Biology subjects

Hermanns, T.

Publications and source records attributed to Hermanns, T..

10 recordsLinked to original sources

bioRxiv2026

TeTIR domains define a new class of animal TIR-like NADase effectors in STAND proteins

TIR domains are found in diverse immune signaling proteins and, in several systems, function as NAD+-cleaving enzymes causing or signaling cell death. In plants, TIR domains occur as effector domains in TNL immune receptors, where they are coupled to a central STAND ATPase and a C-terminal repeat domain. Here, we identify TeTIR (TEP1 extended TIR), a previously unrecognized family of TIR-like domains associated with NACHT-type ATPases and C-terminal WD40 or TPR repeat domains. The TeTIR domain of human TEP1 exhibits robust NADase activity comparable to SARM1-TIR, making TEP1 the only other NADase-active TIR-like protein identified in mammals. NADase activity is conserved in TEP1 homologs from diverse eukaryotes. The crystal structure of TEP1 TeTIR reveals an extended TIR-like fold with additional secondary-structure elements and a distinctive RWG-containing loop. Molecular dynamics simulations indicate that the catalytic glutamate contributes to catalysis and the conformational equilibrium of the active-site region, promoting formation of a substrate-accessible state. Structure-guided mutagenesis identifies conserved residues contributing to NAD+ recognition and catalysis, including features distinct from canonical TIR NADases. Expression of isolated TEP1 TeTIR causes NAD+ depletion and cell death in a catalytic-glutamate-dependent manner. TeTIRs thus define a distinct family of animal TIR-like NADases embedded in NACHT-type STAND architectures.

biochemistry↗

bioRxiv2025

DUCs are C2 domain containing plant-specific deubiquitinases stabilizing endocytic cargo at the plasma membrane

Deubiquitinases (DUBs) remove ubiquitin modifications from proteins in a substrate- or linkage-selective manner and regulate numerous cell-biological processes, including endocytosis. By performing homology-agnostic bioinformatical screens for undescribed deubiquitinase classes, we identified the plant-specific DUC (deubiquitinase with C2) family, whose members are highly selective for cleaving K63-linked ubiquitin chains. The crystal structure of the Arabidopsis member AtDUC1 reveals that DUC enzymes display a papain-like fold with a characteristic DUB-like active site, despite the apparent absence of sequence similarity to other deubiquitinase families. The K63 linkage specificity is attributed to the recognition of both distal and proximal ubiquitin units via highly conserved contact residues. Arabidopsis DUC members localize to the plasma membrane by virtue of their lipid-binding C2-like domains. In protoplast experiments, all three Arabidopsis DUC enzymes demonstrate the capacity to stabilize an endocytotic model cargo at the plasma membrane, a process that requires both the C2-mediated membrane association and catalytic DUB activity.

biochemistry↗

bioRxiv2024

Identification and characterization of a ubiquitin E3 RING ligase of the Chlamydia-like bacterium Simkania negevensis

In the arms race between a pathogen and the host, many bacteria have acquired a sizable armory to counteract or change the defense mechanisms of the host cell, including the eukaryotic ubiquitin system. Simkania negevensis, an obligate intracellular Chlamydia-like bacterium, is one such example. S. negevensis has a biphasic developmental cycle, similar to other members of the Chlamydiales order. The bacterium develops inside a tubular membranous compartment called Simkania-containing vacuole, which is in close contact with the host endoplasmic reticulum (ER) and mitochondria. It can infect a wide range of hosts, with a long infection cycle lasting up to 15 days, and is associated with respiratory tract diseases. Recently, S. negevensis has been discovered to possess an unusually large number of deubiquitinating enzymes, but not much is known about the variety of other ubiquitin-modifying enzymes in this bacterium. Our study provides an initial description of the activity of a so far uncharacterized S. negevensis ubiquitin E3 RING-ligase (SNE_A12920 or SneRING). We report that SneRING primarily generates K63- and K-11 linked ubiquitin chains and preferentially interacts with UbcH5b and UBE2T E2 ubiquitin ligases among the ones we tested. Bacteria express SneRING upon infection of various human cell lines, as well as amoeba. In addition, we demonstrate that a portion of the expressed SneRING co-localizes with mitochondria and ER. Mass spectrometry studies of the SneRING interactome show enrichment in mitochondrial and ER proteins containing a prohibitin domain and involved in organelle morphology, respiration, and stress response. Our work offers the first insights into the function of an S. negevensis RING ligase, an enzyme potentially involved in organelle remodeling to accommodate the unique lifestyle of this intracellular bacterium.

