Biology subjects

Goetze, S.

Publications and source records attributed to Goetze, S..

11 recordsLinked to original sources

bioRxiv2026

The protein and lipid compositions of brain apolipoprotein E particles are cell type-specific

Genetic variation in APOE, which encodes a lipid transporter apolipoprotein E (apoE), is the strongest risk factor for late-onset Alzheimer's disease (AD). Although central nervous system (CNS) apoE is produced by various cell types, including astrocytes, microglia, and pericytes, it is unclear whether apoE lipoproteins are a homogeneous population or if their molecular composition varies depending on their cellular origin. Emerging evidence suggests that CNS apoE lipoproteins contribute to AD-related processes, including amyloid deposition, neuroinflammation, synaptic maintenance, and blood-brain barrier function in a cell type-specific manner. We hypothesized that the compositions of CNS apoE lipoproteins depend on their cellular origin and investigated the lipid and protein profiles of apoE particles secreted by induced pluripotent stem cell (iPSC)-derived brain cells and derived from human cerebrospinal fluid (CSF) using liquid chromatography-mass spectrometry (LC-MS/MS). Our results demonstrate that astrocytes, pericytes, and microglia secrete apoE lipoproteins with distinct, cell type-specific lipid and protein profiles. Astrocyte-secreted particles were enriched in sphingomyelins and proteins linked to lipid metabolism, whereas pericyte-secreted particles contained ether-linked phospholipids and proteins associated with antioxidant activity. The lipidation of microglial apoE was markedly lower, but these particles had higher levels of transferrin, apoC-II, and cathepsin D. Notably, one-third of identified features were cell type-specific, highlighting the substantial molecular specialization across cell types. Most lipids and proteins identified in cell-secreted apoE were also detected in CSF-derived particles. Our findings advance the understanding of apoE biology by shifting the focus from apoE as a single, homogeneous entity to a diverse population of complex particles, the composition of which might be linked to functions in health and neurodegenerative diseases.

neuroscience↗

bioRxiv2026

A generalizable normalization framework to decouple protocol and instrument effects: Application to high-sensitivity proteomics multicentric study (PME13)

Multicenter studies are essential for benchmarking analytical workflows, yet their interpretation is often confounded by the combined effects of experimental protocols and instrumentation. To address this challenge, we introduce a simple normalization-based analytical framework, the recovery metric ({rho}), designed to decouple protocol driven effects from instrument dependent variability. We applied this framework to the 13th Proteomics Multicentric Experiment (PME13), a large multicentric proteomics dataset generated across 27 laboratories using high sensitivity workflows and varying sample preparation protocols. By leveraging a common digested reference sample, {rho} enables direct cross-comparison of all datasets on a unified scale, effectively minimizing instrument-related biases. Using this approach, we demonstrate that apparent instrument dependent trends are largely removed when evaluated through {rho}, revealing consistent protocol driven effects across laboratories. Statistical modeling identified key variables influencing {rho}, including sample input amount, reduction and alkylation, and the use of n-dodecyl-{beta}-D-maltoside (DDM). While DDM was associated with improved {rho}, reduction and alkylation and additional handling steps led to reduced performance, particularly at low input levels. We further highlight practical considerations for the application of ratio based normalization, including the occurrence of values exceeding theoretical bounds, which reflect deviations from underlying assumptions and require appropriate filtering. Overall, this work establishes a generalizable analytical strategy for disentangling confounding factors in multicentric datasets and provides practical guidelines for optimizing high sensitivity proteomics (HSP) workflows. The proposed framework is broadly applicable to other analytical fields where cross laboratory comparability is required.

bioinformatics↗

bioRxiv2026

Multi-omics integration identifies a reproducible inflammatory host-response axis in pediatric sepsis

