How do necrotic cells expose phosphatidylserine to attract their predators—What’s unique and what’s in common with apoptotic cells

Persianolide-A, an eudesmanolide-type sesquiterpene lactone from Artemisia kopetdaghensis, induces apoptosis by regulating ERK signaling pathways

Background and purpose

Herbal components, particularly sesquiterpenes, are progressively recognized as a crucial resource for developing effective therapeutic agents for breast cancer. In this study, the effect of a sesquiterpene lactone known as 8-O-dihydroxy-11a,13-dihydroeudesma-4(15)-en-12,6a-olide (persianolide- A) was examined in breast cancer cell lines.

Experimental approach

MDA-MB-231 and MCF-7 cancer cells were grown in DMEM solution with 10% FBS. Then, an MTT assay was performed to evaluate cell viability. Apoptosis was detected by annexin-PI staining. A caspase 3/7 activity assay kit was used to assess the activity of caspase-3 and caspase-7. Protein expression of Bcl-2, Bax, and p-ERK1/2 was determined by western blotting.

Findings/Results

This study showed that the IC50 values of the persianolide-A for MCF-7 and MDA-MB- 468 cells are 34.76 and 54.48 μM, respectively. In addition, persianolide-A showed a significant increase in apoptosis in both MDAMB-231 and MCF-7 breast cancer cell lines. Persianolide-A significantly increased the expression of the pro-apoptotic protein Bax and decreased the expression of the anti-apoptotic protein Bcl-2. Also, presinolide-A treatment led to a substantial increase in caspase activity with a ratio of 3/7 in both MCF- 7 and MDA-MB-231 cancer cells. In addition, the study showed that persianolide-A decreased the expression of p-ERK1/2 protein.

Conclusion and implications

The results of this study suggest that persianolide-A, sourced from Artemisia kopetdaghensis, induces cell apoptosis in breast cancer cell types. The molecular mechanisms could be implicated in the modulation of the ERK1/2 signaling pathway.

Stain-Free Approach to Determine and Monitor Cell Heath Using Supervised and Unsupervised Image-Based Deep Learning

Cell-based medicinal products (CBMPs) are a growing class of therapeutics that promise new treatments for complex and rare diseases. Given the inherent complexity of the whole human cells comprising CBMPs, there is a need for robust and fast analytical methods for characterization, process monitoring, and quality control (QC) testing during their manufacture. Existing techniques to evaluate and monitor cell quality typically constitute labor-intensive, expensive, and highly specific staining assays. In this work, we combine image-based deep learning with flow imaging microscopy (FIM) to predict cell health metrics using cellular morphology "fingerprints" extracted from images of unstained Jurkat cells (immortalized human T-lymphocyte cells). A supervised (i.e., algorithm trained with human-generated labels for images) fingerprinting algorithm, trained on images of unstained healthy and dead cells, provides a robust stain-free, non-invasive, and non-destructive method for determining cell viability. Results from the stain-free method are in good agreement with traditional stain-based cytometric viability measurements. Additionally, when trained with images of healthy cells, dead cells and cells undergoing chemically induced apoptosis, the supervised fingerprinting algorithm is able to distinguish between the three cell states, and the results are independent of specific treatments or signaling pathways. We then show that an unsupervised variational autoencoder (VAE) algorithm trained on the same images, but without human-generated labels, is able to distinguish between samples of healthy, dead and apoptotic cells along with cellular debris based on learned morphological features and without human input. With this, we demonstrate that VAEs are a powerful exploratory technique that can be used as a process monitoring analytical tool.

Necroptosis contributes to non-alcoholic fatty liver disease pathoetiology with promising diagnostic and therapeutic functions

Nonalcoholic fatty liver disease (NAFLD) is the most prevalent type of chronic liver disease. However, the disease is underappreciated as a remarkable chronic disorder as there are rare managing strategies. Several studies have focused on determining NAFLD-caused hepatocyte death to elucidate the disease pathoetiology and suggest functional therapeutic and diagnostic options. Pyroptosis, ferroptosis, and necroptosis are the main subtypes of non-apoptotic regulated cell deaths (RCDs), each of which represents particular characteristics. Considering the complexity of the findings, the present study aimed to review these types of RCDs and their contribution to NAFLD progression, and subsequently discuss in detail the role of necroptosis in the pathoetiology, diagnosis, and treatment of the disease. The study revealed that necroptosis is involved in the occurrence of NAFLD and its progression towards steatohepatitis and cancer, hence it has potential in diagnostic and therapeutic approaches. Nevertheless, further studies are necessary.

The role of secreted proteins in efferocytosis

The clearance of apoptotic cells known as efferocytosis is the final stage of apoptosis, and includes the recognition, phagocytosis, and degradation of apoptotic cells. The maintenance of tissue homeostasis requires the daily elimination of billions of apoptotic cells from the human body via the process of efferocytosis. Accordingly, aberrations in efferocytosis underlie a growing list of diseases, including atherosclerosis, cancer, and infections. During the initial phase of apoptosis, "Eat-Me" signals are exposed and recognized by phagocytes either directly through phagocyte receptors or indirectly through secreted proteins that function as bridge molecules that cross-link dying cells to phagocytes. Here, we set out to provide a comprehensive review of the molecular mechanisms and biological significance of secreted proteins in apoptotic cell clearance. Specifically, it focuses on how these secreted proteins act as bridging molecules to facilitate the clearance process.