Biology subjects

Wimmer, R.

Publications and source records attributed to Wimmer, R..

10 recordsLinked to original sources

bioRxiv2026

Human cerebral assembloids as a model to study the interaction of glioblastoma with resident glial cells

Glioblastoma (GB) is a highly invasive brain cancer characterized by extensive peritumoral glial reactivity. Although GB infiltration and recurrence remain major therapeutic challenges, the contribution of GB-induced gliosis to tumor progression is poorly understood. To investigate glia-GB interactions, we developed human iPSC-derived cerebral forebrain assembloids containing innate microglia and macroglia and transplanted them with GB cells. The model recapitulates key tumor features observed in patients, including distinct tumor regions, reactive gliosis, and altered tissue stiffness. Single-cell RNA sequencing revealed an unexpected enrichment of extracellular vesicles (EVs) in peritumoral reactive glia, which we validated in patient samples. EV inhibition impaired formation of the peritumoral niche, whereas EVs from GB-containing assembloids induced glial reactivity in GB-free assembloids. Furthermore, the modularity of our system enables incorporation of additional niche components, such as monocytes, allowing investigation of the monocyte-glia-tumor axis and providing a powerful platform to dissect mechanisms regulating the GB microenvironment.

neuroscience↗

bioRxiv2026

Non-ribosomal Peptides as Structural Determinants of Fungal Hydrophobicity

Fungal surfaces must remain hydrophobic to enable growth, dispersal, and survival under fluctuating environmental conditions, yet the molecular basis of this property remains incompletely understood. Here, we identify fungisporins, fusahexins, and related cyclic non-ribosomal peptides (NRPs) as members of a conserved functional class of fungal metabolites, termed WAter Repellent Peptides (WARPs), that are required for fungal surface hydrophobicity. Across filamentous fungi, WARPs vary substantially in sequence and length but share conserved structural features, including cyclization, hydrophobic amino acid composition, and alternating D- and L-configurations, consistent with a flexible amphiphilic scaffold. Loss of WARP-producing non-ribosomal peptide synthetases results in rapid collapse of aerial hyphae upon water exposure, demonstrating that these peptides are required for maintenance of hydrophobic aerial structures. Using phage-display-derived antibodies, we localize WARPs to the hyphal surface, supporting their role as surface-associated structural components. Together, these findings identify a conserved NRPS-encoded peptide system that contributes to fungal hydrophobicity and establish WARPs as a broadly distributed class of surface-associated metabolites with structural function in filamentous fungi.

microbiology↗

bioRxiv2026

A defined 2D system for generating and expanding human basal radial glia from iPSCs

Basal radial glia (bRG) drive human cortical expansion but remain underrepresented in vitro. We established a defined and expandable 2D system for efficient generation of human bRG from iPSCs. These cells recapitulate canonical molecular signatures, hallmark somal translocation behaviours, intrinsic differentiation potential, and integration into organoid tissue. Network-based analyses identified PAK2 as a regulator of mitotic somal translocation, illustrating the systems utility for mechanistic interrogation of bRG biology.

neuroscience↗

bioRxiv2025

Multiplicative couplings facilitate rapid learning and information gating in recurrent neural networks

The mammalian forebrain is the seat of higher cognition with architectural parallels to modern machine learning systems. Specifically, the cortex resembles recurrent neural networks (RNNs) while the thalamus resembles feedforward neural networks (FNNs). How such architectural features endow the forebrain with its learning capacity, is unknown. Here we take inspiration from empirical thalamocortical discovery and develop a multiplicative coupling mechanism between RNN-FNN architectures that collectively enhance their computational strengths and learning. The multiplicative interaction imposes a Hebbian-weight amplification onto synaptic-neuronal coupling, enabling context-dependent gating and rapid switching. We demonstrate that multiplicative feedback-driven synaptic plasticity achieves 2-100 folds of speed improvement in supervised, reinforcement and unsupervised learning settings, boosting memory capacity, model robustness and generalization of RNNs. We further demonstrate the efficacy and biological plausibility of multiplicative gating in modeling multiregional circuits, including a prefrontal cortex-mediodorsal thalamus network for context-dependent decision making, a cortico-thalamic-cortical network for working memory and attention, and an entorhinal cortex-hippocampus network for visuospatial navigation and sequence replay. Taken together, our results demonstrate the profound insights into neuroscience-inspired computation that enable multi-plastic attractor dynamics and computation in recurrent neural circuits.

