Biology subjects

Kasper, L.

Publications and source records attributed to Kasper, L..

14 recordsLinked to original sources

bioRxiv2026

Optimizing histatin 5: Effects of K13 and K17 substitutions on proteolytic stability and antifungal activity

Candida albicans is an opportunistic human fungal pathogen found in the oral cavity. Human saliva contains a 24-amino acid peptide called histatin 5 (Hst5) that has activity against C. albicans, but degradation of Hst5 by secreted aspartyl proteases (Saps) produced by C. albicans and by salivary proteases can reduce its antifungal efficacy. Building on our previous work that identified K13 and K17 as important residues for stability and activity of Hst5, we systematically investigated amino acid modification at these sites. Modifications explored the influence of hydrophobicity, charge, polarity, size, and aromaticity on Hst5s interaction with Saps and saliva. The K13R variant retained proteolytic stability and antifungal activity after incubation with Sap1, Sap2, Sap3, and Sap9, while other K13 variants generally had reduced stability and activity, emphasizing the importance of a positive charge at this position. At K17, substitutions generally enhanced proteolytic stability and improved antifungal activity after incubation with Saps. We introduced the normalized intact peptide (NIP) parameter as a tool for identifying Hst5 variants with improved stability in the presence of multiple Saps, and NIP revealed K17W as the most proteolytically stable variant overall. Additionally, we observed modest differences in peptide stability in saliva, and the K17W variant was the only variant that retained more activity than Hst5 following incubation with saliva. We further assessed the K17W variants ability to prevent biofilm formation and found it to be more effective than the parent peptide Hst5. Our findings highlight the interactions between the Hst5 K13 and K17 residues with Saps and saliva and provide a strong foundation for future Hst5 engineering efforts to improve proteolytic stability and antifungal efficacy in diverse proteolytic environments.

microbiology↗

bioRxiv2025

Differences in layer-specific activation of the insula during interoceptive vs. exteroceptive attention

Attention modulates the relative weighting of information and plays a critical role in modern theories of perception. While its role in exteroceptionthe perception of the external environmenthas been extensively studied, attentional processes in interoception, the perception of bodily states, are less well understood. In this study, we exploited high-field (7 Tesla) functional magnetic resonance imaging and concurrent field monitoring to investigate interoceptive attention at the level of cortical layers during a heartbeat attention task. Voxel-wise analyses reveal increased activity in the bilateral dorsal mid-insula during interoceptive attention (attending to ones heartbeat) compared to exteroceptive attention (attending to auditory white noise). Layer-specific analyses further demonstrate that this difference in activity is significantly higher in upper compared to lower cortical layers. This activation pattern persists after accounting for potential vascular artifacts through a deconvolution analysis with a physiological point spread function (PSF). To our knowledge, these findings represent the first empirical demonstration of layer-specific processes during interoceptive attention in human cortex. They may prove useful to inform and constrain theories of computational principles and physiological implementation of interoception.

neuroscience↗

bioRxiv2025

Evaluating the quality of brainstem ROI registration using structural and diffusion MRI

Accurate registration of regions of interest (ROIs) from standard atlases to participants native spaces is a critical step in fMRI studies, as it directly affects the reliability of sampled BOLD signals. While T1-weighted (T1w) image-based ROI registration is well validated and widely adopted in cortical fMRI, its performance degrades in brainstem studies due to the small size, dense packing, and poor visibility of brainstem nuclei on T1w contrast. We hypothesized that incorporating diffusion MR images, containing more information about internal brainstem architecture, should improve ROI registration accuracy. To test this, we developed four registration pipelines that either included or excluded diffusion-based alignment components and evaluated their performance using data from n=20 healthy participants. Registration accuracy was assessed using Dice coefficient for the red nucleus (RN) and the substantia nigra (SN), and mis-registration fraction--a metric developed for nuclei that cannot be manually delineated--for the dorsal raphe nucleus (DRN). The results showed that diffusion-based pipelines, using fractional anisotropy (FA) images, non-diffusion-weighted (b0) images, and multivariate combination, outperformed the T1w-only baseline. Probabilistic maps derived from inverse-transformed native ROIs further supported improved sensitivity to inter-individual anatomical variability in the diffusion-augmented pipelines. In addition, analysis of gradient magnitude maps from the Jacobian determinants revealed associations between localized deformation and image modality-specific landmarks. These findings demonstrate the potential of diffusion-augmented pipelines for improving brainstem ROI registration, which could enhance the robustness of fMRI studies on brainstem disorders characterized by functional dysregulation.

