SARS-CoV-2 Antigen Rapid Detection Tests: test performance during the COVID-19 pandemic and the impact of COVID-19 vaccination (preprint)

IntroductionDuring the COVID-19 pandemic, SARS-CoV-2 antigen rapid detection tests (RDTs) emerged as point-of-care diagnostics in addition to the RT-qPCR as the gold standard for SARS-CoV-2 diagnostics. Facing the course of the COVID-19 pandemic to an endemic characterised by several SARS-CoV-2 virus variants of concern (VOC) and an increasing public COVID-19 vaccination rate the aim of the study was to investigate the long-term test performance of SARS-CoV-2 RDT in large-scale, clinical screening use during and its influencing factors, above all SARS-CoV-2 VOC and COVID-19 vaccination. MethodsIn a prospective performance assessment conducted at a single centre tertiary care hospital, RDTs from three manufacturers (NADAL(R), Panbio, MEDsan(R)) were compared to RT-qPCR among individuals aged [≥] 6 month. The evaluation involved the determination of standardised viral load from oropharyngeal swabs as well as the evaluation of their influencing factors, especially the COVID-19 vaccination, for detecting SARS-CoV-2 in a clinical point-of-care environment spanning from 12 November 2020 to 30 June 2023 among patients, staff, and visitors of the hospital. ResultsAmong the 78,798 RDT/RT-qPCR tandems analysed, 2,016 (2.6%) tandems tested positive for SARS-CoV-2, with an overall sensitivity of 34.5% (95% CI 32.4-36.6%). A logistic regression revealed that typical COVID-19 symptoms significantly declined over the course of the study and throughout the COVID-19 pandemic, and that among the vaccinated, significantly fewer presented with an infection exhibiting typical symptoms. The employed lasso regression model indicated that only higher viral load and typical COVID-19 symptoms significantly increase the likelihood of a positive RDT result in the case of a SARS-CoV-2 infection directly. ConclusionOur findings indicate that only viral load and COVID-19 symptoms directly influence RDT performance while the obtained effects of COVID-19 vaccination and Omicron VOC both reducing RDT performance were mediated by these two factors. RDTs remain an adequate diagnostic tool for detecting SARS-CoV-2 in individuals showing respiratory symptoms. RDTs show promise beyond SARS-CoV-2, proving adaptable for detecting other pathogens like Influenza and RSV, highlighting their ongoing importance in infection control and prevention efforts.

The abortive SARS-CoV-2 infection of osteoclast precursors promotes their differentiation into osteoclasts (preprint)

The COVID-19 pandemic has resulted in the loss of millions of lives, although a majority of those infected have managed to survive. Consequently, a set of outcomes, identified as long COVID, is now emerging. While the primary target of SARS-CoV-2 is the respiratory system, the impact of COVID-19 extends to various body parts, including the bone. This study aims to investigate the effects of acute SARS-CoV-2 infection on osteoclastogenesis, utilizing both ancestral and Omicron viral strains. Monocyte-derived macrophages (MDM), which serve as precursors to osteoclasts, were exposed to both viral variants. However, the infection proved abortive, even though ACE2 receptor expression increased post-infection, with no significant impact on cellular viability and redox balance. Both SARS-CoV-2 strains heightened osteoclast formation in a dose-dependent manner, as well as CD51/61 expression and bone resorptive ability. Notably, SARS-CoV-2 induced early pro-inflammatory M1 macrophage polarization, shifting towards an M2-like profile. Osteoclastogenesis-related genes (RANK, NFATc1, DC-STAMP, MMP9) were upregulated, and surprisingly, SARS-CoV-2 variants promoted RANKL-independent osteoclast formation. This thorough investigation illuminates the intricate interplay between SARS-CoV-2 and osteoclast precursors, suggesting potential implications for bone homeostasis and opening new avenues for therapeutic exploration in COVID-19.

Parametrization of Worldwide Covid-19 data for multiple variants: How is the SAR-Cov2 virus evolving? (preprint)

We mapped the 2020-2023 daily Covid-19 case data from the World Health Organization (WHO) to the original SIR model of Karmack and McKendrick for multiple pandemic recurrences due to the evolution of the virus to different variants in forty countries worldwide. The aim of the study was to determine how the SIR parameters are changing as the virus evolved into variants. Each peak in cases was analyzed separately for each country and the parameters: reff (pandemic R-parameter), Leff (average number of days an individual is infective) and (the rate of infection for contacts between the set of susceptible persons and the set of infected persons) were computed. Each peak was mapped to circulating variants for each country and the SIR parameters (reff, Leff, ) were averaged over each variant using their values in peaks where 70% of the variant sequences identified belonged to a single variant. This analysis showed that on average, compared to the original Wuhan variant ( = 0.2), the parameter has increased to = 0.5 for the Omicron variants. The value of reff has decreased from around 3.8 to 2.0 and Leff has decreased from 15 days to 10 days. This is as would be expected of a virus that is coming to equilibrium by evolving to increase its infectivity while reducing the effects of infections on the host.

