DeepDynaForecast: Phylogenetic-informed graph deep learning for epidemic transmission dynamic prediction.

In the midst of an outbreak or sustained epidemic, reliable prediction of transmission risks and patterns of spread is critical to inform public health programs. Projections of transmission growth or decline among specific risk groups can aid in optimizing interventions, particularly when resources are limited. Phylogenetic trees have been widely used in the detection of transmission chains and high-risk populations. Moreover, tree topology and the incorporation of population parameters (phylodynamics) can be useful in reconstructing the evolutionary dynamics of an epidemic across space and time among individuals. We now demonstrate the utility of phylodynamic trees for transmission modeling and forecasting, developing a phylogeny-based deep learning system, referred to as DeepDynaForecast. Our approach leverages a primal-dual graph learning structure with shortcut multi-layer aggregation, which is suited for the early identification and prediction of transmission dynamics in emerging high-risk groups. We demonstrate the accuracy of DeepDynaForecast using simulated outbreak data and the utility of the learned model using empirical, large-scale data from the human immunodeficiency virus epidemic in Florida between 2012 and 2020. Our framework is available as open-source software (MIT license) at github.com/lab-smile/DeepDynaForcast.

Bacterial DnaK reduces the activity of anti-cancer drugs cisplatin and 5FU.

BACKGROUND: Chemotherapy is a primary treatment for cancer, but its efficacy is often limited by cancer-associated bacteria (CAB) that impair tumor suppressor functions. Our previous research found that Mycoplasma fermentans DnaK, a chaperone protein, impairs p53 activities, which are essential for most anti-cancer chemotherapeutic responses. METHODS: To investigate the role of DnaK in chemotherapy, we treated cancer cell lines with M. fermentans DnaK and then with commonly used p53-dependent anti-cancer drugs (cisplatin and 5FU). We evaluated the cells' survival in the presence or absence of a DnaK-binding peptide (ARV-1502). We also validated our findings using primary tumor cells from a novel DnaK knock-in mouse model. To provide a broader context for the clinical significance of these findings, we investigated human primary cancer sequencing datasets from The Cancer Genome Atlas (TCGA). We identified F. nucleatum as a CAB carrying DnaK with an amino acid composition highly similar to M. fermentans DnaK. Therefore, we investigated the effect of F. nucleatum DnaK on the anti-cancer activity of cisplatin and 5FU. RESULTS: Our results show that both M. fermentans and F. nucleatum DnaKs reduce the effectiveness of cisplatin and 5FU. However, the use of ARV-1502 effectively restored the drugs' anti-cancer efficacy. CONCLUSIONS: Our findings offer a practical framework for designing and implementing novel personalized anti-cancer strategies by targeting specific bacterial DnaKs in patients with poor response to chemotherapy, underscoring the potential for microbiome-based personalized cancer therapies.

Oral Cannabidiol Treatment Is Associated with an Anti-Inflammatory Gene Expression Signature in Myeloid Cells of People Living with HIV.

Introduction: HIV-related comorbidities appear to be related to chronic inflammation, a condition characterizing people living with HIV (PLWH). Prior work indicates that cannabidiol (CBD) might reduce inflammation; however, the genetics underpinning of this effect are not well investigated. Our main objective is to detect gene expression alterations in human peripheral blood mononuclear cells (PBMCs) from PLWH after at least 1 month of CBD treatment. Materials and Methods: We analyzed ∼41,000 PBMCs from three PLWH at baseline and after CBD treatment (27-60 days) through single-cell RNA sequencing. Results: We obtained a coherent signature, characterized by an anti-inflammatory activity, of differentially expressed genes in myeloid cells. Conclusions: Our study shows how CBD is associated with alterations of gene expression in myeloid cells after CBD treatment. Clinical Trial Registration: NCT05209867.