neuroscience↗

bioRxiv2022

Neurodesk: An accessible, flexible, and portable data analysis environment for reproducible neuroimaging

Neuroimaging data analysis often requires purpose-built software, which can be challenging to install and may produce different results across computing environments. Beyond being a roadblock to neuroscientists, these issues of accessibility and portability can hamper the reproducibility of neuroimaging data analysis pipelines. Here, we introduce the Neurodesk platform, which harnesses software containers to support a comprehensive and growing suite of neuroimaging software (https://www.neurodesk.org/). Neurodesk includes a browser-accessible virtual desktop environment and a command line interface, mediating access to containerized neuroimaging software libraries on various computing platforms, including personal and high-performance computers, cloud computing and Jupyter Notebooks. This community-oriented, open-source platform enables a paradigm shift for neuroimaging data analysis, allowing for accessible, flexible, fully reproducible, and portable data analysis pipelines.

neuroscience↗

bioRxiv2022

Transkingdom mechanism of MAMP generation by chitotriosidase (CHIT1) feeds oligomeric chitin from fungal pathogens and allergens into TLR2-mediated innate immune sensing

Chitin is a highly abundant polysaccharide in nature and linked to immune recognition of fungal infections and asthma in humans. Ubiquitous in fungi and insects, chitin is absent in mammals and plants and, thus, represents a microbe-associated molecular pattern (MAMP). However, the highly polymeric chitin is insoluble, which potentially hampers recognition by host immune sensors. In plants, secreted chitinases degrade polymeric chitin into diffusible oligomers, which are fed to innate immune receptors and co-receptors. In human and murine immune cells, a similar enzymatic activity was shown for human chitotriosidase (CHIT1) and oligomeric chitin is sensed via an innate immune receptor, Toll-like receptor (TLR) 2. However, a complete system of generating MAMPs from chitin and feeding them into a specific receptor/co-receptor-aided sensing mechanism has remained unknown in mammals. Here, we show that the secreted chitinolytic host enzyme, CHIT1, converts inert polymeric chitin into diffusible oligomers that can be sensed by TLR1-TLR2 co-receptor/receptor heterodimers, a process promoted by the lipopolysaccharide binding protein (LBP) and CD14. Furthermore, we observed that Chit1 is induced via the {beta}-glucan receptor Dectin-1 upon direct contact of immortalized human macrophages to the fungal pathogen Candida albicans, whereas the defined fungal secreted aspartyl proteases, Sap2 and Sap6, from C. albicans were able to degrade CHIT1 in vitro. Our study shows the existence of an inducible system of MAMP generation in the human host that enables contact-independent immune activation by diffusible MAMP ligands with striking similarity to the plant kingdom. Moreover, this study highlights CHIT1 as a potential therapeutic target for TLR2-mediated inflammatory processes that are fueled by oligomeric chitin.

immunology↗

bioRxiv2021

H3-K27M-Mutant Nucleosomes Interact with MLL1 to Shape the Glioma Epigenetic Landscape

Cancer-associated mutations in genes encoding histones dramatically reshape chromatin and support tumorigenesis. Lysine to methionine substitution of residue 27 on histone H3 (K27M) is a driver mutation in high-grade pediatric gliomas, known to abrogate Polycomb Repressive Complex 2 (PRC2) activity. We applied single-molecule systems to image individual nucleosomes and delineate the combinatorial epigenetic patterns associated with H3-K27M expression. We found that chromatin marks on H3-K27M-mutant nucleosomes are dictated both by their incorporation preferences and by intrinsic properties of the mutation. Mutant nucleosomes not only preferentially bind PRC2, but also directly interact with MLL1, thus leading to genome-wide redistribution of H3K4me3. H3-K27M-mediated deregulation of both repressive and active chromatin marks leads to unbalanced bivalent chromatin, which may support a poorly differentiated cellular state. This study provides evidence for a direct effect of H3-K27M oncohistone on the MLL1-H3K4me3 pathway and highlights the capability of single-molecule tools to reveal mechanisms of chromatin deregulation in cancer.

cancer biology↗

bioRxiv2021

TAPAS: an open-source software package for Translational Neuromodeling and Computational Psychiatry

Psychiatry faces fundamental challenges with regard to mechanistically guided differential diagnosis, as well as prediction of clinical trajectories and treatment response of individual patients. This has motivated the genesis of two closely intertwined fields: (i) Translational Neuromodeling (TN), which develops "computational assays" for inferring patient-specific disease processes from neuroimaging, electrophysiological, and behavioral data; and (ii) Computational Psychiatry (CP), with the goal of incorporating computational assays into clinical decision making in everyday practice. In order to serve as objective and reliable tools for clinical routine, computational assays require end-to-end pipelines from raw data (input) to clinically useful information (output). While these are yet to be established in clinical practice, individual components of this general end-to-end pipeline are being developed and made openly available for community use. In this paper, we present the Translational Algorithms for Psychiatry-Advancing Science (TAPAS) software package, an open-source collection of building blocks for computational assays in psychiatry. Collectively, the tools in TAPAS presently cover several important aspects of the desired end-to-end pipeline, including: (i) tailored experimental designs and optimization of measurement strategy prior to data acquisition, (ii) quality control during data acquisition, and (iii) artifact correction, statistical inference, and clinical application after data acquisition. Here, we review the different tools within TAPAS and illustrate how these may help provide a deeper understanding of neural and cognitive mechanisms of disease, with the ultimate goal of establishing automatized pipelines for predictions about individual patients. We hope that the openly available tools in TAPAS will contribute to the further development of TN/CP and facilitate the translation of advances in computational neuroscience into clinically relevant computational assays.

neuroscience↗

bioRxiv2020

A Hilbert-based method for processing respiratory timeseries

In this technical note, we introduce a new method for estimating changes in respiratory volume per unit time (RVT) from respiratory bellows recordings. By using techniques from the electrophysiological literature, in particular the Hilbert transform, we show how we can better characterise breathing rhythms, with the goal of improving physiological noise correction in functional magnetic resonance imaging (fMRI). Specifically, our approach leads to a representation with higher time resolution and better captures atypical breathing events than current peak-based RVT estimators. Finally, we demonstrate that this leads to an increase in the amount of respiration-related variance removed from fMRI data when used as part of a typical preprocessing pipeline. Our implementation will be publicly available as part of the PhysIO package, which is distributed as part of the open-source TAPAS toolbox (translationalneuromodeling.org/tapas). HighlightsO_LIWe introduce a new estimator for respiratory volume per unit time from respiratory recordings. C_LIO_LIWe demonstrate how this is able to accurately characterise atypical breathing events. C_LIO_LIThis removes significantly more variance when used as a confound regressor for fMRI data. C_LIO_LIOur implementation will be included in PhysIO, released as part of TAPAS: translationalneuromodeling.org/tapas. C_LI

neuroscience↗

bioRxiv2020

Whole-brain estimates of directed connectivity for human connectomics

Connectomics is essential for understanding large-scale brain networks but requires that individual connection estimates are neurobiologically interpretable. In particular, a principle of brain organization is that reciprocal connections between cortical areas are functionally asymmetric. This is a challenge for fMRI-based connectomics in humans where only undirected functional connectivity estimates are routinely available. By contrast, whole-brain estimates of effective (directed) connectivity are computationally challenging, and emerging methods require empirical validation. Here, using a motor task at 7T, we demonstrate that a novel generative model can infer known connectivity features in a whole-brain network (>200 regions, >40,000 connections) highly efficiently. Furthermore, graph-theoretical analyses of directed connectivity estimates identify functional roles of motor areas more accurately than undirected functional connectivity estimates. These results, which can be achieved in an entirely unsupervised manner, demonstrate the feasibility of inferring directed connections in whole-brain networks and open new avenues for human connectomics.

neuroscience↗

bioRxiv2020

The relationship between resting-state functional connectivity, antidepressant discontinuation and depression relapse