Effectiveness of the 2023-2024 Omicron XBB.1.5-containing mRNA COVID-19 vaccine (mRNA-1273.815) in preventing COVID-19-related hospitalizations and medical encounters among adults in the United States: An interim analysis (preprint)

Background/ObjectivesCOVID-19 continues to pose a significant burden that impacts public health and the healthcare system as the SARS-CoV-2 virus continues to evolve. Regularly updated vaccines are anticipated to boost waning immunity and provide protection against circulating variants. This study evaluated vaccine effectiveness (VE) of mRNA-1273.815, a 2023-2024 Omicron XBB.1.5-containing mRNA COVID-19 vaccine, at preventing COVID-19-related hospitalizations and any medically attended COVID-19 in adults [≥]18 years, overall, and by age and underlying medical conditions. MethodsThis retrospective cohort study used the Veradigm Network EHR linked to claims data to identify US adults [≥]18 years of age who received the mRNA-1273.815 vaccine (exposed) matched 1:1 to individuals who did not receive a 2023-2024 updated COVID-19 vaccine (unexposed). Patients in the unexposed cohort were randomly matched to eligible mRNA-1273.815 recipients. Inverse probability of treatment weighting was used to adjust for differences between the two cohorts. The exposed cohort was vaccinated between September 12, 2023, and December 15, 2023, and individuals in both cohorts were followed up for COVID-19-related hospitalizations and medically attended COVID-19 until December 31, 2023. A Cox regression model was used to estimate the hazard ratio (HR). VE of the mRNA-1273.815 vaccine in preventing COVID-19-related hospitalizations and any medically attended COVID-19 was estimated as 100*(1-HR). Subgroup analyses were performed for adults [≥]50, adults [≥]65, and individuals with underlying medical conditions associated with severe COVID-19 outcomes. ResultsOverall, 859,335 matched pairs of mRNA-1273.815 recipients and unexposed adults were identified. The mean age was 63 years, and 80% of the study population was [≥]50 years old. 61.5% of the mRNA-1273.815 cohort and 66.4% of the unexposed cohort had an underlying medical condition. Among the overall adult population ([≥]18 years), VE was 60.2% (53.4-66.0%) against COVID-19-related hospitalization and 33.1% (30.2%-35.9%) against medically attended COVID-19 over a median follow-up of 63 (IQR: 44-78) days. VE estimates by age and underlying medical conditions were similar. ConclusionsThese results demonstrate the significant protection provided by mRNA-1273.815 against COVID-19-related hospitalizations and any medically attended COVID-19 in adults 18 years and older, regardless of their vaccination history, and support CDC recommendations for vaccination with the 2023-2024 Omicron XBB.1.5-containing COVID-19 vaccine to prevent COVID-19-related outcomes, including hospitalizations.

Application of a High-Resolution Melt Assay for Monitoring SARS-CoV-2 Variants in Burkina Faso and Kenya (preprint)

The rapid emergence and global dissemination of SARS-CoV-2 highlighted a need for robust, adaptable surveillance systems. However, financial and infrastructure requirements for whole genome sequencing (WGS) mean most surveillance data have come from higher-resource geographies, despite unprecedented investment in sequencing in low-middle income countries (LMICs) throughout the SARS-CoV-2 pandemic. Consequently, the molecular epidemiology of SARS-CoV-2 in some LMICs is limited, and there is a need for more cost-accessible technologies to help close data gaps for surveillance of SARS-CoV-2 variants. To address this, we have developed two high-resolution melt curve (HRM) assays that target key variant-defining mutations in the SARS-CoV-2 genome, which give unique signature profiles that define different SARS-CoV-2 variants of concern (VOCs). Extracted RNA from SARS-CoV-2 positive samples collected from 205 participants (112 in Burkina Faso, 93 in Kenya) on the day of enrolment in the MALCOV study (Malaria as a Risk Factor for COVID-19) between February 2021 and February 2022 were analysed using our optimised HRM assays and compared to Next Generation Sequencing (NGS) on Oxford Nanopore MinION . With NGS as a reference, two HRM assays, HRM-VOC-1 and HRM-VOC-2, demonstrated sensitivity/specificity of 100%/99.29% and 92.86/99.39%, respectively, for detecting Alpha, 90.08%/100% and 92.31%/100% for Delta and 93.75%/100% and 100%/99.38% for Omicron. The assays described here provide a lower-cost approach (<$1 per sample) to conducting molecular epidemiology, capable of high-throughput testing. We successfully scaled up the HRM-VOC-2 assay to screen a total of 506 samples from which we were able to show the replacement of Alpha with the introduction of Delta and the replacement of Delta by the Omicron variant in this community in Kisumu, Kenya. These assays are readily adaptable and can focus on local epidemiological surveillance questions or be updated quickly to accommodate the emergence of a novel variant or adapt to novel and emerging pathogens.