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

The coronavirus disease of 2019 (COVID-19) pandemic is characterized by sequential emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants and lineages outcompeting previously circulating ones because of, among other factors, increased transmissibility and immune escape1-3. We devised an unsupervised deep learning AutoEncoder for viral genomes anomaly detection to predict future dominant lineages (FDLs), i.e., lineages or sublineages comprising ≥10% of viral sequences added to the GISAID database on a given week4. The algorithm was trained and validated by assembling global and country-specific data sets from 16,187,950 Spike protein sequences sampled between December 24th, 2019, and November 8th, 2023. The AutoEncoder flags low frequency FDLs (0.01% - 3%), with median lead times of 4-16 weeks. Over time, positive predictive values oscillate, decreasing linearly with the number of unique sequences per data set, showing average performance up to 30 times better than baseline approaches. The B.1.617.2 vaccine reference strain was flagged as FDL when its frequency was only 0.01%, more than one year earlier of being considered for an updated COVID-19 vaccine. Our AutoEncoder, applicable in principle to any pathogen, also pinpoints specific mutations potentially linked to increased fitness, and may provide significant insights for the optimization of public health pre-emptive intervention strategies.

Ancestral Origin and Dissemination Dynamics of Reemerging Toxigenic Vibrio cholerae, Haiti.

The 2010 cholera epidemic in Haiti was thought to have ended in 2019, and the Prime Minister of Haiti declared the country cholera-free in February 2022. On September 25, 2022, cholera cases were again identified in Port-au-Prince. We compared genomic data from 42 clinical Vibrio cholerae strains from 2022 with data from 327 other strains from Haiti and 1,824 strains collected worldwide. The 2022 isolates were homogeneous and closely related to clinical and environmental strains circulating in Haiti during 2012-2019. Bayesian hypothesis testing indicated that the 2022 clinical isolates shared their most recent common ancestor with an environmental lineage circulating in Haiti in July 2018. Our findings strongly suggest that toxigenic V. cholerae O1 can persist for years in aquatic environmental reservoirs and ignite new outbreaks. These results highlight the urgent need for improved public health infrastructure and possible periodic vaccination campaigns to maintain population immunity against V. cholerae.

Seafood-Associated Outbreak of ctx-Negative Vibrio mimicus Causing Cholera-Like Illness, Florida, USA.

Vibrio mimicus caused a seafood-associated outbreak in Florida, USA, in which 4 of 6 case-patients were hospitalized; 1 required intensive care for severe diarrhea. Strains were ctx-negative but carried genes for other virulence determinants (hemolysin, proteases, and types I-IV and VI secretion systems). Cholera toxin-negative bacterial strains can cause cholera-like disease.

ARCA: the interactive database for arbovirus reported cases in the Americas.

BACKGROUND: Accurate case report data are essential to understand arbovirus dynamics, including spread and evolution of arboviruses such as Zika, dengue and chikungunya viruses. Giving the multi-country nature of arbovirus epidemics in the Americas, these data are not often accessible or are reported at different time scales (weekly, monthly) from different sources. RESULTS: We developed a publicly available and user-friendly database for arboviral case data in the Americas: ARCA. ARCA is a relational database that is hosted on the ARCA website. Users can interact with the database through the website by submitting queries through the website, which generates displays results and allows users to download these results in different, convenient file formats. Users can choose to view arboviral case data through a table which containscontaining the number of cases for a particular week, a plot, or through a map. CONCLUSION: Our ARCA database is a useful tool for arboviral epidemiology research allowing for complex queries, data visualization, integration, and formatting.

Network typologies predict future molecular linkages in the network of HIV transmission.

