Biology subjects

Bauer, J.

Publications and source records attributed to Bauer, J..

At least 19 recordsLinked to original sources

bioRxiv2026

Structure of PCPE-2 in complex with the BMP-1 metalloprotease reveals the molecular basis of its mechanism of inhibition

PCPE-2 (procollagen C-proteinase enhancer-2) is an extracellular glycoprotein playing dual functions in the regulation of BMP-1/tolloid-like proteinases. Like its homologue PCPE-1, PCPE-2 can enhance the proteolytic maturation of fibrillar procollagens, but it also acts as a potent and specific inhibitor of BMP-1 through formation of a high-affinity complex with the protease. Here, we investigated the molecular basis of this inhibitory interaction using complementary biochemical, biophysical and structural approaches. We show that the two CUB domains of PCPE-2 must be covalently linked for efficient BMP-1 binding and inhibition, consistent with cooperative engagement of the protease. Cryo-electron microscopy combined with density-guided structural modelling and hydrogen/deuterium-exchange mass spectrometry supports a bipartite interaction complex in which the CUB1 domain of PCPE-2 engages the catalytic domain of BMP-1, while the CUB2 domains of both proteins interact together. The interaction involves the calcium-binding surfaces of the PCPE-2 CUB domains that also participate in procollagen recognition, providing a structural framework for understanding the mutually exclusive interactions of PCPE-2 with BMP-1 and procollagen. Allosteric inhibition is supported by the binding of PCPE-2 CUB1 opposite to the active site cleft, leading to the shielding of the residues surrounding the S1-prime pocket, as observed by HDX-MS. Together, these findings define the architecture of the inhibitory PCPE-2/BMP-1 complex and provide the molecular basis for understanding the distinct regulatory activities of the two procollagen C-proteinase enhancers.

biochemistry↗

bioRxiv2026

Age-related loss of brain demyelinating and remyelinating potential is overcome by microglia renewal

Multiple sclerosis (MS) is an immune-mediated demyelinating disease, with progressive neurodegeneration that is refractory to current therapies. Here, we identify microglia aging as a major factor responsible for age-related loss of beneficial demyelinating and remyelinating brain functions. Brain transcriptomics and in situ spatial gene transcription analysis revealed significant oligodendrocyte loss in both young and aged mice during cuprizone-induced experimental demyelination, but impaired microglial activation in aged mice. Age-related defects in microglial activation were associated with reduced clearance of dead myelin and accumulation of lipid droplets, and impaired remyelination, implying exhaustion of microglial function in the aged mouse brain. Transcriptomic analysis of human brain samples from MS donors with matched disease course and severity and with chronic active and inactive lesions, validated that microglial activation is strongly reduced with increasing age. To investigate microglial responses after repeated demyelinating insults and their direct impact on myelin integrity, we established an experimental model of repeated demyelinating episodes in young and aged mice to recapitulate MS features. Notably, aged mice developed a progressive neuroinflammatory response following sequential demyelinating episodes, in contrast to the alternating cycles of demyelination and remyelination reminiscent of relapsing-remitting MS that were observed in young mice. Microglia depletion and repopulation using a CSF1R antagonist recovered demyelination-remyelination capacity in aged mice. The results indicate that microglia aging is a major determinant in the pathogenesis of progressive MS, and that microglia replacement represents a promising therapeutic approach.

immunology↗

bioRxiv2026

Deep learning enables direct HLA typing from immunopeptidomics data

The immune system eliminates malignant and infected cells through T-cell-mediated recognition of peptides presented by human leukocyte antigen molecules. Mass spectrometry-based immunopeptidomics enables unbiased identification of naturally presented HLA-restricted peptides and has become central to the development of T-cell-based immunotherapies. However, immunopeptidomics data reflects the combined peptide presentation of multiple HLA alleles, and determining which allotypes are represented in this multi-allelic complexity remains an unmet computational challenge. Here, we introduce immunotype, a deep learning-based ensemble predictor for HLA class I allotyping directly from immunopeptidomics data. Immunotype integrates peptide and HLA protein sequence information through transformer encoders and a graph neural network, complemented by a curated mono-allelic reference of known peptide-HLA binding preferences. Immunotype achieves an overall accuracy of 87.2% at protein-level resolution across diverse tissues and thereby enables rapid, cost-effective HLA typing of large-scale immunopeptidomics datasets.