neuroscience↗

bioRxiv2025

Two independent translocation modes drive neural stem cell dissemination into the human fetal cortex

The strong size increase of the human neocortex is supported both by the amplification and the basal translocation of a neural stem cell population, the basal radial glial cells (or bRG cells). Using live imaging of second trimester human fetal tissue and cortical organoids, we identify two independent translocation modes for bRG cell colonization of the human neocortex. On top of an actomyosin-dependent movement called mitotic somal translocation (MST), we identify a microtubule-dependent motion occurring during interphase, that we call interphasic somal translocation (IST). We show that IST is driven by the LINC complex, through the nuclear envelope recruitment of the dynein motor and of its activator LIS1. Consequently, IST severely altered in LIS1 patient-derived cortical organoids. We also demonstrate that MST occurs during prometaphase and is a mitotic spindle translocation event. MST is controlled by the mitotic cell rounding molecular pathway, via Moesin and Vimentin, driving translocation. We report that 85% of bRG cell translocation is due to IST, for a total movement of 0,67 mm per month of human fetal gestation. Our work identifies how bRG cells colonize the human fetal cortex, and further shows that IST and MST are conserved in bRG-related migrating glioblastoma cells.

neuroscience↗

bioRxiv2024

To fly, or not to fly, that is the question: A deep learning model for peptide detectability prediction in mass spectrometry

Identifying detectable peptides, known as flyers, is key in mass spectrometry-based proteomics. Peptide detectability is strongly related with the peptide sequence and its resulting physicochemical properties. Moreover, the high variability in MS data, particularly in peptide detectability and intensity across multiple analyses and samples, makes the development of a generic model for detectability prediction unfeasible. This underlines the need for tools that can be refined for specific experimental conditions. To address this need, we present Pfly, a deep learning model developed to predicts peptide detectability based solely on peptide sequence. Pfly distinguishes itself as a versatile and reliable state-of-the-art tool, offering high performance, accessibility, and easy customizability for end-users. This adaptability allows researchers to tailor the model to their specific experimental conditions, facilitating the creation of lab-specific models. This, in turn, can lead to more accurate results and expand the models applicability across various research fields. The models architecture is an encoder-decoder with an attention mechanism. This tool classifies peptides as either flyers or non-flyers, providing both binary probabilities and detailed categorical probabilities for four distinct classes defined in this study: non-flyer, weak flyer, intermediate flyer, and strong flyer. The model was initially trained on a synthetic peptide library and subsequently fine-tuned with a biological dataset to mitigate bias towards synthesizability, improving the predictive capacity and outperforming state-of-the-art predictors in a benchmark comparison. The study further investigates the influence of protein abundance and the search engine, illustrating the negative impact on peptide identification due to misclassification. Pfly has been integrated in the DLOmix framework and it is accessible on GitHub at https://github.com/wilhelm-lab/dlomix.

bioinformatics↗

bioRxiv2024

Decoding the Impact of Neighboring Amino Acid on MS Intensity Output through Deep Learning

Peptide-level quantification using mass spectrometry (MS) is no trivial task as the physicochemical properties affect both response and detectability. The specific amino acid (AA) sequence affects these properties, however the link from sequence to intensity output remains poorly understood. In this work, we explore combinations of amino acid pairs (i.e., dimer motifs) to determine a potential relationship between the local amino acid environment and MS1 intensity. For this purpose, a deep learning (DL) model, consisting of an encoder-decoder with an attention mechanism, was built. The attention mechanism allowed to identify the most relevant motifs. Specific patterns were consistently observed where a bulky/aromatic and hydrophobic AA followed by a cationic AA as well as consecutive bulky/aromatic and hydrophobic AAs were found important for the MS1 intensity. Correlating attention weights to mean MS1 intensities revealed that some important motifs, particularly containing Trp, His, and Cys, were linked with low responding peptides whereas motifs containing Lys and most bulky hydrophobic AAs were often associated with high responding peptides. Moreover, Asn-Gly was associated with low MS1 response. The model could predict MS1 response with a mean average percentage error of [~]11% and a Pearson correlation coefficient of [~]0.68.

bioinformatics↗

bioRxiv2023

Insight on physicochemical properties governing peptide MS1 response in HPLC-ESI-MS/MS proteomics: A deep learning approach