OBJECTIVE: HIV molecular transmission network typologies have previously demonstrated associations to transmission risk; however, few studies have evaluated their predictive potential in anticipating future transmission events. To assess this, we tested multiple models on statewide surveillance data from the Florida Department of Health. DESIGN: This was a retrospective, observational cohort study examining the incidence of new HIV molecular linkages within the existing molecular network of persons with HIV (PWH) in Florida. METHODS: HIV-1 molecular transmission clusters were reconstructed for PWH diagnosed in Florida from 2006 to 2017 using the HIV-TRAnsmission Cluster Engine (HIV-TRACE). A suite of machine-learning models designed to predict linkage to a new diagnosis were internally and temporally externally validated using a variety of demographic, clinical, and network-derived parameters. RESULTS: Of the 9897 individuals who received a genotype within 12 months of diagnosis during 2012-2017, 2611 (26.4%) were molecularly linked to another case within 1 year at 1.5% genetic distance. The best performing model, trained on two years of data, was high performing (area under the receiving operating curve = 0.96, sensitivity = 0.91, and specificity = 0.90) and included the following variables: age group, exposure group, node degree, betweenness, transitivity, and neighborhood. CONCLUSIONS: In the molecular network of HIV transmission in Florida, individuals' network position and connectivity predicted future molecular linkages. Machine-learned models using network typologies performed superior to models using individual data alone. These models can be used to more precisely identify subpopulations for intervention.

Diversity and Genetic Reassortment of Keystone Virus in Mosquito Populations in Florida.

Keystone orthobunyavirus (KEYV), a member of the genus Orthobunyavirus, was first isolated in 1964 from mosquitoes in Keystone, Florida. Although data on human infections are limited, the virus has been linked to a fever/rash syndrome and, possibly, encephalitis, with early studies suggesting that 20% of persons in the Tampa, Florida, region had antibodies to KEYV. To assess the distribution and diversity of KEYV in other regions of Florida, we collected > 6,000 mosquitoes from 43 sampling sites in St. Johns County between June 2019 and April 2020. Mosquitoes were separated into pools by species and collection date and site. All pools with Aedes spp. (293 pools, 2,171 mosquitoes) were screened with a real-time reverse transcriptase polymerase chain reaction (rRT-PCR) assay that identifies KEYV and other closely related virus species of what was previously designated as the California encephalitis serogroup. In 2020, screening for KEYV was expanded to include 211 pools of Culex mosquitoes from sites where KEYV-positive Aedes spp. had been identified. rRT-PCR-positive samples were inoculated into cell cultures, and five KEYV isolates from Aedes atlanticus pools were isolated and sequenced. Analyses of the KEYV large genome segment sequences revealed two distinct KEYV clades, whereas analyses of the medium and small genome segments uncovered past reassortment events. Our data documented the ongoing seasonal circulation of multiple KEYV clades within Ae. atlanticus mosquito populations along the east coast of Florida, highlighting the need for further studies of the impact of this virus on human health.

Infectivity of three Mayaro Virus geographic isolates in human cell lines.

Mayaro virus (MAYV) is an emergent arthropod-borne virus that causes an acute febrile illness accompanied by arthralgia, similar to chikungunya virus. Increasing urbanization of MAYV outbreaks in the Americas has led to concerns for geographic expansion and spillover. Given the potential importance of this pathogen, we sought to fill critical gaps in knowledge regarding MAYV infectivity and geographic variation. This study describes the cytopathogenicity of MAYV in human dermal fibroblasts, human skeletal muscle satellite cells, human embryonic kidney cells (HEK), peripherally derived human macrophages, and Vero cells. We found that regional differences between these viruses do not affect replication kinetics, with high titers peaking at 37 h post infection. MAYV-U, did however, cause the most cytopathic effect in a time-dependent manner. Compared to the other two prototypic isolates, MAYV-U harbors unique mutations in the E2 protein, D60G and S205F, that are likely to interact with the host cell receptor and could affect infectivity. We further demonstrate that pre-treatment of cells with interferon-ß inhibited viral replication in a dose-dependent manner. Together, these findings advance our understanding of MAYV infection of human target cells and provide initial data regarding variation according to geography.

Machine Learning Prediction and Phyloanatomic Modeling of Viral Neuroadaptive Signatures in the Macaque Model of HIV-Mediated Neuropathology.

