a large increase in low-to-mid-frequency variants in as little as 10 days after treatment, whereas no such change was observed in untreated patients. Some of these variants became fixed in the viral population, including non-synonymous mutations in the spike protein. The variants were distributed across the genome and included unique mutations not commonly found in global omicron genomes. Notably, G-to-A and C-to-T mutations dominated the mutational profile of treated patients, persisting up to 44 days post treatment.

INTERPRETATION

Molnupiravir treatment in immunocompromised patients led to the accumulation of a distinctive pattern of mutations beyond the recommended 5 days of treatment. Treated patients maintained persistent PCR positivity for the duration of monitoring, indicating clear potential for transmission and subsequent emergence of novel variants.

FUNDING

Australian Research Council.

Addressing pandemic-wide systematic errors in the SARS-CoV-2 phylogeny

The SARS-CoV-2 genome occupies a unique place in infection biology - it is the most highly sequenced genome on earth (making up over 20% of public sequencing datasets) with fine scale information on sampling date and geography, and has been subject to unprecedented intense analysis. As a result, these phylogenetic data are an incredibly valuable resource for science and public health. However, the vast majority of the data was sequenced by tiling amplicons across the full genome, with amplicon schemes that changed over the pandemic as mutations in the viral genome interacted with primer binding sites. In combination with the disparate set of genome assembly workflows and lack of consistent quality control (QC) processes, the current genomes have many systematic errors that have evolved with the virus and amplicon schemes. These errors have significant impacts on the phylogeny, and therefore over the last few years, many thousands of hours of researchers time has been spent in "eyeballing" trees, looking for artefacts, and then patching the tree. Given the huge value of this dataset, we therefore set out to reprocess the complete set of public raw sequence data in a rigorous amplicon-aware manner, and build a cleaner phylogeny. Here we provide a global tree of 3,960,704 samples, built from a consistently assembled set of high quality consensus sequences from all available public data as of March 2023, viewable at https://viridian.taxonium.org. Each genome was constructed using a novel assembly tool called Viridian (https://github.com/iqbal-lab-org/viridian), developed specifically to process amplicon sequence data, eliminating artefactual errors and mask the genome at low quality positions. We provide simulation and empirical validation of the methodology, and quantify the improvement in the phylogeny. Phase 2 of our project will address the fact that the data in the public archives is heavily geographically biased towards the Global North. We therefore have contributed new raw data to ENA/SRA from many countries including Ghana, Thailand, Laos, Sri Lanka, India, Argentina and Singapore. We will incorporate these, along with all public raw data submitted between March 2023 and the current day, into an updated set of assemblies, and phylogeny. We hope the tree, consensus sequences and Viridian will be a valuable resource for researchers.

INSaFLU-TELEVIR: an open web-based bioinformatics suite for viral metagenomic detection and routine genomic surveillance

BACKGROUND

Implementation of clinical metagenomics and pathogen genomic surveillance can be particularly challenging due to the lack of bioinformatics tools and/or expertise. In order to face this challenge, we have previously developed INSaFLU, a free web-based bioinformatics platform for virus next-generation sequencing data analysis. Here, we considerably expanded its genomic surveillance component and developed a new module (TELEVIR) for metagenomic virus identification.

RESULTS

The routine genomic surveillance component was strengthened with new workflows and functionalities, including (i) a reference-based genome assembly pipeline for Oxford Nanopore technologies (ONT) data; (ii) automated SARS-CoV-2 lineage classification; (iii) Nextclade analysis; (iv) Nextstrain phylogeographic and temporal analysis (SARS-CoV-2, human and avian influenza, monkeypox, respiratory syncytial virus (RSV A/B), as well as a "generic" build for other viruses); and (v) algn2pheno for screening mutations of interest. Both INSaFLU pipelines for reference-based consensus generation (Illumina and ONT) were benchmarked against commonly used command line bioinformatics workflows for SARS-CoV-2, and an INSaFLU snakemake version was released. In parallel, a new module (TELEVIR) for virus detection was developed, after extensive benchmarking of state-of-the-art metagenomics software and following up-to-date recommendations and practices in the field. TELEVIR allows running complex workflows, covering several combinations of steps (e.g., with/without viral enrichment or host depletion), classification software (e.g., Kaiju, Kraken2, Centrifuge, FastViromeExplorer), and databases (RefSeq viral genome, Virosaurus, etc.), while culminating in user- and diagnosis-oriented reports. Finally, to potentiate real-time virus detection during ONT runs, we developed findONTime, a tool aimed at reducing costs and the time between sample reception and diagnosis.

