Insights into Sex Chromosome Evolution and Aging from the Genome of a Short-Lived Fish.

The killifish Nothobranchius furzeri is the shortest-lived vertebrate that can be bred in the laboratory. Its rapid growth, early sexual maturation, fast aging, and arrested embryonic development (diapause) make it an attractive model organism in biomedical research. Here, we report a draft sequence of its genome that allowed us to uncover an intra-species Y chromosome polymorphism representing-in real time-different stages of sex chromosome formation that display features of early mammalian XY evolution "in action." Our data suggest that gdf6Y, encoding a TGF-ß family growth factor, is the master sex-determining gene in N. furzeri. Moreover, we observed genomic clustering of aging-related genes, identified genes under positive selection, and revealed significant similarities of gene expression profiles between diapause and aging, particularly for genes controlling cell cycle and translation. The annotated genome sequence is provided as an online resource (http://www.nothobranchius.info/NFINgb).

A differentiation checkpoint limits hematopoietic stem cell self-renewal in response to DNA damage.

Checkpoints that limit stem cell self-renewal in response to DNA damage can contribute to cancer protection but may also promote tissue aging. Molecular components that control stem cell responses to DNA damage remain to be delineated. Using in vivo RNAi screens, we identified basic leucine zipper transcription factor, ATF-like (BATF) as a major component limiting self-renewal of hematopoietic stem cells (HSCs) in response to telomere dysfunction and γ-irradiation. DNA damage induces BATF in a G-CSF/STAT3-dependent manner resulting in lymphoid differentiation of HSCs. BATF deletion improves HSC self-renewal and function in response to γ-irradiation or telomere shortening but results in accumulation of DNA damage in HSCs. Analysis of bone marrow from patients with myelodysplastic syndrome supports the conclusion that DNA damage-dependent induction of BATF is conserved in human HSCs. Together, these results provide experimental evidence that a BATF-dependent differentiation checkpoint limits self-renewal of HSCs in response to DNA damage.

GatekeepR: an R Shiny application for the identification of nodes with high dynamic impact in Boolean networks.

MOTIVATION: Boolean networks can serve as straightforward models for understanding processes such as gene regulation, and employing logical rules. These rules can either be derived from existing literature or by data-driven approaches. However, in the context of large networks, the exhaustive search for intervention targets becomes challenging due to the exponential expansion of a Boolean network's state space and the multitude of potential target candidates, along with their various combinations. Instead, we can employ the logical rules and resultant interaction graph as a means to identify targets of specific interest within larger-scale models. This approach not only facilitates the screening process but also serves as a preliminary filtering step, enabling the focused investigation of candidates that hold promise for more profound dynamic analysis. However, applying this method requires a working knowledge of R, thus restricting the range of potential users. We, therefore, aim to provide an application that makes this method accessible to a broader scientific community. RESULTS: Here, we introduce GatekeepR, a graphical, web-based R Shiny application that enables scientists to screen Boolean network models for possible intervention targets whose perturbation is likely to have a large impact on the system's dynamics. This application does not require a local installation or knowledge of R and provides the suggested targets along with additional network information and visualizations in an intuitive, easy-to-use manner. The Supplementary Material describes the underlying method for identifying these nodes along with an example application in a network modeling pancreatic cancer. AVAILABILITY AND IMPLEMENTATION: https://www.github.com/sysbio-bioinf/GatekeepR https://abel.informatik.uni-ulm.de/shiny/GatekeepR/.

Author Correction: Epigenetic stress responses induce muscle stem-cell ageing by Hoxa9 developmental signals.

An Amendment to this paper has been published and can be accessed via a link at the top of the paper.

The chromatin remodeling protein ATRX positively regulates IRF3-dependent type I interferon production and interferon-induced gene expression.

