Biology subjects

Theiss, M.

Publications and source records attributed to Theiss, M..

6 recordsLinked to original sources

bioRxiv2025

An Fc-SPINK1 fusion protein inhibits pancreatic inflammation in a mouse model

Pancreatitis results from premature activation and impaired inactivation of pancreatic proteases, primarily trypsin, leading to self-digestion, tissue necrosis, fibrosis, and inflammation. SPINK1 is a pancreas-specific inhibitor of trypsin that prevents premature trypsin activation, and could be a candidate therapeutic. However, because of its small size, SPINK1 would be subject to rapid renal clearance, making it ineffective. To construct a long half-life therapeutic inhibitor of trypsin for pancreatitis treatment we fused this protein to the C-terminus of an IgG1 antibody Fc element, increasing the size to [~]78 kDa, thereby exceeding the renal clearance threshold and providing for FcRn-mediated recycling out of cells. A non-glycosylated form of Fc-SPINK1 was expressed in the yeast Pichia pastoris. Fc-SPINK1 inhibits trypsin enzyme activity in vitro. The blood pharmacokinetics in mice are consistent with a three-compartment distribution model and a terminal half-life of [~]3 days. In a caerulein-induced mouse model of pancreatitis, Fc-SPINK1 significantly ameliorated cell death and immune cell infiltration. We developed an automated image analysis technique to quantify pancreatitis-associated loss of tissue cohesion, and found that Fc-SPINK1 also reduced this effect. This study demonstrates the potential of Fc-SPINK1 as a rationally designed therapy for pancreatitis.

pharmacology and toxicology↗

bioRxiv2022

LOMAR: LOcalization Microscopy Analysis in R

In single molecule localization microscopy data (SMLM), individual instances of a macromolecular complex come in the form of point sets. Particle averaging, which combines localization data from a large number of instances, is often used to overcome experimental noise and obtain a refined view of the underlying structure. However, SMLM point sets are often heterogeneous due to biological variations in the structure they represent and must be partitioned into groups with similar structure before averaging which calls for being able to compute structurally-relevant similarity measures between sets of points. Here we introduce LOMAR (LOcalization Microscopy Analysis in R) a software package for the R programming language that enables comparison of point sets through implementation of several point sets registration methods and similarity measures derived from topological data analysis. We demonstrate use of the package on real and simulated SMLM data of nuclear pore complexes.

cell biology↗

bioRxiv2022

Simulating structurally variable Nuclear Pore Complexes for Microscopy

MotivationThe Nuclear Pore Complex (NPC) is the only passageway for macromolecules between nucleus and cytoplasm, and one of localization microscopys most important reference standards: it is massive and stereotypically arranged. The average architecture of NPC proteins has been resolved with pseudo-atomic precision, however observed NPC heterogeneities evidence a high degree of divergence from this average. Single Molecule Localization Microscopy (SMLM) images NPCs at protein-level resolution, whereupon image analysis software studies NPC variability. However the true picture of NPC variability is unknown. In quantitative image analysis experiments, it is thus difficult to distinguish intrinsically high SMLM noise from true variability of the underlying structure. ResultsWe introduce CIR4MICS ("ceramics", Configurable, Irregular Rings FOR MICroscopy Simulations), a pipeline that creates artificial datasets of structurally variable synthetic NPCs based on architectural models of the true NPC. Users can select one or more N- or C-terminally tagged NPC proteins, and simulate a wide range of geometric variations. We also represent the NPC as a spring-model such that arbitrary deforming forces, of user-defined magnitudes, simulate irregularly shaped variations. We provide an open-source simulation pipeline, as well as reference datasets of simulated human NPCs. Accompanying ground truth annotations allow to test the capabilities of image analysis software and facilitate a side-by-side comparison with real data. We demonstrate this by synthetically replicating a geometric analysis of real NPC radii and reveal that a wide range of simulated variability parameters can lead to observed results. Our simulator is therefore valuable to benchmark and develop image analysis methods, as well as to inform experimentalists about the requirements of hypothesis-driven imaging studies. AvailabilityCode: https://github.com/uhlmanngroup/cir4mics. Simulated data is available at BioStudies (Accession number S-BSST1058). Contacttheiss@ebi.ac.uk Supplementary informationSupplementary data are available at

bioinformatics↗

bioRxiv2020

USP28 and SPINT2 mediate cell cycle arrest after whole genome doubling

Tetraploidy is frequent in cancer and whole genome doubling shapes the evolution of cancer genomes, thereby driving the transformation, metastasis and drug resistance. Yet, human cells usually arrest when they become tetraploid due to p53 activation that leads to CDKN1A expression, cell cycle arrest, senescence or apoptosis. To uncover the barriers that block proliferation of tetraploids, we performed an RNAi mediated genome-wide screen in a human cancer cell line. We identified 140 genes whose depletion improved survival of tetraploids and characterized in depth two of them: SPINT2 and USP28. We show that SPINT2 is a general regulator of CDKN1A, regulating its transcription via histone acetylation. By mass spectrometry and immunoprecipitation, we show that USP28 interacts with NuMA1 and affects centrosome clustering. Moreover, tetraploid cells accumulate DNA damage and loss of USP28 reduces checkpoint activation. Our results indicate three aspects that contribute to survival of tetraploid cells: i) increased mitogenic signaling and reduced expression of cell cycle inhibitors, ii) the ability to establish functional bipolar spindle, and iii) reduced DNA damage signaling.

