Systems genetics analysis of human body fat distribution genes identifies adipocyte processes.

Excess abdominal fat is a sexually dimorphic risk factor for cardio-metabolic disease and is approximated by the waist-to-hip ratio adjusted for body mass index (WHRadjBMI). Whereas this trait is highly heritable, few causal genes are known. We aimed to identify novel drivers of WHRadjBMI using systems genetics. We used two independent cohorts of adipose tissue gene expression and constructed sex- and depot-specific Bayesian networks to model gene-gene interactions from 8,492 genes. Using key driver analysis, we identified genes that, in silico and putatively in vitro, regulate many others. 51-119 key drivers in each network were replicated in both cohorts. In other cell types, 23 of these genes are found in crucial adipocyte pathways: Wnt signaling or mitochondrial function. We overexpressed or down-regulated seven key driver genes in human subcutaneous pre-adipocytes. Key driver genes ANAPC2 and RSPO1 inhibited adipogenesis, whereas PSME3 increased adipogenesis. RSPO1 increased Wnt signaling activity. In differentiated adipocytes, MIGA1 and UBR1 down-regulation led to mitochondrial dysfunction. These five genes regulate adipocyte function, and we hypothesize that they regulate fat distribution.

Systems genetics analysis of human body fat distribution genes identifies Wnt signaling and mitochondrial activity in adipocytes.

BACKGROUND: Excess fat in the abdomen is a sexually dimorphic risk factor for cardio-metabolic disease. The relative storage between abdominal and lower-body subcutaneous adipose tissue depots is approximated by the waist-to-hip ratio adjusted for body mass index (WHRadjBMI). Genome-wide association studies (GWAS) identified 346 loci near 495 genes associated with WHRadjBMI. Most of these genes have unknown roles in fat distribution, but many are expressed and putatively act in adipose tissue. We aimed to identify novel sex- and depot-specific drivers of WHRadjBMI using a systems genetics approach. METHODS: We used two independent cohorts of adipose tissue gene expression with 362 - 444 males and 147 - 219 females, primarily of European ancestry. We constructed sex- and depot- specific Bayesian networks to model the gene-gene interactions from 8,492 adipose tissue genes. Key driver analysis identified genes that, in silico and putatively in vitro, regulate many others, including the 495 WHRadjBMI GWAS genes. Key driver gene function was determined by perturbing their expression in human subcutaneous pre-adipocytes using lenti-virus or siRNA. RESULTS: 51 - 119 key drivers in each network were replicated in both cohorts. We used single-cell expression data to select replicated key drivers expressed in adipocyte precursors and mature adipocytes, prioritized genes which have not been previously studied in adipose tissue, and used public human and mouse data to nominate 53 novel key driver genes (10 - 21 from each network) that may regulate fat distribution by altering adipocyte function. In other cell types, 23 of these genes are found in crucial adipocyte pathways: Wnt signaling or mitochondrial function. We selected seven genes whose expression is highly correlated with WHRadjBMI to further study their effects on adipogenesis/Wnt signaling (ANAPC2, PSME3, RSPO1, TYRO3) or mitochondrial function (C1QTNF3, MIGA1, PSME3, UBR1).Adipogenesis was inhibited in cells overexpressing ANAPC2 and RSPO1 compared to controls. RSPO1 results are consistent with a positive correlation between gene expression in the subcutaneous depot and WHRadjBMI, therefore lower relative storage in the subcutaneous depot. RSPO1 inhibited adipogenesis by increasing ß-catenin activation and Wnt-related transcription, thus repressing PPARG and CEBPA. PSME3 overexpression led to more adipogenesis than controls. In differentiated adipocytes, MIGA1 and UBR1 downregulation led to mitochondrial dysfunction, with lower oxygen consumption than controls; MIGA1 knockdown also lowered UCP1 expression. SUMMARY: ANAPC2, MIGA1, PSME3, RSPO1, and UBR1 affect adipocyte function and may drive body fat distribution.

Gallium-68-labeled Peptide PET Quantifies Tumor Exposure of PD-L1 Therapeutics.

PURPOSE: Immune checkpoint therapy (ICT) is currently ineffective in a majority of patients. Tumor drug exposure measurements can provide vital insights into mechanisms involved in the resistance of solid tumors to those therapeutics; however, tools to quantify in situ drug exposure are few. We have investigated the potential of programmed death-ligand 1 (PD-L1) pharmacodynamics, quantified using PET, to inform on the tumor exposure of anti-PD-L1 (aPD-L1) therapeutics. EXPERIMENTAL DESIGN: To noninvasively quantify PD-L1 levels, we first developed a novel peptide-based gallium-68-labeled binder, [68Ga]Ga-DK223, and evaluated its in vivo distribution, pharmacokinetics, and PD-L1 specificity in preclinical models of triple-negative breast cancer and urothelial carcinoma with variable PD-L1 expression. We then quantified baseline and accessible PD-L1 levels in tumors as a noninvasive pharmacodynamic measure to assess tumor exposure to two aPD-L1 antibodies (avelumab and durvalumab). RESULTS: DK223 exhibited a KD of 1.01±0.83 nmol/L for PD-L1 and inhibited the PD-1:PD-L1 interaction in a dose-dependent manner. [68Ga]Ga-DK223 provides high-contrast PET images within 60 minutes of administration and detects PD-L1 in an expression-dependent manner in xenograft models. PD-L1 pharmacodynamics measured using [68Ga]Ga-DK223-PET revealed that avelumab and durvalumab had similar exposure early during therapy, but only durvalumab exhibited sustained exposure at the tumor. CONCLUSIONS: [68Ga]Ga-DK223 detected variable PD-L1 levels and exhibited salient features required for clinical translation. [68Ga]Ga-DK223-PET could be useful for quantifying total PD-L1 levels at baseline and accessible PD-L1 levels during therapy to understand drug exposure at the tumor, thus supporting its use for guiding and optimizing ICT.