The chromatin remodeling protein alpha thalassemia/mental retardation syndrome X-linked (ATRX) is a component of promyelocytic leukemia nuclear bodies (PML-NBs) and thereby mediates intrinsic immunity against several viruses including human cytomegalovirus (HCMV). As a consequence, viruses have evolved different mechanisms to antagonize ATRX, such as displacement from PML-NBs or degradation. Here, we show that depletion of ATRX results in an overall impaired antiviral state by decreasing transcription and subsequent secretion of type I IFNs, which is followed by reduced expression of interferon-stimulated genes (ISGs). ATRX interacts with the transcription factor interferon regulatory factor 3 (IRF3) and associates with the IFN-ß promoter to facilitate transcription. Furthermore, whole transcriptome sequencing revealed that ATRX is required for efficient IFN-induced expression of a distinct set of ISGs. Mechanistically, we found that ATRX positively modulates chromatin accessibility specifically upon IFN signaling, thereby affecting promoter regions with recognition motifs for AP-1 family transcription factors. In summary, our study uncovers a novel co-activating function of the chromatin remodeling factor ATRX in innate immunity that regulates chromatin accessibility and subsequent transcription of interferons and ISGs. Consequently, ATRX antagonization by viral proteins and ATRX mutations in tumors represent important strategies to broadly compromise both intrinsic and innate immune responses.

Introducing a machine learning algorithm for delirium prediction-the Supporting SURgery with GEriatric Co-Management and AI project (SURGE-Ahead).

INTRODUCTION: Post-operative delirium (POD) is a common complication in older patients, with an incidence of 14-56%. To implement preventative procedures, it is necessary to identify patients at risk for POD. In the present study, we aimed to develop a machine learning (ML) model for POD prediction in older patients, in close cooperation with the PAWEL (patient safety, cost-effectiveness and quality of life in elective surgery) project. METHODS: The model was trained on the PAWEL study's dataset of 878 patients (no intervention, age ≥ 70, 209 with POD). Presence of POD was determined by the Confusion Assessment Method and a chart review. We selected 15 features based on domain knowledge, ethical considerations and a recursive feature elimination. A logistic regression and a linear support vector machine (SVM) were trained, and evaluated using receiver operator characteristics (ROC). RESULTS: The selected features were American Society of Anesthesiologists score, multimorbidity, cut-to-suture time, estimated glomerular filtration rate, polypharmacy, use of cardio-pulmonary bypass, the Montreal cognitive assessment subscores 'memory', 'orientation' and 'verbal fluency', pre-existing dementia, clinical frailty scale, age, recent falls, post-operative isolation and pre-operative benzodiazepines. The linear SVM performed best, with an ROC area under the curve of 0.82 [95% CI 0.78-0.85] in the training set, 0.81 [95% CI 0.71-0.88] in the test set and 0.76 [95% CI 0.71-0.79] in a cross-centre validation. CONCLUSION: We present a clinically useful and explainable ML model for POD prediction. The model will be deployed in the Supporting SURgery with GEriatric Co-Management and AI project.

The HLA ligandome of oropharyngeal squamous cell carcinomas reveals shared tumour-exclusive peptides for semi-personalised vaccination.

BACKGROUND: The immune peptidome of OPSCC has not previously been studied. Cancer-antigen specific vaccination may improve clinical outcome and efficacy of immune checkpoint inhibitors such as PD1/PD-L1 antibodies. METHODS: Mapping of the OPSCC HLA ligandome was performed by mass spectrometry (MS) based analysis of naturally presented HLA ligands isolated from tumour tissue samples (n = 40) using immunoaffinity purification. The cohort included 22 HPV-positive (primarily HPV-16) and 18 HPV-negative samples. A benign reference dataset comprised of the HLA ligandomes of benign haematological and tissue datasets was used to identify tumour-associated antigens. RESULTS: MS analysis led to the identification of naturally HLA-presented peptides in OPSCC tumour tissue. In total, 22,769 peptides from 9485 source proteins were detected on HLA class I. For HLA class II, 15,203 peptides from 4634 source proteins were discovered. By comparative profiling against the benign HLA ligandomic datasets, 29 OPSCC-associated HLA class I ligands covering 11 different HLA allotypes and nine HLA class II ligands were selected to create a peptide warehouse. CONCLUSION: Tumour-associated peptides are HLA-presented on the cell surfaces of OPSCCs. The established warehouse of OPSCC-associated peptides can be used for downstream immunogenicity testing and peptide-based immunotherapy in (semi)personalised strategies.

Analysis, identification and visualization of subgroups in genomics.

