The Vanderbilt Immunometabolism Research Group (VIMRG)
The Human Adipose Tissue Atlas
Koethe Lab Members: Celestine Wanjalla, Mona Mashayekhi, Curtis Gabriel, Samuel Bailin, John Koethe
Vanderbilt University Medical Center
Department of Medicine
The Koethe Research Group.
Our group is interested in understanding HIV-specific factors that contribute to elevated risk of cardiometabolic diseases in persons with HIV (PWH). As PWH are living longer on effective anti-retroviral therapy, there has been a disproportionate increase in the incidence of cardiometabolic diseases. Several studies have linked elevated inflammation, which does not completely normalize with anti-retroviral therapy, to metabolic disease in PWH. Adipose tissue is a critical regulator of systemic metabolic health and maladaptive changes to adipose tissue in PWH have long been recognized. We are investigating how alteration in immune cell populations and function, viral reservoir and anti-viral immune cell responses, and anti-retroviral therapy may explain the excess risk of cardiometabolic diseases in PWH compared with HIV-negative persons.
The HATIM Cohort
The HIV, Adipose Tissue Immunology, and Metabolism Study (HATIM) was an R01 funded study to assess the role of chronic T cell activation in PWH in adipocyte impairment and insulin resistance. We recruited PWH from the Vanderbilt Comprehensive Care Clinic between August 2017 and June 2018 who were on anti-retroviral therapy for ≥ 18 months, had virologic suppression for ≥ 12 months, and had a CD4 T cell count ≥ 350 cells/mm3 at enrollment. Participants had no known inflammatory condition. Participants were classified as non-diabetic, prediabetic, or diabetic based on fasting blood sugar and/or hemoglobin A1c. The total cohort included 51 non-diabetic PWH, 44 prediabetic PWH, and 39 diabetic PWH. Concurrently, 38 diabetic HIV-negative persons roughly matched by age and body mass index were also recruited. This study is registered with ClinicalTrials.gov (NCT04451980).
HATIM Study Design
Funding
This cohort was funded by National Institutes of Health grant R01DK112262.
HATIM Publications
Hepatic steatosis and ectopic Fat are associated with differences in subcutaneous adipose tissue gene expression in people with HIV. Hepatol Commun. 2021;5:1224-1237. Link
Single-cell analysis shows that adipose tissue of persons with both HIV and diabeties is enriched for clonal, cytotoxic, and CMV-specific CD4+ T cells. Cell Rep Med. 2021;2:100205. Link
Relationship of Subcutaneous Adipose Tissue Inflammation-related Gene Expression with Ectopic Lipid Deposition in Persons with HIV. J Acquir Immune Defic Syndr. 2022[ahead of print]. Link
Relationship of Subcutaneous Adipose Tissue Inflammation-Related Gene Expression With Ectopic Lipid Deposition in Persons With HIV. J Acquir Immune Defic Syndr. 2022;90:175-183. Link
Adipose Tissue in Persons with HIV is Enriched for CD4+ T Effector Memory and T Effector Memory RA+ Cells, Which Show Higher CD69 Expression and CD57, CX3CR1, GPR56 Co-expression with Increasing Glucose Intolerance. Front Immunol. 2019;10:408. Link
Mean Coronary Cross‐Sectional Area as a Measure of Arterial Remodeling Using Noncontrast CT Imaging in Persons With HIV. J Am Heart Assoc. 2022;11:e025768. Link
CD4+ T cells expressing CX3CR1, GPR56, with variable CD57 are associated with cardiometabolic diseases in persons with HIV. Front Immunol. 2023;14:1099356. Link
Study Design.
Fifty-nine PWH (20 non-diabetic, 19 prediabetic, and 20 diabetic) and 32 diabetic HIV-negative persons underwent subcutaneous adipose tissue liposuction and subsequent paired single-cell RNA-seq and CITE-seq analysis. Please see our Github page for processing scripts and reproduction of figures. Link
Overview of Model.
With glucose intolerance, there is a coordinated compositional shift towards lipid-associated and intermediate macrophages and CD4+ and CD8+ T effector memory cells. These same cell types are highly associated with increased myofibroblast (fibrotic cells) and decreased PCOLCE+ fibroblast (interstitial fibroblast that give rise to preadipocytes). The compositional shift is accompanied by a coordinated, inter-cellular, transcriptional regulatory program characterized by: macrophage shift from immunomodulatory role to a lipid metabolism and inflammatory phenotype, T cell polarization from naive cell states towards an effector memory phenotype with expression of cytotoxic, TNF, and IFN-gamma genes, and preadipocyte shift from adipogensis genes to fibrotic genes. Finally, CD4+ T effector cells predominantly expressing CD69, a marker of tissue residency, are associated with whole adipose tissue changes in expression of genes important for insulin activity (GLP1R, e.g.) and lipid metabolism (LPL) that provides a plausible link between immune-cell mediate inflammation and adipocyte functions that gives rise to glucose intolerant phenotypes.