Advancing Real-time Pandemic Forecasting Using Large Language Models: A COVID-19 Case Study (preprint)

Forecasting the short-term spread of an ongoing disease outbreak is a formidable challenge due to the complexity of contributing factors, some of which can be characterized through interlinked, multi-modality variables such as epidemiological time series data, viral biology, population demographics, and the intersection of public policy and human behavior. Existing forecasting model frameworks struggle with the multifaceted nature of relevant data and robust results translation, which hinders their performances and the provision of actionable insights for public health decision-makers. Our work introduces PandemicLLM, a novel framework with multi-modal Large Language Models (LLMs) that reformulates real-time forecasting of disease spread as a text reasoning problem, with the ability to incorporate real-time, complex, non-numerical information that previously unattainable in traditional forecasting models. This approach, through a unique AI-human cooperative prompt design and time series representation learning, encodes multi-modal data for LLMs. The model is applied to the COVID-19 pandemic, and trained to utilize textual public health policies, genomic surveillance, spatial, and epidemiological time series data, and is subsequently tested across all 50 states of the U.S. Empirically, PandemicLLM is shown to be a high-performing pandemic forecasting framework that effectively captures the impact of emerging variants and can provide timely and accurate predictions. The proposed PandemicLLM opens avenues for incorporating various pandemic-related data in heterogeneous formats and exhibits performance benefits over existing models. This study illuminates the potential of adapting LLMs and representation learning to enhance pandemic forecasting, illustrating how AI innovations can strengthen pandemic responses and crisis management in the future.

Transmission Patterns of Co-Circulation of Omicron Sub-Lineages in Hong Kong SAR, China, a City with Rigorous Social Distancing Measures, in 2022 (preprint)

Objective: The study aimed to characterize the changing landscape of circulating SARS-CoV-2 lineages in the local community of Hong Kong throughout 2022. We examined how adjustments to quarantine arrangements influenced the transmission pattern of Omicron variants in a city with relatively rigorous social distancing measures at that time. Methods: In 2022, a total of 4,684 local SARS-CoV-2 genomes were sequenced using the Oxford Nanopore GridION sequencer. SARS-CoV-2 consensus genomes were generated by MAFFT, and the maximum likelihood phylogeny of these genomes were determined using IQ-TREE. The dynamic changes in lineages were depicted in a time tree created by Nextstrain. Statistical analysis was conducted to assess the correlation between changes in the number of lineages and adjustments to quarantine arrangements. Results: By the end of 2022, a total of 83 SARS-CoV-2 lineages were identified in the community. The increase in the number of new lineages was significantly associated with the relaxation of quarantine arrangements (One-way ANOVA, F(5,47)=18.233, p<0.001)). Over time, Omicron BA.5 sub-lineages replaced BA.2.2 and became the predominant Omicron variants in Hong Kong. The influx of new lineages reshaped the dynamics of Omicron variants in the community without fluctuating the death rate and hospitalization rate (One-way ANOVA, F(5,47)=2.037, p=0.091). Conclusion: The study revealed that even with an extended mandatory quarantine period for incoming travelers, it may not be feasible to completely prevent the introduction and subsequent community spread of highly contagious Omicron variants. Ongoing molecular surveillance of COVID-19 remains essential to monitor the emergence of new recombinant variants.