MOTIVATION: Cancer is a complex and heterogeneous disease involving multiple somatic mutations that accumulate during its progression. In the past years, the wide availability of genomic data from patients' samples opened new perspectives in the analysis of gene mutations and alterations. Hence, visualizing and further identifying genes mutated in massive sets of patients are nowadays a critical task that sheds light on more personalized intervention approaches. RESULTS: Here, we extensively review existing tools for visualization and analysis of alteration data. We compare different approaches to study mutual exclusivity and sample coverage in large-scale omics data. We complement our review with the standalone software AVAtar ('analysis and visualization of alteration data') that integrates diverse aspects known from different tools into a comprehensive platform. AVAtar supplements customizable alteration plots by a multi-objective evolutionary algorithm for subset identification and provides an innovative and user-friendly interface for the evaluation of concurrent solutions. A use case from personalized medicine demonstrates its unique features showing an application on vaccination target selection. AVAILABILITY: AVAtar is available at: https://github.com/sysbio-bioinf/avatar. CONTACT: hans.kestler@uni-ulm.de, phone: +49 (0) 731 500 24 500, fax: +49 (0) 731 500 24 502.

Implementing FAIR data management within the German Network for Bioinformatics Infrastructure (de.NBI) exemplified by selected use cases.

This article describes some use case studies and self-assessments of FAIR status of de.NBI services to illustrate the challenges and requirements for the definition of the needs of adhering to the FAIR (findable, accessible, interoperable and reusable) data principles in a large distributed bioinformatics infrastructure. We address the challenge of heterogeneity of wet lab technologies, data, metadata, software, computational workflows and the levels of implementation and monitoring of FAIR principles within the different bioinformatics sub-disciplines joint in de.NBI. On the one hand, this broad service landscape and the excellent network of experts are a strong basis for the development of useful research data management plans. On the other hand, the large number of tools and techniques maintained by distributed teams renders FAIR compliance challenging.

CANTATA-prediction of missing links in Boolean networks using genetic programming.

MOTIVATION: Biological processes are complex systems with distinct behaviour. Despite the growing amount of available data, knowledge is sparse and often insufficient to investigate the complex regulatory behaviour of these systems. Moreover, different cellular phenotypes are possible under varying conditions. Mathematical models attempt to unravel these mechanisms by investigating the dynamics of regulatory networks. Therefore, a major challenge is to combine regulations and phenotypical information as well as the underlying mechanisms. To predict regulatory links in these models, we established an approach called CANTATA to support the integration of information into regulatory networks and retrieve potential underlying regulations. This is achieved by optimizing both static and dynamic properties of these networks. RESULTS: Initial results show that the algorithm predicts missing interactions by recapitulating the known phenotypes while preserving the original topology and optimizing the robustness of the model. The resulting models allow for hypothesizing about the biological impact of certain regulatory dependencies. AVAILABILITY AND IMPLEMENTATION: Source code of the application, example files and results are available at https://github.com/sysbio-bioinf/Cantata. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PREDICT-juvenile-stroke: PRospective evaluation of a prediction score determining individual clinical outcome three months after ischemic stroke in young adults - a study protocol.

BACKGROUND: Although of high individual and socioeconomic relevance, a reliable prediction model for the prognosis of juvenile stroke (18-55 years) is missing. Therefore, the study presented in this protocol aims to prospectively validate the discriminatory power of a prediction score for the 3 months functional outcome after juvenile stroke or transient ischemic attack (TIA) that has been derived from an independent retrospective study using standard clinical workup data. METHODS: PREDICT-Juvenile-Stroke is a multi-centre (n = 4) prospective observational cohort study collecting standard clinical workup data and data on treatment success at 3 months after acute ischemic stroke or TIA that aims to validate a new prediction score for juvenile stroke. The prediction score has been developed upon single center retrospective analysis of 340 juvenile stroke patients. The score determines the patient's individual probability for treatment success defined by a modified Rankin Scale (mRS) 0-2 or return to pre-stroke baseline mRS 3 months after stroke or TIA. This probability will be compared to the observed clinical outcome at 3 months using the area under the receiver operating characteristic curve. The primary endpoint is to validate the clinical potential of the new prediction score for a favourable outcome 3 months after juvenile stroke or TIA. Secondary outcomes are to determine to what extent predictive factors in juvenile stroke or TIA patients differ from those in older patients and to determine the predictive accuracy of the juvenile stroke prediction score on other clinical and paraclinical endpoints. A minimum of 430 juvenile patients (< 55 years) with acute ischemic stroke or TIA, and the same number of older patients will be enrolled for the prospective validation study. DISCUSSION: The juvenile stroke prediction score has the potential to enable personalisation of counselling, provision of appropriate information regarding the prognosis and identification of patients who benefit from specific treatments. TRIAL REGISTRATION: The study has been registered at https://drks.de on March 31, 2022 ( DRKS00024407 ).

