Moeed, A. | Search bioRxiv

bioRxiv2025

The Second Mitochondrial Activator of Caspases (SMAC) regulates growth, inflammation and mitochondrial integrity in cancer cells

SMAC is a mitochondrial intermembrane space protein, which is released during apoptosis and whose known function is antagonism of inhibitor of apoptosis proteins in the cytosol, to facilitate caspase activation. Recent data suggest that SMAC can also be released by sub-lethal signals in the apoptosis pathway, in the absence of cell death. We here explored potential functions of SMAC in non-apoptotic cells. We found that a portion of SMAC is spontaneously released into the cytosol in the absence of apoptosis, regulated by the BCL-2-family proteins BAX and BAK and the fission GTPase DRP1. In cancer cell lines, SMAC was required for the activation of caspases in lethal and non-lethal conditions, while this contribution to caspase-activation was much smaller in non-malignant fibroblast lines. In cells with high levels of cytosolic SMAC, SMAC deficiency reduced in vitro migration, invasion and anchorage-independent growth. SMAC-deficient cells further showed a reduced activity in interferon signalling, associated with reduced cytosolic presence of mitochondrial DNA and activation of the stimulator of interferon genes (STING), and SMAC expression levels correlated with interferon-induced genes in cancer data sets. We further found that SMAC can regulate mitochondrial morphology and integrity. Finally, high gene-expression of SMAC was associated with poor prognosis in patients of several cancer types. These results identify SMAC as a regulator of inflammation and growth behaviour of cancer cells. They further report a mitochondrial function of SMAC and demonstrate a role of SMAC in human cancer biology across several cancer entities.

cell biology↗

bioRxiv2020

Sfaira accelerates data and model reuse in single cell genomics

Exploratory analysis of single-cell RNA-seq data sets is currently based on statistical and machine learning models that are adapted to each new data set from scratch. A typical analysis workflow includes a choice of dimensionality reduction, selection of clustering parameters, and mapping of prior annotation. These steps typically require several iterations and can take up significant time in many single-cell RNA-seq projects. Here, we introduce sfaira, which is a single-cell data and model zoo which houses data sets as well as pre-trained models. The data zoo is designed to facilitate the fast and easy contribution of data sets, interfacing to a large community of data providers. Sfaira currently includes 233 data sets across 45 organs and 3.1 million cells in both human and mouse. Using these data sets we have trained eight different example model classes, such as autoencoders and logistic cell type predictors: The infrastructure of sfaira is model agnostic and allows training und usage of many previously published models. Sfaira directly aids in exploratory data analysis by replacing embedding and cell type annotation workflows with end-to-end pre-trained parametric models. As further example use cases for sfaira, we demonstrate the extraction of gene-centric data statistics across many tissues, improved usage of cell type labels at different levels of coarseness, and an application for learning interpretable models through data regularization on extremely diverse data sets.

bioinformatics↗