CONCLUSIONS

The accessibility, versatility, and functionality of INSaFLU-TELEVIR are expected to supply public and animal health laboratories and researchers with a user-oriented and pan-viral bioinformatics framework that promotes a strengthened and timely viral metagenomic detection and routine genomics surveillance. INSaFLU-TELEVIR is compatible with Illumina, Ion Torrent, and ONT data and is freely available at https://insaflu.insa.pt/ (online tool) and https://github.com/INSaFLU (code).

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.

Whole-genome sequencing of SARS-CoV-2 from the initial cases of domestic cat infections in Canada

Two cat nasal swabs from Canada's earliest confirmed SARS-CoV-2 positive domestic cats were sequenced to over 99% SARS-CoV-2 genome coverage. One cat had lineage A.23.1 SARS-CoV-2 not reported before in animals. Both sequences have multiple spike gene mutations and clustered closely with human-derived sequences in the global SARS-CoV-2 phylogenetic tree.

Bus Riding as Amplification Mechanism for SARS-CoV-2 Transmission, Germany, 20211

To examine the risk associated with bus riding and identify transmission chains, we investigated a COVID-19 outbreak in Germany in 2021 that involved index case-patients among bus-riding students. We used routine surveillance data, performed laboratory analyses, interviewed case-patients, and conducted a cohort study. We identified 191 case-patients, 65 (34%) of whom were elementary schoolchildren. A phylogenetically unique strain and epidemiologic analyses provided a link between air travelers and cases among bus company staff, schoolchildren, other bus passengers, and their respective household members. The attack rate among bus-riding children at 1 school was ≈4 times higher than among children not taking a bus to that school. The outbreak exemplifies how an airborne agent may be transmitted effectively through (multiple) short (<20 minutes) public transport journeys and may rapidly affect many persons.

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.

Properties and Mechanisms of Deletions, Insertions, and Substitutions in the Evolutionary History of SARS-CoV-2

SARS-CoV-2 has accumulated many mutations since its emergence in late 2019. Nucleotide substitutions leading to amino acid replacements constitute the primary material for natural selection. Insertions, deletions, and substitutions appear to be critical for coronavirus's macro- and microevolution. Understanding the molecular mechanisms of mutations in the mutational hotspots (positions, loci with recurrent mutations, and nucleotide context) is important for disentangling roles of mutagenesis and selection. In the SARS-CoV-2 genome, deletions and insertions are frequently associated with repetitive sequences, whereas C>U substitutions are often surrounded by nucleotides resembling the APOBEC mutable motifs. We describe various approaches to mutation spectra analyses, including the context features of RNAs that are likely to be involved in the generation of recurrent mutations. We also discuss the interplay between mutations and natural selection as a complex evolutionary trend. The substantial variability and complexity of pipelines for the reconstruction of mutations and the huge number of genomic sequences are major problems for the analyses of mutations in the SARS-CoV-2 genome. As a solution, we advocate for the development of a centralized database of predicted mutations, which needs to be updated on a regular basis.

