Biology subjects

Leopold, S.

Publications and source records attributed to Leopold, S..

3 recordsLinked to original sources

bioRxiv2024

Longitudinal profiling of the microbiome at four body sites reveals core stability and individualized dynamics during health and disease

To understand dynamic interplay between the human microbiome and host during health and disease, we analyzed the microbial composition, temporal dynamics, and associations with host multi-omics, immune and clinical markers of microbiomes from four body sites in 86 participants over six years. We found that microbiome stability and individuality are body-site-specific and heavily influenced by the host. The stool and oral microbiome were more stable than the skin and nasal microbiomes, possibly due to their interaction with the host and environment. Also, we identified individual-specific and commonly shared bacterial taxa, with individualized taxa showing greater stability. Interestingly, microbiome dynamics correlated across body sites, suggesting systemic coordination influenced by host-microbial-environment interactions. Notably, insulin-resistant individuals showed altered microbial stability and associations between microbiome, molecular markers, and clinical features, suggesting their disrupted interaction in metabolic disease. Our study offers comprehensive views of multi-site microbial dynamics and their relationship with host health and disease. Study HighlightsO_LIThe stability of the human microbiome varies among individuals and body sites. C_LIO_LIHighly individualized microbial genera are more stable over time. C_LIO_LIAt each of the four body sites, systematic interactions between the environment, the host and bacteria can be detected. C_LIO_LIIndividuals with insulin resistance have lower microbiome stability, a more diversified skin microbiome, and significantly altered host-microbiome interactions. C_LI

microbiology↗

bioRxiv2023

Human Th17- and IgG3-associated autoimmunity induced by a translocating gut pathobiont

Extraintestinal autoimmune diseases are multifactorial with translocating gut pathobionts implicated as instigators and perpetuators in mice. However, the microbial contributions to autoimmunity in humans remain largely unclear, including whether specific pathological human adaptive immune responses are triggered by such pathobionts. We show here that the translocating pathobiont Enterococcus gallinarum induces human IFN{gamma}+ Th17 differentiation and IgG3 subclass switch of anti-E. gallinarum RNA and correlating anti-human RNA autoantibody responses in patients with systemic lupus erythematosus and autoimmune hepatitis. Human Th17 induction by E. gallinarum is cell-contact dependent and involves TLR8-mediated human monocyte activation. In murine gnotobiotic lupus models, E. gallinarum translocation triggers IgG3 anti-RNA autoantibody titers that correlate with renal autoimmune pathophysiology and with disease activity in patients. Overall, we define cellular mechanisms of how a translocating pathobiont induces human T- and B-cell-dependent autoimmune responses, providing a framework for developing host- and microbiota-derived biomarkers and targeted therapies in extraintestinal autoimmune diseases. One Sentence SummaryTranslocating pathobiont Enterococcus gallinarum promotes human Th17 and IgG3 autoantibody responses linked to disease activity in autoimmune patients.

immunology↗

bioRxiv2021

Improving statistical power in severe malaria genetic association studies by augmenting phenotypic precision

Severe falciparum malaria has substantially affected human evolution. Genetic association studies of patients with clinically defined severe malaria and matched population controls have helped characterise human genetic susceptibility to severe malaria, but phenotypic imprecision compromises discovered associations. In areas of high malaria transmission the diagnosis of severe malaria in young children and, in particular, the distinction from bacterial sepsis, is imprecise. We developed a probabilistic diagnostic model of severe malaria using platelet and white count data. Under this model we re-analysed clinical and genetic data from 2,220 Kenyan children with clinically defined severe malaria and 3,940 population controls, adjusting for phenotype mis-labelling. Our model, validated by the distribution of sickle trait, estimated that approximately one third of cases did not have severe malaria. We propose a data-tilting approach for case-control studies with phenotype mis-labelling and show that this reduces false discovery rates and improves statistical power in genome-wide association studies.

genetics↗