bioinformatics↗

bioRxiv2026

Marker-based CRISPR screens identify POU2F1 as a regulator of DLL3 and neuroendocrine identity in small cell lung cancer

Small cell lung cancers (SCLC) often exhibit a neuroendocrine lineage identity marked by high expression of Delta-like Ligand 3 (DLL3). Because DLL3 shows minimal expression in normal adult tissues, it serves as an SCLC-selective tumor antigen and is the basis for clinically efficacious targeted therapies. Understanding the mechanisms that regulate DLL3 expression is therefore critical for advancing therapeutic strategies in this disease. Here, we performed transcription factor-focused and genome-wide CRISPR screens to identify regulators of DLL3 expression in SCLC. Both approaches converged on POU2F1 as a top activator of DLL3 in this tumor context. Despite its ubiquitous expression, we identify an SCLC-specific role for POU2F1 in activating DLL3 and a broader set of neuroendocrine lineage genes. Epigenomic analyses reveal tandem POU2F1-ASCL1 motifs within the DLL3 promoter that underlie the strong codependency between POU2F1 and the neuroendocrine master regulator ASCL1 for high-level DLL3 expression in SCLC. We provide evidence that tandem POU2F1-ASCL1 elements are part of a cis-regulatory code for the lung neuroendocrine cell fate. Together, these findings define a previously unrecognized transcriptional logic controlling DLL3 expression and establish POU2F1 as a context-specific regulator of neuroendocrine lineage in small cell lung cancer.

cancer biology↗

bioRxiv2026

Evolution of Origin Sequence and Recognition for Licensing of Eukaryotic DNA Replication

The large size of eukaryotic chromosomes necessitates that the initiation of DNA replication occurs at numerous origins of DNA replication. In S. cerevisiae, origins are highly DNA sequence-specific and are recognized by the Origin Recognition Complex (ORC). In contrast, most eukaryotes lack features in ORC subunits that contribute to DNA sequence-specific recognition, raising the question of how origins are identified. An analysis of origins in the genome of the distantly related budding yeast Yarrowia lipolytica identified considerable variability in origin sequence and structure. High-resolution structures demonstrate that Y. lipolytica origins are recognized by a combination of ORC and Cdc6 in a manner different from S. cerevisiae. The structure of Yarrowia ORC-Cdc6 bound to different origins shows considerable plasticity in protein-DNA interactions. We compare these yeast structures to the structure of human ORC-CDC6 bound to DNA. These studies reveal information on the evolution of origins and origin recognition. Nomenclature note: There is a different nomenclature for proteins in yeast and human cells. For example, Cdc6 in yeasts is CDC6 in human cells.

