Unlocking capacities of genomics for the COVID-19 response and future pandemics

Multiple introductions and sustained local transmission of Monkeypox virus in Southern Brazil between 2022-2023

Mpox is a zoonotic viral disease caused by the Monkeypox virus (MPXV). Human cases have been mainly restricted to the African continent until the worldwide multi-country outbreak unfolded in 2022. We reconstructed epidemiological links of 53 MPXV infections using genomic epidemiology in Rio Grande do Sul State, southern Brazil, during 2022 and 2023. We detected five well-supported clades, three representing local transmission chains that were mostly restricted to the 2022 virus spread, one supported year-long maintenance encompassing samples from 2022 and 2023, and one new importation from Europe in 2023. Our results provide new insights into the geographic extent of community transmission and its association with viral diversity during the more pronounced 2022 mpox upsurge and during the following lower incidence phase. These findings highlight the power of continued genomic surveillance to uncover hidden transmission chains to understand viral dynamics and inform public health responses. The detection of sustained transmission in the state is important to guide targeted control measures to curtail further community and international transmission and highlight the need for maintaining genomic surveillance efforts.

Assessing different next-generation sequencing technologies for wastewater-based epidemiology

Wastewater-based epidemiology has proven to be an important public health asset during the COVID-19 pandemic. It can provide less biassed and more cost-effective population-level monitoring of the disease burden as compared to clinical testing. An essential component of SARS-CoV-2 wastewater monitoring is next-generation sequencing, providing genomic data to identify and quantify circulating viral strains rapidly. However, the specific choice of sequencing method influences the quality and timeliness of generated data and hence its usefulness for wastewater-based pathogen surveillance. Here, we systematically benchmarked Illumina Novaseq 6000, Element Aviti, ONT R9.4.1 MinION flow cell, and ONT R9.4.1 Flongle flow cell sequencing data to facilitate the selection of sequencing technology. Using a time series of wastewater samples from influent of six wastewater treatment plants throughout Switzerland, along with spike-in experiments, we show that higher sequencing error rates of ONT Nanopore sequencing reduce the accuracy of estimates of the relative abundance of viral variants, but the overall trend is in good concordance among all technologies. We find that the sequencing runtime for ONT Nanopore flow cells can be reduced to as little as five hours without significant impact on the quality of variant estimates. Our findings suggest that SARS-CoV-2 variant tracking is readily achievable with all tested technologies, albeit with different tradeoffs in terms of cost, timeliness and accuracy.

Investigating and forecasting infectious disease dynamics using epidemiological and molecular surveillance data

The integration of viral genomic data into public health surveillance has revolutionized our ability to track and forecast infectious disease dynamics. This review addresses two critical aspects of infectious disease forecasting and monitoring: the methodological workflow for epidemic forecasting and the transformative role of molecular surveillance. We first present a detailed approach for validating epidemic models, emphasizing an iterative workflow that utilizes ordinary differential equation (ODE)-based models to investigate and forecast disease dynamics. We recommend a more structured approach to model validation, systematically addressing key stages such as model calibration, assessment of structural and practical parameter identifiability, and effective uncertainty propagation in forecasts. Furthermore, we underscore the importance of incorporating multiple data streams by applying both simulated and real epidemiological data from the COVID-19 pandemic to produce more reliable forecasts with quantified uncertainty. Additionally, we emphasize the pivotal role of viral genomic data in tracking transmission dynamics and pathogen evolution. By leveraging advanced computational tools such as Bayesian phylogenetics and phylodynamics, researchers can more accurately estimate transmission clusters and reconstruct outbreak histories, thereby improving data-driven modeling and forecasting and informing targeted public health interventions. Finally, we discuss the transformative potential of integrating molecular epidemiology with mathematical modeling to complement and enhance epidemic forecasting and optimize public health strategies.