Federated electronic data capture (fEDC): Architecture and prototype.

In clinical research as well as patient care, structured documentation of findings is an important task. In many cases, this is achieved by means of electronic case report forms (eCRF) using corresponding information technology systems. To avoid double data entry, eCRF systems can be integrated with electronic health records (EHR). However, when researchers from different institutions collaborate in collecting data, they often use a single joint eCRF system on the Internet. In this case, integration with EHR systems is not possible in most cases due to information security and data protection restrictions. To overcome this shortcoming, we propose a novel architecture for a federated electronic data capture system (fEDC). Four key requirements were identified for fEDC: Definitions of forms have to be available in a reliable and controlled fashion, integration with electronic health record systems must be possible, patient data should be under full local control until they are explicitly transferred for joint analysis, and the system must support data sharing principles accepted by the scientific community for both data model and data captured. With our approach, sites participating in a joint study can run their own instance of an fEDC system that complies with local standards (such as being behind a network firewall) while also being able to benefit from using identical form definitions by sharing metadata in the Operational Data Model (ODM) format published by the Clinical Data Interchange Standards Consortium (CDISC) throughout the collaboration. The fEDC architecture was validated with a working open-source prototype at five German university hospitals. The fEDC architecture provides a novel approach with the potential to significantly improve collaborative data capture: Efforts for data entry are reduced and at the same time, data quality is increased since barriers for integrating with local electronic health record systems are lowered. Further, metadata are shared and patient privacy is ensured at a high level.

Capturing dynamic relevance in Boolean networks using graph theoretical measures.

MOTIVATION: Interaction graphs are able to describe regulatory dependencies between compounds without capturing dynamics. In contrast, mathematical models that are based on interaction graphs allow to investigate the dynamics of biological systems. However, since dynamic complexity of these models grows exponentially with their size, exhaustive analyses of the dynamics and consequently screening all possible interventions eventually becomes infeasible. Thus, we designed an approach to identify dynamically relevant compounds based on the static network topology. RESULTS: Here, we present a method only based on static properties to identify dynamically influencing nodes. Coupling vertex betweenness and determinative power, we could capture relevant nodes for changing dynamics with an accuracy of 75% in a set of 35 published logical models. Further analyses of the selected compounds' connectivity unravelled a new class of not highly connected nodes with high impact on the networks' dynamics, which we call gatekeepers. We validated our method's working concept on logical models, which can be readily scaled up to complex interaction networks, where dynamic analyses are not even feasible. AVAILABILITY AND IMPLEMENTATION: Code is freely available at https://github.com/sysbio-bioinf/BNStatic. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Weighted average ensemble-based semantic segmentation in biological electron microscopy images.

Semantic segmentation of electron microscopy images using deep learning methods is a valuable tool for the detailed analysis of organelles and cell structures. However, these methods require a large amount of labeled ground truth data that is often unavailable. To address this limitation, we present a weighted average ensemble model that can automatically segment biological structures in electron microscopy images when trained with only a small dataset. Thus, we exploit the fact that a combination of diverse base-learners is able to outperform one single segmentation model. Our experiments with seven different biological electron microscopy datasets demonstrate quantitative and qualitative improvements. We show that the Grad-CAM method can be used to interpret and verify the prediction of our model. Compared with a standard U-Net, the performance of our method is superior for all tested datasets. Furthermore, our model leverages a limited number of labeled training data to segment the electron microscopy images and therefore has a high potential for automated biological applications.

Improved automatic detection of herpesvirus secondary envelopment stages in electron microscopy by augmenting training data with synthetic labelled images generated by a generative adversarial network.

