Biology subjects

Luedde, T.

Publications and source records attributed to Luedde, T..

14 recordsLinked to original sources

bioRxiv2024

Phosphatase-independent suppression of mucosal inflammation and disease progression by Salmonella SopB

Salmonella enterica subsp. enterica serovar Typhimurium (S. Typhimurium) translocates effector molecules via its Salmonella pathogenicity island (SPI)1 encoded type 3 secretion system (T3SS) to induce internalization by intestinal epithelial cells and manipulate cellular responses. Among these effector molecules, the Salmonella outer protein B (SopB) was shown to possess phosphatidyl-inositol phosphatase activity and induce bacterial internalisation, promote cell survival, influence endosomal trafficking and alter host cell signalling. Using a neonatal S. Typhimurium infection model, we here show that SopB in vivo suppresses early epithelial chemokine expression, delays mucosal immune cell recruitment, reduces barrier impairment by enterocyte necroptosis, and prevents disease progression and premature death. Unexpectedly, this immunosuppressive effect was independent of the phosphatidyl-inositol phosphatase and phosphotransferase activity of SopB but required an intact N-terminal domain. Thus, SopB exerts a potent phosphatase-independent immunosuppressive effect to delay local tissue inflammation and disease progression likely to promote host transmission.

microbiology↗

bioRxiv2024

The ferroptosis mediator ACSL4 fails to prevent disease progression in mouse models of MASLD

Metabolic dysfunction-associated steatotic liver disease is an increasingly prevalent condition, representing a major risk factor for the development of progressive chronic liver damage. This can potentially give rise to the development of steatohepatitis and hepatocellular carcinoma (HCC). It is already known, that patients with metabolic dysfunction-associated steatotic liver disease (MASLD) show increased systemic and hepatic iron concentrations as well as perturbed lipid metabolism, suggesting involvement of ferroptotic cell death in the development and progression of MASLD. Consequently, inhibition of ferroptosis represents a potential therapeutic option for patients with MASLD. The aim of this study was to determine whether a liver parenchymal cell specific conditional deletion of the pro-ferroptotic gene acyl-CoA synthetase long-chain family member 4 (ACSL4LPC-KO) can ameliorate the onset and progression of MASLD in mice. To this end, ACSL4LPC-KO and floxed wild-type littermates were fed a choline-deficient high-fat diet over the course of 20 and 40 weeks to monitor the progression of metabolic liver injury as well as the development of metabolic syndrome. In contrast to the recently published studies by Duan et al. [1], our results show no significant differences between ACSL4LPC-KO and WT mice with regard to the development of MASLD or the progression of metabolic syndrome. Furthermore, no differences were observed in metabolic parameters (i.e. weight gain, glucose tolerance test, hepatic steatosis) or MASLD-associated inflammatory response. Our analyses therefore suggest that loss of ACSL4 has no effect on the progression of MASLD induced by choline-deficient high fat diet. The discrepancy between our and previously published results could be due to differences in the diets or the influence of a distinct microbiome, so the results obtained with liver parenchymal cell specific ACSL4 null mice should be taken with caution.

systems biology↗

bioRxiv2024

Shift in cellular crosstalk reveals the immunosuppressive environment in cholangiocarcinoma patients.

Background and aimsCholangiocarcinoma (CCA) is a deadly disease, and this cancer entity is characterized by an abundant stroma. The tumor microenvironment (TME) plays an important role in aggressive behavior and poor response to therapeutics; however, underlying pathways are unknown. MethodsTo fill this gap, we used multiplexed immunohistochemistry, high-dimensional phenotyping, and transcriptomics to analyze human CCA samples and identify cell cluster crosstalk in patients with a poor prognosis. ResultsOur findings confirmed the presence of Tregs and the lack of effector memory cells in the tumor. New findings are the spatiality of the effector memory cells being more present in the peripheral tissue, for some reason these immune cells fail to reach the tumor niche. We revealed cancer crosstalk with macrophages and stromal cells and identified responsible genes in the poor prognosis group. Amongst the responsible ligand pairs are GAS6-AXL belonging to the TAM family. We then identified VCAN-TLR2 to be present and influencing the ECM in a way to supports immune exhaustion. Last, EGFR-TGF-{beta} is expressed in macrophages and this finding is important in Tregs induction and blocking cytotoxic T cell function. ConclusionsThe multiple mechanisms leading to the exclusion of relevant immune cells needed for an anti-cancer response and mechanisms leading to active immune suppression are part of complex cell-cell crosstalk. This study provides a deeper insight into the immune exhausted phenotype in CCA.

cancer biology↗

bioRxiv2023

KIF23 regulation by miR-107 controls replicative tumor cell fitness in mouse and human hepatocellular carcinoma

Background & AimsIn hepatocellular carcinoma, there is a lack of successful translation of experimental targets identified in mouse models to human patients. In this study, we used a comprehensive transcriptomic approach in mice to identify novel potential targets for therapeutic intervention in humans. MethodsWe analysed combined genome-wide miRNA and mRNA expression data in three pathogenically distinct mouse models of liver cancer. Effects of target genes on hepatoma cell fitness were evaluated by proliferation, survival and motility assays. TCGA and GEO databases, in combination with tissue microarrays (TMA), were used to validate the mouse targets and their impact on human HCC prognosis. Finally, the functional effects of the identified targets on tumorigenesis and tumor therapy were tested in hydrodynamic tail vein injection (HDTVi)-based preclinical HCC models in vivo. ResultsThe expression of miR-107 was found to be significantly reduced in mouse models of liver tumors of various etiologies and in cohorts of human HCC patients. Overexpression of miR-107 or inhibition of its novel target Kinesin family member 23 (Kif23) significantly reduced proliferation by interfering with cytokinesis, thereby controlling survival and motility of mouse and human hepatoma cells. In humans, KIF23 expression was found to be a prognostic marker in liver cancer, with high expression associated with poor prognosis. HDTVi of vectors carrying either pre-miR- 107 or anti-Kif23 shRNA inhibited the development of highly aggressive cMyc-NRas- induced liver cancers in mice. ConclusionsDisruption of the miR-107/Kif23 axis inhibited hepatoma cell proliferation in vitro and prevented oncogene-induced liver cancer development in vivo, offering a novel potential avenue for the treatment of HCC in humans. Impact and implicationsA comprehensive analysis integrating in silico prediction, miRNA and mRNA data in three pathogenically distinct mouse models provided novel targets for the treatment of human HCC, bridging the translational gap between mouse data and human HCC. Our functional findings on the novel miR-107/Kif23 module provide important new insights into the control of mitosis in liver cancer cells. The findings that miR-107 overexpression or Kif23 inhibition had a dramatic functional effect on inhibiting the growth of liver cancer cells in vitro and in vivo suggest that the miR-107/Kif23 axis may be a promising novel target and potential adjunct to sequential systemic therapy of HCC. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=164 SRC="FIGDIR/small/565448v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@afda02org.highwire.dtl.DTLVardef@111da7borg.highwire.dtl.DTLVardef@11eeeecorg.highwire.dtl.DTLVardef@1d7d9f7_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LImiR-107 is globally downregulated in mouse liver cancers of different etiologies and represents a potential biomarker in human HCC. C_LIO_LIIntegration of in-silico-prediction, miRNA and mRNA transcriptomics identified KIF23, a mitotic spindle-associated protein, as a specific target mediating the biological effects of miR-107. C_LIO_LIThe miR-107/KIF23 module promotes replicative fitness of liver cancer cells through an essential function in cytokinesis C_LIO_LIMice receiving shRNA targeting Kif23 were completely protected from oncogene-induced liver cancer. C_LI

cancer biology↗

bioRxiv2023

TIGIT drives the immunosuppressive environment by downregulation of metalloproteinases MMP2 and MMP14 in perihilar cholangiocarcinoma.

BackgroundCheckpoint blockade in cholangiocarcinoma (CCA) is promising; however, little is known about the response to treatment in CCA subtypes. In this study, we investigated the spatial immune environment in combination with checkpoint expression in perihilar CCA (pCCA). Materials & MethodsThe levels of checkpoint molecules (PD-1, PD-L1, PD-L2, LAG-3, ICOS, TIGIT, TIM-3, and CTLA-4), macrophages (CD68), and T cells (CD4 and CD8) were assessed by multiplex immunofluorescence (mIF) in 50 patients. We investigated the transcriptomic profile using the NanoString Cancer Progression Panel, and validation was performed by mIF on tissue sections from 24 patients. ResultsThe expression of checkpoint molecules TIGIT, CTLA-4, and LAG-3 alone and in combination with other checkpoint molecules was more abundant in the Central Tumor (CT) and Invasive Margin (IM) than in peritumoral tissue (PT) (CD4 and CD8 TIGIT p<0.0001 for both CD4 and CD8 CTLA-4, p<0.0001 and p < 0.001, respectively, and CD8 LAG-3 p < 0.05). MMP2 and MMP14 were differentially expressed in patients with high TIGIT expression. ConclusionThe immune environment in pCCA is characterized by the expression of multiple checkpoints, demonstrating the complexity of ICI treatment. High TIGIT expression drives an immunosuppressive environment by modulating the extracellular matrix. Future clinical trials in pCCA could consider TIGIT as a therapeutically relevant target for (combination) treatment.

cancer biology↗

bioRxiv2023

Uncovering novel roles of miR-122 in the pathophysiology of the liver: Potential interaction with NRF1 and E2F4 signaling

MicroRNA miR-122 plays a pivotal role in liver function. Despite numerous studies investigating this miRNA, the global network of genes regulated by miR-122 and its contribution to the underlying pathophysiological mechanisms remain largely unknown. To gain a deeper understanding of miR-122 activity, we employed two complementary approaches. Firstly, through transcriptome analysis of polyribosome-bound RNAs, we discovered that miR-122 exhibits potential antagonistic effects on specific transcription factors known to be dysregulated in liver disease, including nuclear respiratory factor-1 (NRF1) and the E2F Transcription Factor 4 (E2F4). Secondly, through proteome analysis of hepatoma cell transfected with either miR-122 mimic or antagomiR we discovered changes in several proteins associated with increased malignancy. Interestingly, many of these proteins were reported to be transcriptionally regulated by NRF1 and E2F4, six of which we validated as miR-122 targets. Among these, a negative correlation was observed between miR-122 and glucose-6-phosphate dehydrogenase levels in the livers of patients with hepatitis B virus-associated hepatocellular carcinoma. This study provides novel insights into potential alterations of molecular pathway occurring at the early stages of liver disease, driven by the dysregulation of miR-122 and its associated genes.

cancer biology↗

bioRxiv2023

Differential modulation of miR-122 transcription by TGFbeta1/BMP6: implications for nonresolving inflammation and hepatocarcinogenesis

Chronic inflammation is widely recognized as a significant factor that promotes and worsens the development of malignancies, including hepatocellular carcinoma. This study aimed to explore the potential role of microRNAs in inflammation-associated nonresolving hepatocarcinogenesis. By conducting a comprehensive analysis of altered microRNAs in animal models with liver cancer of various etiologies, we identified miR-122 as the most significantly downregulated microRNA in the liver of animals with inflammation-associated liver cancer. Although previous research has indicated the importance of miR-122 in maintaining hepatocyte function, its specific role as either the trigger or the consequence of underlying diseases remains unclear. Through extensive analysis of animals and in vitro models, we have successfully demonstrated that MIR122 transcription is differentially regulated by the immunoregulatory cytokines by the transforming growth factor-beta 1 (TGF{beta}1) and the bone morphogenetic protein- 6 (BMP6). Furthermore, we presented convincing evidence directly linking reduced MIR122 transcription to inflammation and in chronic liver diseases. The results of this study strongly suggest that prolonged activation of signaling pathways, leading to disruption of cytokine-mediated regulation of MIR122, may significantly contribute to the onset and exacerbation of chronic liver disease.

cancer biology↗

bioRxiv2023

An optimized workflow for analyzing extracellular vesicles as biomarkers in liver diseases.