Metagenomic next generation sequencing of plasma RNA for diagnosis of unexplained, acute febrile illness in Uganda

Metagenomic next generation metagenomic sequencing (mNGS) has proven to be a useful tool in the diagnosis and identification of novel human pathogens and pathogens not identified on routine clinical microbiologic tests. In this study, we applied mNGS to characterize plasma RNA isolated from 42 study participants with unexplained acute febrile illness (AFI) admitted to tertiary referral hospitals in Mubende and Arua, Uganda. Study participants were selected based on clinical criteria suggestive of viral infection (i.e., thrombocytopenia, leukopenia). The study population had a median age of 28 years (IQR:24 to 38.5) and median platelet count of 114 x103 cells/mm3 (IQR:66,500 to 189,800). An average of 25 million 100 bp reads were generated per sample. We identified strong signals from diverse virus, bacteria, fungi, or parasites in 10 (23.8%) of the study participants. These included well recognized pathogens like Helicobacter pylori, human herpes virus-8, Plasmodium falciparum, Neisseria gonorrhoeae, and Rickettsia conorii. We further confirmed Rickettsia conorii infection, the cause of Mediterranean Spotted Fever (MSF), using PCR assays and Sanger sequencing. mNGS was a useful addition for detection of otherwise undetected pathogens and well-recognized non-pathogens. This is the first report to describe the molecular confirmation of a hospitalized case of MSF in sub-Saharan Africa (SSA). Further studies are needed to determine the utility of mNGS for disease surveillance in similar settings.

Early detection of emerging viral variants through analysis of community structure of coordinated substitution networks

The emergence of viral variants with altered phenotypes is a public health challenge underscoring the need for advanced evolutionary forecasting methods. Given extensive epistatic interactions within viral genomes and known viral evolutionary history, efficient genomic surveillance necessitates early detection of emerging viral haplotypes rather than commonly targeted single mutations. Haplotype inference, however, is a significantly more challenging problem precluding the use of traditional approaches. Here, using SARS-CoV-2 evolutionary dynamics as a case study, we show that emerging haplotypes with altered transmissibility can be linked to dense communities in coordinated substitution networks, which become discernible significantly earlier than the haplotypes become prevalent. From these insights, we develop a computational framework for inference of viral variants and validate it by successful early detection of known SARS-CoV-2 strains. Our methodology offers greater scalability than phylogenetic lineage tracing and can be applied to any rapidly evolving pathogen with adequate genomic surveillance data.

V-pipe 3.0: a sustainable pipeline for within-sample viral genetic diversity estimation

The large amount and diversity of viral genomic datasets generated by next-generation sequencing technologies poses a set of challenges for computational data analysis workflows, including rigorous quality control, scaling to large sample sizes, and tailored steps for specific applications. Here, we present V-pipe 3.0, a computational pipeline designed for analyzing next-generation sequencing data of short viral genomes. It is developed to enable reproducible, scalable, adaptable, and transparent inference of genetic diversity of viral samples. By presenting 2 large-scale data analysis projects, we demonstrate the effectiveness of V-pipe 3.0 in supporting sustainable viral genomic data science.

A Synergistic Lipid Nanoparticle Encapsulating mRNA Shingles Vaccine Induces Potent Immune Responses and Protects Guinea Pigs from Viral Challenges

Shingles is caused by the reactivation of varicella zoster virus (VZV) and manifests as painful skin rashes. While the recombinant protein-based vaccine proves highly effective, it encounters supply chain challenges due to a shortage of the necessary adjuvant. Messenger RNA (mRNA)-based vaccines can be rapidly produced on a large scale, but their effectiveness relies on efficient delivery and sequence design. Here, an mRNA-based VZV vaccine using a synergistic lipid nanoparticle (Syn-LNP) containing two different ionizable lipids is developed. Syn-LNP shows superior mRNA expression compared to LNPs formulated with either type of ionizable lipid and to a commercialized LNP. After encapsulating VZV glycoprotein E (gE)-encoding mRNA, mgE@Syn-LNP induces robust humoral and cellular immune responses in two strains of mice. The magnitude of these responses is similar to that induced by adjuvanted recombinant gE proteins and significantly higher than that observed with live-attenuated VZV. mgE@Syn-LNP exhibits durable humoral responses for over 7 months without obvious adverse effects. In addition, mgE@Syn-LNP protects vaccinated guinea pigs against live VZV challenges. Preliminary studies on the mRNA antigen design reveal that the removal of glycosylation sites of gE greatly reduces its immune responses. Collectively, Syn-LNP encapsulating gE-encoded mRNA holds great promise as a shingles vaccine.

