The human malaria parasite proliferates in red blood cells following repeated cycles of invasion, multiplication, and egress. serine repeat antigen 5 (PfSERA5), a putative serine protease, plays an important role in merozoite egress. However, regulation of its activity leading to merozoite egress is poorly understood. In this study, we show that PfSERA5 undergoes phosphorylation prior to merozoite egress. Immunoprecipitation of parasite lysates using anti-PfSERA5 serum followed by MS analysis identified calcium-dependent protein kinase 1 (PfCDPK1) as an interacting kinase. Association of PfSERA5 with PfCDPK1 was corroborated by co-sedimentation, co-immunoprecipitation, and co-immunolocalization analyses. Interestingly, PfCDPK1 phosphorylated PfSERA5 in the presence of Ca and enhanced its proteolytic activity. A PfCDPK1 inhibitor, purfalcamine, blocked the phosphorylation and activation of PfSERA5 both as well as in schizonts, which, in turn, blocked merozoite egress. Together, these results suggest that phosphorylation of PfSERA5 by PfCDPK1 following a rise in cytosolic Ca levels activates its proteolytic activity to trigger merozoite egress.
The human malaria parasite Plasmodium falciparum possesses sophisticated systems of protein secretion to modulate host cell invasion and remodeling. In the present study, we provide insights into the function of the AP-1 complex in P. falciparum. We utilized GFP fusion constructs for live cell imaging, as well as fixed parasites in immunofluorescence analysis, to study adaptor protein mu1 (Pfμ1) mediated protein trafficking in P. falciparum. In trophozoites Pfμ1 showed similar dynamic localization to that of several Golgi/ER markers, indicating Golgi/ER localization. Treatment of transgenic parasites with Brefeldin A altered the localization of Golgi-associated Pfμ1, supporting the localization studies. Co-localization studies showed considerable overlap of Pfμ1 with the resident rhoptry proteins, rhoptry associated protein 1 (RAP1) and Cytoadherence linked asexual gene 3.1 (Clag3.1) in schizont stage. Immunoprecipitation experiments with Pfμ1 and PfRAP1 revealed an interaction, which may be mediated through an intermediate transmembrane cargo receptor. A specific role for Pfμ1 in trafficking was suggested by treatment with AlF4, which resulted in a shift to a predominantly ER-associated compartment and consequent decrease in co-localization with the Golgi marker GRASP. Together, these results suggest a role for the AP-1 complex in rhoptry protein trafficking in P. falciparum.
Introduction/Aims: Body mass index (BMI) is linked to amyotrophic lateral sclerosis (ALS) risk and prognosis, but additional research is needed. The aim of this study was to identify whether and when historical changes in BMI occurred in ALS participants, how these longer term trajectories associated with survival, and whether metabolomic profiles provided insight into potential mechanisms.Methods: ALS and control participants self-reported body height and weight 10 (reference) and 5 years earlier, and at study entry (diagnosis for ALS participants). Generalized estimating equations evaluated differences in BMI trajectories between cases and controls. ALS survival was evaluated by BMI trajectory group using accelerated failure time models. BMI trajectories and survival associations were explored using published metabolomic profiling and correlation networks.Results: Ten-year BMI trends differed between ALS and controls, with BMI loss in the 5 years before diagnosis despite BMI gains 10 to 5 years beforehand in both groups. An overall 10-year drop in BMI associated with a 27.1% decrease in ALS survival (P = .010). Metabolomic networks in ALS participants showed dysregulation in sphingomyelin, bile acid, and plasmalogen subpathways.Discussion: ALS participants lost weight in the 5-year period before enrollment. BMI trajectories had three distinct groups and the group with significant weight loss in the past 10 years had the worst survival. Participants with a high BMI and increase in weight in the 10 years before symptom onset also had shorter survival. Certain metabolomics profiles were associated with the BMI trajectories. Replicating these findings in prospective cohorts is warranted.
Modern analytical methods allow for the simultaneous detection of hundreds of metabolites, generating increasingly large and complex data sets. The analysis of metabolomics data is a multi-step process that involves data processing and normalization, followed by statistical analysis. One of the biggest challenges in metabolomics is linking alterations in metabolite levels to specific biological processes that are disrupted, contributing to the development of disease or reflecting the disease state. A common approach to accomplishing this goal involves pathway mapping and enrichment analysis, which assesses the relative importance of predefined metabolic pathways or other biological categories. However, traditional knowledge-based enrichment analysis has limitations when it comes to the analysis of metabolomics and lipidomics data. We present a Java-based, user-friendly bioinformatics tool named Filigree that provides a primarily data-driven alternative to the existing knowledge-based enrichment analysis methods. Filigree is based on our previously published differential network enrichment analysis (DNEA) methodology. To demonstrate the utility of the tool, we applied it to previously published studies analyzing the metabolome in the context of metabolic disorders (type 1 and 2 diabetes) and the maternal and infant lipidome during pregnancy.
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