Unsupervised identification of significant lineages of SARS-CoV-2 through scalable machine learning methods

Since its emergence in late 2019, SARS-CoV-2 has diversified into a large number of lineages and caused multiple waves of infection globally. Novel lineages have the potential to spread rapidly and internationally if they have higher intrinsic transmissibility and/or can evade host immune responses, as has been seen with the Alpha, Delta, and Omicron variants of concern. They can also cause increased mortality and morbidity if they have increased virulence, as was seen for Alpha and Delta. Phylogenetic methods provide the "gold standard" for representing the global diversity of SARS-CoV-2 and to identify newly emerging lineages. However, these methods are computationally expensive, struggle when datasets get too large, and require manual curation to designate new lineages. These challenges provide a motivation to develop complementary methods that can incorporate all of the genetic data available without down-sampling to extract meaningful information rapidly and with minimal curation. In this paper, we demonstrate the utility of using algorithmic approaches based on word-statistics to represent whole sequences, bringing speed, scalability, and interpretability to the construction of genetic topologies. While not serving as a substitute for current phylogenetic analyses, the proposed methods can be used as a complementary, and fully automatable, approach to identify and confirm new emerging variants.

Scalable neighbour search and alignment with uvaia

Despite millions of SARS-CoV-2 genomes being sequenced and shared globally, manipulating such data sets is still challenging, especially selecting sequences for focused phylogenetic analysis. We present a novel method, uvaia, which is based on partial and exact sequence similarity for quickly extracting database sequences similar to query sequences of interest. Many SARS-CoV-2 phylogenetic analyses rely on very low numbers of ambiguous sites as a measure of quality since ambiguous sites do not contribute to single nucleotide polymorphism (SNP) differences. Uvaia overcomes this limitation by using measures of sequence similarity which consider partially ambiguous sites, allowing for more ambiguous sequences to be included in the analysis if needed. Such fine-grained definition of similarity allows not only for better phylogenetic analyses, but could also lead to improved classification and biogeographical inferences. Uvaia works natively with compressed files, can use multiple cores and efficiently utilises memory, being able to analyse large data sets on a standard desktop.

ViTAL: Vision TrAnsformer based Low coverage SARS-CoV-2 lineage assignment

MOTIVATION

Rapid spread of viral diseases such as Coronavirus disease 2019 (COVID-19) highlights an urgent need for efficient surveillance of virus mutation and transmission dynamics, which requires fast, inexpensive and accurate viral lineage assignment. The first two goals might be achieved through low-coverage whole-genome sequencing (LC-WGS) which enables rapid genome sequencing at scale and at reduced costs. Unfortunately, LC-WGS significantly diminishes the genomic details, rendering accurate lineage assignment very challenging.

RESULTS

We present ViTAL, a novel deep learning algorithm specifically designed to perform lineage assignment of low coverage-sequenced genomes. ViTAL utilizes a combination of MinHash for genomic feature extraction and Vision Transformer for fine-grain genome classification and lineage assignment. We show that ViTAL outperforms state-of-the-art tools across diverse coverage levels, reaching up to 87.7% lineage assignment accuracy at 1× coverage where state-of-the-art tools such as UShER and Kraken2 achieve the accuracy of 5.4% and 27.4% respectively. ViTAL achieves comparable accuracy results with up to 8× lower coverage than state-of-the-art tools. We explore ViTAL's ability to identify the lineages of novel genomes, i.e. genomes the Vision Transformer was not trained on. We show how ViTAL can be applied to preliminary phylogenetic placement of novel variants.

AVAILABILITY AND IMPLEMENTATION

The data underlying this article are available in https://github.com/zuherJahshan/vital and can be accessed with 10.5281/zenodo.10688110.