Antigenic cartography using hamster sera identifies SARS-CoV-2 JN.1 evasion seen in human XBB.1.5 booster sera (preprint)

Antigenic assessments of SARS-CoV-2 variants inform decisions to update COVID-19 vaccines. Primary infection sera are often used for assessments, but such sera are rare due to population immunity from SARS-CoV-2 infections and COVID-19 vaccinations. Here, we show that neutralization titers and breadth of matched human and hamster pre-Omicron variant primary infection sera correlate well and generate similar antigenic maps. The hamster antigenic map shows modest antigenic drift among XBB sub-lineage variants, with JN.1 and BA.4/BA.5 variants within the XBB cluster, but with five to six-fold antigenic differences between these variants and XBB.1.5. Compared to sera following only ancestral or bivalent COVID-19 vaccinations, or with post-vaccination infections, XBB.1.5 booster sera had the broadest neutralization against XBB sub-lineage variants, although a five-fold titer difference was still observed between JN.1 and XBB.1.5 variants. These findings suggest that antibody coverage of antigenically divergent JN.1 could be improved with a matched vaccine antigen.

Can self-testing be enhanced to hasten safe return of healthcare workers in pandemics? Random order, open label trial using two manufacturers' SARS-CoV-2 lateral flow devices concurrently (preprint)

ObjectivesTo inform management of competing risks from Covid-19 and key-worker absence, we evaluated whether using two manufacturers lateral flow tests (LFTs) concurrently improved SARS-CoV-2 Omicron detection and was acceptable to hospital staff. In a nested study, to understand the risks of return to work after a fixed number of days of isolation or quarantine, we examined virus culture at Days 5-7 after positive test or significant exposure. Methods and Analysis1419 fully-vaccinated Liverpool (UK) University Hospitals staff participated in a random-order, open-label trial testing whether dual LFTs improved SARS-CoV2 detection, and whether dual swabbing was acceptable to users. Main outcome was self-reported LFT result. Staff enrolled via routine testing sites for symptomatic staff and close contacts. Recruitment took place between 7th February and 8th May 2022. Participants employed nose-throat swab Innova and nose-only swab Orient Gene LFTs for 10 days, with daily LFTs taken in random order. A swab for polymerase chain reaction (PCR) analysis was taken at Day-5 and, if positive, Day-10. A questionnaire on acceptability was administered on exit. Selected participants gave swabs for viral culture on Days 5-7; swabs were delivered and returned by courier. Cultures were considered positive if cytopathic effect was apparent or the SARs-COV2 N gene sub-genomic RNA was detected by sequencing. Results226 individuals reported 1466 pairs of LFT results. Tests disagreed in 127 cases (8.7%). Orient Gene was more likely (78 cf. 49, P=0.03) to be positive. Orient Gene positive Innova negative result-pairs became more frequent over time (P<0.001). If Innova was swabbed second, it was less likely to agree with a positive Orient Gene result (P=0.005); swabbing first with Innova made no significant difference (P=0.85). Of 311 individuals completing the exit questionnaire, 90.7% reported dual swabbing was easy, 57.1% said it was no barrier to their daily routine and 65.6% preferred dual testing. Respondents had more confidence in dual c.f. single test results (median 9 cf. 8 on 10-point scale, P<0.001). Viral cultures from swabs taken at Days 5-7 were positive for 6/31 (19.4%, 7.5%-37.5%) and indeterminate for 11/31 (35.5%, 19.2%-54.6%) LFT-positive participants, indicating they were likely still infectious. ConclusionsDual brand testing increased LFT detection of SARS-CoV-2 antigen by a small but meaningful margin and was acceptable to hospital workers. Viral cultures demonstrated that policies recommending safe return to work [~]5 days after Omicron infection/exposure were flawed. Key-workers should be prepared for dynamic self-testing protocols in future pandemics. Trial registrationhttps://www.isrctn.com/ISRCTN47058442 (IRAS Project ID:311842) Key messagesO_ST_ABSWhat is already known on this topicC_ST_ABSO_LIOmicron BA.1 and BA.2 waves caused large-scale healthcare worker absence in late 2021 - early 2022, risking patient safety from both Covid-19 and reduced care capacity C_LIO_LILateral flow tests (LFTs) reliably detected SARS-CoV-2 antigen, more so with Omicron than prior variants, identifying the most infectious individuals C_LIO_LISelf-testing with LFT SARS-CoV-2 rapid antigen tests reduced Covid-19 transmission, mitigating risks of return to work, including healthcare settings C_LI What this study addsO_LIDual c.f. single brand LFT testing increased SARS-CoV-2 antigen detection marginally, but more than can be explained by extending swabbing from nose-only to nose-throat C_LIO_LINHS deployment of nose-only LFTs in response to compound pressures from Omicron, winter and pandemic burnout was safe and acceptable to most participating hospital staff C_LIO_LICulturable virus was detected confidently in a fifth (and potentially in a further third) of LFT-positive hospital workers 5-7 days after their self-referral for testing, indicating substantial protracted infectiousness C_LI How this study might affect research, practice or policyO_LIThis study shows international Covid-19 policies for return to work after fixed periods (e.g. 5 days after positive test) were flawed: too little emphasis was placed on variation in infectivity between individuals C_LIO_LIFuture pandemic preparedness needs to plan testing quality assurance unified across healthcare and community self-testing contexts, including continuous study of serial daily antigen, nucleic acid and culturable virus test results C_LI

