Identification of Anthrax as the Cause of a Cluster of Unexplained Deaths, Uganda, 2023: The Role of Metagenomic Next-Generation Sequencing and Postmortem Specimens.

Between April and November 2023, 27 unexplained human deaths that presented with swelling of the arms, skin sores with black centers, difficulty in breathing, obstructed swallowing, headaches, and other body aches were reported in Kyotera District, Uganda by the Public Health Emergency Operations Center. Subsequently, the death of cattle on farms and the consumption of carcass meat by some residents were also reported. Field response teams collected clinical/epidemiological data and autopsy samples to determine the cause of deaths.

Rates of evolution differ between cell types identified by single-cell RNAseq in threespine stickleback.

Rates of evolutionary change vary by gene. While some broad gene categories are highly conserved with little divergence over time, others undergo continuous selection pressure and are highly divergent. Here, we combine single-cell RNA sequencing (scRNAseq) with evolutionary genomics to understand whether certain cell types exhibit faster evolutionary divergence (using their characteristic genes), than other types of cells.

Markov Field network model of multi-modal data predicts effects of immune system perturbations on intravenous BCG vaccination in macaques.

Analysis of multi-modal datasets can identify multi-scale interactions underlying biological systems, but can be beset by spurious connections due to indirect impacts propagating through an unmapped biological network. For example, studies in macaques have shown that BCG vaccination by an intravenous route protects against tuberculosis, correlating with changes across various immune data modes. To eliminate spurious correlations and identify critical immune interactions in a public multi-modal dataset (systems serology, cytokines, cytometry) of vaccinated macaques, we applied Markov Fields (MF), a data-driven approach that explains vaccine efficacy and immune correlations via multivariate network paths, without requiring large numbers of samples (i.

Markov field network model of multi-modal data predicts effects of immune system perturbations on intravenous BCG vaccination in macaques.

Analysis of multi-modal datasets can identify multi-scale interactions underlying biological systems but can be beset by spurious connections due to indirect impacts propagating through an unmapped biological network. For example, studies in macaques have shown that Bacillus Calmette-Guerin (BCG) vaccination by an intravenous route protects against tuberculosis, correlating with changes across various immune data modes. To eliminate spurious correlations and identify critical immune interactions in a public multi-modal dataset (systems serology, cytokines, and cytometry) of vaccinated macaques, we applied Markov fields (MFs), a data-driven approach that explains vaccine efficacy and immune correlations via multivariate network paths, without requiring large numbers of samples (i.

The Dominance of Coinfecting Parasites' Indirect Genetic Effects on Host Traits.

AbstractIndirect genetic effects (IGEs) exist when there is heritable variation in one organism's ability to alter a second organism's traits. For example, parasites have antigens that can induce a host immune response, as well as disparate strategies to evade or suppress host immunity; among-parasite genetic variation in these antigens generates among-host variation in immune traits. Here, we experimentally show that the cestode parasite exerts an IGE on an immune trait (peritoneal fibrosis) in its threespine stickleback host: stickleback developed strong fibrosis after exposure to some parasite genotypes but not others.