Kockx, M. | Search bioRxiv

bioRxiv2023

ApoA-I Protects Pancreatic β-cells from Cholesterol-induced Mitochondrial Damage and Restores their Ability to Secrete Insulin

Objective: High cholesterol levels in pancreatic {beta}-cells cause oxidative stress and decrease insulin secretion. {beta}-cells can internalize apolipoprotein (apo) A-I, which increases insulin secretion. This study asks whether internalization of apoA-I improves {beta}-cell insulin secretion by reducing oxidative stress. Approach: Ins-1E cells were cholesterol-loaded by incubation with cholesterol-methyl-{beta}-cyclodextrin. Insulin secretion in the presence of 2.8 or 25 mM glucose was quantified by radioimmunoassay. Internalization of fluorescently labelled apoA-I by {beta}-cells was monitored by flow cytometry. The effects of apoA-I internalization on {beta}-cell gene expression was evaluated by RNA sequencing. ApoA-I binding partners on the {beta}-cell surface were identified by mass spectrometry. Mitochondrial oxidative stress was quantified in {beta}-cells and isolated islets with MitoSOX and confocal microscopy. Results: An F1-ATPase {beta}-subunit on the {beta}-cell surface was identified as the main apoA-I binding partner. {beta}-cell internalization of apoA-I was time-, concentration-, temperature-, cholesterol- and F1-ATPase {beta}-subunit-dependent. {beta}-cells with internalized apoA-I (apoA-I+ cells) had higher cholesterol and cell surface F1-ATPase {beta}-subunit levels than {beta}-cells without internalized apoA-I (apoA-I- cells). The internalized apoA-I co-localized with mitochondria and was associated with reduced oxidative stress and increased insulin secretion. The ATPase inhibitory factor 1, IF1, attenuated apoA-I internalization and increased oxidative stress in Ins-1E {beta}-cells and isolated mouse islets. Differentially expressed genes in apoA-I+ and apoA-I- Ins-1E cells were related to protein synthesis, the unfolded protein response, insulin secretion and mitochondrial function. Conclusions: These results establish that {beta}-cells are functionally heterogeneous and apoA-I restores insulin secretion in {beta}-cells with elevated cholesterol levels by improving mitochondrial redox balance.

cell biology↗

bioRxiv2023

Automated tumor immunophenotyping predicts clinical benefit from anti-PD-L1 immunotherapy

BackgroundCancer immunotherapy has transformed the clinical approach to patients with malignancies as profound benefits can be seen in a subset of patients. To identify this subset, biomarker analyses increasingly focus on phenotypic and functional evaluation of the tumor microenvironment (TME) to determine if density, spatial distribution, and cellular composition of immune cell infiltrates can provide prognostic and/or predictive information. Attempts have been made to develop standardized methods to evaluate immune infiltrates in the routine assessment of certain tumor types; however, broad adoption of this approach in clinical decision-making is still missing. MethodsWe developed approaches to categorize solid tumors into "Desert", "Excluded" and "Inflamed" types according to the spatial distribution of CD8+ immune effector cells to determine the prognostic and/or predictive implications of such labels. To overcome the limitations of this subjective approach we incrementally developed four automated analysis pipelines of increasing granularity and complexity for density and pattern assessment of immune effector cells. ResultsWe show that categorization based on "manual" observation is predictive for clinical benefit from anti-programmed cell death ligand-1 (PD-L1) therapy in two large cohorts of patients with non-small cell lung cancer (NSCLC) or triple-negative breast cancer (TNBC). For the automated analysis we demonstrate that a combined approach outperforms individual pipelines and successfully relates spatial features to pathologist-based read-outs and patient response to therapy. ConclusionsOur findings suggest tumor immunophenotype (IP) generated by automated analysis pipelines should be evaluated further as potential predictive biomarkers for cancer immunotherapy. What is already known on this topicClinical benefit from checkpoint inhibitor-targeted therapies is realized only in a subset of patients. Robust biomarkers to identify patients who may respond to such therapies are needed. What this study addsWe have developed manual and automated approaches to categorize tumors into immunophenotypes based on the spatial distribution of CD8+ T effector cells that predict clinical benefit from anti-PD-L1 immunotherapy for patients with advanced non-small cell lung cancer or triple-negative breast cancer. How this study might affect research, practice or policyTumor immunophenotypes should be further validated as predictive biomarker for checkpoint inhibitor-targeted therapies in prospective clinical studies.

pathology↗