Background & AimsExtracellular vesicles (EVs) play an important role in intercellular communication, serving as vehicles for the exchange of biological materials and being involved in the regulation of physiological processes. EVs and their associated cargoes are considered a promising source of disease-associated biomarkers. The purpose of this study was to establish an easy-to-use, reproducible, and scalable workflow to efficiently analyze EVs in the context of liver disease. MethodsAn optimized workflow was established for the pre-analytical processing and isolation of EVs from plasma and serum. Nanoparticle Tracking Analysis (NTA) was used to characterize circulating EVs in the serum of patients with nonalcoholic fatty liver disease (NAFLD), autoimmune liver disease (AIH), and animal models with impaired liver function. EVs were separated from soluble proteins by an optimized, polyethylene glycol (PEG)-based enrichment protocol. Enriched EVs were either labeled and functionally characterized by monitoring cellular uptake or lysed for biomarker identification. ResultsCirculating EVs in the serum of patients with NAFLD or AIH and in different animal models have been characterized by NTA. Here we show that both the quantity and size of EVs in the serum of patients/animal models are significantly different from those of healthy individuals. We show that isolated EVs are functional, and their uptake by acceptor cells can be quantified after fluorescence labelling. Enriched EVs were directly used to analyze RNA biomarkers. Several microRNAs, including miR-15b, -16, -21, -122 and -223, were found to be significantly up-regulated in EVs isolated from the sera of patients with NAFLD and AIH. We show that EVs transport cytokines, and that IL-2, IL-6 and IL-8 were significantly up-regulated in EVs enriched from patients with cholangiocarcinoma (CCA) compared to healthy controls. ConclusionsThe workflow presented here represents an accessible and easy-to-use approach that enables the analysis and enrichment of EVs from complex biological fluids and their preparation for functional characterization or downstream analysis. In this study, the levels of several miRNAs were found to be significantly increased in EVs isolated from AIH and NAFLD patients compared with healthy controls. HighlightsO_LIEVs circulating in crude serum reflect the diseased stage of the donors. C_LIO_LIEnrichment of EVs with the approach presented here efficiently separates soluble proteins from EVs, providing optimal material for further characterization. C_LIO_LIExosomal markers are present in the EVs-enriched fraction. C_LIO_LIEnriched EVs are intact and are functionally taken up by acceptor cells. C_LIO_LIEnriched EVs are suitable, and have been used for, biomarkers identification both at RNA and protein level. C_LI

molecular biology↗

bioRxiv2022

PD1+ T-cells correlate with Nerve Fiber Density as a prognostic biomarker in patients with resected perihilar cholangiocarcinoma.

Background & AimsPerihilar cholangiocarcinoma (pCCA) is a rare hepatobiliary malignancy. Nerve fiber invasion (NFI) shows cancer invading the nerve and is considered an aggressive feature. Nerve fiber density (NFD) consists of small nerve fibers without cancer invasion and is divided into high NFD (high numbers of small nerve fibers) or low NFD (low numbers of small nerve fibers). We aim to explore differences in immune cell populations and survival. MethodsWe applied multiplex immunofluorescence (mIF) on 47 pCCA surgically resected patients and investigated the immune cell composition in the tumor microenvironment (TME) of different nerve fiber phenotypes (NFI, high and low NFD). Extensive group comparison was carried out and the association with overall survival (OS) was assessed. ResultsThe NFI ROI was measured with highest CD68+ macrophage levels among 3 ROIs (NFI compared to tumor free p= 0.016 and to tumor p=0.034). Further, for NFI patients the density of co-inhibitory markers CD8+PD1+ and CD68+PD1+ were more abundant in the tumor rather than NFI ROI (p= 0.004 and p= 0.0029 respectively). Comparison between patients with NFD and NFI groups, the signals of co-expression of CD8+PD1+ as well as CD68+PD1+ were significantly higher in the high NFD group (p= 0.027 and p= 0.044, respectively). The OS for high NFD patients was 92 months median OS (95% CI:41-142), for low NFD patients 20 months ((95% CI: 4-36) and for NFI group of patients 19 months (95% CI 7-33). The OS for high NFD patients was significantly better compared to low NFD (p= 0.046) and NFI (p= 0.032). ConclusionsPD1+ T-cells correlate with high NFD as a prognostic biomarker, the biological pathway behind this needs to be investigated. Lay summaryNerve fibers play a dual role in the tumor microenvironment in pCCA. In our previous study, we showed that the presence of high numbers of small nerve fibers is associated with a better overall survival. In addition, we found that in high NFD patients PD1+ T-cells are significantly overexpressed. Therefore, we present high NFD as a promising prognostic biomarker. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=128 SRC="FIGDIR/small/475344v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@e30fadorg.highwire.dtl.DTLVardef@11a4e4aorg.highwire.dtl.DTLVardef@a04d8org.highwire.dtl.DTLVardef@1c425db_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2021