Comparison of Nanopore and Synthesis-Based Next-Generation Sequencing Platforms for SARS-CoV-2 Variant Monitoring in Wastewater

Shortly after the beginning of the SARS-CoV-2 pandemic, many countries implemented sewage sentinel systems to monitor the circulation of the virus in the population. A fundamental part of these surveillance programs is the variant tracking through sequencing approaches to monitor and identify new variants or mutations that may be of importance. Two of the main sequencing platforms are Illumina and Oxford Nanopore Technologies. Here, we compare the performance of MiSeq (Illumina) and MinION (Oxford Nanopore Technologies), as well as two different data processing pipelines, to determine the effect they may have on the results. MiSeq showed higher sequencing coverage, lower error rate, and better capacity to detect and accurately estimate variant abundances than MinION R9.4.1 flow cell data. The use of different variant callers (LoFreq and iVar) and approaches to calculate the variant proportions had a remarkable impact on the results generated from wastewater samples. Freyja, coupled with iVar, may be more sensitive and accurate than LoFreq, especially with MinION data, but it comes at the cost of having a higher error rate. The analysis of MinION R10.4.1 flow cell data using Freyja combined with iVar narrows the gap with MiSeq performance in terms of read quality, accuracy, sensitivity, and number of detected mutations. Although MiSeq should still be considered as the standard method for SARS-CoV-2 variant tracking, MinION's versatility and rapid turnaround time may represent a clear advantage during the ongoing pandemic.

An updated review on pathogenic coronaviruses (CoVs) amid the emergence of SARS-CoV-2 variants: A look into the repercussions and possible solutions

SARS-CoV-2, responsible for COVID-19, shares 79% and 50% of its identity with SARS-CoV-1 and MERS-CoV, respectively. It uses the same main cell attachment and entry receptor as SARS-CoV-1, which is the ACE-2 receptor. However, key residues in the receptor-binding domain of its S-protein seem to give it a stronger affinity for the receptor and a better ability to hide from the host immune system. Like SARS-CoV-1 and MERS-CoV, cytokine storms in critically ill COVID-19 patients cause ARDS, neurological pathology, multiorgan failure, and increased death. Though many issues remain, the global research effort and lessons from SARS-CoV-1 and MERS-CoV are hopeful. The emergence of novel SARS-CoV-2 variants and subvariants raised serious concerns among the scientific community amid the emergence of other viral diseases like monkeypox and Marburg virus, which are major concerns for healthcare settings worldwide. Hence, an updated review on the comparative analysis of various coronaviruses (CoVs) has been developed, which highlights the evolution of CoVs and their repercussions.

A robust platform for integrative spatial multi-omics analysis to map immune responses to SARS-CoV-2 infection in lung tissues

The SARS-CoV-2 (COVID-19) virus has caused a devastating global pandemic of respiratory illness. To understand viral pathogenesis, methods are available for studying dissociated cells in blood, nasal samples, bronchoalveolar lavage fluid and similar, but a robust platform for deep tissue characterization of molecular and cellular responses to virus infection in the lungs is still lacking. We developed an innovative spatial multi-omics platform to investigate COVID-19-infected lung tissues. Five tissue-profiling technologies were combined by a novel computational mapping methodology to comprehensively characterize and compare the transcriptome and targeted proteome of virus infected and uninfected tissues. By integrating spatial transcriptomics data (Visium, GeoMx and RNAScope) and proteomics data (CODEX and PhenoImager HT) at different cellular resolutions across lung tissues, we found strong evidence for macrophage infiltration and defined the broader microenvironment surrounding these cells. By comparing infected and uninfected samples, we found an increase in cytokine signalling and interferon responses at different sites in the lung and showed spatial heterogeneity in the expression level of these pathways. These data demonstrate that integrative spatial multi-omics platforms can be broadly applied to gain a deeper understanding of viral effects on cellular environments at the site of infection and to increase our understanding of the impact of SARS-CoV-2 on the lungs.