Evaluating biomedical feature fusion on machine learning's predictability and interpretability of COVID-19 severity types (preprint)

BackgroundAccurately differentiating severe from non-severe COVID-19 clinical types is critical for the healthcare system to optimize workflow, as severe patients require intensive care. Current techniques lack the ability to accurately predict COVID-19 patients clinical type, especially as SARS-CoV-2 continues to mutate. ObjectiveIn this work, we explore both predictability and interpretability of multiple state-of-the-art machine learning (ML) techniques trained and tested under different biomedical data types and COVID-19 variants. MethodsComprehensive patient-level data were collected from 362 patients (214 severe, 148 non-severe) with the original SARS-CoV-2 variant in 2020 and 1000 patients (500 severe, 500 non-severe) with the Omicron variant in 2022-2023. The data included 26 biochemical features from blood testing and 26 clinical features from each patients clinical characteristics and medical history. Different types of ML techniques, including penalized logistic regression (LR), random forest (RF), k-nearest neighbors (kNN), and support vector machines (SVM) were applied to build predictive models based on each data modality separately and together for each variant set. ResultsAll ML models performed similarly under different testing scenarios. The fused characteristic modality yielded the highest area under the curve (AUC) score achieving 0.914 on average. The second highest AUC was 0.876 achieved by the biochemical modality alone, followed by 0.825 achieved by clinical modality alone. All ML models were robust when cross-tested with original and Omicron variant patient data. Upon model interpretation, our models ranked elevated d-dimer (biochemical feature), elevated high sensitivity troponin I (biochemical feature), and age greater than 55 years (clinical feature) as the most predictive features of severe COVID-19. ConclusionsWe found ML to be a powerful tool for predicting severe COVID-19 based on comprehensive individual patient-level data. Further, ML models trained on the biochemical and clinical modalities together witness enhanced predictive power. The improved performance of these ML models when trained and cross-tested with Omicron variant data supports the robustness of ML as a tool for clinical decision support.

Discovery of orally bioavailable SARS-CoV-2 papain-like protease inhibitor as a potential treatment for COVID-19 (preprint)

The RNA-dependent RNA polymerase (RdRp), 3C-like protease (3CLpro), and papain-like protease (PLpro) are pivotal components in the viral life cycle of SARS-CoV-2, presenting as promising therapeutic targets. Currently, all FDA-approved antiviral drugs against SARS-CoV-2 are RdRp or 3CLpro inhibitors. However, the mutations causing drug resistance have been observed in RdRp and 3CLpro from SARS-CoV-2, which makes it necessary to develop antivirals with novel mechanisms. Through the application of a structure-based drug design (SBDD) approach, we discovered a series of novel potent non-covalent PLpro inhibitors with remarkable in vitro potency and in vivo PK properties. The co-crystal structures of PLpro with leads revealed that the residues E164 and Q269 around the S2 site are critical for improving the inhibitor\'s potency. The lead compound GZNL-P36 not only inhibited SARS-CoV-2 and its variants at the cellular level with EC50 ranging from 58.2 nM to 306.2 nM, but also inhibited HCoV-NL63 and HCoV-229E with EC50 of 81.6 nM and 2.66 M, respectively. Oral administration of the compound resulted in significantly improved survival and notable reductions in lung viral loads and lesions in SARS-CoV-2 infection mouse model, consistent with RNA-seq data analysis. Our results indicate that PLpro inhibitor is a promising SARS-CoV-2 therapy.

Comparative analysis of serological assays and sero-surveillance for SARS-CoV-2 exposure in US cattle (preprint)