biochemistry↗

bioRxiv2024

A family of bacterial Josephin-like deubiquitinases with an unusual cleavage mode

Many intracellular bacteria secrete deubiquitinase (DUB) effectors into eukaryotic host cells to keep the bacterial surface or the enclosing vesicle membrane free of ubiquitin marks. Here, we describe a new family of bacterial DUBs that is structurally related to eukaryotic Josephins, but contains members that catalyze a unique destructive substrate deubiquitination. These ubiquitin C-terminal clippases (UCCs) cleave ubiquitin before the C-terminal diGly motif, thereby truncating the modifier and leaving a remnant on the substrate. By comparing the crystal structures of substrate-bound clippases and a closely related conventional DUB, we identified the factors causing the shift and found them conserved in other clippases, including one highly specific for M1-linked ubiquitin chains. This new enzyme class has great potential as tools to study the ubiquitin system, in particular aspects involving branched chains.

biochemistry↗

bioRxiv2023

The structural basis for deubiquitination by the fingerless USP-type effector TssM

Intracellular bacteria are threatened by ubiquitin-mediated autophagy, whenever the bacterial surface or enclosing membrane structures become targets of host ubiquitin ligases. As a countermeasure, many intracellular pathogens encode deubiquitinase (DUB) effectors to keep their surfaces free of ubiquitin modifications. Most bacterial DUBs belong to the OTU or CE-clan families. The betaproteobacteria B. pseudomallei and B. mallei, causative agents of melioidosis and glanders, respectively, encode the TssM effector, the only known bacterial DUB belonging to the USP class. TssM is much shorter than typical eukaryotic USP enzymes and lacks the canonical ubiquitin-recognition region. By solving the crystal structures of isolated TssM and its complex with ubiquitin, we found that TssM lacks the entire Fingers subdomain of the USP fold. Instead, the TssM family has evolved the functionally analog Littlefinger loop, which is located towards the end of the USP domain and recognizes different ubiquitin interfaces than those used by canonical USPs. The structures also revealed the presence of an N-terminal immunoglobulin-fold domain, which is able to form a strand-exchange dimer and might mediate TssM localization to the bacterial surface.

biochemistry↗

bioRxiv2023

Proteomic-based stratification of intermediate-risk prostate cancer patients

Gleason grading is an important prognostic indicator for prostate adenocarcinoma and is crucial for patient treatment decisions. However, intermediate-risk patients diagnosed in Gleason Grade Groups (GG) 2 and GG3 can harbour either aggressive or non-aggressive disease, resulting in under- or over-treatment of a significant number of patients. Here, we performed proteomic, differential expression, machine learning, and survival analyses for 1,348 matched tumour and benign sample runs from 278 patients. Three proteins (F5, TMEM126B and EARS2) were identified as candidate biomarkers in patients with biochemical recurrence. Multivariate Cox regression yielded 18 proteins, from which a risk score was constructed to dichotomise prostate cancer patients into low- and high-risk groups. This 18-protein signature is prognostic for the risk of biochemical recurrence and completely independent of the intermediate GG. Our results suggest that markers generated by computational proteomic profiling have the potential for clinical applications including integration into prostate cancer management.

cancer biology↗

bioRxiv2021

Single-Cell Proteomics Defines the Cellular Heterogeneity of Localized Prostate Cancer

Localized prostate cancer exhibits multiple genomic alterations and heterogeneity at the proteomic level. Single-cell technologies capture important cell-to-cell variability responsible for heterogeneity in biomarker expression that may be overlooked when molecular alterations are based on bulk tissue samples. The aim of this study was to identify novel prognostic biomarkers and describe the heterogeneity of prostate cancer and the associated immune cell infiltrates by simultaneously quantifying 36 proteins using single-cell mass cytometry analysis of over 1,6 million cells from 58 men with localized prostate cancer. To perform this task, we proposed a novel computational pipeline, Franken, which showed unprecedented combination of performance, sensitivity and scalability for high dimensional clustering compared to state of the art methods. We were able to describe subpopulations of immune, stromal, and prostate cells, including unique changes occurring in tumor tissues and high grade disease providing insights into the coordinated progression of prostate cancer. Our results further indicated that men with localized disease already harbor rare subpopulations that typically occur in castration-resistant and metastatic disease, which were confirmed through imaging. Our methodology could be used to discover novel prognostic biomarkers to personalize treatment and improve outcomes.

