Antitumor effect of memantine is related to the formation of the splicing isoform of GLG1, a decoy FGF‑binding protein

The Golgi Apparatus as an Anticancer Therapeutic Target

Although the discovery of the Golgi apparatus (GA) was made over 125 years ago, only a very limited number of therapeutic approaches have been developed to target this complex organelle. The GA serves as a modification and transport center for proteins and lipids and also has more recently emerged as an important store for some ions. The dysregulation of GA functions is implicated in many cellular processes associated with cancer and some GA proteins are indeed described as cancer biomarkers. This dysregulation can affect protein modification, localization, and secretion, but also cellular metabolism, redox status, extracellular pH, and the extracellular matrix structure. Consequently, it can directly or indirectly affect cancer progression. For these reasons, the GA is an appealing anticancer pharmacological target. Despite this, no anticancer drug specifically targeting the GA has reached the clinic and few have entered the clinical trial stage. Advances in nanodelivery approaches may help change this scenario by specifically targeting tumor cells and/or the GA through passive, active, or physical strategies. This article aims to examine the currently available anticancer GA-targeted drugs and the nanodelivery strategies explored for their administration. The potential benefits and challenges of modulating and specifically targeting the GA function in the context of cancer therapy are discussed.

exFINDER: identify external communication signals using single-cell transcriptomics data

Cells make decisions through their communication with other cells and receiving signals from their environment. Using single-cell transcriptomics, computational tools have been developed to infer cell-cell communication through ligands and receptors. However, the existing methods only deal with signals sent by the measured cells in the data, the received signals from the external system are missing in the inference. Here, we present exFINDER, a method that identifies such external signals received by the cells in the single-cell transcriptomics datasets by utilizing the prior knowledge of signaling pathways. In particular, exFINDER can uncover external signals that activate the given target genes, infer the external signal-target signaling network (exSigNet), and perform quantitative analysis on exSigNets. The applications of exFINDER to scRNA-seq datasets from different species demonstrate the accuracy and robustness of identifying external signals, revealing critical transition-related signaling activities, inferring critical external signals and targets, clustering signal-target paths, and evaluating relevant biological events. Overall, exFINDER can be applied to scRNA-seq data to reveal the external signal-associated activities and maybe novel cells that send such signals.

Autophagy modulators influence the content of important signalling molecules in PS-positive extracellular vesicles

Extracellular vesicles (EVs) are important mediators of intercellular communication in the tumour microenvironment. Many studies suggest that cancer cells release higher amounts of EVs exposing phosphatidylserine (PS) at the surface. There are lots of interconnections between EVs biogenesis and autophagy machinery. Modulation of autophagy can probably affect not only the quantity of EVs but also their content, which can deeply influence the resulting pro-tumourigenic or anticancer effect of autophagy modulators. In this study, we found that autophagy modulators autophinib, CPD18, EACC, bafilomycin A1 (BAFA1), 3-hydroxychloroquine (HCQ), rapamycin, NVP-BEZ235, Torin1, and starvation significantly alter the composition of the protein content of phosphatidylserine-positive EVs (PS-EVs) produced by cancer cells. The greatest impact had HCQ, BAFA1, CPD18, and starvation. The most abundant proteins in PS-EVs were proteins typical for extracellular exosomes, cytosol, cytoplasm, and cell surface involved in cell adhesion and angiogenesis. PS-EVs protein content involved mitochondrial proteins and signalling molecules such as SQSTM1 and TGFβ1 pro-protein. Interestingly, PS-EVs contained no commonly determined cytokines, such as IL-6, IL-8, GRO-α, MCP-1, RANTES, and GM-CSF, which indicates that secretion of these cytokines is not predominantly mediated through PS-EVs. Nevertheless, the altered protein content of PS-EVs can still participate in the modulation of the fibroblast metabolism and phenotype as p21 was accumulated in fibroblasts influenced by EVs derived from CPD18-treated FaDu cells. The altered protein content of PS-EVs (data are available via ProteomeXchange with identifier PXD037164) also provides information about the cellular compartments and processes that are affected by the applied autophagy modulators. Video Abstract.

exFINDER: identify external communication signals using single-cell transcriptomics data

Cells make decisions through their communication with other cells and receiving signals from their environment. Using single-cell transcriptomics, computational tools have been developed to infer cell-cell communication through ligands and receptors. However, the existing methods only deal with signals sent by the measured cells in the data, the received signals from the external system are missing in the inference. Here, we present exFINDER, a method that identifies such external signals received by the cells in the single-cell transcriptomics datasets by utilizing the prior knowledge of signaling pathways. In particular, exFINDER can uncover external signals that activate the given target genes, infer the external signal-target signaling network (exSigNet), and perform quantitative analysis on exSigNets. The applications of exFINDER to scRNA-seq datasets from different species demonstrate the accuracy and robustness of identifying external signals, revealing critical transition-related signaling activities, inferring critical external signals and targets, clustering signal-target paths, and evaluating relevant biological events. Overall, exFINDER can be applied to scRNA-seq data to reveal the external signal-associated activities and maybe novel cells that send such signals.