Sepsis is a major cause of morbidity and mortality in children, yet biological heterogeneity in host responses has limited progress toward targeted therapies and patient stratification. Multiomics integration can combine complementary molecular layers to identify coordinated disease programs not captured by individual assays. Here, we integrated genomic, bulk transcriptomic, proteomic and metabolomic data from blood samples of 22 children with culture-confirmed bacterial sepsis enrolled in the Swiss Pediatric Sepsis Study. Multi-Omics Factor Analysis identified a dominant host-response axis reflecting systemic inflammation. This axis was driven primarily by transcriptomic variation and supported by coordinated proteomic and metabolomic signals, including circulating inflammatory mediators and altered amino-acid metabolism. It was associated with C-reactive protein and a severity score proxy. Projection into an independent pediatric sepsis cohort (n = 22) reproduced the inflammatory and severity-related interpretation of this axis. Single-omic projections showed that the integrated signal could be approximated from individual layers, particularly transcriptomics. In three external pediatric whole-blood transcriptomic datasets, the RNA-derived projection separated septic shock from healthy controls and increased across clinical inflammatory syndromes. These findings define a reproducible inflammatory host-response axis in pediatric sepsis and support multi-omics-guided selection of molecular readouts suitable for clinical translation.

immunology↗

bioRxiv2024

MultiOmicsAgent: Guided extreme gradient-boosted decision trees-based approaches for biomarker-candidate discovery in multi-omics data

MultiOmicsAgent (MOAgent) is an innovative, Python based open-source tool for biomarker discovery, utilizing machine learning techniques specifically extreme gradient-boosted decision trees to process multi-omics data. With its cross-platform compatibility, user-oriented graphical interface and a well-documented API, MOAgent not only meets the needs of both coding professionals and those new to machine learning but also addresses common data analysis challenges like data incompleteness, class imbalances and data leakage between disjoint data splits. MOAgents guided data analysis strategy opens up data-driven insights from digitized clinical biospecimen cohorts and makes advanced data analysis accessible and reliable for a wide audience. Biographical NoteJens Settelmeier, Julia Boshart, Martin Gesell are Ph.D. candidates, Jianbo Fu, Sebastian N. Steiner are Post Doc candidates and Sandra Goetze, Patrick Pedrioli senior scientists at the Institute of Translational Medicine at Health Sciences and Technology department at ETH Zurich, Switzerland, within Professor Bernd Wollscheids research group who has been working in the fields of bioinformatics, clinical multi-omics with a focus on spatial cell surface proteomics. Peter J. Schuffler is professor at the institute of Pathology at the TU Munich, Germany and has been working in the field of digital pathology and clinical multi-modal studies. Diyora Salimova is junior professor at the department of Applied Mathematics at the Albert-Ludwigs-University of Freibug, Germany and has been working in the field of stochastic processes, approximation theory and machine learning related topics. Key PointsO_LIMOAgent enables a guided biomarker-candidate discovery in multi-omics studies, providing a graphical interface and well-documented API. C_LIO_LIA user can run MOAgent on a personal computer without the requirement of coding a single line. C_LIO_LIMOAgent is a Python-based solution for biomarker-candidate discovery, using machine learning to analyze multi-omics data. C_LIO_LIMOAgent can address challenges like data incompleteness and class imbalances, ensuring reliable analysis. C_LIO_LIMOAgent makes advanced data analysis accessible, enhancing insights from clinical data. C_LI

bioinformatics↗

bioRxiv2023

Mapping the dynamic cell surface interactome of high-density lipoprotein reveals Aminopeptidase N as modulator of its endothelial uptake

Heterogeneous high-density lipoprotein (HDL) particles, which can contain hundreds of proteins, affect human health and disease through dynamic molecular interactions with cell surface proteins. How HDL mediates its long-range signaling functions and interactions with various cell types is largely unknown. Due to the complexity of HDL, we hypothesize that multiple receptors engage with HDL particles resulting in condition-dependent receptor-HDL interaction clusters at the cell surface. Here we used the mass spectrometry-based and light-controlled proximity labeling strategy LUX-MS in a discovery-driven manner to decode HDL-receptor interactions. Surfaceome nanoscale organization analysis of hepatocytes and endothelial cells using LUX-MS revealed that the previously known HDL-binding protein scavenger receptor SCRB1 is embedded in a cell surface protein community, which we term HDL synapse. Modulating the endothelial HDL synapse, composed of 60 proteins, by silencing individual members showed that the HDL synapse can be assembled in the absence of SCRB1 and that the members are interlinked. The aminopeptidase AMPN (also known as CD13) was identified as an HDL synapse member that directly influences HDL uptake into the primary human aortic endothelial cells (HAECs). Our data indicate that preformed cell surface residing protein complexes modulate HDL function and suggest new theragnostic opportunities.