cancer biology↗

bioRxiv2020

An evolutionary approach to systematic discovery of novel deubiquitinases, applied to Legionella

Deubiquitinating enzymes (DUBs) are important regulators of the posttranslational protein ubiquitination system. Mammalian genomes encode about hundred different DUBs, which can be grouped into seven different classes. Members of other DUB classes are found in pathogenic bacteria, which use them to target the host defense. By combining bioinformatical and experimental approaches, we address the question if the known DUB families have a common evolutionary ancestry and share conserved features that set them apart from other proteases. By systematically comparing family-specific Hidden-Markov-Models, we uncovered distant relationships between established DUBs and other cysteine protease families. Most DUB families share a conserved aromatic residue linked to the active site, which restricts the cleavage of substrates with sidechains at the S2 position, corresponding to Gly-75 in ubiquitin. By applying these criteria to Legionella pneumophila ORFs, we identified lpg1621 and lpg1148 as deubiquitinases, characterized their cleavage specificities, and confirmed the importance of the aromatic gatekeeper motif for substrate selection.Competing Interest StatementThe authors have declared no competing interest.View Full Text

biochemistry↗

bioRxiv2020

Proteogenomic heterogeneity of localized human prostate cancer progression

BackgroundTumor-specific genomic aberrations are routinely determined by high throughput genomic measurements. It remains unclear though, how complex genome alterations affect molecular networks through changing protein levels, and consequently biochemical states of tumor tissues. ResultsHere, we investigated the propagation of genomic effects along the axis of gene expression during prostate cancer progression. For that, we quantified genomic, transcriptomic and proteomic alterations based on 105 prostate samples, consisting of benign prostatic hyperplasia regions and malignant tumors, from 39 prostate cancer patients. Our analysis revealed convergent effects of distinct copy number alterations impacting on common downstream proteins, which are important for establishing the tumor phenotype. We devised a network-based approach that integrates perturbations across different molecular layers, which identified a sub-network consisting of nine genes whose joint activity positively correlated with increasingly aggressive tumor phenotypes and was predictive of recurrence-free survival. Further, our data revealed a wide spectrum of intra-patient network effects, ranging from similar to very distinct alterations on different molecular layers. ConclusionsThis study uncovered molecular networks with remarkably convergent alterations across tumor sites and patients, but it also exposed a diversity of network effects: we could not identify a single sub-network that was perturbed in all high-grade tumor regions.

systems biology↗

bioRxiv2018

Automated Gleason grading of prostate cancer tissue microarrays via deep learning

The Gleason grading system remains the most powerful prognostic predictor for patients with prostate cancer since the 1960s. Its application requires highly-trained pathologists, is tedious and yet suffers from limited inter-pathologist reproducibility, especially for the intermediate Gleason score 7. Automated annotation procedures constitute a viable solution to remedy these limitations.\n\nIn this study, we present a deep learning approach for automated Gleason grading of prostate cancer tissue microarrays with Hematoxylin and Eosin (H&E) staining. Our system was trained using detailed Gleason annotations on a discovery cohort of 641 patients and was then evaluated on an independent test cohort of 245 patients annotated by two pathologists. On the test cohort, the inter-annotator agreements between the model and each pathologist, quantified via Cohens quadratic kappa statistic, were 0.75 and 0.71 respectively, comparable with the inter-pathologist agreement (kappa=0.71). Furthermore, the models Gleason score assignments achieved pathology expert-level stratification of patients into prognostically distinct groups, on the basis of disease-specific survival data available for the test cohort.\n\nOverall, our study shows promising results regarding the applicability of deep learning-based solutions towards more objective and reproducible prostate cancer grading, especially for cases with heterogeneous Gleason patterns.

bioinformatics↗