Pooled multicolour tagging for visualizing subcellular protein dynamics.

Imaging-based methods are widely used for studying the subcellular localization of proteins in living cells. While routine for individual proteins, global monitoring of protein dynamics following perturbation typically relies on arrayed panels of fluorescently tagged cell lines, limiting throughput and scalability. Here, we describe a strategy that combines high-throughput microscopy, computer vision and machine learning to detect perturbation-induced changes in multicolour tagged visual proteomics cell (vpCell) pools. We use genome-wide and cancer-focused intron-targeting sgRNA libraries to generate vpCell pools and a large, arrayed collection of clones each expressing two different endogenously tagged fluorescent proteins. Individual clones can be identified in vpCell pools by image analysis using the localization patterns and expression level of the tagged proteins as visual barcodes, enabling simultaneous live-cell monitoring of large sets of proteins. To demonstrate broad applicability and scale, we test the effects of antiproliferative compounds on a pool with cancer-related proteins, on which we identify widespread protein localization changes and new inhibitors of the nuclear import/export machinery. The time-resolved characterization of changes in subcellular localization and abundance of proteins upon perturbation in a pooled format highlights the power of the vpCell approach for drug discovery and mechanism-of-action studies.

Pharmacological perturbation of the phase-separating protein SMNDC1.

SMNDC1 is a Tudor domain protein that recognizes di-methylated arginines and controls gene expression as an essential splicing factor. Here, we study the specific contributions of the SMNDC1 Tudor domain to protein-protein interactions, subcellular localization, and molecular function. To perturb the protein function in cells, we develop small molecule inhibitors targeting the dimethylarginine binding pocket of the SMNDC1 Tudor domain. We find that SMNDC1 localizes to phase-separated membraneless organelles that partially overlap with nuclear speckles. This condensation behavior is driven by the unstructured C-terminal region of SMNDC1, depends on RNA interaction and can be recapitulated in vitro. Inhibitors of the protein's Tudor domain drastically alter protein-protein interactions and subcellular localization, causing splicing changes for SMNDC1-dependent genes. These compounds will enable further pharmacological studies on the role of SMNDC1 in the regulation of nuclear condensates, gene regulation and cell identity.

Assessment of portal hypertension severity using machine learning models in patients with compensated cirrhosis.

BACKGROUND & AIMS: In individuals with compensated advanced chronic liver disease (cACLD), the severity of portal hypertension (PH) determines the risk of decompensation. Invasive measurement of the hepatic venous pressure gradient (HVPG) is the diagnostic gold standard for PH. We evaluated the utility of machine learning models (MLMs) based on standard laboratory parameters to predict the severity of PH in individuals with cACLD. METHODS: A detailed laboratory workup of individuals with cACLD recruited from the Vienna cohort (NCT03267615) was utilised to predict clinically significant portal hypertension (CSPH, i.e., HVPG ≥10 mmHg) and severe PH (i.e., HVPG ≥16 mmHg). The MLMs were then evaluated in individual external datasets and optimised in the merged cohort. RESULTS: Among 1,232 participants with cACLD, the prevalence of CSPH/severe PH was similar in the Vienna (n = 163, 67.4%/35.0%) and validation (n = 1,069, 70.3%/34.7%) cohorts. The MLMs were based on 3 (3P: platelet count, bilirubin, international normalised ratio) or 5 (5P: +cholinesterase, +gamma-glutamyl transferase, +activated partial thromboplastin time replacing international normalised ratio) laboratory parameters. The MLMs performed robustly in the Vienna cohort. 5P-MLM had the best AUCs for CSPH (0.813) and severe PH (0.887) and compared favourably to liver stiffness measurement (AUC: 0.808). Their performance in external validation datasets was heterogeneous (AUCs: 0.589-0.887). Training on the merged cohort optimised model performance for CSPH (AUCs for 3P and 5P: 0.775 and 0.789, respectively) and severe PH (0.737 and 0.828, respectively). CONCLUSIONS: Internally trained MLMs reliably predicted PH severity in the Vienna cACLD cohort but exhibited heterogeneous results on external validation. The proposed 3P/5P online tool can reliably identify individuals with CSPH or severe PH, who are thus at risk of hepatic decompensation. IMPACT AND IMPLICATIONS: We used machine learning models based on widely available laboratory parameters to develop a non-invasive model to predict the severity of portal hypertension in individuals with compensated cirrhosis, who currently require invasive measurement of hepatic venous pressure gradient. We validated our findings in a large multicentre cohort of individuals with advanced chronic liver disease (cACLD) of any cause. Finally, we provide a readily available online calculator, based on 3 (platelet count, bilirubin, international normalised ratio) or 5 (platelet count, bilirubin, activated partial thromboplastin time, gamma-glutamyltransferase, choline-esterase) widely available laboratory parameters, that clinicians can use to predict the likelihood of their patients with cACLD having clinically significant or severe portal hypertension.

SMNDC1 links chromatin remodeling and splicing to regulate pancreatic hormone expression.

Insulin expression is primarily restricted to the pancreatic ß cells, which are physically or functionally depleted in diabetes. Identifying targetable pathways repressing insulin in non-ß cells, particularly in the developmentally related glucagon-secreting α cells, is an important aim of regenerative medicine. Here, we perform an RNA interference screen in a murine α cell line to identify silencers of insulin expression. We discover that knockdown of the splicing factor Smndc1 triggers a global repression of α cell gene-expression programs in favor of increased ß cell markers. Mechanistically, Smndc1 knockdown upregulates the ß cell transcription factor Pdx1 by modulating the activities of the BAF and Atrx chromatin remodeling complexes. SMNDC1's repressive role is conserved in human pancreatic islets, its loss triggering enhanced insulin secretion and PDX1 expression. Our study identifies Smndc1 as a key factor connecting splicing and chromatin remodeling to the control of insulin expression in human and mouse islet cells.