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Cellular stresses trigger autophagy to remove damaged macromolecules and organelles. Lysosomes ‘host’ multiple stress-sensing mechanisms that trigger the coordinated biogenesis of autophagosomes and lysosomes. For example, transcription factor (TF)EB, which regulates autophagy and lysosome biogenesis, is activated following the inhibition of mTOR, a lysosome-localized nutrient sensor. Here we show that reactive oxygen species (ROS) activate TFEB via a lysosomal Ca 2+ -dependent mechanism independent of mTOR. Exogenous oxidants or increasing mitochondrial ROS levels directly and specifically activate lysosomal TRPML1 channels, inducing lysosomal Ca 2+ release. This activation triggers calcineurin-dependent TFEB-nuclear translocation, autophagy induction and lysosome biogenesis. When TRPML1 is genetically inactivated or pharmacologically inhibited, clearance of damaged mitochondria and removal of excess ROS are blocked. Furthermore, TRPML1’s ROS sensitivity is specifically required for lysosome adaptation to mitochondrial damage. Hence, TRPML1 is a ROS sensor localized on the lysosomal membrane that orchestrates an autophagy-dependent negative-feedback programme to mitigate oxidative stress in the cell. Reactive oxygen species (ROS) damage cell components, necessitating their clearance through autophagy. Here, the authors show that ROS can induce autophagy by triggering TRPML1 to release Ca 2+ from the lysosomal lumen, in turn activating the autophagy and lysosomal biogenesis regulator TFEB.

Targeting exosome biogenesis and release may have potential clinical implications for cancer therapy. Herein, we have optimized a quantitative high throughput screen (qHTS) assay to identify compounds that modulate exosome biogenesis and/or release by aggressive prostate cancer (PCa) CD63-GFP-expressing C4-2B cells. A total of 4,580 compounds were screened from the LOPAC library (a collection of 1,280 pharmacologically active compounds) and the NPC library (NCGC collection of 3,300 compounds approved for clinical use). Twenty-two compounds were found to be either potent activators or inhibitors of intracellular GFP signal in the CD63-GFP-expressing C4-2B cells. The activity of lead compounds in modulating the secretion of exosomes was validated by a tunable resistive pulse sensing (TRPS) system (qNano-IZON) and flow cytometry. The mechanism of action of the lead compounds in modulating exosome biogenesis and/or secretion were delineated by immunoblot analysis of protein markers of the endosomal sorting complex required for transport (ESCRT)-dependent and ESCRT-independent pathways. The lead compounds tipifarnib, neticonazole, climbazole, ketoconazole, and triademenol were validated as potent inhibitors and sitafloxacin, forskolin, SB218795, fenoterol, nitrefazole and pentetrazol as activators of exosome biogenesis and/or secretion in PC cells. Our findings implicate the potential utility of drug-repurposing as novel adjunct therapeutic strategies in advanced cancer.

Significance Lysosomes are the cell’s degradation center. To adapt to different environmental conditions, the cell has evolved a set of delicate mechanisms to rapidly change lysosome function, which is referred to as lysosomal adaptation. Notably, lysosomal adaptation is required for cell survival under low nutrient conditions. In this study, we identified TRPML1, a lysosomal Ca ²⁺-permeant ion channel, as an essential player required for lysosomal adaptation. The activity of TRPML1 is potently (up to 10-fold) and rapidly increased upon nutrient starvation. Furthermore, pharmacological inhibition or genetic deletion of TRPML1 completely abolished the effects of starvation on boosting the degradation capability of lysosomes. Upon nutrient starvation, autophagy digests unwanted cellular components to generate catabolites that are required for housekeeping biosynthesis processes. A complete execution of autophagy demands an enhancement in lysosome function and biogenesis to match the increase in autophagosome formation. Here, we report that mucolipin-1 (also known as TRPML1 or ML1), a Ca ²⁺ channel in the lysosome that regulates many aspects of lysosomal trafficking, plays a central role in this quality-control process. By using Ca ²⁺ imaging and whole-lysosome patch clamping, lysosomal Ca ²⁺ release and ML1 currents were detected within hours of nutrient starvation and were potently up-regulated. In contrast, lysosomal Na ⁺-selective currents were not up-regulated. Inhibition of mammalian target of rapamycin (mTOR) or activation of transcription factor EB (TFEB) mimicked a starvation effect in fed cells. The starvation effect also included an increase in lysosomal proteostasis and enhanced clearance of lysosomal storage, including cholesterol accumulation in Niemann–Pick disease type C (NPC) cells. However, this effect was not observed when ML1 was pharmacologically inhibited or genetically deleted. Furthermore, overexpression of ML1 mimicked the starvation effect. Hence, lysosomal adaptation to environmental cues such as nutrient levels requires mTOR/TFEB-dependent, lysosome-to-nucleus regulation of lysosomal ML1 channels and Ca ²⁺ signaling.