Coronavirus disease-2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) continues to pose a significant threat to public health globally. Notably, SARS-CoV-2 demonstrates a unique capacity to infect various non-human animal species, documented in captive and free-living animals. However, experimental studies revealed low susceptibility of domestic cattle (Bos taurus) to ancestral B.1 lineage SARS-CoV-2 infection, with limited viral replication and seroconversion. Despite the emergence of viral variants with potentially altered host tropism, recent experimental findings indicate greater permissiveness of cattle to SARS-CoV-2 Delta variant infection compared to other variants, though with limited seroconversion and no clear evidence of transmission. While some studies detected SARS-CoV-2 antibodies in cattle in Italy and Germany, there is no evidence of natural SARS-CoV-2 infection in cattle from the United States or elsewhere. Since serological tests have inherent problems of false positives and negatives, we conducted a comprehensive assessment of multiple serological assays on over 600 cattle serum samples, including pre-pandemic and pandemic cattle sera. We found that SARS-CoV-2 pseudovirus neutralization assays with a luciferase reporter system can produce false positive results, and care must be taken to interpret serological diagnosis using these assays. We found no serological evidence of natural SARS-CoV-2 infection or transmission among cattle in the USA. Hence, it is critical to develop more reliable serological assays tailored to accurately detect SARS-CoV-2 antibodies in cattle populations and rigorously evaluate diagnostic tools. This study underscores the importance of robust evaluation when employing serological assays for SARS-CoV-2 detection in cattle populations.

Within-host genetic diversity of SARS-CoV-2 across animal species (preprint)

Infectious disease transmission to different host species makes eradication very challenging and expands the diversity of evolutionary trajectories taken by the pathogen. Since the beginning of the ongoing COVID-19 pandemic, SARS-CoV-2 has been transmitted from humans to many different animal species, and viral variants of concern could potentially evolve in a non-human animal. Previously, using available whole genome consensus sequences of SARS-CoV-2 from four commonly sampled animals (mink, deer, cat, and dog) we inferred similar numbers of transmission events from humans to each animal species but a relatively high number of transmission events from mink back to humans (Naderi et al., 2023). Using a genome-wide association study (GWAS), we identified 26 single nucleotide variants (SNVs) that tend to occur in deer -- more than any other animal -- suggesting a high rate of viral adaptation to deer. Here we quantify intra-host SARS-CoV-2 across animal species and show that deer harbor more intra-host SNVs (iSNVs) than other animals, providing a larger pool of genetic diversity for natural selection to act upon. Within-host diversity is particularly high in deer lymph nodes compared to nasopharyngeal samples, suggesting tissue-specific differences in viral population sizes or selective pressures. Neither mixed infections involving more than one viral lineage nor large changes in the strength of selection are likely to explain the higher intra-host diversity within deer. Rather, deer are more likely to contain larger viral population sizes, to be infected for longer periods of time, or to be systematically sampled at later stages of infections. Combined with extensive deer-to-deer transmission, the high levels of within-deer viral diversity help explain the apparent rapid adaptation of SARS-CoV-2 to deer.

Multi-ancestry GWAS of diarrhea during acute SARS-CoV2 infection identifies multiple novel loci and contrasting etiological roles of irritable bowel syndrome subtypes (preprint)

A substantial proportion of acute SARSCoV2 infection cases exhibit gastrointestinal symptoms, yet the genetic determinants of these extrapulmonary manifestations are poorly understood. Using survey data from 239,866 individuals who tested positively for SARSCoV2, we conducted a multi-ancestry GWAS of 80,289 cases of diarrhea occurring during acute COVID19 infection (33.5%). Six loci (CYP7A1, LZFTl1/CCR9, TEME182, NALCN, LFNG, GCKR) met genomewide significance in a trans-ancestral analysis. The top significant GWAS hit mapped to the CYP7A1 locus, which plays an etiologic role in bile acid metabolism and is in high LD (r2= 0.93) with the SDCBP gene, which was previously implicated in antigen processing and presentation in the COVID-19 context. Another association was observed with variants in the LZTFL1/CCR9 region, which is a known locus for COVID19 susceptibility and severity. PheWAS showed a shared association across three of the six SNPs with irritable bowel syndrome (IBS) and its subtypes. Mendelian randomization showed that genetic liability to IBS-diarrhea increased (OR=1.40,95%,CI[1.33,1.47]), and liability to IBS-constipation decreased (OR=0.86, 95%CI[0.79,0.94]) the relative odds of experiencing COVID19+ diarrhea. Our genetic findings provide etiological insights into the extrapulmonary manifestations of acute SARSCoV2 infection.

