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Global SARS-CoV-2 genomic surveillance efforts have provided critical data on the ongoing evolution of the virus to inform best practices in clinical care and public health throughout the pandemic. Impactful genomic surveillance strategies generally follow a multi-disciplinary pipeline involving clinical sample collection, viral genotyping, metadata linkage, data reporting, and public health responses. Unfortunately, current limitations in each of these steps have compromised the overall effectiveness of these strategies. Biases from convenience-based sampling methods can obfuscate the true distribution of circulating variants. The lack of standardization in genotyping strategies and bioinformatic expertise can create bottlenecks in data processing and complicate interpretation. Limitations and inconsistencies in clinical and demographic data collection and sharing can slow the compilation and limit the utility of comprehensive datasets. This likewise can complicate data reporting, restricting the availability of timely data. Finally, gaps and delays in the implementation of genomic surveillance data in the public health sphere can prevent officials from formulating effective mitigation strategies to prevent outbreaks. In this review, we outline current SARS-CoV-2 global genomic surveillance methods and assess roadblocks at each step of the pipeline to identify potential solutions. Evaluating the current obstacles that impede effective surveillance can improve both global coordination efforts and pandemic preparedness for future outbreaks.

Human immunodeficiency virus (HIV) relies upon a broad array of host factors in order to replicate and evade the host antiviral response. Cleavage and polyadenylation specificity factor 6 (CPSF6) is one such host factor that is recruited by incoming HIV-1 cores to regulate trafficking, nuclear import, uncoating, and integration site selection. Despite these well-described roles, the impact of CPSF6 perturbation on HIV-1 infectivity varies considerably by cell type. Here, we report that CPSF6 knock-out in primary CD4+ T cells leads to increased permissivity to HIV-1 infection due to broad transcriptional reprogramming. Knock-out of CPSF6 results in widespread differential gene expression, including downregulation of genes involved in the innate immune response and enhanced expression of the HIV-1 co-receptors. Accordingly, these cells are less responsive to interferon and express lower levels of antiretroviral restriction factors, including TRIM5α. These transcriptional changes are linked to global shortening of mRNA 3' untranslated regions (UTRs) through changes in alternative polyadenylation (APA), which are triggered by disruption of the CPSF6-containing Cleavage Factor Im (CFIm) complex. Furthermore, we find that recruitment of CPSF6 by HIV-1 cores is sufficient to perturb CPSF6 function, leading to 3' UTR shortening and subsequent transcriptional rewiring. These results suggest a model in which HIV-1 transcriptionally reprograms target cells through recruitment of CPSF6 to incoming cores to circumvent the antiviral response and enhance permissivity to infection.

Neonatal health is dependent on early risk stratification, diagnosis, and timely management of potentially devastating conditions, particularly in the setting of prematurity. Many of these conditions are poorly predicted in real-time by clinical data and current diagnostics. Umbilical cord blood may represent a novel source of molecular signatures that provides a window into the state of the fetus at birth. In this study, we comprehensively characterized the cord blood proteome of infants born between 25 to 42 weeks using untargeted mass spectrometry and functional enrichment analysis. We determined that the cord blood proteome at birth varies significantly across gestational development. Proteins that function in structural development and growth (e.g., extracellular matrix organization, lipid particle remodeling, and blood vessel development) are more abundant earlier in gestation. In later gestations, proteins with increased abundance are in immune response and inflammatory pathways, including complements and calcium-binding proteins. These data contribute to the knowledge of the physiologic state of neonates across gestational age, which is crucial to understand as we strive to best support postnatal development in preterm infants, determine mechanisms of pathology causing adverse health outcomes, and develop cord blood biomarkers to help tailor our diagnosis and therapeutics for critical neonatal conditions.

The impact of remdesivir on SARS-CoV-2 diversity and evolution in vivo has remained unclear. In this single-center, retrospective cohort study, we assessed SARS-CoV-2 diversification and diversity over time in a cohort of hospitalized patients who did or did not receive remdesivir. Whole-genome sequencing was performed on 98 paired specimens collected from 49 patients before and after remdesivir administration. The genetic divergence between paired specimens was not significantly different in this cohort compared with that in a control group of patients who did not receive the drug. However, when we focused on minority variants, several positions showed preferential diversification after remdesivir treatment, some of which were associated with specific variants of concern. Most notably, remdesivir administration resulted in strong selection for a nonsynonymous mutation in nsp12, G671S, previously associated with enhanced viral fitness. This same mutation was found to be enriched in a second cohort of 143 inpatients with specimens collected after remdesivir administration compared with controls. Only one other mutation previously implicated in remdesivir resistance (nsp12:V792I) was found to be preferentially selected for after remdesivir administration. These data suggest that SARS-CoV-2 variants with enhanced replicative fitness may be selected for in the presence of antiviral therapy as an indirect means to overcome this selective pressure.