Detailed analysis of secondary envelopment of the herpesvirus human cytomegalovirus (HCMV) by transmission electron microscopy (TEM) is crucial for understanding the formation of infectious virions. Here, we present a convolutional neural network (CNN) that automatically recognises cytoplasmic capsids and distinguishes between three HCMV capsid envelopment stages in TEM images. 315 TEM images containing 2,610 expert-labelled capsids of the three classes were available for CNN training. To overcome the limitation of small training datasets and thus poor CNN performance, we used a deep learning method, the generative adversarial network (GAN), to automatically increase our labelled training dataset with 500 synthetic images and thus to 9,192 labelled capsids. The synthetic TEM images were added to the ground truth dataset to train the Faster R-CNN deep learning-based object detector. Training with 315 ground truth images yielded an average precision (AP) of 53.81% for detection, whereas the addition of 500 synthetic training images increased the AP to 76.48%. This shows that generation and additional use of synthetic labelled images for detector training is an inexpensive way to improve detector performance. This work combines the gold standard of secondary envelopment research with state-of-the-art deep learning technology to speed up automatic image analysis even when large labelled training datasets are not available.

Epigenetic stress responses induce muscle stem-cell ageing by Hoxa9 developmental signals.

The functionality of stem cells declines during ageing, and this decline contributes to ageing-associated impairments in tissue regeneration and function. Alterations in developmental pathways have been associated with declines in stem-cell function during ageing, but the nature of this process remains poorly understood. Hox genes are key regulators of stem cells and tissue patterning during embryogenesis with an unknown role in ageing. Here we show that the epigenetic stress response in muscle stem cells (also known as satellite cells) differs between aged and young mice. The alteration includes aberrant global and site-specific induction of active chromatin marks in activated satellite cells from aged mice, resulting in the specific induction of Hoxa9 but not other Hox genes. Hoxa9 in turn activates several developmental pathways and represents a decisive factor that separates satellite cell gene expression in aged mice from that in young mice. The activated pathways include most of the currently known inhibitors of satellite cell function in ageing muscle, including Wnt, TGFß, JAK/STAT and senescence signalling. Inhibition of aberrant chromatin activation or deletion of Hoxa9 improves satellite cell function and muscle regeneration in aged mice, whereas overexpression of Hoxa9 mimics ageing-associated defects in satellite cells from young mice, which can be rescued by the inhibition of Hoxa9-targeted developmental pathways. Together, these data delineate an altered epigenetic stress response in activated satellite cells from aged mice, which limits satellite cell function and muscle regeneration by Hoxa9-dependent activation of developmental pathways.

Synergistic targeting and resistance to PARP inhibition in DNA damage repair-deficient pancreatic cancer.

OBJECTIVE: ATM serine/threonine kinase (ATM) is the most frequently mutated DNA damage response gene, involved in homologous recombination (HR), in pancreatic ductal adenocarcinoma (PDAC). DESIGN: Combinational synergy screening was performed to endeavour a genotype-tailored targeted therapy. RESULTS: Synergy was found on inhibition of PARP, ATR and DNA-PKcs (PAD) leading to synthetic lethality in ATM-deficient murine and human PDAC. Mechanistically, PAD-induced PARP trapping, replication fork stalling and mitosis defects leading to P53-mediated apoptosis. Most importantly, chemical inhibition of ATM sensitises human PDAC cells toward PAD with long-term tumour control in vivo. Finally, we anticipated and elucidated PARP inhibitor resistance within the ATM-null background via whole exome sequencing. Arising cells were aneuploid, underwent epithelial-mesenchymal-transition and acquired multidrug resistance (MDR) due to upregulation of drug transporters and a bypass within the DNA repair machinery. These functional observations were mirrored in copy number variations affecting a region on chromosome 5 comprising several of the upregulated MDR genes. Using these findings, we ultimately propose alternative strategies to overcome the resistance. CONCLUSION: Analysis of the molecular susceptibilities triggered by ATM deficiency in PDAC allow elaboration of an efficient mutation-specific combinational therapeutic approach that can be also implemented in a genotype-independent manner by ATM inhibition.

Prospective longitudinal study of immune checkpoint molecule (ICM) expression in immune cell subsets during curative conventional therapy of head and neck squamous cell carcinoma (HNSCC).