Accurate and absolute quantification of individual peptides in complex mixtures is a challenge not easily overcome. A potential solution is the use of quantitative mass spectrometry (MS) based methods, however, current state of the art requires foreground knowledge and isotopically labeled standards for each peptide to be accurately quantified. This increases analytical expenses, time consumption, and labor, limiting the number of peptides that can be quantified. A key step in developing less restrictive label-free quantitative peptidomics methods is understanding of the physicochemical properties of peptides that influence the MS response. In this work, a deep learning model was developed to identify the most relevant physicochemical properties based on repository MS data from equimolar peptide pools. Using an autoencoder with attention mechanism and correlating attention weights with corresponding physicochemical property indices from AAindex1, we were able to obtain insight on the properties governing the peptide-level MS1 response. These properties can be grouped in three main categories related to peptide hydrophobicity, charge, and structural propensities. Moreover, we present a model for predicting the MS1 intensity output based solely on peptide sequence input. Using a refined training dataset, the model predicted log-transformed peptide MS1 intensities with an average error of 11%.

bioinformatics↗

bioRxiv2021

Dynamin-2 controls complement receptor 3- mediated phagocytosis completion and closure

Phagocytosis is the mechanism of the internalization of large particles, microorganisms and cellular debris. The complement pathway represents one of the first mechanisms of defense against infection and the complement receptor 3 (CR3), which is highly expressed on macrophages, is a major receptor for many pathogens and debris. Key to dissecting the mechanisms by which CR3-mediated phagocytosis occurs, is understanding how the complex actin binding protein machinery and associated regulators interact with actin during phagocytosis, from triggering of receptor, through to phagosome formation and closure. However, how CR3-mediated phagosome completion and closure are orchestrated is not known. Here, we reveal that dynamin-2 is recruited concomitantly with polymerised actin at the site of the nascent phagosomes and accumulates until membrane scission. Inhibition of dynamin activity leads to stalled phagocytic cups and a decrease in the amount of F-actin at the site of phagocytosis. Acute inhibition of dynamin activity in living phagocytosing cells established that dynamin-2 plays a critical role in the effective scission of the CR3-phagosome from the plasma membrane. Thus, dynamin-2 has two distinct roles in CR3-mediated phagocytosis, in the assembly of the F-actin phagocytic cup and during phagosome scission.

cell biology↗

bioRxiv2019

The Proteome of Tetrasphaera elongata is Prepadapted to Changing Environmental Conditions in Wastewater Treatment Plants

The activated sludge in wastewater treatment plants (WWTP) designed for enhanced biological phosphorus removal (EBPR) experiences periodically changing nutrient and oxygen availability. Tetrasphaera is the most abundant genus in Danish WWTP and represent up to 20-30% of the activated sludge community based on 16S rRNA amplicon sequencing and quantitative fluorescence in situ hybridization analyses, although it is in low abundance in the influent wastewater. Here we investigate how Tetrasphaera can successfully out-compete most other microorganisms in such highly dynamic ecosystems. To achive this, we analyze the physiological adaptations of the WWTP isolate T. elongata str. LP2 during an aerobic to anoxic shift by label-free quantitative proteomics and NMR-metabolomics. Escherichia coli was used as reference organism as it shares several metabolic capabilities and is regularly introduced to wastewater treatment plants, but without succeeding there. When compared to E. coli, only minor changes in the proteome of T. elongata were observed after the switch to anoxic conditions. This indicates that metabolic pathways for anaerobic energy harvest were already expressed during the aerobic growth. This allows continuous growth of Tetrasphaera immediately after the switch to anoxic conditions. Metabolomics furthermore revealed that the substrates provided were exploited far more efficiently by Tetrasphaera than by E. coli. These results suggest that T. elongata prosper in the dynamic WWTP environment due to adaptation to the changing environmental conditions. Significance of the studyMembers of the genus Tetrasphaera are widely distributed and highly abundant in most well-operating WWTPs with EBPR configuration. However, despite their high abundance in situ, little is known about their physiology and ecological role. Although the importance of Tetrasphaera in engineered wastewater treatment systems is slowly being recognized, additional information is needed to understand the full extent of functions the microorganisms have in many of the essential biological processes in the WWTP. Such information may improve available process models and ultimately lead to better wastewater treatment as well as resource recovery. This study supplies proteomic and metabolomic data on the aerobic/anoxic adaptation of Tetrasphaera and provides a hypothesis on how Tetrasphaera might compete in dynamic engineered systems.

microbiology↗