BACKGROUNDSymptoms of early-onset neonatal sepsis (EOS) in preterm infants are nonspecific and overlap with normal postnatal physiological adaptations and noninfectious pathologies. This clinical uncertainty and the lack of reliable EOS diagnostics results in liberal use of antibiotics in the first days to weeks of life, leading to increased risk of antibiotic-related morbidities in infants who do not have an invasive infection. METHODSTo identify potential biomarkers for EOS in newborn infants, we used unlabeled tandem mass spectrometry proteomics to identify differentially abundant proteins in the umbilical cord blood of infants with and without culture-confirmed EOS. Proteins were then confirmed using immunoassay, and logistic regression and random forest models were built, including both biomarker concentration and clinical variables to predict EOS. RESULTSThese data identified 5 proteins that were significantly upregulated in infants with EOS, 3 of which (serum amyloid A, C-reactive protein, and lipopolysaccharide-binding protein) were confirmed using a quantitative immunoassay. The random forest classifier for EOS was applied to a cohort of infants with culture-negative presumed sepsis. Most infants with presumed sepsis were classified as resembling infants in the control group, with low EOS biomarker concentrations.CONCLUSIONThese results suggest that cord blood biomarker screening may be useful for early stratification of EOS risk among neonates, improving targeted, evidence-based use of antibiotics early in life. FUNDINGNIH, Gerber Foundation, Friends of Prentice, Thrasher Research Fund, Ann & Robert H. Lurie Children's Hospital, and Stanley Manne Children's Research Institute of Lurie Children's.

SARS-CoV-2, the causative agent of COVID-19, has had a devastating impact, resulting in over 7 million deaths worldwide. Like most viruses, SARS-CoV-2 has continued to evolve in order to adapt to selective pressures such as therapeutics and vaccines. These mutations have resulted in variants that demonstrate enhanced transmissibility, immune escape, or antiviral resistance. Genomic surveillance provides a framework in which to track the emergence of these mutations. We begin by outlining the genomic surveillance pipeline and address challenges and potential solutions associated with stage. Integrating these solutions, we conducted genomic surveillance to assess the association between SARS-CoV-2 clade and patient outcome over a two-year period. By the inclusion of population-level confounders, which includes sampling bias, we highlight the importance of integrating non-virological factors when examining patient risk. Furthermore, using the same approach, we examined the impact of remdesivir on SARS-CoV-2 diversity and evolution in vivo. Using sequences collected from patients before and after remdesivir administration, we identified several positions that showed preferential diversification after remdesivir treatment, several of which were associated with enhanced viral fitness. Taken together, our studies highlight the significant impact of SARS-CoV-2 mutations on disease dynamics as well as therapeutic interventions and emphasizes the necessity of using an integrative modeling approach to assess the impact of viral mutations. These findings underscore the ongoing evolution of the virus and the critical role of genomic surveillance in informing public health interventions and treatment strategies

BackgroundReferral of patients with heart failure (HF) who are at high mortality risk for specialist evaluation is recommended. Yet, most tools for identifying such patients are difficult to implement in electronic health record (EHR) systems.ObjectiveTo assess the performance and ease of implementation of Machine learning Assessment of RisK and EaRly mortality in Heart Failure (MARKER-HF), a machine-learning model that uses structured data that is readily available in the EHR, and compare it with two commonly used risk scores: the Seattle Heart Failure Model (SHFM) and Meta‐Analysis Global Group in Chronic (MAGGIC) Heart Failure Risk Score.DesignRetrospective, cohort study.ParticipantsData from 6764 adults with HF were abstracted from EHRs at a large integrated health system from 1/1/10 to 12/31/19.Main measuresOne-year survival from time of first cardiology or primary care visit was estimated using MARKER-HF, SHFM, and MAGGIC. Discrimination was measured by the area under the receiver operating curve (AUC). Calibration was assessed graphically.Key resultsCompared to MARKER-HF, both SHFM and MAGGIC required a considerably larger amount of data engineering and imputation to generate risk score estimates. MARKER-HF, SHFM, and MAGGIC exhibited similar discriminations with AUCs of 0.70 (0.69–0.73), 0.71 (0.69–0.72), and 0.71 (95% CI 0.70–0.73), respectively. All three scores showed good calibration across the full risk spectrum.ConclusionsThese findings suggest that MARKER-HF, which uses readily available clinical and lab measurements in the EHR and required less imputation and data engineering than SHFM and MAGGIC, is an easier tool to identify high-risk patients in ambulatory clinics who could benefit from referral to a HF specialist.