Biology subjects

Friess, H.

Publications and source records attributed to Friess, H..

6 recordsLinked to original sources

bioRxiv2024

Quantifying immune cell telomere content at single-cell resolution in context of PD-1 checkpoint immunotherapy

Biological processes such as aging, carcinogenesis, and immune responses depend on the ability of cells to maintain or rapidly expand populations. This capacity is constrained by a cells replicative potential, which is reflected in its telomere content. Despite the central role of telomeres in cancer and immunity, their analysis at single-cell resolution across diverse cell types remains challenging. Here we show that scATAC-seq data enables quantitative telomeromics when key technical and biological confounders are accounted for. We present a computational framework that addresses read sparsity, telomere representation, and chromatin-state-dependent competition for sequencing signal, enabling robust estimation of telomere content and telomeric variant repeat composition from scATAC-seq data. By inferring global chromatin condensation directly from scATAC-seq profiles, our approach corrects for cell-cycle-associated biases while simultaneously capturing chromatin-state dysregulation in cancer. Applied to a large cancer atlas, this framework reveals patient-specific telomere maintenance phenotypes, including telomerase-associated and alternative lengthening of telomeres (ALT)-like profiles preserved across subclonal populations. Extending beyond cancer cells, we observe that telomere content in exhausted T cell subpopulations prior to immunotherapy is predictive for effective response to PD-1 checkpoint blockade. Together, these results establish scATAC-seq as a robust platform for single-cell telomeromics in cancer and immunity. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=78 SRC="FIGDIR/small/609339v2_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@4f9498org.highwire.dtl.DTLVardef@a4b787org.highwire.dtl.DTLVardef@d1e25org.highwire.dtl.DTLVardef@136196b_HPS_FORMAT_FIGEXP M_FIG C_FIG Created in BioRender. Popp, F. (2026) https://BioRender.com/mi2jove

immunology↗

bioRxiv2024

Orally delivered biodegradable targeted inflammation resolving pectin-coated nanoparticles induce anastomotic healing post intestinal surgery

Although medical treatment is sucessful in most cases in patients with inflammatory bowel diseases (IBD), a percentage of patients require surgical resection of diseased bowel segments at least once in their lifetime. Healing success of the intestinal anastomosis is at high risk, especially in presence of acute inflammation. Failure of anastomotic healing is a life-threatening complication and causes high socioeconomic costs. Common anti-inflammatory medications can have detrimental effects on wound healing. Thus, targeted perioperative therapeutics supporting anastomotic healing during colitis are an urgent medical need. Here, we develop a novel basal membrane targeted controlled release, pectin-coated polymeric nanoparticle (NP) encapsulating a highly potent inflammation resolving mediator, the peptide Ac2-26. These NPs can undergo gastric passage and facilitate localized release of the therapeutic peptide in the colon via degradation of their pectin-chitosan coating by microbial pectinases, which subsequently exposes a collagen IV targeted NP surface, allowing for further binding and retention of the NPs at the intestinal wound. To test these NPs, we used a murine surgical model combining the formation of an intestinal anastomosis with the induction of a preoperative colitis by dextran sodium sulfate. In this model, perioperative administration of pectin-chitosan coated NPs containing Ac2-26 (P-C-Col IV-Ac2-26-NP) led to the reduction of colitis activity in the postoperative phase. Macroscopic wound closure was improved by P-C-Col IV-Ac2-26-NP treatment as evaluated by endoscopy and intraabdominal adhesion scoring. Microscopic analysis of the healing process showed an improved semiquantitative healing score in the treatment group. In this proof-of-concept study we demonstrate that novel P-C-Col IV-Ac2-26-NP could be a promising and clinically feasible perioperative treatment strategy for IBD patients. TOC graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=135 SRC="FIGDIR/small/569918v1_ufig1.gif" ALT="Figure 1"> View larger version (53K): org.highwire.dtl.DTLVardef@14f726dorg.highwire.dtl.DTLVardef@e1739aorg.highwire.dtl.DTLVardef@16ca7d0org.highwire.dtl.DTLVardef@7d87fe_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2022

Proteogenomic analysis reveals RNA as an important source for tumor-agnostic neoantigen identification correlating with T-cell infiltration

