Coinfection frequency in water flea populations is a mere reflection of parasite diversity

In nature, parasite species often coinfect the same host. Yet, it is not clear what drives the natural dynamics of coinfection prevalence. The prevalence of coinfections might be affected by interactions among coinfecting species, or simply derive from parasite diversity. Identifying the relative impact of these parameters is crucial for understanding patterns of coinfections. We studied the occurrence and likelihood of coinfections in natural populations of water fleas (Daphnia magna). Coinfection prevalence was within the bounds expected by chance and parasite diversity had a strong positive effect on the likelihood of coinfections. Additionally, coinfection prevalence increased over the season and became as common as a single infection. Our results demonstrate how patterns of coinfection, and particularly their temporal variation, are affected by overlapping epidemics of different parasites. We suggest that monitoring parasite diversity can help predict where and when coinfection prevalence will be high, potentially leading to increased health risks to their hosts

Tertiary Lymphoid Structures:Autoimmunity Goes Local

Tertiary lymphoid structures (TLS) are frequently observed in target organs of autoimmune diseases. TLS present features of secondary lymphoid organs such as segregated T and B cell zones, presence of follicular dendritic cell networks, high endothelial venules and specialized lymphoid fibroblasts and display the mechanisms to support local adaptive immune responses toward locally displayed antigens. TLS detection in the tissue is often associated with poor prognosis of disease, auto-antibody production and malignancy development. This review focuses on the contribution of TLS toward the persistence of the inflammatory drive, the survival of autoreactive lymphocyte clones and post-translational modifications, responsible for the pathogenicity of locally formed autoantibodies, during autoimmune disease development

Sequential infection of Daphnia magna by a gut microsporidium followed by a haemolymph yeast decreases transmission of both parasites

Over the course of seasonal epidemics, populations of susceptible hosts may encounter a wide variety of parasites. Parasite phenology affects the order in which these species encounter their hosts, leading to sequential infections, with potentially strong effects on within-host growth and host population dynamics. Here, the cladoceran Daphnia magna was exposed sequentially to a haemolymph-infecting yeast (Metschnikowia bicuspidata) and a gut microsporidium (Ordospora colligata), with experimental treatments reflecting two possible scenarios of parasite succession. The effects of single and co-exposure were compared on parasite infectivity, spore production and the overall virulence experienced by the host. We show that neither parasite benefited from coinfection; instead, when hosts encountered Ordospora, followed by Metschnikowia, higher levels of host mortality contributed to an overall decrease in the transmission of both parasites. These results showcase an example of sequential infections generating unilateral priority effects, in which antagonistic interactions between parasites can alleviate the intensity of infection and coincide with maladaptive levels of damage inflicted on the host

<b>Data & Code of - "Coinfection frequency in water flea populations is a mere reflection of parasite diversity."</b>

The data files contain all the data that was used in the diffrent analyses and figures (but do not contain data that was discrded following the criterions that were detailed in the paper). The Python script is acompanied with six data files which are all read in the beginning of the script. each file contain a different data set or subset (only_endo.xlsx & endoparasites_status.xlsx are splited sets of endoparasites_status_full.xlsx for convinience reasons). It is recomended to read the help file and the instructions that were embeded in the code for easier use. The R script acompanied with two data files, one for each of the anlyses performend on this software. Again, it is recomended to read the help file for better understanding of the data.List of data files for the Python script: abiotics.xlsx, densities.xlsx, disection.xlsx, endoparasites_status.xlsx, endoparasites_status_full.xlsx & only_endo.xlsx.List of data files for the R script: data_for_the_model_selection.xlsx & densities_dataIt is recomended to have both script and its related file in the same folder.</p