Imaging and subsequent segmentation analysis in three-dimensional (3D) culture models are complicated by the light scattering that occurs when collecting fluorescent signal through multiple cell and extracellular matrix layers. For 3D cell culture models to be usable for drug discovery, effective and efficient imaging and analysis protocols need to be developed that enable high-throughput data acquisition and quantitative analysis of fluorescent signal. Here we report the first high-throughput protocol for optical clearing of spheroids, fluorescent high-content confocal imaging, 3D nuclear segmentation, and post-segmentation analysis. We demonstrate nuclear segmentation in multiple cell types, with accurate identification of fluorescently-labeled subpopulations, and develop a metric to assess the ability of clearing to improve nuclear segmentation deep within the tissue. Ultimately this analysis pipeline allows for previously unattainable segmentation throughput of 3D culture models due to increased sample clarity and optimized batch-processing analysis.

Age-related Macular Degeneration (AMD), a blinding eye disease, is characterized by pathological protein- and lipid-rich drusen deposits underneath the retinal pigment epithelium (RPE) and atrophy of the RPE monolayer in advanced disease stages - leading to photoreceptor cell death and vision loss. Currently, there are no drugs that stop drusen formation or RPE atrophy in AMD. Here we provide an iPSC-RPE AMD model that recapitulates drusen and RPE atrophy. Drusen deposition is dependent on AMD-risk-allele CFH(H/H) and anaphylatoxin triggered alternate complement signaling via the activation of NF-κB and downregulation of autophagy pathways. Through high-throughput screening we identify two drugs, L-745,870, a dopamine receptor antagonist, and aminocaproic acid, a protease inhibitor that reduce drusen deposits and restore RPE epithelial phenotype in anaphylatoxin challenged iPSC-RPE with or without the CFH(H/H) genotype. This comprehensive iPSC-RPE model replicates key AMD phenotypes, provides molecular insight into the role of CFH(H/H) risk-allele in AMD, and discovers two candidate drugs to treat AMD. Age-related macular degeneration is characterized by lipid-rich drusen deposits underneath the retinal pigment epithelium (RPE). Here the authors report an in vitro iPSC-RPE model for AMD that recapitulates drusen and RPE atrophy, and identify two drugs that reduce drusen deposits and restore RPE epithelial phenotype.

Pancreatic cancer treatment often relies on multi-drug regimens, but optimal combinations remain elusive. This study evaluates predictive approaches to identify synergistic drug combinations using a dataset from the National Center for Advancing Translational Sciences (NCATS). Screening 496 combinations of 32 anticancer compounds against the PANC-1 cells experimentally determined the degree of synergism and antagonism. Three research groups (NCATS, University of North Carolina, and Massachusetts Institute of Technology) leverage these data to apply machine learning (ML) approaches, predicting synergy across 1.6 million combinations. Of the 88 tested, 51 show synergy, with graph convolutional networks achieving the best hit rate and random forest the highest precision. Beyond highlighting the potential of ML, this work delivers 307 experimentally validated synergistic combinations, demonstrating its practical impact in treating pancreatic cancer. Finding optimal multi-drug combinations for pancreatic cancer remains a complex task. Here, authors across three different groups apply machine learning approaches to predict synergy across 1.6 million combinations of drugs for pancreatic cancer, 307 of which are validated experimentally.

