Host genetic variants regulates CCR5 expression on immune cells: a study in people living with HIV and healthy controls

dos Santos, J. C.; Zhang, Z.; van Eekeren, L. E.; Fok, E. T.; Vadaq, N.; van de Wijer, L.; van der Heijden, W.; Koeken, V. A.; Koenen, H. J.; Mhlanga, M.; Netea, M.; van der Ven, A. J.; Li, Y.

doi:10.1101/2022.06.19.496757

bioRxiv · 10.1101/2022.06.19.496757

Host genetic variants regulates CCR5 expression on immune cells: a study in people living with HIV and healthy controls

dos Santos, J. C.·Zhang, Z.·van Eekeren, L. E.·Fok, E. T.·Vadaq, N.·van de Wijer, L.·van der Heijden, W.·Koeken, V. A.·Koenen, H. J.·Mhlanga, M.·Netea, M.·van der Ven, A. J.·Li, Y.

View original source ↗Export metadata ↓

Abstract

C-C chemokine receptor 5 (CCR5) is the main HIV co-receptor affecting susceptibility and disease course. Quantitative trait loci (QTL) mapping analysis was performed to assess genetic variants associated with CCR5 expression on circulating immune cells in 209 PLHIV using ART and 304 healthy controls, all of Western European ancestry. The proportions of CCR5 positive cells and CCR5 mean fluorescence intensity (MFI) were assessed by flow cytometry in monocytes and CD4+ and CD8+ T cell subsets using flow cytometry. We identified the rs60939770, which is an intergenic variant in cis-region to CCR5 gene not in linkage disequilibrium with CCR5d32, related to the proportion of CCR5+ memory T regulatory cells, both in PLHIV and healthy controls. Two genome-wide significant loci, in linkage equilibrium with CCR5d32, were found to be associated with CCR5 MFI of multiple subsets of mostly differentiated memory T cells in both groups. The expression of nearby chemokines receptors (CCR1, CCR2, CCR3, CCRL2), existing in the same the same topologically associating domain, were also influenced by these genetic variants. Furthermore, we show the genetic variants which modulate CCR5 surface expression affect the production of other inflammatory mediators, including monocyte- and lymphocyte-derived cytokines as well as CCL4 and IL-8. Our data indicate that the genetic regulation of CCR5 expression is cell-specific and affects the production of various inflammatory mediators. Author SummaryCCR5 plays a important role in the acquisition of HIV and it is associated to immune activation in people living with HIV (PLHIV). Using samples of cohorts composed of healthy individuals and PLHIV, we sought to map genomic regions that influence CCR5 expression on monocytes and subsets of CD4+ and CD8+ cells. We identified distinct genetic variants that are associated with CCR5 cell proportions or mean fluorescence intensity in subpopulations of T cells with memory functions in both healthy and PLHIV. The genetic variants also influenced the expression of other nearby chemokine receptors and the production of inflammatory mediators. Thus, we demonstrated that the genetic regulation of CCR5 expression is cell-type specific and may impact HIV susceptibility and disease progression.

Explore related subjects

systems biology Preprint

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

dos Santos, J. C., Zhang, Z., van Eekeren, L. E., Fok, E. T., Vadaq, N., van de Wijer, L., van der Heijden, W., Koeken, V. A., Koenen, H. J., Mhlanga, M., Netea, M., van der Ven, A. J., Li, Y.. 2022-06-20. Host genetic variants regulates CCR5 expression on immune cells: a study in people living with HIV and healthy controls. https://doi.org/10.1101/2022.06.19.496757

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Low-fidelity simulator selection for multi-fidelity inference of epithelial vertex models

Vertex models link cell division, neighbour exchange, and intercellular forces to epithelial tissue form through parameters such as junctional line tension and perimeter contractility. Bayesian inference of these parameters from data quantifies their uncertainty, but for stochastic models of proliferating tissues it requires many costly simulations. Multi-fidelity simulation-based inference reduces this cost by training on many runs of a cheaper, less accurate ('low-fidelity') simulator before refining with a few full ('high-fidelity') runs. Low-fidelity versions can be constructed in several ways, for example by stopping simulations early, coarsening the timestep, or averaging fewer replicates, but it is unclear which to choose before committing to costly training. To address this, we introduce PRISM (Paired Ranking Index for Simulator Matching), a screen that compares each candidate with the full simulator on paired runs, scoring agreement in cell-packing statistics against cost. For a vertex model of a proliferating tissue, PRISM flags stopping early as unreliable before training; once trained, this choice can produce confidently wrong parameter estimates. By contrast, a coarser timestep, within the range tested, is cheap and accurate; while fewer replicates are accurate but too costly. Using a screened low-fidelity simulator, multi-fidelity inference outperforms approximate Bayesian computation and neural-network inference trained on full simulations alone, at matched cost and with well-calibrated uncertainty. The same pattern recurs for a second vertex model fitted to published Drosophila pupal-wing imaging data, although there stopping halfway proves safe. Together, our findings suggest that which approximations are safe depends on the model, and that screening them before training makes multi-fidelity inference of tissue mechanics practical.

systems biology↗

bioRxiv2026

Calibrating developmental dynamics from single-cell atlases: lineage paths outrun least-action routes

Single-cell atlases infer developmental dynamics from destructive snapshots, yet how far realized lineage paths depart from such inferred dynamics has never been measured with controls. We introduce LADD (Least-Action Developmental Dynamics), a measurement framework that learns a stochastic developmental vector field from time-series single-cell RNA-seq and computes minimum-action and minimum-time paths, together with a three-layer calibration protocol that separates genuine path deviation from the generic cost of centroid measurement, and we compare its forecasting component with optimal-transport and flow-matching methods. In the lineage-resolved Caenorhabditis elegans atlas, the raw action ratio of observed lineage-centroid trajectories to optimal paths is 3.79, yet forward-simulated ensembles of the learned model itself score 2.07, leaving a genuine excess of about two-fold. In zebrafish (Danio rerio), the observed ratio sits entirely at the model's floor. Three mechanisms that could explain the residual deviation (detours, flow-opposition, and fate-map complexity) are each excluded; what remains is speed, with lineages moving 30-43% faster along the learned flow. We report validity boundaries for temporal resolution, cross-species comparison, and energetic interpretation, delimiting where action-based trajectory comparisons are meaningful.

systems biology↗

bioRxiv2026

Choice of annotation data significantly impacts automated reconstruction of genome-scale metabolic networks

Genome-scale metabolic models are valuable tools for studying metabolism in many applications, such as metabolic engineering or understanding human disease. However, curated models are available for only a limited number of organisms, and generating high-quality models for additional organisms remains a challenge. To help address this challenge, several software tools have been developed that can produce draft models in an automated fashion, but the quality of these draft models lags behind manually curated ones. Thus, there is still a need to improve these tools. Previous work has focused primarily on developing better methods for each step in the reconstruction process, such as gene annotation and gap-filling. Here, we focus on the data that underlies these methods, such as known connections between protein sequence and function. We find that changing the source of reconstruction data has a substantial impact on the contents and predictions of generated models. In fact, the source of data can have a bigger impact than the choice of reconstruction method. Additionally, we show that the reconstruction process is sensitive to the accuracy of gene annotations in the reconstruction data. Finally, we demonstrate the importance of having functional annotation data from an organism that is closely related to the one for which a model is being built. These observations highlight the importance of reconstruction data and identify principles that should be considered when selecting data for reconstruction tools.

systems biology↗