molecular biology↗

bioRxiv2026

t2pmhc: A Structure-Informed Graph Neural Network to predict TCR-pMHC Binding

Mapping of T cell receptors (TCRs) to their cognate MHC-presented peptides (pMHC) is central for the development of precision immunotherapies and vaccine design. However, accurate prediction of TCR affinity to peptide antigens remains an open challenge. Most approaches rely solely on sequence information, although increasing evidence suggests that TCR-pMHC binding is primarily determined by three-dimensional structural interactions within the entire TCR-pMHC complex. Consequently, sequence-based methods often fail to generalize to peptides not included in the training data (unseen peptides). Here we introduce t2pmhc, a structure-based graph neural network framework for predicting TCR-pMHC binding using predicted structures of the entire TCR-pMHC complex. We evaluated a Graph Convolutional Network (GCN) and a Graph Attention Network, both demonstrating improved generalization to unseen peptides compared to state-of-the-art models across a variety of public datasets. Evaluation with crystallographic structures yields high-confidence predictions, indicating that current limitations of structure-based models are largely driven by the accuracy of structure prediction. Analysis of node attention patterns in t2pmhc-GCN reveals biologically consistent patterns, assigning high attention to the peptide and the CDR3 regions. Within the peptide sequence, canonical MHC anchor residues are consistently downweighted, whereas potential TCR-binding residues are upweighted. These findings establish t2pmhc as a structure-informed framework for robust TCR-pMHC binding prediction, enabling improved generalization to unseen antigens and providing a foundation for integrating TCR repertoire sequencing into vaccine design and immunotherapy.

bioinformatics↗

bioRxiv2026

A structured RNA balances DEAD-box RNA helicase function in plant alternative splicing control

Eukaryotic gene expression is a multi-layered process influenced by multiple factors. One of them is the secondary structure of precursor mRNAs that can impact various aspects of their processing including alternative splicing (AS). Here, we report the functional characterization of the conserved RNA structural element DEAD that is located in DEAD-box RNA helicase (DRH) genes from land plants and serves as a sensor for RNA helicase activity by controlling AS. In Arabidopsis thaliana, it is found in DRH1 and its closest paralog, regulating usage of an alternative splice site as part of a negative feedback loop. Accordingly, opening of the structure shifts splicing towards non-coding variants, thereby balancing transcript and protein levels. Interestingly, the system is specific to DRH1 and its paralog and does not react to related helicases, which is at least partially conferred by the disordered and RGG/RG motif-containing C-terminus of DRH1. The importance of DEAD is underlined by the observation that releasing this attenuation mechanism causes massive changes in AS - mainly intron retention and exon skipping - and gene expression and results in a severe stress phenotype. Thus, DEAD provides a critical buffering mechanism to fine-tune helicase levels and their global impact on RNA structure-responsive gene expression.

plant biology↗

bioRxiv2025

A Computer Vision Dataset for Pollinator Detection under Real Field Conditions

The widespread decline in biodiversity and abundance of pollinator insects is expected to provoke cascading effects on food security and jeopardize ecosystem services crucial for many crops and wild plants. Pollinator monitoring is a crucial element in preventing further decline of pollinators, to which computer vision approaches can make essential contributions. To facilitate research in such approaches, we present a dataset for pollinator detection with accurate annotations. We develop the dataset with an iterative semi-automatic annotation approach, which leverages YOLO to assist with human annotation. We quantify the impact of multiple levels of errors in annotations on training and report the increase in mAP of 28.7% at the final iteration when compared to the manual annotations. Our dataset encompasses pollinator detection for honeybees and bumblebees across various flower treatments over multiple days. Our dataset facilitates the development of deep learning-based methods for automatic large-scale pollinator detection under various real-world field conditions, as well as adjacent computer vision tasks such as small object detection and label correction.

ecology↗

bioRxiv2025

Decoding the functional role of the calcium ATPase YloB in microbially induced calcite precipitation and sporulation in Solibacillus silvestris

Calcium homeostasis is essential for bacterial physiology, yet its regulation and role in specialized processes such as microbially induced calcite precipitation (MICP) and sporulation remain poorly understood. Here, we characterized the sole annotated P-type calcium ATPase in Solibacillus silvestris, which was previously shown to be upregulated under MICP conditions. We named the protein YloB based on homology to the Bacillus subtilis protein. Structural modeling confirmed that YloB possesses the conserved domains and motifs typical of calcium binding P-type ATPases. Deletion of yloB did not affect growth under elevated calcium levels nor did it reduce MICP yields, indicating that YloB is dispensable for calcium detoxification and biomineralization. In contrast, {Delta}yloB cells produced only partially dehydrated spores with severely reduced heat resistance, a defect rescued by complementation. A YloB-mNeonGreen fluorescent fusion localized specifically to the cell membrane surrounding the forespore during early engulfment, and was retained at this site throughout the remainder of sporulation. A transcriptional PyloB-msfGFP reporter demonstrated that yloB transcription was induced in the early stages of sporulation, but did not respond to a calcium stimulus. These results show that YloB does not act to detoxify calcium from the cytoplasm of S. silvestris, but rather functions to pump calcium into the forespore, enabling Ca-dipicolinic acid accumulation and proper spore maturation. Our study reveals a specialized role for a bacterial calcium ATPase in sporulation, distinguishing calcium transport for endospore development from biomineralization and global calcium homeostasis.

microbiology↗

bioRxiv2025

Brownian DNA Computing

Conventional silicon computing is constrained by energy demands, limited parallelism, and poor compatibility with living systems, motivating the exploration of molecular alternatives. Here, we realize Brownian DNA computing, a hitherto theoretical framework, for energy efficient computation in which coupled molecular balances on a DNA origami scaffold form Brownian Logic Elements (BLEs) that harnesses thermal fluctuations for computation. By encoding multiple logic gates into a programmed energy landscape, these BLEs execute all fundamental Boolean logic gates, complex circuits such as half-adders, and multi-input operations, while also enabling non-Boolean logic with multi-valued outputs in a single compact gate. Computation arises through thermally driven exploration of a near-flat configurational energy landscape, without reliance on consumable fuel strands for cascading signal propagation, making the process fast, resettable, and compatible with operation near reversible thermodynamic limits. Transient-input protocols further allow the BLE energy landscape to be controlled quasistatically, providing a route toward reversible Brownian computation. In combination with single-molecule readout, Brownian DNA computing establishes a new foundation for molecular information processing with potential applications in biocomputing, soft robotics, and energy-efficient nanodevices.

biophysics↗

bioRxiv2025

Elucidating cancer cachexia-mediated aberrant cardiac wasting signaling in human iPSC-derived cardiac muscle

Cancer cachexia is a highly debilitating clinical syndrome of involuntary body mass loss featuring profound muscle wasting leading to high mortality. Notably, cardiac wasting is prominent in cancer patients and cancer survivors. Cachexia studies present significant challenges due to the absence of human models and mainly short-term animal studies. To address this translational gap, we have developed a robust human-based cachexia experimental approach characterized by marked cardiac muscle wasting and contractile dysfunction, with increased expression of protein degradation markers. Using human iPSC-derived cardiac muscle, we investigated morphological, functional, and metabolic alterations in the key stages of cachexia and in the post-cachexia phase. C26 and HCT116 tumor cell lines were used to induce cachexia by two methods, pulse addition of cancer cell conditioned media or in transwell-adapted co-culture. Cachectic cardiac myocytes exhibited reduced contraction amplitude, prolonged relaxation time, and increased oxygen consumption rate (OCR), as assessed by video-based and Seahorse analyses. Mechanistic investigations centered on the Atrogin-1/Calcineurin A/NFAT axis revealed this signaling pathway as a central driver of cachexia-induced cardiac atrophy. Cachectic cardiac myocytes exhibited significant upregulation of Atrogin-1, leading to a marked decrease in Calcineurin A protein levels. This, in turn, impaired nuclear translocation of NFAT, thereby suppressing its transcriptional activity and downstream cell growth signaling. These molecular changes were accompanied by increased autophagic flux, as indicated by elevated LC3BII/LC3BI ratios. Furthermore, withdrawal of cachexia-inducing stimuli followed by regular media changes for one week led to normalization of Atrogin-1 and autophagy markers; however, functional impairments and metabolic dysregulation persisted, highlighting delayed recovery. Our new findings establish the Atrogin-1/Calcineurin A/NFAT axis as a key regulatory mechanism in cardiac muscle wasting and suggest this aberrant signaling axis may serve as a targetable mechanism for treatment of cachexia-induced cardiac dysfunction. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=154 HEIGHT=200 SRC="FIGDIR/small/670675v2_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@12e9borg.highwire.dtl.DTLVardef@19ad494org.highwire.dtl.DTLVardef@1fa664corg.highwire.dtl.DTLVardef@5232a6_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2025

Transient recurrent dynamics shape representations in mice

Brain networks are high-dimensional and interacting complex systems that exhibit substantial structural heterogeneity as well as temporal variability. Yet, when exposed to a stimulus, their recurrent circuits perform reliable computations. The mechanisms underlying this robustness are, however, still mostly unknown. Here, we combine analyses of Neuropixels recordings of awake, behaving mice with models and theory of recurrent neural networks to identify three core computational characteristics that emerge from the interplay of many network constituents and drive dynamic, reliably classifiable stimulus representations. We find that the level of recurrent inhibition in circuits and the microscopic chaos of dynamics respectively drive mean population responses as well as the within- and across-class stimulus response similarities. These core characteristics in turn non-trivially interact to predict and shape the experimentally observed separability of visual and tactile stimulus representations in mouse superior colliculus. Using these characteristics to assess the information transmitted through the network for multiple stimuli reveals a trade-off in coding space: increasing the number of stimuli conveys more information but also reduces their separability due to their larger overlap in the finitedimensional neuronal space. Our analysis predicts that, only for the experimentally observed small population activity, information keeps increasing with the number of stimuli, revealing another crucial advantage of sparse coding.

neuroscience↗

bioRxiv2025

Lifestyle, Early-life, and Genetic Health Risk Factors Underlying the Brain Age Gap: A Mega-Analysis Across 3,934 Individuals from the ENIGMA MDD Consortium

BackgroundLarge-scale studies show that adults with major depressive disorder (MDD) generally have a higher imaging-predicted age relative to their chronological age (i.e., positive brain age gap) compared to controls, though considerable within-group variation exists. This study examines lifestyle, early-life, and genetic health risk factors contributing to the brain age gap. Identifying risk and resilience factors could help protect brain and mental health. MethodsUsing an established model trained on FreeSurfer-derived brain regions (www.photon-ai.com/enigma_brainage), we generated brain age predictions for 1,846 controls and 2,088 individuals with MDD (aged 18-75) from 12 international cohorts. Polygenic risk scores (PRS) were calculated for major depression, C-reactive protein, and body mass index (BMI) using large-scale GWAS results. Linear mixed models were applied to assess lifestyle (BMI, smoking, education), early-life childhood trauma, and genetic (PRS) health risk associations with the brain age gap. Additionally, we evaluated the link between the brain age gap and peripheral biological age indicators (epigenetic clocks). ResultsHigher brain age gaps were significantly associated with BMI ({beta}=0.01, PFDR=0.02) and smoking ({beta}=0.11, PFDR=0.02), while lower brain age gaps were linked to higher education ({beta}=-0.02, PFDR=0.02). Higher childhood trauma scores predicted a higher brain age gap ({beta}=0.04, P=0.01). Higher brain age gaps were positively associated with all PRS ({beta}s=0.04-0.16, PsFDR=0.02-0.03). There were no significant interactions between diagnosis and assessed factors on the brain age gap. In a multivariable model, only modifiable health factors--BMI, smoking, and education--remained uniquely associated with brain age gaps. ConclusionsGenetic liability for depression and related traits is linked to poorer brain health, but health behaviors potentially offer a key opportunity for intervention. This study underscores the importance of targeting modifiable lifestyle factors to mitigate poor brain health in depressed individuals, an approach perhaps under-recognized in clinical practice.

neuroscience↗

bioRxiv2025

Dissecting heterogeneity in cortical thickness abnormalities in major depressive disorder: a large-scale ENIGMA MDD normative modelling study

ImportanceMajor depressive disorder (MDD) is highly heterogeneous, with marked individual differences in clinical presentation and neurobiology, which may obscure identification of structural brain abnormalities in MDD. To explore this, we used normative modeling to index regional patterns of variability in cortical thickness (CT) across individual patients. ObjectiveTo use normative modeling in a large dataset from the ENIGMA MDD consortium to obtain individualised CT deviations from the norm (relative to age, sex and site) and examine the relationship between these deviations and clinical characteristics. Design, setting, and participantsA normative model adjusting for age, sex and site effects was trained on 35 CT measures from FreeSurfer parcellation of 3,181 healthy controls (HC) from 34 sites (40 scanners). Individualised z-score deviations from this norm for each CT measure were calculated for a test set of 2,119 HC and 3,645 individuals with MDD. For each individual, each CT z-score was classified as being within the normal range (95% of individuals) or within the extreme range (2.5% of individuals with the thinnest or thickest cortices). Main outcome measuresZ-score deviations of CT measures of MDD individuals as estimated from a normative model based on HC. ResultsZ-score distributions of CT measures were largely overlapping between MDD and HC (minimum 92%, range 92-98%), with overall thinner cortices in MDD. 34.5% of MDD individuals, and 30% of HC individuals, showed an extreme deviation in at least one region, and these deviations were widely distributed across the brain. There was high heterogeneity in the spatial location of CT deviations across individuals with MDD: a maximum of 12% of individuals with MDD showed an extreme deviation in the same location. Extreme negative CT deviations were associated with having an earlier onset of depression and more severe depressive symptoms in the MDD group, and with higher BMI across MDD and HC groups. Extreme positive deviations were associated with being remitted, of not taking antidepressants and less severe symptoms. Conclusions and relevanceOur study illustrates a large heterogeneity in the spatial location of CT abnormalities across patients with MDD and confirms a substantial overlap of CT measures with HC. We also demonstrate that individualised extreme deviations can identify protective factors and individuals with a more severe clinical picture. Key points QuestionCan z-scores derived from normative modelling shed light on the heterogeneous group-level findings of cortical thickness abnormalities in major depression and what characterises individuals at the extreme ends of cortical thickness abnormalities? FindingWe confirmed a large overlap in z-score distributions between depressed individuals and healthy controls and a heterogeneous spatial distribution of extreme z-deviations across brain regions across individual patients. Lower z-scores for cortical thickness were related to more severe clinical characteristics. MeaningOur findings confirm the heterogeneity in individual variation in the location and extent of CT abnormalities across patients with MDD and stress the importance of individualised predictions when examining cortical thickness abnormalities.

neuroscience↗

bioRxiv2025

A neural basis for flexible perceptual inference

What seems obvious in one context can take on an entirely different meaning if that context shifts. While context-dependent inference has been widely studied, a fundamental question remains: how does the brain simultaneously infer both the meaning of sensory input and the underlying context itself, especially when the context is changing? Here, we study flexible perceptual inference--the ability to adapt rapidly to implicit contextual shifts without trial and error. We introduce a novel change-detection task in dynamic environments that requires tracking latent state and context. We find that mice exhibit first-trial behavioral adaptation to latent context shifts driven by inference rather than reward feedback. By deriving the Bayes-optimal policy under a partially observable Markov decision process, we show that rapid adaptation emerges from sequential updates of an internal belief state. In addition, we show that artificial neural networks trained via reinforcement learning achieve near-optimal performance, implementing Bayesian inference-like mechanisms within their recurrent dynamics. These networks develop flexible internal representations that enable real-time adjustments to the inference model. Our findings establish flexible perceptual inference as a core principle of cognitive flexibility, offering computational and neural-mechanistic insights into adaptive behavior in uncertain environments.

neuroscience↗

bioRxiv2025

Minimally Invasive DNA-Mediated Photostabilization for Extended Single-Molecule and Super-resolution Imaging

Photobleaching of fluorescence labels poses a major limitation in single-molecule and super-resolution microscopy. Conventional photostabilization methods, such as oxygen removal and addition of high concentrations of photostabilization additives, often require careful fluorophore selection and can disrupt the biological environment. To address these limitations, we developed a modular and minimally invasive photostabilization approach that utilizes DNA-mediated delivery of a photostabilizer directly to the imaging site. Under lower excitation intensities, the DNA-mediated strategy outperformed solution-based approaches, achieving efficient photostabilization at significantly lower additive concentrations. However, at higher excitation intensities, the stability of a single photostabilizer molecule became the limiting factor. To overcome this and reduce the loss of localizations in DNA-PAINT experiments we have also implemented a recovery scheme where the photostabilizer is continuously replenished at the imaging site. We further extended the approach to cell imaging, demonstrating improved localization rate and precision in 3D-DNA PAINT measurements. DNA-mediated photostabilization offers a promising solution for imaging applications where high additive concentrations are prohibited. Its modularity enables adaptation to various imaging schemes and ultimately expands the repertoire of fluorophores suitable for single-molecule and super-resolution imaging.

biophysics↗

bioRxiv2024

Movie reconstruction from mouse visual cortex activity

The ability to reconstruct images represented by the brain has the potential to give us an intuitive understanding of what the brain sees. Reconstruction of visual input from human fMRI data has garnered significant attention in recent years. Comparatively less focus has been directed towards vision reconstruction from single-cell recordings, despite its potential to provide a more direct measure of the information represented by the brain. Here, we achieve high-quality reconstructions of natural movies presented to mice, from the activity of neurons in their visual cortex for the first time. Using our method of video optimization via backpropagation through a state-of-the-art dynamic neural encoding model we reliably reconstruct 10-second movies at 30 Hz from two-photon calcium imaging data. We achieve a pixel-level correction of 0.57 between the ground truth movie and the reconstructions from single-trial neural responses. We find that critical for high-quality reconstructions are the number of neurons in the dataset and the use of model ensembling. This paves the way for movie reconstruction to be used as a tool to investigate a variety of visual processing phenomena.

neuroscience↗

bioRxiv2023

CSF plasma cell expansion in LGI1-/CASPR2-autoimmune encephalitis is associated with loss of regulatory MAIT cells

Anti-Leucine-rich glioma inactivated-1 (LGI1) and anti-contactin-associated-protein-2 (CASPR2) associated autoimmune encephalitis (AIE) variants are characterized by directly pathogenic autoantibodies present in serum and CSF. The dynamics and drivers of intrathecal and systemic autoantibody production are incompletely understood. We aimed to elucidate the immunologic basis of the LGI1-/CASPR2-associated AIE variants by performing multi-omic profiling of CSF/blood in untreated patients. We validated findings by flow cytometry in independent cohorts and confirmed functionality using rodent immunization. We identified clonal IgG2 and IgG4 plasma cell expansion and affinity maturation in the CSF together with clonally restricted, activated, antigen-experienced CD8 and CD4 T cells as a hallmark of these encephalitis variants. Using recombinant cloning, we confirmed that expanded CSF plasma cell clones almost exclusively bound the respective neuronal autoantigen. In addition, we found a loss of regulatory mucosa-associated invariant T (MAIT) cells and gamma delta T cells in the CSF and - to a lesser degree - in blood. We validated the functional role of these invariant T cells using a novel murine active immunization paradigm using both autoantigens: MAIT cells suppressed systemic formation of LGI1 and CASPR2-specific anti-neuronal antibodies. We propose that loss of systemic and intrathecal regulatory mechanisms mediated by innate-like T cells promote plasma cell expansion and autoantibody production as a shared mechanism in AIE. One sentence summaryCerebrospinal fluid (CSF) and peripheral blood (PB) single cell transcriptomics of patients with untreated anti-LGI1 and anti-CASPR2 autoimmune encephalitis demonstrated CSF specific expansion of autoantigen-specific plasma cell clones and systemic loss of invariant mucosa-associated T-cells (MAIT).

neuroscience↗