The dynamic landscape of parasitaemia dependent intestinal microbiota shifting at species level and the correlated gut transcriptome during Plasmodium yoelii infection
Zong, Y.; Cheng, L.; Cheng, X.; Liao, B.; Ye, X.; Liu, T.; Li, J.; Zhou, X.; Xu, W.; Ren, B.
Show abstract
BackgroundMalaria, caused by Plasmodium, is a global life-threatening infection disease especially during the COVID-19 pandemic. However, it is still unclear about the dynamic change and the interactions between intestinal microbiota and host immunity. Here, we investigated the change of intestinal microbiome and transcriptome during the whole Plasmodium infection process in mice to analyze the dynamic landscape of parasitaemia dependent intestinal microbiota shifting and related to host immunity. ResultsThere were significant parasitaemia dependent changes of intestinal microbiota and transcriptome, and the microbiota was significantly correlated to the intestinal immunity. We found that (i) the diversity and composition of the intestinal microbiota represented a significant correlation along with the Plasmodium infection in family, genus and species level; (ii) the up-regulated genes from the intestinal transcriptome were mainly enriched in immune cell differentiation pathways along with the malaria development, particularly, naive CD4+ T cells differentiation; (iii) the abundance of the parasitaemia phase-specific microbiota represented a high correlation with the phase-specific immune cells development, particularly, Th1 cell with family Bacteroidales BS11 gut group, genera Prevotella 9, Ruminococcaceae UCG 008, Moryella and specie Sutterella*, Th2 cell with specie Sutterella*, Th17 cell with family Peptococcaceae, genus Lachnospiraceae FCS020 group and spices Ruminococcus 1*, Ruminococcus UGG 014* and Eubacterium plexicaudatum ASF492, Tfh and B cell with genera Moryella and species Erysipelotrichaceae bacterium canine oral taxon 255. ConclusionThere was a remarkable dynamic landscape of the parasitaemia dependent shifting of intestinal microbiota and immunity, and a notable correlation between the abundance of intestinal microbiota.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Integrated analysis of intestinal microbiota and metabolomic reveals that decapod iridescent virus 1 (DIV1) infection induces secondary bacterial infection and metabolic reprogramming in Marsupenaeus japonicus 94%
- Altered gut microbiota and immunity defines Plasmodium vivax survival in Anopheles stephensi 93%
- Peptides derived of kunitz-type serine protease inhibitor as potential vaccine against experimental schistosomiasis 93%
Similar papers in this journal
Similar papers in this journal
- Serological evidence and factors associated to liver damage in malaria-typhoid infected patients consulting in two health facilities, Yaoundé-Cameroon 95%
- Stress-mediating Inflammatory Cytokine Profiling Reveals Unique Patterns in Malaria and Typhoid Fever Patients 95%
- Dual oxidase gene Duox and Toll-like receptor 3 gene TLR3 in the Toll pathway suppress zoonotic pathogens through regulating the intestinal bacterial community homeostasis in Hermetia illucens L. 94%
Similar papers in this journal
- Metatranscriptomic Insights into Host-Microbiome Interactions Underlying Asymptomatic COVID-19 Cases 93%
- Gut microbial communities associated with phenotypically divergent populations of the striped stem borer Chilo suppressalis 93%
- Impact of HIV infection and integrase strand transfer inhibitors-based treatment on gut virome 93%
Similar papers in this journal
- Schistosoma Japonicum infection in Treg-specific USP21 knock-out mice 96%
- Endothelial Protein C Receptor Could Contribute to Experimental Malaria-Associated Acute Respiratory Distress Syndrome 92%
- Immunoinformatics Prediction of Epitope Based Peptide Vaccine Against Listeria Monocytogenes Fructose Bisphosphate Aldolase Protein 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.