Current Uses and Future Perspectives of Genomic Technologies in Clinical Microbiology

Recent advancements in sequencing technology and data analytics have led to a transformative era in pathogen detection and typing. These developments not only expedite the process, but also render it more cost-effective. Genomic analyses of infectious diseases are swiftly becoming the standard for pathogen analysis and control. Additionally, national surveillance systems can derive substantial benefits from genomic data, as they offer profound insights into pathogen epidemiology and the emergence of antimicrobial-resistant strains. Antimicrobial resistance (AMR) is a pressing global public health issue. While clinical laboratories have traditionally relied on culture-based antimicrobial susceptibility testing, the integration of genomic data into AMR analysis holds immense promise. Genomic-based AMR data can furnish swift, consistent, and highly accurate predictions of resistance phenotypes for specific strains or populations, all while contributing invaluable insights for surveillance. Moreover, genome sequencing assumes a pivotal role in the investigation of hospital outbreaks. It aids in the identification of infection sources, unveils genetic connections among isolates, and informs strategies for infection control. The One Health initiative, with its focus on the intricate interconnectedness of humans, animals, and the environment, seeks to develop comprehensive approaches for disease surveillance, control, and prevention. When integrated with epidemiological data from surveillance systems, genomic data can forecast the expansion of bacterial populations and species transmissions. Consequently, this provides profound insights into the evolution and genetic relationships of AMR in pathogens, hosts, and the environment.

COWID: an efficient cloud-based genomics workflow for scalable identification of SARS-COV-2

Implementing a specific cloud resource to analyze extensive genomic data on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses a challenge when resources are limited. To overcome this, we repurposed a cloud platform initially designed for use in research on cancer genomics (https://cgc.sbgenomics.com) to enable its use in research on SARS-CoV-2 to build Cloud Workflow for Viral and Variant Identification (COWID). COWID is a workflow based on the Common Workflow Language that realizes the full potential of sequencing technology for use in reliable SARS-CoV-2 identification and leverages cloud computing to achieve efficient parallelization. COWID outperformed other contemporary methods for identification by offering scalable identification and reliable variant findings with no false-positive results. COWID typically processed each sample of raw sequencing data within 5 min at a cost of only US$0.01. The COWID source code is publicly available (https://github.com/hendrick0403/COWID) and can be accessed on any computer with Internet access. COWID is designed to be user-friendly; it can be implemented without prior programming knowledge. Therefore, COWID is a time-efficient tool that can be used during a pandemic.

Immune landscape and redox imbalance during neurological disorders in COVID-19

The outbreak of Coronavirus Disease 2019 (COVID-19) has prompted the scientific community to explore potential treatments or vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes the illness. While SARS-CoV-2 is mostly considered a respiratory pathogen, several neurological complications have been reported, raising questions about how it may enter the Central Nervous System (CNS). Receptors such as ACE2, CD147, TMPRSS2, and NRP1 have been identified in brain cells and may be involved in facilitating SARS-CoV-2 entry into the CNS. Moreover, proteins like P2X7 and Panx-1 may contribute to the pathogenesis of COVID-19. Additionally, the role of the immune system in the gravity of COVID-19 has been investigated with respect to both innate and adaptive immune responses caused by SARS-CoV-2 infection, which can lead to a cytokine storm, tissue damage, and neurological manifestations. A redox imbalance has also been linked to the pathogenesis of COVID-19, potentially causing mitochondrial dysfunction, and generating proinflammatory cytokines. This review summarizes different mechanisms of reactive oxygen species and neuro-inflammation that may contribute to the development of severe COVID-19, and recent progress in the study of immunological events and redox imbalance in neurological complications of COVID-19, and the role of bioinformatics in the study of neurological implications of COVID-19.

Improving the Odds—COVID-Omics and Predicting Patient Outcomes

Purpose of Review

The global COVID-19 pandemic has claimed millions of lives and harmed hundreds of millions more. Amidst this crisis, scientists have used multi-omics to understand and combat the virus. The purpose of this review is to provide the latest and most impactful work in COVID-omics.

Recent Findings

Multi-omics has identified risk-stratification criteria to predict viral severity among COVID-19 patients. Omic methods have also unlocked targetable biomarkers in viral pathways and enabled public health agencies to curb transmission by genomic tracing. Transplant researchers have used multi-omics to assess the safety of transplanting organs from COVID-positive donors, and whether patient immunosuppression regimens should be maintained. Lastly, maximizing multi-omic impact by nurturing future collaborations between mutli-omic labs and public health agencies and pharmaceutical companies will be critical in successfully facing the next pandemic.

Summary

This review focuses on contributions within the field of COVID-omics, including patient risk stratification and viral pathway analysis, genomic public health surveillance, and transplant clinician recommendations.

LAPIS is a fast web API for massive open virus sequencing data

BACKGROUND

Recent epidemic outbreaks such as the SARS-CoV-2 pandemic and the mpox outbreak in 2022 have demonstrated the value of genomic sequencing data for tracking the origin and spread of pathogens. Laboratories around the globe generated new sequences at unprecedented speed and volume and bioinformaticians developed new tools and dashboards to analyze this wealth of data. However, a major challenge that remains is the lack of simple and efficient approaches for accessing and processing sequencing data.

RESULTS

The Lightweight API for Sequences (LAPIS) facilitates rapid retrieval and analysis of genomic sequencing data through a REST API. It supports complex mutation- and metadata-based queries and can perform aggregation operations on massive datasets. LAPIS is optimized for typical questions relevant to genomic epidemiology. Using a newly-developed in-memory database engine, it has a high speed and throughput: between 25 January and 4 February 2023, the SARS-CoV-2 instance of LAPIS, which contains 14.5 million sequences, processed over 20 million requests with a mean response time of 411 ms and a median response time of 1 ms. LAPIS is the core engine behind our dashboards on genspectrum.org and we currently maintain public LAPIS instances for SARS-CoV-2 and mpox.

CONCLUSIONS

Powered by an optimized database engine and available through a web API, LAPIS enhances the accessibility of genomic sequencing data. It is designed to serve as a common backend for dashboards and analyses with the potential to be integrated into common database platforms such as GenBank.

Genomic surveillance identifies potential risk factors for SARS-CoV-2 transmission at a mid-sized university in a small rural town

Understanding transmission dynamics of SARS-CoV-2 in institutions of higher education (IHEs) is important because these settings have potential for rapid viral spread. Here, we used genomic surveillance to retrospectively investigate transmission dynamics throughout the 2020-2021 academic year for the University of Idaho ("University"), a mid-sized IHE in a small rural town. We generated genome assemblies for 1168 SARS-CoV-2 samples collected during the academic year, representing 46.8% of positive samples collected from the University population and 49.8% of positive samples collected from the surrounding community ("Community") at the local hospital during this time. Transmission dynamics differed for the University when compared to the Community, with more infection waves that lasted shorter lengths of time, potentially resulting from high-transmission congregate settings along with mitigation efforts implemented by the University to combat outbreaks. We found evidence for low transmission rates between the University and Community, with approximately 8% of transmissions into the Community originating from the University, and approximately 6% of transmissions into the University originating from the Community. Potential transmission risk factors identified for the University included congregate settings such as sorority and fraternity events and residences, holiday travel, and high caseloads in the surrounding community. Knowledge of these risk factors can help the University and other IHEs develop effective mitigation measures for SARS-CoV-2 and similar pathogens.

Systematic Guidelines for Effective Utilization of COVID-19 Databases in Genomic, Epidemiologic, and Clinical Research

The pandemic has led to the production and accumulation of various types of data related to coronavirus disease 2019 (COVID-19). To understand the features and characteristics of COVID-19 data, we summarized representative databases and determined the data types, purpose, and utilization details of each database. In addition, we categorized COVID-19 associated databases into epidemiological data, genome and protein data, and drug and target data. We found that the data present in each of these databases have nine separate purposes (clade/variant/lineage, genome browser, protein structure, epidemiological data, visualization, data analysis tool, treatment, literature, and immunity) according to the types of data. Utilizing the databases we investigated, we created four queries as integrative analysis methods that aimed to answer important scientific questions related to COVID-19. Our queries can make effective use of multiple databases to produce valuable results that can reveal novel findings through comprehensive analysis. This allows clinical researchers, epidemiologists, and clinicians to have easy access to COVID-19 data without requiring expert knowledge in computing or data science. We expect that users will be able to reference our examples to construct their own integrative analysis methods, which will act as a basis for further scientific inquiry and data searching.