Dispersion patterns of SARS-CoV-2 variants Gamma, Lambda and Mu in Latin America and the Caribbean

Latin America and Caribbean (LAC) regions were an important epicenter of the COVID-19 pandemic and SARS-CoV-2 evolution. Through the COVID-19 Genomic Surveillance Regional Network (COVIGEN), LAC countries produced an important number of genomic sequencing data that made possible an enhanced SARS-CoV-2 genomic surveillance capacity in the Americas, paving the way for characterization of emerging variants and helping to guide the public health response. In this study we analyzed approximately 300,000 SARS-CoV-2 sequences generated between February 2020 and March 2022 by multiple genomic surveillance efforts in LAC and reconstructed the diffusion patterns of the main variants of concern (VOCs) and of interest (VOIs) possibly originated in the Region. Our phylogenetic analysis revealed that the spread of variants Gamma, Lambda and Mu reflects human mobility patterns due to variations of international air passenger transportation and gradual lifting of social distance measures previously implemented in countries. Our results highlight the potential of genetic data to reconstruct viral spread and unveil preferential routes of viral migrations that are shaped by human mobility patterns.

Randomized controlled trial of molnupiravir SARS-CoV-2 viral and antibody response in at-risk adult outpatients

Viral clearance, antibody response and the mutagenic effect of molnupiravir has not been elucidated in at-risk populations. Non-hospitalised participants within 5 days of SARS-CoV-2 symptoms randomised to receive molnupiravir (n = 253) or Usual Care (n = 324) were recruited to study viral and antibody dynamics and the effect of molnupiravir on viral whole genome sequence from 1437 viral genomes. Molnupiravir accelerates viral load decline, but virus is detectable by Day 5 in most cases. At Day 14 (9 days post-treatment), molnupiravir is associated with significantly higher viral persistence and significantly lower anti-SARS-CoV-2 spike antibody titres compared to Usual Care. Serial sequencing reveals increased mutagenesis with molnupiravir treatment. Persistence of detectable viral RNA at Day 14 in the molnupiravir group is associated with higher transition mutations following treatment cessation. Viral viability at Day 14 is similar in both groups with post-molnupiravir treated samples cultured up to 9 days post cessation of treatment. The current 5-day molnupiravir course is too short. Longer courses should be tested to reduce the risk of potentially transmissible molnupiravir-mutated variants being generated. Trial registration: ISRCTN30448031.

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.

Robust expansion of phylogeny for fast-growing genome sequence data

Massive sequencing of SARS-CoV-2 genomes has urged novel methods that employ existing phylogenies to add new samples efficiently instead of de novo inference. 'TIPars' was developed for such challenge integrating parsimony analysis with pre-computed ancestral sequences. It took about 21 seconds to insert 100 SARS-CoV-2 genomes into a 100k-taxa reference tree using 1.4 gigabytes. Benchmarking on four datasets, TIPars achieved the highest accuracy for phylogenies of moderately similar sequences. For highly similar and divergent scenarios, fully parsimony-based and likelihood-based phylogenetic placement methods performed the best respectively while TIPars was the second best. TIPars accomplished efficient and accurate expansion of phylogenies of both similar and divergent sequences, which would have broad biological applications beyond SARS-CoV-2. TIPars is accessible from https://tipars.hku.hk/ and source codes are available at https://github.com/id-bioinfo/TIPars.

Bases-dependent Rapid Phylogenetic Clustering (Bd-RPC) enables precise and efficient phylogenetic estimation in viruses

Understanding phylogenetic relationships among species is essential for many biological studies, which call for an accurate phylogenetic tree to understand major evolutionary transitions. The phylogenetic analyses present a major challenge in estimation accuracy and computational efficiency, especially recently facing a wave of severe emerging infectious disease outbreaks. Here, we introduced a novel, efficient framework called Bases-dependent Rapid Phylogenetic Clustering (Bd-RPC) for new sample placement for viruses. In this study, a brand-new recoding method called Frequency Vector Recoding was implemented to approximate the phylogenetic distance, and the Phylogenetic Simulated Annealing Search algorithm was developed to match the recoded distance matrix with the phylogenetic tree. Meanwhile, the indel (insertion/deletion) was heuristically introduced to foreign sequence recognition for the first time. Here, we compared the Bd-RPC with the recent placement software (PAGAN2, EPA-ng, TreeBeST) and evaluated it in Alphacoronavirus, Alphaherpesvirinae, and Betacoronavirus by using Split and Robinson-Foulds distances. The comparisons showed that Bd-RPC maintained the highest precision with great efficiency, demonstrating good performance in new sample placement on all three virus genera. Finally, a user-friendly website (http://www.bd-rpc.xyz) is available for users to classify new samples instantly and facilitate exploration of the phylogenetic research in viruses, and the Bd-RPC is available on GitHub (http://github.com/Bin-Ma/bd-rpc).