DeepMed: A unified, modular pipeline for end-to-end deep learning in computational pathology

The interpretation of digitized histopathology images has been transformed thanks to artificial intelligence (AI). End-to-end AI algorithms can infer high-level features directly from raw image data, extending the capabilities of human experts. In particular, AI can predict tumor subtypes, genetic mutations and gene expression directly from hematoxylin and eosin (H&E) stained pathology slides. However, existing end-to-end AI workflows are poorly standardized and not easily adaptable to new tasks. Here, we introduce DeepMed, a Python library for predicting any high-level attribute directly from histopathological whole slide images alone, or from images coupled with additional meta-data (https://github.com/KatherLab/deepmed). Unlike earlier computational pipelines, DeepMed is highly developer-friendly: its structure is modular and separates preprocessing, training, deployment, statistics, and visualization in such a way that any one of these processes can be altered without affecting the others. Also, DeepMed scales easily from local use on laptop computers to multi-GPU clusters in cloud computing services and therefore can be used for teaching, prototyping and for large-scale applications. Finally, DeepMed is user-friendly and allows researchers to easily test multiple hypotheses in a single dataset (via cross-validation) or in multiple datasets (via external validation). Here, we demonstrate and document DeepMeds abilities to predict molecular alterations, histopathological subtypes and molecular features from routine histopathology images, using a large benchmark dataset which we release publicly. In summary, DeepMed is a fully integrated and broadly applicable end-to-end AI pipeline for the biomedical research community.

cancer biology↗

bioRxiv2021

Swarm learning for decentralized artificial intelligence in cancer histopathology

Artificial Intelligence (AI) can extract clinically actionable information from medical image data. In cancer histopathology, AI can be used to predict the presence of molecular alterations directly from routine histopathology slides. However, training robust AI systems requires large datasets whose collection faces practical, ethical and legal obstacles. These obstacles could be overcome with swarm learning (SL) where partners jointly train AI models, while avoiding data transfer and monopolistic data governance. Here, for the first time, we demonstrate the successful use of SL in large, multicentric datasets of gigapixel histopathology images comprising over 5000 patients. We show that AI models trained using Swarm Learning can predict BRAF mutational status and microsatellite instability (MSI) directly from hematoxylin and eosin (H&E)-stained pathology slides of colorectal cancer (CRC). We trained AI models on three patient cohorts from Northern Ireland, Germany and the United States of America and validated the prediction performance in two independent datasets from the United Kingdom using SL-based AI models. Our data show that SL enables us to train AI models which outperform most locally trained models and perform on par with models which are centrally trained on the merged datasets. In addition, we show that SL-based AI models are data efficient and maintain a robust performance even if only subsets of local datasets are used for training. In the future, SL can be used to train distributed AI models for any histopathology image analysis tasks, overcoming the need for data transfer and without requiring institutions to give up control of the final AI model.

bioinformatics↗

bioRxiv2020

Nerve fibers in the Tumor Microenvironment are co-localized with Tertiary Lymphoid Structures