Molecular epidemiology of invasive Group A Streptococcal infections before and after the COVID-19 pandemic in Switzerland (preprint)

Group A Streptococcus (GAS, aka Streptococcus pyogenes) poses a significant public health concern, causing a diverse spectrum of infections with high mortality rates. Following the COVID-19 pandemic, a resurgence of invasive GAS (iGAS) infections has been documented, necessitating efficient outbreak detection methods. Whole genome sequencing (WGS) serves as the gold standard for GAS molecular typing, albeit constrained by time and costs. This study aimed to characterize the postpandemic increased prevalence of iGAS on the molecular epidemiological level in order to assess whether new, more virulent variants have emerged, as well as to assess the performance of the rapid and cost-effective Fourier-transform infrared (FTIR) spectroscopy as an alternative to WGS for detecting and characterizing GAS transmission routes. A total of 66 iGAS strains isolated from nine Swiss hospitals during the COVID-19 post-pandemic increased GAS prevalence were evaluated and compared to 15 strains collected before and 12 during the COVID-19 pandemic. FT-IR measurements and WGS were conducted for network analysis. Demographic, clinical, and epidemiological data were collected. Skin and soft tissue infection was the most common diagnosis, followed by primary bacteremia and pneumonia. Viral co-infections were found in 25% of cases and were significantly associated with more severe disease requiring intensive care unit admission. WGS analysis did not reveal emerging GAS genetic distinct variants after the COVID-19 pandemic, indicating the absence of a pandemic-induced shift. FT-IR spectroscopy exhibited limitations in differentiating genetically distant GAS strains, yielding poor overlap with WGS-derived clusters. The emm1/ST28 gebotype was predominant in our cohort and was associated with five of the seven deaths recorded, in accordance with the molecular epidemiological data before the pandemic. Additionally, no notable shift in antibiotic susceptibility patterns was observed. Our data suggest that mainly non-pathogen related factors contributed to the recent increased prevalence of iGAS.

SARS-CoV-2 disrupts host gene networks: unveiling key hub genes as potential therapeutic targets for COVID-19 management (preprint)

Background Although the end of COVID-19 as a public health emergency was declared on May 2023, still new cases of the infection are reported and the risk remains of new variants emerging that may cause new surges in cases and deaths. While clinical symptoms have been rapidly defined worldwide, the basic body responses and pathogenetic mechanisms acting in patients with SARS-CoV-2 infection over time until recovery or death require further investigation. The understanding of the molecular mechanisms underlying the development and course of the disease is essential in designing effective preventive and therapeutic approaches, and ultimately reducing mortality and disease spreading.Methods The current investigation aimed to identify the key genes engaged in SARS-CoV-2 infection and uncover their molecular implication in disease severity. To achieve this goal high-throughput RNA sequencing of peripheral blood samples collected from healthy donors and COVID-19 patients was performed. The resulting sequence data were processed using a wide range of bioinformatics tools to obtain detailed modifications within five transcriptomic phenomena: expression of genes and long non-coding RNAs, alternative splicing, allel-specific expression and circRNA production. The in silico procedure was completed with a functional analysis of the identified alterations.Results The transcriptomic analysis revealed that SARS-CoV-2 has a significant impact on multiple genes encoding ribosomal proteins (RPs). Results show that these genes differ not only in terms of expression but also manifest biases in alternative splicing and ASE ratios. The integrated functional analysis exposed that RPs mostly affected pathways and processes related to infection—COVID-19 and NOD-like receptor signaling pathway, SARS-CoV-2-host interactions and response to the virus. Furthermore, our results linked the multiple intronic ASE variants and exonic circular RNA differentiations with SARS-CoV-2 infection, suggesting that these molecular events play a crucial role in mRNA maturation and transcription during COVID-19 disease.Conclusions By elucidating the genetic mechanisms induced by the virus, the current research provides significant information that can be employed to create new targeted therapeutic strategies for future research and treatment related to COVID-19. Moreover, the findings highlight potentially promising therapeutic biomarkers for early risk assessment of critically ill patients.

Machine Learning Insights into Regional Dynamics and Prevalence of COVID-19 Variants in US Health and Human Services Regions (preprint)

