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Michel Georges

Publications and source records attributed to Michel Georges.

2 recordsLinked to original sources

Multi-level aggregation analysis of microbiome composition and host gene expression reveals associations with systemic and local immunity.

The human gut microbiome plays a critical role in immune regulation, yet the molecular links between microbiome composition and host gene expression remain incompletely understood. We analyzed associations between host gene expression and microbiome composition in a cohort of 315 healthy individuals, integrating microarray-based gene expression data from three intestinal sites (ileum, transverse colon, and rectum) and six immune cell types with microbiome sequencing data. Using a hierarchical feature aggregation strategy combining principal component analysis, clustering, and covariate correction, we discovered significant associations primarily related to immunity. While microbial profiles were similar across the three intestinal sites, the transverse colon yielded the most "microbiome-host gene expression" associations. Among the immune cell types, CD8+ cells showed the highest number of associations. The first principal component of microbiome composition, reflecting a gradient from commensals (e.g., Ruminococcaceae and Christensenellaceae) to proinflammatory taxa ([Ruminococcus] gnavus and Lachnoclostridium), correlated with the expression of TNF-α-linked genes (HMOX1, CPI17, HSD3B2, and SLC5A1). Among individual genera, Catenibacterium abundance was associated with gene expression in both intestinal and immune cells, including negative associations with MRPS21 (related to mitochondrial function) in the transverse colon and with CD8+ gene programs related to T cell differentiation. These findings align with emerging evidence implicating mitochondrial dysfunction in intestinal inflammation. Our results identify multi-level associations between the gut microbiome and host gene expression, suggesting potential mechanisms by which microbiota shape local and systemic immunity and vice versa. The implicated genes and taxa represent candidates for experimental validation to improve understanding of host-microbiome homeostasis and its disruption in disease.IMPORTANCEThe gut microbiome and immune system are engaged in a complex interplay throughout human life. While most associative studies focus on case-control comparisons-typically examining patients with conditions such as inflammatory bowel disease or metabolic diseases-less is known about the molecular links between the microbiome and immune system in healthy individuals. In this study of a large cohort of healthy individuals, we addressed this gap by applying multiscale modeling to tackle the high dimensionality of host-microbiome data. We identified multi-level associations between microbiome composition and host gene expression in both intestinal tissues and immune cells. These findings offer a valuable reference for understanding baseline host-microbiome communication and highlight molecular candidates-such as TNF-α-related genes and mitochondrial pathways-for future experimental validation.

Humans

A viral clonality evenness score to predict progression to adult T-cell leukaemia in asymptomatic carriers of human T-lymphotropic virus type 1 in Japan: a retrospective longitudinal cohort study.

BACKGROUND: Adult T-cell leukaemia/lymphoma (ATL) is a highly aggressive T-cell malignancy that occurs in approximately 2-7% of individuals with human T-lymphotropic virus type 1 (HTLV-1), after decades of asymptomatic infection. To address the urgent need for predictive biomarkers to identify asymptomatic carriers of HTLV-1 at high risk of progression to ATL, we aimed to evaluate viral clonality sequencing as a potential tool for risk stratification. METHODS: This retrospective longitudinal cohort study involved HTLV-1 carriers enrolled in the Joint Study on Predisposing Factors of ATL Development, a nationwide cohort study initiated in Japan in 2002. Participants were selected from this cohort on the basis of their baseline proviral load at the time of enrolment as an asymptomatic carrier, length of follow-up, and clinical outcome. The cohort was subdivided into three subgroups: the first comprising HTLV-1 carriers who developed ATL, the second comprising carriers with high proviral load (&#x2265;4%) who did not progress to ATL, and the third comprising carriers with low proviral load (<4%) who did not progress to ATL. DNA extracted from peripheral blood mononuclear cells collected at enrolment and at least one follow-up visit was analysed by HTLV-1 clonality sequencing and the proviral load was quantified. We calculated a viral clonality evenness (VCE) score, based on the Shannon Evenness Index, to quantify the uniformity of the clonal distribution of samples, for which 0 represents a perfectly monoclonal architecture and 1 indicates a completely polyclonal landscape. We then estimated the performance of proviral load thresholds and VCE scoring to classify the risk of progression to ATL using the area under the receiver operating characteristic curve (AUC), the accuracy, and Matthews correlation coefficient. VCEs were compared between participant subgroups with the Wilcoxon rank sum test. FINDINGS: 56 participants followed up by JSPFAD between Feb 6, 2003, and July 19, 2022, were included in this study: 17 who progressed to ATL (mean follow-up 8&#xb7;3 years [SD 4&#xb7;0]), 18 who had a high proviral load and did not progress to ATL (9&#xb7;7 years [3&#xb7;4]), and 21 who had a low proviral load and did not progress to ATL (7&#xb7;5 years [3&#xb7;0]). Clonality sequencing of samples from 39 participants who did not progress to ATL revealed hundreds to thousands of HTLV-1 integration sites at both timepoints, corresponding to multiple clones of low and uniform abundance, and these participants had high VCE scores (&#x2265;0&#xb7;694) at baseline. By contrast, most participants (14 of 17) who progressed to ATL had a single predominant clone or two to four predominant clones at both timepoints, and lower VCE scores (<0&#xb7;694) at baseline than those who did not progress (p<0&#xb7;0001). AUCs were very similar for proviral load thresholds (91 [95% CI 80-98]) and VCE scoring (91 [78-100]), although when using methods that give equal weight to every individual, VCE scoring outperformed proviral load thresholds in predicting progression to ATL (accuracy: proviral load 0&#xb7;76 [95% CI 0&#xb7;76-0&#xb7;77], VCE scoring 1&#xb7;00 [0&#xb7;99-1&#xb7;00]; Matthews correlation coefficient: proviral load 0&#xb7;23 [95% CI 0&#xb7;19-0&#xb7;24], VCE scoring 0&#xb7;91 [0&#xb7;80-1&#xb7;00]). Prediction based on VCE scoring indicated no false positives, compared with 20% when using proviral load, although VCE scoring yields a greater number of false negatives (0&#xb7;3% vs 0&#xb7;1%). INTERPRETATION: The implementation of VCE scoring in clinical practice could inform early pre-emptive therapeutic interventions, exclusively targeting individuals with HTLV-1 at high risk and aiming to prevent progression to aggressive, treatment-refractory disease. Further validation, including independent confirmation of the performance of VCE scoring in multiple populations and the characterisation of its temporal dynamics, will be crucial to determine its clinical utility and potential integration into care pathways. FUNDING: Association Jules Bordet, FNRS-T&#xe9;l&#xe9;vie, FCC, WALInnov, FLF, JSPS-KAKENHI, and CoBiA.

Humans