In human immunodeficiency virus (HIV) infection, virus replication in and adaptation to the central nervous system (CNS) can result in neurocognitive deficits in approximately 25% of patients with unsuppressed viremia. While no single viral mutation can be agreed upon as distinguishing the neuroadapted population, earlier studies have demonstrated that a machine learning (ML) approach could be applied to identify a collection of mutational signatures within the virus envelope glycoprotein (Gp120) predictive of disease. The S[imian]IV-infected macaque is a widely used animal model of HIV neuropathology, allowing in-depth tissue sampling infeasible for human patients. Yet, translational impact of the ML approach within the context of the macaque model has not been tested, much less the capacity for early prediction in other, noninvasive tissues. We applied the previously described ML approach to prediction of SIV-mediated encephalitis (SIVE) using gp120 sequences obtained from the CNS of animals with and without SIVE with 97% accuracy. The presence of SIVE signatures at earlier time points of infection in non-CNS tissues indicated these signatures cannot be used in a clinical setting; however, combined with protein structural mapping and statistical phylogenetic inference, results revealed common denominators associated with these signatures, including 2-acetamido-2-deoxy-beta-d-glucopyranose structural interactions and high rate of alveolar macrophage (AM) infection. AMs were also determined to be the phyloanatomic source of cranial virus in SIVE animals, but not in animals that did not develop SIVE, implicating a role for these cells in the evolution of the signatures identified as predictive of both HIV and SIV neuropathology. IMPORTANCE HIV-associated neurocognitive disorders remain prevalent among persons living with HIV (PLWH) owing to our limited understanding of the contributing viral mechanisms and ability to predict disease onset. We have expanded on a machine learning method previously used on HIV genetic sequence data to predict neurocognitive impairment in PLWH to the more extensively sampled SIV-infected macaque model in order to (i) determine the translatability of the animal model and (ii) more accurately characterize the predictive capacity of the method. We identified eight amino acid and/or biochemical signatures in the SIV envelope glycoprotein, the most predominant of which demonstrated the potential for aminoglycan interaction characteristic of previously identified HIV signatures. These signatures were not isolated to specific points in time or to the central nervous system, limiting their use as an accurate clinical predictor of neuropathogenesis; however, statistical phylogenetic and signature pattern analyses implicate the lungs as a key player in the emergence of neuroadapted viruses.

Application of Phylodynamic Tools to Inform the Public Health Response to COVID-19: Qualitative Analysis of Expert Opinions.