Programmed-death-1 (PD1) antibodies are approved for recurrent and metastatic head and neck squamous cell carcinoma. Multiple drugs targeting costimulatory and coinhibitory immune checkpoint molecules (ICM) have been discovered. However, it remains unknown how these ICM are affected by curative conventional therapy on different immune cell subsets during the course of treatment. In the prospective noninterventional clinical study titled "Immune Response Evaluation to Curative conventional Therapy" (NCT03053661), 22 patients were prospectively enrolled. Blood samples were drawn at defined time points throughout curative conventional treatment and follow-up. Immune cells (IC) from the different time points were assessed by multicolor flow cytometry. The following ICM were measured by flow cytometry: PD1, CTLA4, BTLA, CD137, CD27, GITR, OX40, LAG3 and TIM3. Dynamics of ICM expression were assessed using nonparametric paired samples tests. Significant changes were noted for PD1, BTLA and CD27 on multiple IC types during or after radiotherapy. Nonsignificant trends for increased expression of OX40 and GITR from baseline until the end of RT were observed on CD4 T cells and CD4+ CD39+ T cells. In patients with samples at recurrence of disease, a nonsignificant increase of TIM3 and LAG3 positive CD4+ CD39+ T cells was evident, accompanied by an increase of double positive cells for TIM3/LAG3. Potential future targets to be combined with RT in the conventional treatment and anti-PD1/PD-L could be BTLA agonists, or agonistic antibodies to costimulatory ICM like CD137, OX40 or GITR. The combination of cetuximab with CD27 agonistic antibodies enhancing ADCC or the targeting of TIM3/LAG3 may be another promising strategy.

Protein Kinase D1, Reduced in Human Pancreatic Tumors, Increases Secretion of Small Extracellular Vesicles From Cancer Cells That Promote Metastasis to Lung in Mice.

BACKGROUND & AIMS: Pancreatic tumor cells release small extracellular vesicles (sEVs, exosomes) that contain lipids and proteins, RNA, and DNA molecules that might promote formation of metastases. It is not clear what cargo these vesicles contain and how they are released. Protein kinase D1 (PRKD1) inhibits cell motility and is believed to be dysregulated in pancreatic ductal adenocarcinomas. We investigated whether it regulates production of sEVs in pancreatic cancer cells and their ability to form premetastatic niches for pancreatic cancer cells in mice. METHODS: We analyzed data from UALCAN and human pancreatic tissue microarrays to compare levels of PRKD1 between tumor and nontumor tissues. We studied mice with pancreas-specific disruption of Prkd1 (PRKD1KO mice), mice that express oncogenic KRAS (KC mice), and KC mice with disruption of Prkd1 (PRKD1KO-KC mice). Subcutaneous xenograft tumors were grown in NSG mice from Panc1 cells; some mice were then given injections of sEVs. Pancreata and lung tissues from mice were analyzed by histology, immunohistochemistry, and/or quantitative polymerase chain reaction; we performed nanoparticle tracking analysis of plasma sEVs. The Prkd1 gene was disrupted in Panc1 cells using CRISPR-Cas9 or knocked down with small hairpin RNAs, or PRKD1 activity was inhibited with the selective inhibitor CRT0066101. Pancreatic cancer cell lines were analyzed by gene-expression microarray, quantitative polymerase chain reaction, immunoblot, and immunofluorescence analyses. sEVs secreted by Panc1 cell lines were analyzed by flow cytometry, transmission electron microscopy, and mass spectrometry. RESULTS: Levels of PRKD1 were reduced in human pancreatic ductal adenocarcinoma tissues compared with nontumor tissues. PRKD1KO-KC mice developed more pancreatic intraepithelial neoplasia, at a faster rate, than KC mice, and had more lung metastases and significantly shorter average survival time. Serum from PRKD1KO-KC mice had increased levels of sEVs compared with KC mice. Pancreatic cancer cells with loss or inhibition of PRKD1 increased secretion of sEVs; loss of PRKD1 reduced phosphorylation of its substrate, cortactin, resulting in increased F-actin levels at the plasma membrane. sEVs from cells with loss or reduced expression of PRKD1 had altered content, and injection of these sEVs into mice increased metastasis of xenograft tumors to lung, compared with sEVs from pancreatic cells that expressed PRKD1. PRKD1-deficient pancreatic cancer cells showed increased loading of integrin α6ß4 into sEVs-a process that required CD82. CONCLUSIONS: Human pancreatic ductal adenocarcinoma has reduced levels of PRKD1 compared with nontumor pancreatic tissues. Loss of PRKD1 results in reduced phosphorylation of cortactin in pancreatic cancer cell lines, resulting in increased in F-actin at the plasma membrane and increased release of sEVs, with altered content. These sEVs promote metastasis of xenograft and pancreatic tumors to lung in mice.