Background The COVID-19 pandemic arising from the emergence of SARS-CoV-2 in late 2019 has led to global devastation with millions of lives lost by January 2024. Despite the WHO's declaration of the end of the global health emergency in May 2023, the virus persists, propelled by mutations. Variants continue to challenge vaccination efforts, underscoring the necessity for ongoing vigilance. This study aimed at contributing to a more data-driven approach to pandemic management by employing random forest regression to analyze regional variant prevalence.Methods This study utilized data from various sources including National COVID Cohort Collaborative database, Bureau of Transportation Statistics, World Weather Online, EPA, and US Census. Key variables include pollution, weather, travel patterns, and demographics. Preprocessing steps involved merging and normalization of datasets. Training data spanned from January 2021 to February 2023. The Random Forest Regressor was chosen for its accuracy in modeling. To prevent data leakage, time series splits were employed. Model performance was evaluated using metrics such as MSE and R-squared.Results The Alpha variant was predominant in the Southeast, with less than 80% share even at its peak. Delta surged initially in Kansas City and maintained dominance there for over 5 months. Omicron subvariant BA.5 spread nationwide, becoming predominant across all Health and Human Services regions simultaneously, with New York seeing the earliest and fastest decline in its share. Variant XBB.1.5 concentrated more in the Northeast, but limited data hindered full analysis. Using RF regressor, key features affecting spread patterns were identified, with high predictive accuracy. Each variant showed specific environmental correlations; for instance, Alpha with air quality index and temperature, Delta with ozone density, BA.5 with UV index, and XBB.1.5 with location, land area, and income. Correlation analysis further highlighted variant-specific associations.Conclusions This research provides a comprehensive analysis of the regional distribution of COVID-19 variants, offering critical insights for devising targeted public health strategies. By utilizing machine learning, the study uncovers the complex factors contributing to variant spread and reveals how specific factors contribute to variant prevalence, offering insights crucial for pandemic management.

Synthetic coevolution reveals adaptive mutational trajectories of neutralizing antibodies and SARS-CoV-2 (preprint)

The Covid-19 pandemic showcases a coevolutionary race between the human immune system and SARS-CoV-2, mirroring the Red Queen hypothesis of evolutionary biology. The immune system generates neutralizing antibodies targeting the SARS-CoV-2 spike protein's receptor binding domain (RBD), crucial for host cell invasion, while the virus evolves to evade antibody recognition. Here, we establish a synthetic coevolution system combining high-throughput screening of antibody and RBD variant libraries with protein mutagenesis, surface display, and deep sequencing. Additionally, we train a protein language machine learning model that predicts antibody escape to RBD variants. Synthetic coevolution reveals antagonistic and compensatory mutational trajectories of neutralizing antibodies and SARS-CoV-2 variants, enhancing the understanding of this evolutionary conflict.

Enhanced mucosal B- and T-cell responses against SARS-CoV-2 after heterologous intramuscular mRNA prime/intranasal protein boost vaccination with a combination adjuvant. (preprint)

Current COVID-19 mRNA vaccines delivered intramuscularly (IM) induce effective systemic immunity, but with suboptimal immunity at mucosal sites, limiting their ability to impart sterilizing immunity. There is strong interest in rerouting immune responses induced in the periphery by parenteral vaccination to the portal entry site of respiratory viruses, such as SARS-CoV-2, by mucosal vaccination. We previously demonstrated the combination adjuvant, NE/IVT, consisting of a nanoemulsion (NE) and an RNA-based RIG-I agonist (IVT) induces potent systemic and mucosal immune responses in protein-based SARS-CoV-2 vaccines administered intranasally (IN). Herein, we demonstrate priming IM with mRNA followed by heterologous IN boosting with NE/IVT adjuvanted recombinant antigen induces strong mucosal and systemic antibody responses and enhances antigen-specific T cell responses in mucosa-draining lymph nodes compared to IM/IM and IN/IN prime/boost regimens. While all regimens induced cross-neutralizing antibodies against divergent variants and sterilizing immunity in the lungs of challenged mice, mucosal vaccination, either as homologous prime/boost or heterologous IN boost after IM mRNA prime was required to impart sterilizing immunity in the upper respiratory tract. Our data demonstrate the benefit of hybrid regimens whereby strong immune responses primed via IM vaccination are rerouted by IN vaccination to mucosal sites to provide optimal protection to SARS-CoV-2.

Broad-Spectrum Coronavirus Inhibitors Discovered by Modeling Viral Fusion Dynamics (preprint)

Broad-spectrum therapeutics capable of inhibiting SARS-CoV-2, its variants, and related coronaviruses hold promise in curbing the spread of COVID-19 and averting future pandemics. Here, we employed a multidisciplinary approach that included molecular dynamics simulation (MDS) and artificial intelligence (AI)-based docking predictions to identify potent inhibitors that target a conserved region within the SARS-CoV-2 spike protein that mediates membrane fusion by undergoing large-scale mechanical rearrangements. In silico binding screens honed in on this region, leading to the discovery of FDA-approved drugs and novel molecules predicted to disrupt spike protein conformational changes. These compounds significantly inhibited SARS-CoV-2 infection and blocked the entry of spike protein-bearing pseudotyped , {beta}, {gamma}, {delta} variants as well as SARS-CoV and MERS-CoV in cultured human ACE2-expressing cells. The optimized lead compound significantly inhibited SARS-CoV2 infection in mice when administered orally.