BACKGROUND: In the wake of the SARS-CoV-2 pandemic, scientists have scrambled to collect and analyze SARS-CoV-2 genomic data to inform public health responses to COVID-19 in real time. Open source phylogenetic and data visualization platforms for monitoring SARS-CoV-2 genomic epidemiology have rapidly gained popularity for their ability to illuminate spatial-temporal transmission patterns worldwide. However, the utility of such tools to inform public health decision-making for COVID-19 in real time remains to be explored. OBJECTIVE: The aim of this study is to convene experts in public health, infectious diseases, virology, and bioinformatics-many of whom were actively engaged in the COVID-19 response-to discuss and report on the application of phylodynamic tools to inform pandemic responses. METHODS: In total, 4 focus groups (FGs) occurred between June 2020 and June 2021, covering both the pre- and postvariant strain emergence and vaccination eras of the ongoing COVID-19 crisis. Participants included national and international academic and government researchers, clinicians, public health practitioners, and other stakeholders recruited through purposive and convenience sampling by the study team. Open-ended questions were developed to prompt discussion. FGs I and II concentrated on phylodynamics for the public health practitioner, while FGs III and IV discussed the methodological nuances of phylodynamic inference. Two FGs per topic area to increase data saturation. An iterative, thematic qualitative framework was used for data analysis. RESULTS: We invited 41 experts to the FGs, and 23 (56%) agreed to participate. Across all the FG sessions, 15 (65%) of the participants were female, 17 (74%) were White, and 5 (22%) were Black. Participants were described as molecular epidemiologists (MEs; n=9, 39%), clinician-researchers (n=3, 13%), infectious disease experts (IDs; n=4, 17%), and public health professionals at the local (PHs; n=4, 17%), state (n=2, 9%), and federal (n=1, 4%) levels. They represented multiple countries in Europe, the United States, and the Caribbean. Nine major themes arose from the discussions: (1) translational/implementation science, (2) precision public health, (3) fundamental unknowns, (4) proper scientific communication, (5) methods of epidemiological investigation, (6) sampling bias, (7) interoperability standards, (8) academic/public health partnerships, and (9) resources. Collectively, participants felt that successful uptake of phylodynamic tools to inform the public health response relies on the strength of academic and public health partnerships. They called for interoperability standards in sequence data sharing, urged careful reporting to prevent misinterpretations, imagined that public health responses could be tailored to specific variants, and cited resource issues that would need to be addressed by policy makers in future outbreaks. CONCLUSIONS: This study is the first to detail the viewpoints of public health practitioners and molecular epidemiology experts on the use of viral genomic data to inform the response to the COVID-19 pandemic. The data gathered during this study provide important information from experts to help streamline the functionality and use of phylodynamic tools for pandemic responses.

Molecular Basis of the Toxigenic Vibrio cholerae O1 Serotype Switch from Ogawa to Inaba in Haiti.

Toxigenic Vibrio cholerae O1 serotype Ogawa was introduced involuntarily into Haiti in October 2010, and virtually all of the clinical strains isolated during the first 5 years of the epidemic were Ogawa. Inaba strains were identified intermittently prior to 2015, with diverse mutations resulting in a common phenotype. In 2015, the percentage of clinical infections due to the Inaba serotype began to rapidly increase, with Inaba supplanting Ogawa as the dominant serotype during the subsequent 4 years. We investigated the molecular basis of the serotype switch and confirmed that all Inaba strains had the same level of mRNA expression of the wbeT genes, as well as the same translation levels for the truncated WbeT proteins in the V. cholerae Inaba isolates. Neither wbeT gene expression levels, differential mutations, or truncation size of the WbeT proteins appeared to be responsible for the successful Inaba switch in 2015. Our phylodynamic analysis demonstrated that the V. cholerae Inaba strains in Haiti evolved directly from Ogawa strains and that a significant increase of diversifying selection at the population level occurred at the time of the Ogawa-Inaba switch. We conclude that the emergence of the Inaba serotype was driven by diversifying selection, independent of the mutational pattern in the wbeT gene. IMPORTANCE Our phylodynamic analysis demonstrated that Vibrio cholerae Inaba strains in Haiti evolved directly from Ogawa strains. Our results support the hypothesis that after an initial Ogawa-dominated epidemic wave, V. cholerae Inaba was able to become the dominant strain thanks to a selective advantage driven by ongoing diversifying selection, independently from the mutational pattern in the wbeT gene.

Persistence of Severe Acute Respiratory Syndrome Coronavirus 2 Omicron Variant in Children and Utility of Rapid Antigen Testing as an Indicator of Culturable Virus.

We screened 65 longitudinally collected nasal swab samples from 31 children aged 0-16 years who were positive for severe acute respiratory syndrome coronavirus 2 Omicron BA.1. By day 7 after onset of symptoms, 48% of children remained positive by rapid antigen test. In a sample subset, we found 100% correlation between antigen test results and virus culture.

Emergence and Evolutionary Response of Vibrio cholerae to Novel Bacteriophage, Democratic Republic of the Congo1.