Systemic pan-tumor analyses may reveal the significance of common features implicated in cancer immunogenicity and patient survival. Here, we provide a comprehensive multi-omics data set for 32 patients across 25 tumor types by combining proteogenomics with phenotypic and functional analyses. By using an optimized computational approach, we discovered a large number of novel tumor-specific and tumor-associated antigens including shared common target candidates. To create a pipeline for the identification of neoantigens in our cohort, we combined deep DNA and RNA sequencing with MS- based immunopeptidomics of tumor specimens, followed by the assessment of their immunogenicity. In fact, we could detect a broad variety of non-wild type HLA-binding peptides in the majority of patients and confirmed the immunogenicity of 24 neoantigens. Most interestingly, the majority of total and immunogenic neoantigens originated from variants identified in the RNA dataset, illustrating the importance of RNA as a still understudied source of cancer antigens. Moreover, the amount of these mainly RNA-based immunogenic neoantigens correlated positively with overall CD8+ tumor-infiltrating T cells. This study therefore underlines the importance of RNA-centered variant detection for the identification of shared biomarkers and potentially relevant neoantigen candidates. Statement of significanceThe significance of this study lies not only in the potential of our optimized proteogenomic workflow for the discovery of neoantigens (in particular RNA-derived neoantigens) for clinical application, but sheds light on the entity-agnostic prevalence of HLA class I peptide presentation of RNA processing events to be used for tumor targeting.

cancer biology↗

bioRxiv2022

The gut microbiota promotes liver regeneration through hepatic membrane phospholipid synthesis

Background & AimsHepatocyte growth and proliferation is dependent on the synthesis of membrane phospholipids. Lipid synthesis, in turn, requires short chain fatty acids (SCFA) generated by bacterial fermentation, delivered through the gut- liver axis. We therefore hypothesized that dysbiotic insults like antibiotics treatment not only negatively affect gut microbiota, but also impair hepatic lipid synthesis and liver regeneration. MethodsStable isotope labelling and 70% partial hepatectomy (PHx) was carried out in C57Bl/6J wildtype mice, in mice treated with broad-spectrum antibiotics, in germfree mice and gnotobiotic mice colonized with minimal microbiota. Microbiome was analysed by 16S rRNA gene sequencing and microbial culture. Gut content, liver and blood were tested by lipidomics mass spectrometry, qRT-PCR, immunoblot and immunohistochemistry for expression of proliferative and lipogenic markers. Matched biopsies from hyperplastic and hypoplastic liver tissue of human patients subjected to portal vein embolization were analysed by qRT-PCR for lipogenic enzymes and results were correlated with liver volumetry. ResultsThree days of antibiotics treatment induced persistent dysbiosis with significantly decreased beta-diversity and richness, but massive increase of Proteobacteria, accompanied by decreased colonic SCFA. After PHx, antibiotics- treated mice showed delayed liver regeneration, increased mortality, impaired hepatocyte proliferation and decreased hepatic phospholipid synthesis. Expression of the key lipogenic enzyme SCD1 was upregulated after PHx, but delayed by antibiotics-treatment. Germfree mice essentially recapitulated the phenotype of antibiotics-treatment. Importantly, phospholipid synthesis, hepatocyte proliferation, liver regeneration and survival were rescued in gnotobiotic mice colonized with a minimal SCFA-producing microbial community. SCD1 was required for human hepatoma cell proliferation, and its hepatic expression was associated with liver regeneration and hyperproliferation in human patients. ConclusionGut microbiota are pivotal for hepatic membrane phospholipid synthesis and liver regeneration. Lay SummaryGut microbiota affects the liver lipid metabolism through the gut-liver axis, and microbial metabolites promote liver regeneration. Perturbations of the microbiome, e.g., by antibiotics treatment, impair the production of bacterial metabolites, which serve as building blocks for new membrane lipids in liver cells. As a consequence, hepatocyte growth and proliferation, and ultimately, liver regeneration and survival after liver surgery is impaired. HighlightsO_LIPartial hepatectomy in mice pretreated with antibiotics is associated with impaired hepatocyte proliferation and phospholipid synthesis, delayed liver regeneration and increased mortality C_LIO_LIThe delay in liver regeneration and impaired lipogenesis upon antibiotics treatment is preceded by dysbiosis of gut microbiota, increase of Proteobacteria and decreased short-chain fatty acids in the gut C_LIO_LIPartial hepatectomy in germfree mice essentially phenocopies the detrimental effects of antibiotic treatment C_LIO_LILiver regeneration and mortality, as well as phospholipid synthesis and hepatocyte proliferation in germfree mice are fully rescued upon colonisation with a minimal gut bacterial consortium capable of short-chain fatty acid production C_LIO_LIIn human patients, the intrahepatic expression of lipid synthesis enzymes positively correlates with proliferation and liver regeneration in the clinic C_LIO_LIThus, liver regeneration is affected by composition of gut microbiota C_LIO_LIClinically, pre-operative analysis of the gut microbiome may serve as biomarker to determine the extent of liver resections C_LI

