Post-acute sequelae of COVID-19 (PASC) represent an emerging global crisis. However, quantifiable risk-factors for PASC and their biological associations are poorly resolved. We executed a deep multi-omic, longitudinal investigation of 309 COVID-19 patients from initial diagnosis to convalescence (2-3 months later), integrated with clinical data, and patient-reported symptoms. We resolved four PASC-anticipating risk factors at the time of initial COVID-19 diagnosis: type 2 diabetes, SARS-CoV-2 RNAemia, Epstein-Barr virus viremia, and specific autoantibodies. In patients with gastrointestinal PASC, SARS-CoV-2-specific and CMV-specific CD8 + T cells exhibited unique dynamics during recovery from COVID-19. Analysis of symptom-associated immunological signatures revealed coordinated immunity polarization into four endotypes exhibiting divergent acute severity and PASC. We find that immunological associations between PASC factors diminish over time leading to distinct convalescent immune states. Detectability of most PASC factors at COVID-19 diagnosis emphasizes the importance of early disease measurements for understanding emergent chronic conditions and suggests PASC treatment strategies.

We present an integrated analysis of the clinical measurements, immune cells and plasma multi-omics of 139 COVID-19 patients representing all levels of disease severity, from serial blood draws collected during the first week of infection following diagnosis. We identify a major shift between mild and moderate disease, at which point elevated inflammatory signaling is accompanied by the loss of specific classes of metabolites and metabolic processes. Within this stressed plasma environment at moderate disease, multiple unusual immune cell phenotypes emerge and amplify with increasing disease severity. We condensed over 120,000 immune features into a single axis to capture how different immune cell classes coordinate in response to SARS-CoV-2. This immune-response axis independently aligns with the major plasma composition changes, with clinical metrics of blood clotting, and with the sharp transition between mild and moderate disease. This study suggests that moderate disease may provide the most effective setting for therapeutic intervention.

Human respiratory syncytial virus (HRSV) is the main cause of acute lower respiratory infections in children under 2 years of age and causes repeated infections throughout life. We investigated the genetic variability of RSV-A circulating in Ontario during 2010–2011 winter season by sequencing and phylogenetic analysis of the G glycoprotein gene. Among the 201 consecutive RSV isolates studied, RSV-A (55.7%) was more commonly observed than RSV-B (42.3%). 59.8% and 90.1% of RSV-A infections were among children ≤12 months and ≤5 years old, respectively. On phylogenetic analysis of the second hypervariable region of the 112 RSV-A strains, 110 (98.2%) clustered within or adjacent to the NA1 genotype; two isolates were GA5 genotype. Eleven (10%) NA1-related isolates clustered together phylogenetically as a novel RSV-A genotype, named ON1, containing a 72 nucleotide duplication in the C-terminal region of the attachment (G) glycoprotein. The predicted polypeptide is lengthened by 24 amino acids and includes a23 amino acid duplication. Using RNA secondary structural software, a possible mechanism of duplication occurrence was derived. The 23 amino acid ON1 G gene duplication results in a repeat of 7 potential O-glycosylation sites including three O-linked sugar acceptors at residues 270, 275, and 283. Using Phylogenetic Analysis by Maximum Likelihood analysis, a total of 19 positively selected sites were observed among Ontario NA1 isolates; six were found to be codons which reverted to the previous state observed in the prototype RSV-A2 strain. The tendency of codon regression in the G-ectodomain may infer a decreased avidity of antibody to the current circulating strains. Further work is needed to document and further understand the emergence, virulence, pathogenicity and transmissibility of this novel RSV-A genotype with a72 nucleotide G gene duplication.

Background: The extent to which climate change may affect human health by increasing risk from vector-borne diseases has been under considerable debate.Objectives: We quantified potential effects of future climate change on the basic reproduction number (R0) of the tick vector of Lyme disease, Ixodes scapularis, and explored their importance for Lyme disease risk, and for vector-borne diseases in general.Methods: We applied observed temperature data for North America and projected temperatures using regional climate models to drive an I. scapularis population model to hindcast recent, and project future, effects of climate warming on R0. Modeled R0 increases were compared with R0 ranges for pathogens and parasites associated with variations in key ecological and epidemiological factors (obtained by literature review) to assess their epidemiological importance.Results: R0 for I. scapularis in North America increased during the years 1971–2010 in spatio-temporal patterns consistent with observations. Increased temperatures due to projected climate change increased R0 by factors (2–5 times in Canada and 1.5–2 times in the United States), comparable to observed ranges of R0 for pathogens and parasites due to variations in strains, geographic locations, epidemics, host and vector densities, and control efforts.Conclusions: Climate warming may have co-driven the emergence of Lyme disease in northeastern North America, and in the future may drive substantial disease spread into new geographic regions and increase tick-borne disease risk where climate is currently suitable. Our findings highlight the potential for climate change to have profound effects on vectors and vector-borne diseases, and the need to refocus efforts to understand these effects.Citation: Ogden NH, Radojević M, Wu X, Duvvuri VR, Leighton PA, Wu J. 2014. Estimated effects of projected climate change on the basic reproductive number of the Lyme disease vector Ixodes scapularis. Environ Health Perspect 122:631–638; http://dx.doi.org/10.1289/ehp.1307799

Human respiratory syncytial virus (RSV)A), not significantly different to that of Ontario ON1. The estimated mean reproductive number (R 0 = ∼ 1.01) from global and Ontario sequences showed no significant difference and implies stability among global RSV-A ON1. This study suggests that local epidemics exhibit similar underlying evolutionary and epidemiological dynamics to that of the persistent global RSV-A ON1 population. These findings underscore the importance of continual molecular surveillance of RSV in order to gain a better understanding of epidemics.

A better understanding of the metabolic alterations in immune cells during severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection may elucidate the wide diversity of clinical symptoms experienced by individuals with coronavirus disease 2019 (COVID-19). Here, we report the metabolic changes associated with the peripheral immune response of 198 individuals with COVID-19 through an integrated analysis of plasma metabolite and protein levels as well as single-cell multiomics analyses from serial blood draws collected during the first week after clinical diagnosis. We document the emergence of rare but metabolically dominant T cell subpopulations and find that increasing disease severity correlates with a bifurcation of monocytes into two metabolically distinct subsets. This integrated analysis reveals a robust interplay between plasma metabolites and cell-type-specific metabolic reprogramming networks that is associated with disease severity and could predict survival.

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We report here on antigens from the SARS-CoV-2 virus spike protein, that when presented by Class I MHC, can lead to cytotoxic CD8 + T cell anti-viral responses in COVID-19 patients. We present a method in which the SARS-CoV-2 spike protein is converted into a library of peptide antigen-Major Histocompatibility Complexes (pMHCs) as single chain trimers that contain the peptide antigen, the MHC HLA allele subunit, and the β-2 microglobulin subunit. This library is used to detect the evolution of virus-specific T cell populations in four COVID-19 study participants two of which share one HLA allele, and the other two a second HLA allele, at two time points over the initial course of infection. HLA-matched participants exhibit similar virusspecific T cell populations, but very different time-trajectories of those populations. This strategy can be used to track those virus-specific T cell populations over the course of an infection, thus providing deep insight into the SARS-CoV-2 immune system trajectories observed in different COVID-19 patients.