Herpes simplex virus (HSV) infection poses global public health concerns with lifelong impacts. Acyclovir, the standard therapy, has limited efficacy in preventing subclinical shedding, and drug resistance occurs in immunocompromised patients, highlighting the need for novel therapeutics. Here we show that acyclovir is significantly less effective in skin-derived keratinocytes than donor-matched fibroblasts. Using 3D bioprinted human skin equivalents (HSEs) in a 96-well plate format, we have screened 738 compounds with broad targets and mechanisms of action, identifying potent antivirals, including 23 known or experimental HSV treatments. Unlike acyclovir, antivirals against HSV helicase/primase or host replication pathways display similar potency across cell types and donor sources in both 2D and 3D models. The reduced potency in keratinocytes may explain acyclovir’s limited clinical efficacy. Our 3D bioprinted HSE assay platform enables the integration of patient-derived cells early in drug development and offers a physiologically relevant approach for HSV drug discovery. Using a 3D bioprinted human skin equivalent assay, this study shows a keratinocyte-specific limitation of acyclovir against herpes simplex virus and identifies antivirals effective across various cell types and 2D and 3D platforms.

Background Borderline Personality Disorder (BPD) is a severe and disabling condition. The Borderline Personality Disorder Checklist (BPDCL) was designed to specifically assess the subjective burden of a patient due to BPD symptoms. Various translations have been developed, but an assessment of the psychometric properties of these translations is needed. The aim was to examine the psychometric qualities of the BPDCL across different languages (i.e., Italian, Dutch, German, Spanish, English, and Greek). Methods Secondary data was used by reaching out to various researchers, who administered the BPDCL in previous studies. Five studies (N = 3199) conducted in Spain, Germany, Italy, the Netherlands, Australia, England, and Greece, were included in the current data set. The BPDCL was administered to BPD patients (N = 1131), Axis I disorder patients (N = 57), patients with other personality disorders (N = 225), and healthy controls (N = 1786). Item analyses and analyses assessing the known‐groups and convergent validity were performed to investigate the psychometric properties of the checklist. Results Each version of the BPDCL, differing in language, demonstrated high‐reliability coefficients (Cronbach's Alpha ranged from 0.93 to 0.96 and was 0.96 for the entire sample). The correlations between the BPDCL and other instruments, used in the studies, were weak to strong. Correlations greater than 0.55 were observed between the BPDCL and the scales BPDSI, SCL‐90 and the BSI. In addition, the BPDCL seems to differentiate well between diagnostic groups. BPD patients scored the highest, followed by patients with other personality disorders, who in turn scored higher than Axis I disorder patients and healthy controls. Conclusions In general, the BPDCL possesses good psychometric properties and seems to be an adequate self‐report instrument to measure the subjective burden of BPD patients.

En este artículo detallaremos los principios teóricos y procedimentales para realizar un análisis de clústeres aplicado a los estudios históricos medievales utilizando el método de la distancia de Gower. Los análisis de clústeres son una herramienta usada frecuentemente en la arqueología para clasificar materiales encontrados en los yacimientos, pero estos métodos estadísticos también pueden ser usados para definir estrategias de explotación y ocupación de un territorio. La distancia de Gower es una prueba no paramétrica muy útil cuando queremos saber como de diferentes son los distintos registros dentro de un grupo de datos mixtos. A partir del trabajo previo con las fuentes documentales y cartográficas, mostraremos los resultados obtenidos en aplicar un análisis de conglomerados para definir las estrategias de explotación agrarias y forestales en el conjunto de asentamientos medievales existentes a lo largo del siglo XI en la comarca del Baix Montseny (Cataluña).

The tumour microenvironment contributes to cancer metastasis and drug resistance. However, most high throughput screening (HTS) assays for drug discovery use cancer cells grown in monolayers. Here we show that a multilayered culture containing primary human fibroblasts, mesothelial cells and extracellular matrix can be adapted into a reliable 384- and 1,536-multi-well HTS assay that reproduces the human ovarian cancer (OvCa) metastatic microenvironment. We validate the identified inhibitors in secondary in vitro and in vivo biological assays using three OvCa cell lines: HeyA8, SKOV3ip1 and Tyk-nu. The active compounds directly inhibit at least two of the three OvCa functions: adhesion, invasion and growth. In vivo , these compounds prevent OvCa adhesion, invasion and metastasis, and improve survival in mouse models. Collectively, these data indicate that a complex three-dimensional culture of the tumour microenvironment can be adapted for quantitative HTS and may improve the disease relevance of assays used for drug screening. Tumour microenvironment affects the outcome of pharmacological anticancer treatments. Here, Kenny et al . show that organotypic cultures of ovarian cancer cells can recapitulate metastasis. They identify several new compounds that block cancer invasion and metastasis and improve survival in mouse models.