Performance of methods for SARS-CoV-2 variant detection and abundance estimation within mixed population samples

Background

The accurate identification of SARS-CoV-2 (SC2) variants and estimation of their abundance in mixed population samples (e.g., air or wastewater) is imperative for successful surveillance of community level trends. Assessing the performance of SC2 variant composition estimators (VCEs) should improve our confidence in public health decision making. Here, we introduce a linear regression based VCE and compare its performance to four other VCEs: two re-purposed DNA sequence read classifiers (Kallisto and Kraken2), a maximum-likelihood based method (Lineage deComposition for Sars-Cov-2 pooled samples (LCS)), and a regression based method (Freyja).

Methods

We simulated DNA sequence datasets of known variant composition from both Illumina and Oxford Nanopore Technologies (ONT) platforms and assessed the performance of each VCE. We also evaluated VCEs performance using publicly available empirical wastewater samples collected for SC2 surveillance efforts. Bioinformatic analyses were performed with a custom NextFlow workflow (C-WAP, CFSAN Wastewater Analysis Pipeline). Relative root mean squared error (RRMSE) was used as a measure of performance with respect to the known abundance and concordance correlation coefficient (CCC) was used to measure agreement between pairs of estimators.

Results

Based on our results from simulated data, Kallisto was the most accurate estimator as it had the lowest RRMSE, followed by Freyja. Kallisto and Freyja had the most similar predictions, reflected by the highest CCC metrics. We also found that accuracy was platform and amplicon panel dependent. For example, the accuracy of Freyja was significantly higher with Illumina data compared to ONT data; performance of Kallisto was best with ARTICv4. However, when analyzing empirical data there was poor agreement among methods and variations in the number of variants detected (e.g., Freyja ARTICv4 had a mean of 2.2 variants while Kallisto ARTICv4 had a mean of 10.1 variants).

Conclusion

This work provides an understanding of the differences in performance of a number of VCEs and how accurate they are in capturing the relative abundance of SC2 variants within a mixed sample (e.g., wastewater). Such information should help officials gauge the confidence they can have in such data for informing public health decisions.

SARS-CoV-2 diagnostic testing rates determine the sensitivity of genomic surveillance programs

The first step in SARS-CoV-2 genomic surveillance is testing to identify people who are infected. However, global testing rates are falling as we emerge from the acute health emergency and remain low in many low- and middle-income countries (mean = 27 tests per 100,000 people per day). We simulated COVID-19 epidemics in a prototypical low- and middle-income country to investigate how testing rates, sampling strategies and sequencing proportions jointly impact surveillance outcomes, and showed that low testing rates and spatiotemporal biases delay time to detection of new variants by weeks to months and can lead to unreliable estimates of variant prevalence, even when the proportion of samples sequenced is increased. Accordingly, investments in wider access to diagnostics to support testing rates of approximately 100 tests per 100,000 people per day could enable more timely detection of new variants and reliable estimates of variant prevalence. The performance of global SARS-CoV-2 genomic surveillance programs is fundamentally limited by access to diagnostic testing.

Detection of the Omicron SARS-CoV-2 Lineage and Its BA.1 Variant with Multiplex RT-qPCR

Whole genome sequencing (WGS) is considered the best instrument to track both virus evolution and the spread of new, emerging variants. However, WGS still does not allow the analysis of as many samples as qPCR does. Epidemiological and clinical research needs to develop advanced qPCR methods to identify emerging variants of SARS-CoV-2 while collecting data on their spreading in a faster and cheaper way, which is critical for introducing public health measures. This study aimed at designing a one-step RT-qPCR assay for multiplex detection of the Omicron lineage and providing additional data on its subvariants in clinical samples. The RT-qPCR assay demonstrated high sensitivity and specificity on multiple SARS-CoV-2 variants and was cross-validated by WGS.

Mosaic Recombination Inflicted Various SARS-CoV-2 Lineages to Emerge into Novel Virus Variants: a Review Update

Human Coronaviruses (hCoVs) belongs to the enormous and dissimilar family of positive-sense, non-segmented, single-stranded RNA viruses. The RNA viruses are prone to high rates of mutational recombination resulting in emergence of evolutionary variant to alter various features including transmissibility and severity. The evolutionary changes affect the immune escape and reduce effectiveness of diagnostic and therapeutic measures by becoming undetectable by the currently available diagnostics and refractory to therapeutics and vaccines. Whole genome sequencing studies from various countries have adequately reported mosaic recombination between different lineage strain of SARS-CoV-2 whereby RNA dependent RNA polymerase (RdRp) gene reconnects with a homologous RNA strand at diverse position. This all lead to evolutionary emergence of new variant/ lineage as evident with the emergence of XBB in India at the time of writing this review. The continuous periodical genomic surveillance is utmost required for understanding the various lineages involved in recombination to emerge into hybrid variant. This may further help in assessing virus transmission dynamics, virulence and severity factor to help health authorities take appropriate timely action for prevention and control of any future COVID-19 outbreak.