cell biology↗

bioRxiv2019

A machine learning algorithm predicts molecular subtypes in pancreatic ductal adenocarcinoma with differential response to gemcitabine-based versus FOLFIRINOX chemotherapy

PurposeDevelopment of a supervised machine-learning model capable of predicting clinically relevant molecular subtypes of pancreatic ductal adenocarcinoma (PDAC) from diffusion-weighted-imaging-derived radiomic features.\n\nMethodsThe retrospective observational study assessed 55 surgical PDAC patients. Molecular subtypes were defined by immunohistochemical staining of KRT81. Tumors were manually segmented and 1606 radiomic features were extracted with PyRadiomics. A gradient-boosted-tree algorithm (XGBoost) was trained on 70% of the patients (N=28) and tested on 30% (N=17) to predict KRT81+ vs. KRT81-tumor subtypes. The average sensitivity, specificity and ROC-AUC value were calculated. Chemotherapy response was assessed stratified by subtype. Radiomic feature importance was ranked.\n\nResultsThe mean{+/-}STDEV sensitivity, specificity and ROC-AUC were 0.90{+/-}0.07, 0.92{+/-}0.11, and 0.93{+/-}0.07, respectively. Patients with a KRT81+ subtype experienced significantly diminished median overall survival compared to KRT81-patients (7.0 vs. 22.6 months, HR 1.44, log-rank-test P=<0.001) and a significantly improved response to gemcitabine-based chemotherapy over FOLFIRINOX (10.14 vs. 3.8 months median overall survival, HR 0.85, P=0.037) compared to KRT81-patients, who responded significantly better to FOLFIRINOX over gemcitabine-based treatment (30.8 vs. 13.4 months median overall survival, HR 0.88, P=0.027).\n\nConclusionsThe machine-learning based analysis of radiomic features enables the prediction of subtypes of PDAC, which are highly relevant for overall patient survival and response to chemotherapy.

bioinformatics↗

bioRxiv2019

A prospectively validated machine learning model for the prediction of survival and tumor subtype in pancreatic ductal adenocarcinoma

PurposeTo develop a supervised machine learning algorithm capable of predicting above vs. below-median overall survival from medical imaging-derived radiomic features in a cohort of patients with pancreatic ductal adenocarcinoma (PDAC).\n\nMaterials and Methods102 patients with histopathologically proven PDAC were retrospectively assessed as the training cohort and 30 prospectively enrolled patients served as the external validation cohort. Tumors were segmented in pre-operative diffusion weighted-(DW)-MRI derived ADC maps and radiomic features were extracted. A Random Forest machine learning algorithm was fit to the training cohort and tested in the external validation cohort. The histopathological subtype of the tumor samples was assessed by immunohistochemistry in 21/30 patients of the external validation cohort. Individual radiomic feature importance was evaluated.\n\nResultsThe machine learning algorithm achieved a sensitivity of 87% and a specificity of 80% (ROC-AUC 90%) for the prediction of above- vs. below-median survival on the unseen data of the external validation cohort. Heterogeneity-related features were highly ranked by the model. Of the 21 patients for whom the histopathological subtype was determined, 8/9 patients predicted by the model to experience below-median overall survival exhibited the quasi-mesenchymal subtype, while 11/12 patients predicted to experience above-median survival exhibited a non-quasi-mesenchymal subtype (Fishers exact test P<0.001).\n\nConclusionThe application of machine-learning to the radiomic analysis of DW-MRI-derived ADC maps allowed the prediction of overall survival with high diagnostic accuracy in a prospectively collected cohort. The high overlap of clinically relevant histopathological subtypes with model predictions underlines the potential of quantitative imaging workflows in pre-operative subtyping and risk assessment in PDAC.

bioinformatics↗