systems biology↗

bioRxiv2022

Targeting tumour-intrinsic neural vulnerabilities of glioblastoma

Glioblastoma is the most common yet deadliest primary brain cancer1. The neural behavior of glioblastoma, including the formation of synaptic circuitry and tumour microtubes, is increasingly understood to be pivotal for disease manifestation2-9. Nonetheless, the few approved treatments for glioblastoma target its oncological nature, while its neural vulnerabilities remain incompletely mapped and clinically unexploited. Here, we systematically survey the neural molecular dependencies and cellular heterogeneity across glioblastoma patients and diverse model systems. In 27 surgical patient samples, we identify cancer cell morphologies indicative of poor prognosis, and discover repurposable neuroactive drugs with anti-glioblastoma efficacy by image-based drug screening. Glioblastoma cells exhibit functional dependencies on highly expressed neuroactive drug targets, while interpretable molecular machine learning (COSTAR) reveals their downstream convergence on AP-1-driven tumour suppression. This drug-target connectivity signature is confirmed by accurate in silico drug screening on >1 million compounds, as well as by multi-omic profiling of glioblastoma drug responses. Thus, Ca2+-driven AP-1 pathway induction represents a tumour-intrinsic vulnerability at the intersection of oncogenesis and neural activity-dependent signaling. Opportunities for clinical translation of this neural vulnerability are epitomized by the antidepressant Vortioxetine synergizing with current standard of care treatments in vivo. Together, the results presented here provide a mechanistic foundation and conceptual framework for the treatment of glioblastoma based on its neural origins.

cancer biology↗

bioRxiv2022

Decoding functional high-density lipoprotein particle surfaceome interactions

High-density lipoprotein (HDL) is a mixture of complex particles mediating reverse cholesterol transport in the human body and several cytoprotective activities. Despite its relevance for human health, many aspects of HDL-mediated lipid trafficking and cellular signaling remain elusive at the molecular level. During HDLs journey throughout the body, its function is mediated through interactions with cell surface receptors on different cell types. Using four different cellular model systems HDL is interacting with, we comparatively analyzed their surfaceomes to define the HDL receptome. Surfaceome analysis of EA.hy926, HEPG2, foam cells, and human aortic endothelial cells (HAEC) revealed the main currently known HDL-receptor scavenger receptor B1 (SCRB1), as well as 154 shared cell surface receptors representing potential HDL interaction receptor candidates. Since vascular endothelial growth factor A (VEGF-A) was recently found as a regulatory factor of transendothelial transport of HDL, we next analyzed the VEGF modulated surfaceome of HAEC using auto-CSC technology. VEGF-A treatment led to a remodeling of the surfaceome of HAEC cells including the previously reported higher surfaceome abundance of SCRB1 upon treatment. 165 additional receptors were found on HAEC upon VEGF-A treatment representing SCRB1 co-regulated receptors potentially involved in HDL function. Using HATRIC-based ligand receptor capturing (HATRIC-LRC) technology on human endothelial cells, we specifically aimed for the identification of other bona fide (co-)receptors of HDL beyond SCRB1. HATRIC-LRC enabled, next to SCRB1, the identification of the receptor tyrosine-protein kinase Mer (MERTK), which we show is directly contributing to endothelial HDL binding and uptake. Subsequent proximity ligation assays (PLA) demonstrate furthermore the spatial vicinity of MERTK and SCRB1 on the endothelial cell surface. The data shown provide direct evidence for a complex and dynamic HDL receptome and that receptor nanoscale organization may influence functional HDL binding and uptake. List of human gene and protein names discussed in the paperABCA1: Phospholipid-transporting ATPase ABCA1 (ABCA1) ABCA7: Phospholipid-transporting ATPase ABCA7 (ABCA7) ABCG1: ATB-binding cassette G1 (ABCG1) ACVR2A: Activin receptor type-2A (AVR2A) APOA1: Apolipoprotein A1 (APOA1) APOB: Apolipoprotein B (APOB) APOC3: Apolipoprotein C-III (APOC3) APOH: Beta-2-glycoprotein 1 (APOH) APOM: Apolipoprotein M (APOM) ATP5MF: Ecto-F1-ATPase (ATPK) CD4: T-cell surface glycoprotein CD4 (CD4) CD14: Monocyte differentiation antigen CD14 (CD14) CD36: platelet glycoprotein 4 (CD36) EGFR: Epidermal growth factor receptor (EGFR) ITGAM: Integrin alpha-M (ITAM) ITGAV: Integrin alpha-V (ITAV) KDR: Vascular endothelial growth factor receptor 2 (VGFR2) KIT: Mast/stem cell growth factor receptor Kit (KIT) MEGF8: Multiple epidermal growth factor-like domains protein 8 (MEGF8) MEGF10: Multiple epidermal growth factor-like domains protein 10 (MEGF10) MERTK: tyrosine-protein kinase Mer (MERTK) MRC2: C-type mannose receptor 2 (MRC2) MSR1: Macrophage scavenger receptor types I and II (MSRE) PCDHAC1: Protocadherin alpha-C1 (PCDC1) PLTP: Phospholipid transfer protein (PLTP) S1PR1: sphingosine 1-phosphate receptor 1 (S1PR1) S1PR2: sphingosine 1-phosphate receptor 2 (S1PR2) S1PR3: sphingosine 1-phosphate receptor 3 (S1PR3) SCARA3: Scavenger receptor class A member 3 (SCAR3) SCARB1: Scavenger receptor B1 (SCRB1) SLC8A3: Sodium/calcium exchanger 2 (NAC2) TF: Transferrin (TRFE) TFRC: Transferrin receptor protein 1 (TFR1) VEGFA: vascular endothelial growth factor A (VEGF-A) Significance statementA molecular understanding of the high-density lipoprotein (HDL) receptome would provide the rational basis for modulating reverse cholesterol transport. Here we used cellular model systems mimicking tissues involved in HDL-trafficking and reverse cholesterol transport to define the HDL receptome. The receptor tyrosine-protein kinase Mer (MERTK) was identified as a novel mediator of HDL binding and uptake in human aortic endothelial cells. Endothelial MERTK resides within the surfaceome proximal to SCRB1 representing a potential co-receptor. The molecular nanoscale organization of the cell-specific surfaceome may influence cellular HDL interactions and functionality.