Figure 1. Overall Model. Abbreviations: FIB, fibroblast; PVM, perivascular macrophage; LAM, lipid-associated macrophage; Tem, T effector memory
How to contact us.
Lab Members.
John R. Koethe, MD, MSCI. Email:john.r.koethe@vumc.org
Samuel S. Bailin, MD. Email:samuel.bailin@vumc.org
Celestine N. Wanjalla. MD, PhD. Email: celestine.wanjall@vumc.org
Curtis L. Gabriel, MD, PhD. Email: curtis.l.gabriel@vumc.org
Mona Mashayekhi, MD, PhD. Email: mona.mashayekhi@vumc.org
Cell information vs cell information on dimension reduction
In this tab, users can visualise two cell informations side-by-side on low-dimensional representions.Dimension Reduction
Gene expression vs gene expression on dimension reduction
In this tab, users can visualise two gene expressions side-by-side on low-dimensional representions.Dimension Reduction
Coexpression of two genes on reduced dimensions
In this tab, users can visualise the coexpression of two genes on low-dimensional representions.Dimension Reduction
Cell information / gene expression violin plot / box plot
In this tab, users can visualise the gene expression or continuous cell information (e.g. Number of UMIs / module score) across groups of cells (e.g. libary / clusters).Proportion / cell numbers across different cell information
In this tab, users can visualise the composition of single cells based on one discrete cell information across another discrete cell information. Usage examples include the library or cellcycle composition across clusters.Gene expression bubbleplot / heatmap
In this tab, users can visualise the gene expression patterns of multiple genes grouped by categorical cell information (e.g. library / cluster).The normalised expression are averaged, log-transformed and then plotted.
Cell information vs gene expression on reduced dimensions
In this tab, users can visualise both cell information and gene expression side-by-side on low-dimensional representions.Dimension Reduction
Cluster-defining gene markers
In this tab, users can subset on the cluster of interest and find cluster-defining gene markers.Cell information vs ADT expression on reduced dimensions
In this tab, users can visualise both cell information and ADT expression side-by-side on low-dimensional representions.Dimension Reduction
Cell information vs average ADT cluster expression on dimension reduction
In this tab, users can visualise cell information with average ADT cluster expression side-by-side on low-dimensional representions.Dimension Reduction
Gene expression vs gene expression on dimension reduction
In this tab, users can visualise two gene expressions side-by-side on low-dimensional representions.Dimension Reduction
Coexpression of two genes on reduced dimensions
In this tab, users can visualise the coexpression of two genes on low-dimensional representions.Dimension Reduction
Cell information / gene expression violin plot / box plot
In this tab, users can visualise the gene expression or continuous cell information (e.g. Number of UMIs / module score) across groups of cells (e.g. libary / clusters).Proportion / cell numbers across different cell information
In this tab, users can visualise the composition of single cells based on one discrete cell information across another discrete cell information. Usage examples include the library or cellcycle composition across clusters.Gene expression bubbleplot / heatmap
In this tab, users can visualise the gene expression patterns of multiple genes grouped by categorical cell information (e.g. library / cluster).The normalised expression are averaged, log-transformed and then plotted.
Adjusted Spearman's correlation between cell proportions
In this tab, users can visualise the relationship of cell proportions with each other.Cell Selection
Adjusted Spearman's correlation between cell proportion & age or BMI
In this tab, users can visualise the relationship of cell proportions with age or BMI adjusted for sex, diabetes status, age (outcome BMI) and BMI (outcome Age).Demographic Variable
Adjusted Spearman's correlation between cell proportion & continuous measure of glucose.
In this tab, users can visualise the relationship of cell proportions with continuous measures of glucose adjusted for age, BMI, and sex.Glucose measure
Ordinal linear regression to determine relationship of sex with cell proportion.
In this tab, users can visualise the relationship of cell proportions with sex (reference: male) adjusted for BMI, age, and diabetes status.Subset selection
Pseudobulk differential gene expression between prediabetic and non-diabetic PWH
In this tab, users can visualise the differentially expressed genes between prediabetic and non-diabetic PWH adjusted for age, BMI, and sex.Select cluster
Pseudobulk differential gene expression between diabetic and non-diabetic PWH
In this tab, users can visualise the differentially expressed genes between diabetic and non-diabetic PWH adjusted for age, BMI, and sex.Select cluster
Linear regression analysis of CD4 TEM proportion (independent) and adipocyte gene Expression (dependent)
In this tab, users can visualise the association between CD4 TEM proportion and adipocyte gene expression.Select gene
Inter-cellular expression programs among immune cells and fibroadipogenic cells in adipose tissue.
In this tab, users can visualise the multicellular expression program for MP1. Note that some genes are plotted multiple times for each cell type expressing them.Reference: Bailin SS, Kropski JA, Gangula RD, et. al. Changes in Subcutaneous White Adipose Tissue Cellular Composition and Molecular Programs Underlie Glucose Intolerance in Persons with HIV. Front. Immunol. 2023;14. Link
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