Using big sequencing data to identify chronic SARS-Coronavirus-2 infections

The evolution of SARS-Coronavirus-2 (SARS-CoV-2) has been characterized by the periodic emergence of highly divergent variants. One leading hypothesis suggests these variants may have emerged during chronic infections of immunocompromised individuals, but limited data from these cases hinders comprehensive analyses. Here, we harnessed millions of SARS-CoV-2 genomes to identify potential chronic infections and used language models (LM) to infer chronic-associated mutations. First, we mined the SARS-CoV-2 phylogeny and identified chronic-like clades with identical metadata (location, age, and sex) spanning over 21 days, suggesting a prolonged infection. We inferred 271 chronic-like clades, which exhibited characteristics similar to confirmed chronic infections. Chronic-associated mutations were often high-fitness immune-evasive mutations located in the spike receptor-binding domain (RBD), yet a minority were unique to chronic infections and absent in global settings. The probability of observing high-fitness RBD mutations was 10-20 times higher in chronic infections than in global transmission chains. The majority of RBD mutations in BA.1/BA.2 chronic-like clades bore predictive value, i.e., went on to display global success. Finally, we used our LM to infer hundreds of additional chronic-like clades in the absence of metadata. Our approach allows mining extensive sequencing data and providing insights into future evolutionary patterns of SARS-CoV-2.

SARS-CoV-2 lineage assignments using phylogenetic placement/UShER are superior to pangoLEARN machine-learning method

With the rapid spread and evolution of SARS-CoV-2, the ability to monitor its transmission and distinguish among viral lineages is critical for pandemic response efforts. The most commonly used software for the lineage assignment of newly isolated SARS-CoV-2 genomes is pangolin, which offers two methods of assignment, pangoLEARN and pUShER. PangoLEARN rapidly assigns lineages using a machine-learning algorithm, while pUShER performs a phylogenetic placement to identify the lineage corresponding to a newly sequenced genome. In a preliminary study, we observed that pangoLEARN (decision tree model), while substantially faster than pUShER, offered less consistency across different versions of pangolin v3. Here, we expand upon this analysis to include v3 and v4 of pangolin, which moved the default algorithm for lineage assignment from pangoLEARN in v3 to pUShER in v4, and perform a thorough analysis confirming that pUShER is not only more stable across versions but also more accurate. Our findings suggest that future lineage assignment algorithms for various pathogens should consider the value of phylogenetic placement.

Tracking SARS-CoV-2 variants during the 2023 flu season and beyond in Lebanon

BACKGROUND

Early SARS-CoV-2 variant detection relies on testing and genomic surveillance. The Omicron variant (B.1.1.529) has quickly become the dominant type among the previous circulating variants worldwide. Several subvariants have emerged exhibiting greater infectivity and immune evasion. In this study we aimed at studying the prevalence of the Omicron subvariants during the flu season and beyond in Lebanon through genomic screening and at determining the overall standing and trajectory of the pandemic in the country.

METHODS

A total of 155 SARS-CoV-2 RNA samples were sequenced, using Nanopore sequencing technology.