molecular biology↗

bioRxiv2020

Standardization and Harmonization of Distributed Multi-National Proteotype Analysis supporting Precision Medicine Studies

Cancer has no borders: Generation and analysis of molecular data across multiple centers worldwide is necessary to gain statistically significant clinical insights for the benefit of patients. Here we conceived and standardized a proteotype data generation and analysis workflow enabling distributed data generation and evaluated the quantitative data generated across laboratories of the international Cancer Moonshot consortium. Using harmonized mass spectrometry (MS) instrument platforms and standardized data acquisition procedures, we demonstrated robust, sensitive, and reproducible data generation across eleven sites in nine countries on seven consecutive days in a 24/7 operation mode. The data presented from the high-resolution MS1-based quantitative data-independent acquisition (HRMS1-DIA) workflow shows that coordinated proteotype data acquisition is feasible from clinical specimens using such standardized strategies. This work paves the way for the distributed multi-omic digitization of large clinical specimen cohorts across multiple sites as a prerequisite for turning molecular precision medicine into reality.

molecular biology↗

bioRxiv2019

A proteogenomic resource enabling integrated analysis of Listeria genotype-proteotype-phenotype relationships

Listeria monocytogenes is an opportunistic foodborne pathogen responsible for listeriosis, a potentially fatal foodborne disease. Many different Listeria strains and serotypes exist, but a proteogenomic resource that bridges the gap in our molecular understanding of the relationships between the Listeria genotypes and phenotypes via proteotypes is still missing. Here we devised a next-generation proteogenomics strategy that enables the community to rapidly proteotype Listeria strains and relate this information back to the genotype. Based on sequencing and de novo assembly of the two most commonly used Listeria model strains, EGD-e and ScottA, we established two comprehensive Listeria proteogenomic databases. A genome comparison established core- and strain-specific genes potentially responsible for virulence differences. Next, we established a DIA/SWATH-based proteotyping strategy, including a new and robust sample preparation workflow, that enables the reproducible, sensitive, and relative quantitative measurement of Listeria proteotypes. This reusable and publically available DIA/SWATH library covers 70% of open reading frames of Listeria and represents the most extensive spectral library for Listeria proteotype analysis to date. We used these two new resources to investigate the Listeria proteotype in states mimicking the upper gastrointestinal passage. Exposure of Listeria to bile salts at 37 {degrees}C, which simulates conditions encountered in the duodenum, showed significant proteotype perturbations including an increase of FlaA, the structural protein of flagella. Given that Listeria is known to lose its flagella above 30 {degrees}C, this was an unexpected finding. The formation of flagella, which might have implications on infectivity, was validated by parallel reaction monitoring and light and scanning electron microscopy. flaA transcript levels were not significantly different with and without exposure to bile salts at 37 {degrees}C, suggesting regulation at the post-transcriptional level. Together, these analyses provide a comprehensive proteogenomic resource and toolbox for the Listeria community enabling the analysis of Listeria genotype-proteotype-phenotype relationships.

systems biology↗

bioRxiv2017

An integrative strategy to identify the entire protein coding potential of prokaryotic genomes by proteogenomics

Accurate annotation of all protein-coding sequences (CDSs) is an essential prerequisite to fully exploit the rapidly growing repertoire of completely sequenced prokaryotic genomes. However, large discrepancies among the number of CDSs annotated by different resources, missed functional short open reading frames (sORFs), and overprediction of spurious ORFs represent serious limitations.\n\nOur strategy towards accurate and complete genome annotation consolidates CDSs from multiple reference annotation resources, ab initio gene prediction algorithms and in silico ORFs in an integrated proteogenomics database (iPtgxDB) that covers the entire protein-coding potential of a prokaryotic genome. By extending the PeptideClassifier concept of unambiguous peptides for prokaryotes, close to 95% of the identifiable peptides imply one distinct protein, largely simplifying downstream analysis. Searching a comprehensive Bartonella henselae proteomics dataset against such an iPtgxDB allowed us to unambiguously identify novel ORFs uniquely predicted by each resource, including lipoproteins, differentially expressed and membrane-localized proteins, novel start sites and wrongly annotated pseudogenes. Most novelties were confirmed by targeted, parallel reaction monitoring mass spectrometry, including unique ORFs and variants identified in a re-sequenced laboratory strain that are not present in its reference genome. We demonstrate the general applicability of our strategy for genomes with varying GC content and distinct taxonomic origin, and release iPtgxDBs for B. henselae, Bradyrhozibium diazoefficiens and Escherichia coli as well as the software to generate such proteogenomics search databases for any prokaryote.

genomics↗

bioRxiv2017

Lysinibacillus fusiformis M5 induces increased complexity in Bacillus subtilis 168 colony biofilms via hypoxanthine

In recent years, biofilms have become a central subject of research in the fields of microbiology, medicine, agriculture, or systems biology amongst others. The sociomicrobiology of multispecies biofilms, however, is still poorly understood. Here, we report a screening system that allowed us to identify soil bacteria, which induce architectural changes in biofilm colonies when cocultured with B. subtilis. We identified the soil bacterium Lysinibacillus fusiformis M5 as inducer of wrinkle-formation in B. subtilis colonies mediated by a diffusible signaling molecule. This compound was isolated by bioassay-guided chromatographic fractionation. The elicitor was identified to be the purine hypoxanthine using mass spectrometry and nuclear magnetic resonance (NMR) spectroscopy. We show that the induction of wrinkle formation by hypoxanthine is not dependent on signal recognition by the histidine kinases KinA, KinB, KinC, and KinD, which are generally involved in phosphorylation of the master regulator Spo0A. Likewise, we show that hypoxanthine signaling does not induce the expression of biofilm-matrix related operons epsA-O and tasA-sipW-tapA. Finally, we demonstrate that the purine permease PbuO, but not PbuG, is necessary for hypoxanthine to induce an increase in wrinkle formation of B. subtilis biofilm colonies. Our results suggest that hypoxanthine-stimulated wrinkle development is not due to a direct induction of biofilm-related gene expression, but rather caused by the excess of hypoxanthine within B. subtilis cells, which may lead to cell stress and death.\n\nIMPORTANCEBiofilms are a bacterial lifestyle with high relevance regarding diverse human activities. Biofilms can be favorable, for instance in crop protection. In nature, biofilms are commonly found as multispecies communities displaying complex social behaviors and characteristics. The study of interspecies interactions will thus lead to a better understanding and use of biofilms as they occur outside laboratory conditions. Here, we present a screening method suitable for the identification of multispecies interactions, and showcase L. fusiformis as a soil bacterium that is able to live alongside B. subtilis and modify the architecture of its biofilms.

microbiology↗