Cholera causes substantial illness and death in Africa. We analyzed 24 toxigenic Vibrio cholerae O1 strains isolated in 2015-2017 from patients in the Great Lakes region of the Democratic Republic of the Congo. Strains originating in southern Asia appeared to be part of the T10 introduction event in eastern Africa. We identified 2 main strain lineages, most recently a lineage corresponding to sequence type 515, a V. cholerae cluster previously reported in the Lake Kivu region. In 41% of fecal samples from cholera patients, we also identified a novel ICP1 (Bangladesh cholera phage 1) bacteriophage, genetically distinct from ICP1 isolates previously detected in Asia. Bacteriophage resistance occurred in distinct clades along both internal and external branches of the cholera phylogeny. This bacteriophage appears to have served as a major driver for cholera evolution and spread, and its appearance highlights the complex evolutionary dynamic that occurs between predatory phage and bacterial host.

Molecular Epidemiology of HIV-1 Subtype B Infection across Florida Reveals Few Large Superclusters with Metropolitan Origin.

Florida is considered an epicenter of HIV in the United States. The U.S. federal plan for Ending the HIV Epidemic (EHE) within 10 years prioritizes seven of Florida's 67 counties for intervention. We applied molecular epidemiology methods to characterize the HIV infection networks in the state and infer whether the results support the EHE. HIV sequences (N = 34,446) and associated clinical/demographic metadata of diagnosed people with HIV (PWH), during 2007 to 2017, were retrieved from the Florida Department of Health. HIV genetic networks were investigated using MicrobeTrace. Associates of clustering were identified through boosted logistic regression. Assortative trait mixing was also assessed. Bayesian phylogeographic methods were applied to evaluate evidence of imported HIV-1 lineages and illustrate spatiotemporal flows within Florida. We identified nine large clusters spanning all seven EHE counties but little evidence of external introductions, suggesting-in the absence of undersampling-an epidemic that evolved independently from the rest of the country or other external influences. Clusters were highly assortative by geography. Most of the sampled infections (82%) did not cluster with others in the state using standard molecular surveillance methods despite satisfactory sequence sampling in the state. The odds of being unclustered were higher among PWH in rural regions, and depending on demographics. A significant number of unclustered sequences were observed in counties omitted from EHE. The large number of missing sequence links may impact timely detection of emerging transmission clusters and ultimately hinder the success of EHE in Florida. Molecular epidemiology may help better understand infection dynamics at the population level and underlying disparities in disease transmission among subpopulations; however, there is also a continuous need to conduct ethical discussions to avoid possible harm of advanced methodologies to vulnerable groups, especially in the context of HIV stigmatization. IMPORTANCE The large number of missing phylogenetic linkages in rural Florida counties and among women and Black persons with HIV may impact timely detection of ongoing and emerging transmission clusters and ultimately hinder the success of epidemic elimination goals in Florida.

Analysis of Zika Virus Sequence Data Associated with a School Cohort in Haiti.

Zika virus (ZIKV) infections occurred in epidemic form in the Americas in 2014-2016, with some of the earliest isolates in the region coming from Haiti. We isolated ZIKV from 20 children with acute undifferentiated febrile illness who were part of a cohort of children seen at a school clinic in the Gressier region of Haiti. The virus was also isolated from three pools of Aedes aegypti mosquitoes collected at the same location. On phylogenetic analysis, three distinct ZIKV clades were identified. Strains from all three clades were present in Haiti in 2014, making them among the earliest isolates identified in the Western Hemisphere. Strains from all three clades were also isolated in 2016, indicative of their persistence across the time period of the epidemic. Mosquito isolates were collected in 2016 and included representatives from two of the three clades; in one instance, ZIKV was isolated from a pool of male mosquitoes, suggestive of vertical transmission of the virus. The identification of multiple ZIKV clades in Haiti at the beginning of the epidemic suggests that Haiti served as a nidus for transmission within the Caribbean.

Rapid SARS-CoV-2 diagnosis using disposable strips and a metal-oxide-semiconductor field-effect transistor platform.