cell biology↗

bioRxiv2020

Connectomics analysis reveals first, second, and third order thermosensory and hygrosensory neurons in the adult Drosophila brain

Animals exhibit innate and learned preferences for temperature and humidity - conditions critical for their survival and reproduction. Here, we leveraged a whole adult brain electron microscopy volume to study the circuitry associated with antennal thermosensory and hygrosensory neurons, which target specific ventroposterior (VP) glomeruli in the Drosophila melanogaster antennal lobe. We have identified two new VP glomeruli, in addition to the five known ones, and the projection neurons (VP PNs) that relay VP information to higher brain centres, including the mushroom body and lateral horn, seats of learned and innate olfactory behaviours, respectively. Focussing on the mushroom body lateral accessory calyx (lACA), a known thermosensory neuropil, we present a comprehensive connectome by reconstructing neurons downstream of heating- and cooling-responsive VP PNs. We find that a few lACA-associated mushroom body intrinsic neurons (Kenyon cells) solely receive thermosensory inputs, while most receive additional olfactory and thermo- or hygrosensory PN inputs in the main calyx. Unexpectedly, we find several classes of lACA-associated neurons that form a local network with outputs to other brain neuropils, suggesting that the lACA serves as a general hub for thermosensory circuitry. For example, we find DN1 pacemaker neurons that link the lACA to the accessory medulla, likely mediating temperature-based entrainment of the circadian clock. Finally, we survey strongly connected downstream partners of VP PNs across the protocerebrum; these include a descending neuron that receives input mainly from dry-responsive VP PNs, meaning that just two synapses might separate hygrosensory inputs from motor neurons in the nerve cord. (249) HIGHLIGHTSO_LITwo novel thermo/hygrosensory glomeruli in the fly antennal lobe C_LIO_LIFirst complete set of thermosensory and hygrosensory projection neurons C_LIO_LIFirst connectome for a thermosensory centre, the lateral accessory calyx C_LIO_LINovel third order neurons, including a link to the circadian clock C_LI

neuroscience↗

bioRxiv2018

Automated classification of synaptic vesicles in electron tomograms of C. elegans using machine learning

1. AbstractSynaptic vesicles (SVs) are a key component of neuronal signaling and fulfil different roles depending on their composition. In electron micrograms of neurites, two types of vesicles can be distinguished by morphological criteria, the classical \"clear core\" vesicles (CCV) and the typically larger \"dense core\" vesicles (DCV), with differences in electron density due to their diverse cargos. Compared to CCVs, the precise function of DCVs is less defined. DCVs are known to store neuropeptides, which function as neuronal messengers and modulators [1]. In C. elegans, they play a role in locomotion, dauer formation, egg-laying, and mechano- and chemosensation [2]. Another type of DCVs, also referred to as granulated vesicles, are known to transport Bassoon, Piccolo and further constituents of the presynaptic density in the center of the active zone (AZ), and therefore are important for synaptogenesis [3].\n\nTo better understand the role of different types of SVs, we present here a new automated approach to classify vesicles. We combine machine learning with an extension of our previously developed vesicle segmentation workflow, the ImageJ macro 3D ART VeSElecT. With that we reliably distinguish CCVs and DCVs in electron tomograms of C. elegans NMJs using image-based features. Analysis of the underlying ground truth data shows an increased fraction of DCVs as well as a higher mean distance between DCVs and AZs in dauer larvae compared to young adult hermaphrodites. Our machine learning based tools are adaptable and can be applied to study properties of different synaptic vesicle pools in electron tomograms of diverse model organisms.\n\n2. Author summaryVesicles are important components of the cell, and synaptic vesicles are central for neuronal signaling. Two types of synaptic vesicles can be distinguished by electron microscopy: the classical \"clear core\" vesicles (CCVs) and the typically larger \"dense core\" vesicles (DCVs). The distinct appearance of vesicles is caused by their different cargos. To rapidly distinguish between both vesicle types, we present here a new automated approach to classify vesicles in electron tomograms. We combine machine learning with an extension of our previously developed vesicle segmentation workflow, an ImageJ macro, to reliably distinguish CCVs and DCVs using specific image-based features. The approach was trained and validated using data-sets that were hand curated by microscopy experts. Our technique can be transferred to more extensive comparisons in both stages as well as to other neurobiology questions regarding synaptic vesicles.

neuroscience↗