Velocity at maximal oxygen uptake best predicts 3 km race time in collegiate distance runners

Purpose: There is a lack of scientific investigation into the predictors of 3 km race performance in collegiate distance runners. The purpose of this investigation was to determine what physiological variables best predict 3 km race time in a group of collegiate distance runners. Methods: Twenty-one endurance trained runners (11 men, 10 women) volunteered for this investigation. Running economy (RE) and maximal oxygen uptake (VO2max) testing were conducted within 9 ± 6 days of the race in a single session. All participants ran in a 3 km race at an NCAA sanctioned track meet. Pearson’s product moment correlations were performed between 3 km race time and velocity at VO2max (vVO2max), relative VO2max, RE at 9.7, 11.3, 12.9, and 14.5 km•hr-1 and percent of VO2max. A stepwise multiple regression was performed with 3 km race time as the dependent variable and independent variables of vVO2max, VO2max, RE9.7, RE11.3, RE12.9, RE14.5. Results: The results revealed that vVO2max was the best predictor of 3 km race performance in a heterogeneous group of collegiate distance runners (R2=0.90). For the men, vVO2max remained the best predictor of 3 km race performance (R2=0.49). For the women, the best predictors of 3 km performance were vVO2max and VO2max (R2=0.97). Conclusions: Distance coaches should consider emphasizing vVO2max as a primary factor in training to improve 3 km race performance and conversely, the pace achieved in a 3-km race is a good predictor of vVO2max

Glycan dependent phenotype differences of HIV-1 generated from macrophage versus CD4+ T helper cell populations

Human immunodeficiency virus type 1 (HIV-1) is able to infect a variety of cell types with differences in entry efficiency and replication kinetics determined by the host cell type or the viral phenotype. The phenotype of the virus produced from these various cell types, including infectivity, co-receptor usage and neutralisation sensitivity, may also be affected by the characteristics of the producing cell. This can be due to incorporation of variant cell-specific molecules or differences in post-translational modifications of the gp41/120 envelope. In this study we produced genetically identical virus strains from macrophages, CD4-enriched lymphocytes as well as Th1 and Th2 CD4+ cell lines and compared each different virus stock for their infectivity in various cell types and sensitivity to neutralisation. In order to study the effect of the producer host cell on the virus phenotype, virus stocks were normalised on infectivity and were sequenced to confirm env gene homogeneity. Virus production by Th1 or Th2 cells did not compromise infectivity of the variant cell types tested. We observed no difference in sensitivity to co-receptor blocking agents upon viral passage through Th1 and Th2 CD4+ cell lineages nor did this affect DC-SIGN-mediated viral capture as measured in a transfer assay to CD4+ lymphocytes. Virus produced by macrophages was comparably sensitive to CC-chemokine inhibition as was virus generated from the array of CD4+ lymphocytes. We identified that virus produced from macrophages was fourteen times more resistant to 2G12 neutralisation than virus produced from CD4+ lymphocytes. Macrophage-produced dual-tropic (R5/X4) virus was six times more efficiently transmitted to CD4+ cells than lymphocyte-derived HIV-1 (p&amp;lt;0.0001) after DCSIGN capture. These results provide further insights to what extent the host cell influences viral phenotype and thereby various aspects of HIV-1 pathogenesis but suggest that viruses generated from Th1 versus Th2 cells are consistent in phenotype.</jats:p

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Structure of a bacterial 30S ribosomal subunit at 5.5 Å resolution

The 30S ribosomal subunit binds messenger RNA and the anticodon stem-loop of transfer RNA during protein synthesis. A crystallographic analysis of the structure of the subunit from the bacterium Thermus thermophilus is presented. At a resolution of 5.5 Å, the phosphate backbone of the ribosomal RNA is visible, as are the α-helices of the ribosomal proteins, enabling double-helical regions of RNA to be identified throughout the subunit, all seven of the small-subunit proteins of known crystal structure to be positioned in the electron density map, and the fold of the entire central domain of the small-subunit ribosomal RNA to be determined

Structure of a bacterial 30S ribosomal subunit at 5.5 Å resolution

The 30S ribosomal subunit binds messenger RNA and the anticodon stem-loop of transfer RNA during protein synthesis. A crystallographic analysis of the structure of the subunit from the bacterium Thermus thermophilus is presented. At a resolution of 5.5 Å, the phosphate backbone of the ribosomal RNA is visible, as are the α-helices of the ribosomal proteins, enabling double-helical regions of RNA to be identified throughout the subunit, all seven of the small-subunit proteins of known crystal structure to be positioned in the electron density map, and the fold of the entire central domain of the small-subunit ribosomal RNA to be determined

Assembly of chimeric connexin-aequorin proteins into functional gap junction channels - Reporting intracellular and plasma membrane calcium environments

Chimeric proteins comprising connexins 26, 32, and 43 and aequorin, a chemiluminescent calcium indicator, were made by fusing the amino terminus of aequorin to the carboxyl terminus of connexins. The retention of function by the chimeric partners was investigated. Connexin 32-aequorin and connexin 43-aequorin retained chemiluminescent activity whereas that of connexin 26-aequorin was negligible. Immunofluorescent staining of COS-7 cells expressing the chimerae showed they were targeted to the plasma membrane. Gap junction intercellular channel formation by the chimerae alone and in combination with wild-type connexins was investigated. Stable HeLa cells expressing connexin 43-aequorin were functional, as demonstrated by Lucifer yellow transfer. Pairs of Xenopus oocytes expressing connexin 43-aequorin were electrophysiologically coupled, but those expressing chimeric connexin 26 or 32 showed no detectable levels of coupling. The formation of heteromeric channels constructed of chimeric connexin 32 or connexin 43 and the respective wild-type connexins was inferred from the novel voltage gating properties of the junctional conductance. The results show that the preservation of function by each partner of the chimeric protein is dictated mainly by the nature of the connexin, especially the length of the cytoplasmic carboxyl-terminal domain. The aequorin partner of the connexin 43 chimera reported calcium levels in COS-7 cells in at least two different calcium environments