Analysis of research hotspots in COVID-19 genomics based on citespace software: Bibliometric analysis

Introduction

To analyze the current state, hotspots, and cutting-edge trends of genomics research on the outbreak of Corona Virus Disease 2019 (COVID-19) from 2019 to the present (March 2022).

Methods

Statistical and visual analysis of COVID-19 genomics results published in the 2019-2022 Web of Science Core Collection Database (WOSCC) was performed using CiteSpace software, including data on countries, institutions, authors, journals, co-citations, keywords, etc.

Results

A total of 9133 English literature were included. The number of publications has significantly increased in 2021, and it is expected that this upward trend will last into the future. The research hotspots of COVID-19 revolve around quarantine, biological management, angiotensin-converting enzyme-2, RNA-dependent RNA polymerase, etc. Research frontiers and trends focus on molecular docking, messenger RNA, functional receptor, etc.

Conclusion

The last two years have seen a significant increase in research interest in the field of novel coronavirus pneumonia genomics.

Omicron variant (B.1.1.529) and its sublineages: What do we know so far amid the emergence of recombinant variants of SARS-CoV-2?

Since the start of the COVID-19 pandemic, numerous variants of SARS-CoV-2 have been reported worldwide. The advent of variants of concern (VOCs) raises severe concerns amid the serious containment efforts against COVID-19 that include physical measures, pharmacological repurposing, immunization, and genomic/community surveillance. Omicron variant (B.1.1.529) has been identified as a highly modified, contagious, and crucial variant among the five VOCs of SARS-CoV-2. The increased affinity of the spike protein (S-protein), and host receptor, angiotensin converting enzyme-2 (ACE-2), due to a higher number of mutations in the receptor-binding domain (RBD) of the S-protein has been proposed as the primary reason for the decreased efficacy of majorly available vaccines against the Omicron variant and the increased transmissible nature of the Omicron variant. Because of its significant competitive advantage, the Omicron variant and its sublineages swiftly surpassed other variants to become the dominant circulating lineages in a number of nations. The Omicron variant has been identified as a prevalent strain in the United Kingdom and South Africa. Furthermore, the emergence of recombinant variants through the conjunction of the Omicron variant with other variants or by the mixing of the Omicron variant's sublineages/subvariants poses a major threat to humanity. This raises various issues and hazards regarding the Omicron variant and its sublineages, such as an Omicron variant breakout in susceptible populations among fully vaccinated persons. As a result, understanding the features and genetic implications of this variant is crucial. Hence, we explained in depth the evolution and features of the Omicron variant and analyzed the repercussions of spike mutations on infectiousness, dissemination ability, viral entry mechanism, and immune evasion. We also presented a viewpoint on feasible strategies for precluding and counteracting any future catastrophic emergence and spread of the omicron variant and its sublineages that could result in a detrimental wave of COVID-19 cases.

SARS-CoV-2 diagnostic testing rates determine the sensitivity of genomic surveillance programs

The first step in SARS-CoV-2 genomic surveillance is testing to identify infected people. However, global testing rates are falling as we emerge from the acute health emergency and remain low in many low- and middle-income countries (LMICs) (mean = 27 tests/100,000 people/day). We simulated COVID-19 epidemics in a prototypical LMIC to investigate how testing rates, sampling strategies, and sequencing proportions jointly impact surveillance outcomes and showed that low testing rates and spatiotemporal biases delay time-to-detection of new variants by weeks-to-months and can lead to unreliable estimates of variant prevalence even when the proportion of samples sequenced is increased. Accordingly, investments in wider access to diagnostics to support testing rates of ~100 tests/100,000 people/day could enable more timely detection of new variants and reliable estimates of variant prevalence. The performance of global SARS-CoV-2 genomic surveillance programs is fundamentally limited by access to diagnostic testing.