RESULTS

Nanopore sequencing of 155 genomes revealed their distribution over 39 Omicron variants. XBB.1.5 (23.29 %) was the most common, followed by XBB.1.9.1 (10.96 %) and XBB.1.42 (7.5 %). The first batch collected between September and November 2022, included the BA.2.75.2, BA.5.2, BA.5.2.20, BA.5.2.25 and BQ.1.1.5 lineages. Between December 2022 and January 2023, those lineages were replaced by BA.2.75.5, BN.1, BN.1.4, BQ.1, BQ.1.1, BQ.1.1.23, CH.1.1, CM.4 and XBK. Starting February 2023, we observed a gradual emergence and dominance of the recombinant XBB and its sub-lineages (XBB.1, XBB.1.5, XBB.1.5.2, XBB.1.5.3, XBB.1.9, XBB.1.9.1, XBB.1.9.2, XBB.1.16, XBB.1.22 and XBB.1.42).

CONCLUSIONS

The timely detection and characterization of SARS-CoV-2 variants is important to reduce transmission through established disease control measures and to avoid introductions into animal populations that could lead to serious public health implications.

Impact of reference design on estimating SARS-CoV-2 lineage abundances from wastewater sequencing data

BACKGROUND

Sequencing of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA from wastewater samples has emerged as a valuable tool for detecting the presence and relative abundances of SARS-CoV-2 variants in a community. By analyzing the viral genetic material present in wastewater, researchers and public health authorities can gain early insights into the spread of virus lineages and emerging mutations. Constructing reference datasets from known SARS-CoV-2 lineages and their mutation profiles has become state-of-the-art for assigning viral lineages and their relative abundances from wastewater sequencing data. However, selecting reference sequences or mutations directly affects the predictive power.

RESULTS

Here, we show the impact of a mutation- and sequence-based reference reconstruction for SARS-CoV-2 abundance estimation. We benchmark 3 datasets: (i) synthetic "spike-in"' mixtures; (ii) German wastewater samples from early 2021, mainly comprising Alpha; and (iii) samples obtained from wastewater at an international airport in Germany from the end of 2021, including first signals of Omicron. The 2 approaches differ in sublineage detection, with the marker mutation-based method, in particular, being challenged by the increasing number of mutations and lineages. However, the estimations of both approaches depend on selecting representative references and optimized parameter settings. By performing parameter escalation experiments, we demonstrate the effects of reference size and alternative allele frequency cutoffs for abundance estimation. We show how different parameter settings can lead to different results for our test datasets and illustrate the effects of virus lineage composition of wastewater samples and references.

CONCLUSIONS

Our study highlights current computational challenges, focusing on the general reference design, which directly impacts abundance allocations. We illustrate advantages and disadvantages that may be relevant for further developments in the wastewater community and in the context of defining robust quality metrics.

A magnetic control enrichment technique combined with terahertz metamaterial biosensor for detecting SARS-CoV-2 spike protein

The highly contagious SARS-CoV-2 virus, responsible for the COVID-19 pandemic continues to pose significant challenges to public health. Developing new methods for early detection and diagnosis is crucial in combatting the disease, mitigating its impact and be prepared for future challenges in pandemic diseases. In this study, we propose a terahertz (THz) biosensing technology that capitalizes on the properties of THz metamaterial in conjunction with magnetic nanoparticles. This approach can accurately identify the SARS-CoV-2 spike protein by pinpointing its location on the THz resonance sources grooved surface. The magnetic nanoparticles are employed to selectively bind with target molecules, and migrate towards the THz metamaterial unit cell when exposed to an applied magnetic field. The presence of target molecules in to the metamaterial variation in the frequency, amplitude, and phase of the resonance response, thus enabling swift, accurate and sensitive detection. To assess the effectiveness of the proposed technique, we have conducted a comparative analysis between real samples on platforms controlled by magnetic manipulation and those without the control. It was confirmed that the proposed THz sensing method demonstrated a linear detection range spanning from 0.005 ng mL-1 to 1000 ng mL-1 with a detection limit of 0.002 ng mL-1. Furthermore, it exhibited a frequency shift of 24 GHz and a stability index of 95%. The THz biosensing technique may pave a new avenue in identifying and preempting the spread of potential pandemic diseases.