BackgroundB cells and tertiary lymphoid structures (TLS) are reported to be important in the improvement of survival of cancer patients. These secondary lymphoid organs have been associated with the generation of an anti-tumor response. Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancer types and the stromal architecture shapes the intratumoral heterogeneity. The stroma of PDAC is a complex system in which crosstalk takes place between cancer-associated fibroblasts, immune cells, endothelial cells and the cancer cells. Besides immune cells and fibroblasts, there is some limited data about the influence of nerve fibers on cancer progression. Patients and methodsNerve Fiber Density (NFD) was analysed in our cohort of 188 patients with Pancreatic Ductal Adenocarcinoma who underwent pancreatic surgery. We used immunohistochemistry and multiplex imaging to phenotype the immune cell infiltrate. The cell detection classifier measured distance from immune cell to cancer gland and with a heat map we could count TLS. By using Machine learning we were able to define the spatial distribution and counting Tertiary Lymphoid Structures. ResultsHigh NFD is significantly associated with prolonged overall survival (HR 1.676 (95%CI 1.126,2.495) for low vs. high NFD, p-value 0.0109). The immune cells surrounding the nerve fibers were phenotyped in B cells, T cells and dendritic follicular cells, matching a TLS. Here we show that small nerve fibers are located at the TLS in Pancreatic Cancer and a high Nerve Fiber Density combined with more than 5 TLS is associated with a better survival (HR 0.388 (95%CI 0.218, 0.689). ConclusionThe co-localization of small nerve fibers with TLS is a new finding which has not been described before. However the precise roles of these TLS and nerve fibers remains unknown. These findings unravel future pathways and has the potential to reach new directions into already existing targeted therapy.

cancer biology↗

bioRxiv2019

Pan-cancer image-based detection of clinically actionable genetic alterations

Precision treatment of cancer relies on genetic alterations which are diagnosed by molecular biology assays.1 These tests can be a bottleneck in oncology workflows because of high turnaround time, tissue usage and costs.2 Here, we show that deep learning can predict point mutations, molecular tumor subtypes and immune-related gene expression signatures3,4 directly from routine histological images of tumor tissue. We developed and systematically optimized a one-stop-shop workflow and applied it to more than 4000 patients with breast5, colon and rectal6, head and neck7, lung8,9, pancreatic10, prostate11 cancer, melanoma12 and gastric13 cancer. Together, our findings show that a single deep learning algorithm can predict clinically actionable alterations from routine histology data. Our method can be implemented on mobile hardware14, potentially enabling point-of-care diagnostics for personalized cancer treatment in individual patients.

genetics↗

bioRxiv2019

Deep learning detects virus presence in cancer histology

Oncogenic viruses like human papilloma virus (HPV) or Epstein Barr virus (EBV) are a major cause of human cancer. Viral oncogenesis has a direct impact on treatment decisions because virus-associated tumors can demand a lower intensity of chemotherapy and radiation or can be more susceptible to immune check-point inhibition. However, molecular tests for HPV and EBV are not ubiquitously available.\n\nWe hypothesized that the histopathological features of virus-driven and non-virus driven cancers are sufficiently different to be detectable by artificial intelligence (AI) through deep learning-based analysis of images from routine hematoxylin and eosin (HE) stained slides. We show that deep transfer learning can predict presence of HPV in head and neck cancer with a patient-level 3-fold cross validated area-under-the-curve (AUC) of 0.89 [0.82; 0.94]. The same workflow was used for Epstein-Barr virus (EBV) driven gastric cancer achieving a cross-validated AUC of 0.80 [0.70; 0.92] and a similar performance in external validation sets. Reverse-engineering our deep neural networks, we show that the key morphological features can be made understandable to humans.\n\nThis workflow could enable a fast and low-cost method to identify virus-induced cancer in clinical trials or clinical routine. At the same time, our approach for feature visualization allows pathologists to look into the black box of deep learning, enabling them to check the plausibility of computer-based image classification.

cancer biology↗