Dataset Information

Tracking resilience to infections by mapping disease space

ABSTRACT: Individuals that recover from infections travel a path through disease space that returns to its origin. By following Plasmodium infected mice longitudinally from sickness to recovery, we show that these routes trace simple looping paths that can serve as maps. These maps can predict outcomes, reveal safe avenues and can help us avoid bad neighborhoods. Hosts that will suffer adverse effects enter regions of disease space that are not traversed by resilient individuals. Here we show that we can map disease outcomes using both longitudinal mouse data and cross-sectional human data. These maps identify relationships that could be useful in predicting and treating infections; they also show us that we commonly lack samples in regions of disease space that would help to build better predictive models.

ORGANISM(S): Mus musculus

PROVIDER: GSE61191 | GEO | 2016/04/18

SECONDARY ACCESSION(S): PRJNA260493

REPOSITORIES: GEO

ACCESS DATA

Similar Datasets

Project description:Approximately 20% of sleeping sickness patients exhibit respiratory complications which are commonly attributed to secondary bacterial infections, with an unknown role of the parasite. Using a Glossina morsitans tsetse fly initiated Trypansoma brucei infection in mice we found that parasites rapidly and permanently colonize the lungs, representing one of the major target organs next to the adipose tissue. Trypanosomes were found by immunofluorescence staining and scanning electron microscopy to occupy the extravascular spaces surrounding the blood vessels of the alveoli and bronchi. Parasites were even found in the lung cartilage. Trypanosomes were often observed as nests of multiplying parasites exhibiting close interactions with collagen and highly active secretion of extracellular vesicles that are engaged in intercellular communication. The local immune response was analysed by flow cytometry after 10 and 21 days of infection and was characterized by a substantial increase of CD11b+ Ly6C+ monocytes, CD11b+ Ly6C+ F4/80+ macrophages and CD11b+ CD11c+ dendritic cells. CD11b+ Ly6G+ neutrophils only accumulated prominently at the late infection time point. Interestingly, parasite presence resulted in a significant reduction of B220+ IgM+ B cells, CD11b+ CD11clo/- SiglecF+ eosinophils and TcR-β- NK1.1+ natural killer cells. Digital transcriptomics revealed infection-induced upregulation of Il-10, IFN-ɣ- and IFN-α-responses, IL-2-, IL-6- and TNF-signalling, a Th1 pro-inflammatory signature, negative immune checkpoint regulators and a predominant M1 macrophage polarization. Il12a and genes associated with complement and the B cell receptor were downregulated. No infection-associated pulmonary dysfunction could be detected by in vivo lung function measurements, mirroring the limited pulmonary complications during sleeping sickness. However, the substantial reduction of eosinophils, B cells and NK cells may render individuals more susceptible to opportunistic infections. Collectively, these observations provide essential insights in the peculiar parasite biology, immunological reactions and physiological function of a largely overlooked target organ which may trigger new diagnostic approaches for sleeping sickness.

Dataset Information

Tracking resilience to infections by mapping disease space

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure