Biology subjects

Ehinger, A.

Publications and source records attributed to Ehinger, A..

7 recordsLinked to original sources

bioRxiv2025

Arabidopsis HYPERSENSITIVE INDUCED REACTION 2 affects plasma membrane receptor pathways and organization

The distribution of proteins across the plasma membrane is not uniform; however, the principles governing their organization remain not fully understood. Hypersensitive-induced reaction (HIR) proteins are plant-specific members of the stomatin/prohibitin/flotillin/HflK/C (SPFH) family that have been shown to influence membrane organization. Arabidopsis thaliana HIR2 interacts with multiple plasma membrane proteins, including receptor kinases such as BAK1-INTERACTING RECEPTORS 2 and 3 (BIR2 and 3), BRI1-ASSOCIATED KINASE 1 (BAK1), FLAGELLIN SENSING 2 (FLS2), and BRASSINOSTEROID INSENSITIVE 1 (BRI1). These interactions connect HIR2 to BAK1-mediated signaling pathways, as evidenced by impaired growth and immunity phenotypes in hir2 mutants. HIR2 is anchored to the inner leaflet of the plasma membrane through a hydrophobic interaction domain and S-acylation. Using single-particle tracking photoactivated localization microscopy (sptPALM), we showed that HIR2 affects receptor kinase dynamics and clustering, suggesting a role in spatially coordinating receptor complex activities. Structural modeling with AlphaFold 3 predicts a multimeric circular cup-like assembly for HIR2, consistent with high molecular weight complexes identified through blue native polyacrylamide gel electrophoresis. These findings indicate that HIR2 forms a discrete membrane compartment, providing a novel structural framework for spatial membrane organization and thereby modulating the function of membrane-resident receptors.

plant biology↗

bioRxiv2024

Simultaneous and Dynamic Super-Resolution Imaging of Two Proteins in Arabidopsis thaliana using dual-color sptPALM

Super-resolution microscopy techniques have revolutionized cell biology by providing insights into single-molecule dynamics and nanoscale organization within living cells. However, the application of dynamic live-cell methods in plants remains limited by the availability of suitable fluorophores for simultaneous visualization of multiple proteins. To address this challenge, we implemented a dual-color single-particle tracking photoactivated localization microscopy (sptPALM) approach based on codon-optimized photoactivatable fluorescent proteins PA-GFP and PATagRFP. Recently, we demonstrated their individual performance in single-color experiments in Nicotiana benthamiana and Arabidopsis thaliana cells. Here, we establish their combined use for dual-color sptPALM, enabling the simultaneous tracking of two distinct protein species within the same plant cell. This approach provides a framework to investigate the coordinated dynamics, interactions, and spatial organization of multiple proteins in living plant cells.

plant biology↗

bioRxiv2023

GreenMemTraX: A User-Friendly Software for Super-Resolution Analysis of Membrane Protein Dynamics and Nanoscale Organization in Plants

Super-resolution microscopy (SRM) approaches revolutionize cell biology by providing insights into the nanoscale organization and dynamics of macromolecular assemblies and single molecules in living cells. A major hurdle limiting SRM democratization is post-acquisition data analysis which is often complex and time-consuming. Here, we present OneFlowTraX, a user-friendly and open-source software dedicated to the analysis of single-molecule localization microscopy (SMLM) approaches such as single-particle tracking photoactivated localization microscopy (sptPALM). Through an intuitive graphical user interface, OneFlowTraX provides an automated all-in-one solution for single-molecule localization, tracking, as well as mobility and clustering analyses. OneFlowTraX allows the extraction of diffusion and clustering parameters of millions of molecules in a few minutes. Finally, OneFlowTraX greatly simplifies data management following the FAIR (Findable, Accessible, Interoperable, Reusable) principles. We provide a detailed step-by-step manual and guidelines to assess the quality of single-molecule analyses. Applying different fluorophores including mEos3.2, PA-GFP, and PATagRFP, we exemplarily used OneFlowTraX to analyze the dynamics of plant plasma membrane-localized proteins including an aquaporin, the brassinosteroid receptor Brassinosteroid Insensitive 1 (BRI1) and the Receptor-Like Protein 44 (RLP44).

plant biology↗

bioRxiv2021

The TIR-NBS-LRR protein CSA1 is required for autoimmune cell death in Arabidopsis pattern recognition co-receptor bak1 and bir3 mutants

The BRI1-associated kinase BAK1/SERK3 is a positive regulator of multiple leucine rich receptor kinase-mediated signaling pathways including pattern triggered immunity (PTI). Absence or overexpression of BAK1 leads to spontaneous cell death formation. BAK1-interacting receptors (BIR) constitutively interact with BAK1, and plants lacking or overexpressing BIR proteins phenocopy the cell death symptoms observed in bak1 knock outs or overexpressors. In the interactome of BIR3, the TIR-NBS-LRR protein CONSTITUTIVE SHADE-AVOIDANCE 1 (CSA1) was identified by mass spectrometry. CSA1 physically interacts with BIR proteins and can be detected in complexes with BAK1. Direct interaction was shown only for CSA1 with BIR proteins but not BAK1. Double mutant bak1 bir3 genotypes develop strong dwarfism and cell death symptoms that are dependent on EDS1 and salicylic acid. Loss of CSA1 blocks bak1 and bak1 bir3-mediated cell death formation thus demonstrating that CSA1 is causal for this type of cell death. We propose that CSA1 guards BIR proteins and initiates autoimmune cell death that is observed when BAK1 BIR complexes are impaired. Our findings reveal how cell death in the absence of BAK1 and BIR3 is executed and links BAK1, a common co-receptor of many pattern recognition receptors, to NLR proteins typically implicated in effector-triggered immunity.

plant biology↗

bioRxiv2020

Spatial Deconvolution of HER2-positive Breast Tumors Reveals Novel Intercellular Relationships

In the past decades, transcriptomic studies have revolutionized cancer treatment and diagnosis. However, tumor sequencing strategies typically result in loss of spatial information, critical to understand cell interactions and their functional relevance. To address this, we investigate spatial gene expression in HER2-positive breast tumors using Spatial Transcriptomics technology. We show that expression-based clustering enables data-driven tumor annotation and assessment of intra-and interpatient heterogeneity; from which we discover shared gene signatures for immune and tumor processes. We integrate and spatially map tumor-associated types from single cell data to find: segregated epithelial cells, interactions between B and T-cells and myeloid cells, co-localization of macrophage and T-cell subsets. A model is constructed to infer presence of tertiary lymphoid structures, applicable across tissue types and technical platforms. Taken together, we combine different data modalities to define novel interactions between tumor-infiltrating cells in breast cancer and provide tools generalizing across tissues and diseases.

cancer biology↗

bioRxiv2020

The Mutational Landscape of the SCAN-B Real-World Primary Breast Cancer Transcriptome

Breast cancer is a disease of genomic alterations, of which the complete panorama of somatic mutations and how these relate to molecular subtypes and therapy response is incompletely understood. Within the Sweden Cancerome Analysis Network-Breast project (SCAN-B; ClinicalTrials.gov NCT02306096), an ongoing study elucidating the tumor transcriptomic profiles for thousands of breast cancers prospectively, we developed an optimized pipeline for detection of single nucleotide variants and small insertions and deletions from RNA sequencing (RNA-seq) data, and profiled a large real-world population-based cohort of 3,217 breast tumors. We use it to describe the mutational landscape of primary breast cancer viewed through the transcriptome of a large population-based cohort of patients, and relate it to patient overall survival. We demonstrate that RNA-seq can be used to call mutations in important breast cancer genes such as PIK3CA, TP53, and ERBB2, as well as the status of key molecular pathways and tumor mutational burden, and identify potentially druggable genes in 86.8% percent of tumors. To make this rich and growing mutational portraiture of breast cancer available for the wider research community, we developed an open source web-based application, the SCAN-B MutationExplorer, accessible at http://oncogenomics.bmc.lu.se/MutationExplorer. These results add another dimension to the use of RNA-seq as a potential clinical tool, where both gene expression-based and gene mutation-based biomarkers can be interrogated simultaneously and in real-time within one week of tumor sampling.

cancer biology↗

bioRxiv2018

Multidimensional transcriptomics provides detailed information about immune cell distribution and identity in HER2+ breast tumors

The comprehensive analysis of tumor tissue heterogeneity is crucial for determining specific disease states and establishing suitable treatment regimes. Here, we analyze tumor tissue sections from ten patients diagnosed with HER2+ breast cancer. We obtain and analyze multidimensional, genome-wide transcriptomics data to resolve spatial immune cell distribution and identity within the tissue sections. Furthermore, we determine the extent of immune cell infiltration in different regions of the tumor tissue, including invasive cancer regions. We combine cross-sectioning and computational alignment to build three-dimensional images of the transcriptional landscape of the tumor and its microenvironment. The three-dimensional data clearly demonstrates the heterogeneous nature of tumor-immune interactions and reveal interpatient differences in immune cell infiltration patterns. Our study shows the potential for an improved stratification and description of the tumor-immune interplay, which is likely to be essential in treatment decisions.

cancer biology↗