The SARS-CoV-2 pandemic has had a significant impact worldwide. Currently, the most common detection methods for the virus are polymerase chain reaction (PCR) and lateral flow tests. PCR takes more than an hour to obtain the results and lateral flow tests have difficulty with detecting the virus at low concentrations. In this study, 60 clinical human saliva samples, which included 30 positive and 30 negative samples confirmed with RT-PCR, were screened for COVID-19 using disposable glucose biosensor strips and a reusable printed circuit board. The disposable strips were gold plated and functionalized to immobilize antibodies on the gold film. After functionalization, the strips were connected to the gate electrode of a metal-oxide-semiconductor field-effect transistor on the printed circuit board to amplify the test signals. A synchronous double-pulsed bias voltage was applied to the drain of the transistor and strips. The resulting change in drain waveforms was converted to digital readings. The RT-PCR-confirmed saliva samples were tested again using quantitative PCR (RT-qPCR) to determine cycling threshold (Ct) values. Ct values up to 45 refer to the number of amplification cycles needed to detect the presence of the virus. These PCR results were compared with digital readings from the sensor to better evaluate the sensor technology. The results indicate that the samples with a range of Ct values from 17.8 to 35 can be differentiated, which highlights the increased sensitivity of this sensor technology. This research exhibits the potential of this biosensor technology to be further developed into a cost-effective, point-of-care, and portable rapid detection method for SARS-CoV-2.

Development of a Monoclonal Antibody to a Vibriophage as a Proxy for Vibrio cholerae Detection.

Cholera is an acute watery, diarrheal disease that causes high rates of morbidity and mortality without treatment. Early detection of the etiologic agent of toxigenic Vibrio cholerae is important to mobilize treatment and mitigate outbreaks. Monoclonal antibody (mAb) based rapid diagnostic tests (RDTs) enable early detection in settings without laboratory capacity. However, the odds of an RDT testing positive are reduced by nearly 90% when the common virulent bacteriophage ICP1 is present. We hypothesize that adding a mAb for the common, and specific, virulent bacteriophage ICP1 as a proxy for V. cholerae to an RDT will increase diagnostic sensitivity when virulent ICP1 phage is present. In this study, we used an in-silico approach to identify immunogenic ICP1 protein targets that were conserved across disparate time periods and locations. Specificity of targets to cholera patients with known ICP1 was determined, and specific targets were used to produce mAbs in a murine model. Candidate mAbs to the head protein demonstrated specificity to ICP1 by Enzyme linked immunosorbent assay (ELISA) and an ICP1 phage neutralization assay. The limit of detection of the final mAb candidate for ICP1 phage particles spiked into cholera stool matrix was 8 × 105 PFU by Western blotting analysis. This mAb will be incorporated into a RDT prototype for evaluation in a future diagnostic study to test the guiding hypothesis behind this study.

Lessons Learned and Yet-to-Be Learned on the Importance of RNA Structure in SARS-CoV-2 Replication.

SARS-CoV-2, the etiological agent responsible for the COVID-19 pandemic, is a member of the virus family Coronaviridae, known for relatively extensive (~30-kb) RNA genomes that not only encode for numerous proteins but are also capable of forming elaborate structures. As highlighted in this review, these structures perform critical functions in various steps of the viral life cycle, ultimately impacting pathogenesis and transmissibility. We examine these elements in the context of coronavirus evolutionary history and future directions for curbing the spread of SARS-CoV-2 and other potential human coronaviruses. While we focus on structures supported by a variety of biochemical, biophysical, and/or computational methods, we also touch here on recent evidence for novel structures in both protein-coding and noncoding regions of the genome, including an assessment of the potential role for RNA structure in the controversial finding of SARS-CoV-2 integration in "long COVID" patients. This review aims to serve as a consolidation of previous works on coronavirus and more recent investigation of SARS-CoV-2, emphasizing the need for improved understanding of the role of RNA structure in the evolution and adaptation of these human viruses.