The risk of relapsing into depression after stopping antidepressants is high, but no established predictors exist. Resting-state functional magnetic resonance imaging (rsfMRI) measures may help predict relapse and identify the mechanisms by which relapses occur. rsfMRI data were acquired from healthy controls and from patients with remitted major depressive disorder on antidepressants. Patients were assessed a second time either before or after discontinuation of the antidepressant, and followed up for six months to assess relapse. A seed-based functional connectivity analysis was conducted focusing on the left subgenual anterior cingulate cortex and left posterior cingulate cortex. Seeds in the amygdala and dorsolateral prefrontal cortex were explored. 44 healthy controls (age: 33.8 (10.5), 73% female) and 84 patients (age: 34.23 (10.8), 80% female) were included in the analysis. 29 patients went on to relapse and 38 remained well. The seed-based analysis showed that discontinuation resulted in an increased functional connectivity between the right dorsolateral prefrontal cortex and the parietal cortex in non-relapsers. In an exploratory analysis, this functional connectivity predicted relapse risk with a balanced accuracy of 0.86. Further seed-based analyses, however, failed to reveal differences in functional connectivity between patients and controls, between relapsers and non-relapsers before discontinuation and changes due to discontinuation independent of relapse. In conclusion, changes in the connectivity between the dorsolateral prefrontal cortex and the posterior default mode network were associated with and predictive of relapse after open-label antidepressant discontinuation. This finding requires replication in a larger dataset.

neuroscience↗

bioRxiv2019

Neural Arbitration between Social and Individual Learning Systems

Decision making often requires integrating self-gathered information with information acquired from observing others. Depending on the situation, it may be beneficial to rely more on one than the other source, taking into account that either information may be imprecise or deceiving. The process by which one source is selected over the other based on perceived reliability, here defined as arbitration, has not been fully elucidated. In this study, we formalised arbitration as the relative reliability (precision) of predictions afforded by each learning system using hierarchical Bayesian models. In a probabilistic learning task, participants predicted the outcome of a lottery using recommendations from a more informed advisor and self-sampled outcomes. The number of points participants wagered on their predictions reflected arbitration: The higher the relative precision of one learning system over the other and the lower the intention volatility, the more points participants wagered on a given trial. Functional neuroimaging demonstrated that the arbitration signal was independent of decision confidence and involved modalityspecific brain regions. Arbitrating in favour of self-gathered information activated the dorsolateral prefrontal cortex and the midbrain whereas arbitrating in favour of social information engaged ventromedial prefrontal cortex and the temporoparietal junction. These findings are in line with domain specificity and indicate that relative precision captures arbitration between social and individual learning systems at both the behavioural and neural level.

neuroscience↗

bioRxiv2019

Advances in Spiral fMRI: A High-resolution Study with Single-shot Acquisition

Spiral fMRI has been put forward as a viable alternative to rectilinear echo-planar imaging, in particular due to its enhanced average k-space speed and thus high acquisition efficiency. This renders spirals attractive for contemporary fMRI applications that require high spatiotemporal resolution, such as laminar or columnar fMRI. However, in practice, spiral fMRI is typically hampered by its reduced robustness and ensuing blurring artifacts, which arise from imperfections in both static and dynamic magnetic fields. Recently, these limitations have been overcome by the concerted application of an expanded signal model that accounts for such field imperfections, and its inversion by iterative image reconstruction. In the challenging ultra-high field environment of 7 Tesla, where field inhomogeneity effects are aggravated, both multi-shot and single-shot 2D spiral imaging at sub-millimeter resolution was demonstrated with high depiction quality and anatomical congruency. In this work, we further these advances towards a time series application of spiral readouts, namely, single-shot spiral BOLD fMRI at 0.8 mm in-plane resolution. We demonstrate that high-resolution spiral fMRI at 7 T is not only feasible, but delivers both excellent image quality, BOLD sensitivity, and spatial specificity of the activation maps, with little artifactual blurring. Furthermore, we show the versatility of the approach with a combined in/out spiral readout at a more typical resolution (1.5 mm), where the high acquisition efficiency allows to acquire two images per shot for improved sensitivity by echo combination. HighlightsO_LIThis work reports the first fMRI study at 7T with high-resolution spiral readout gradient waveforms. C_LIO_LIWe achieve spiral fMRI with sub-millimeter resolution (0.8 mm, FOV 230 mm), acquired in a single shot (36 slices in 3.3 s). C_LIO_LISpiral images exhibit intrinsic geometric congruency to anatomical scans, and spatially specific activation patterns. C_LIO_LIImage reconstruction rests on a signal model expanded by measured trajectories and static field maps, inverted by cg-SENSE. C_LIO_LIWe assess generalizability of the approach for spiral in/out readouts, providing two images per shot (1.5 mm resolution). C_LI

neuroscience↗

bioRxiv2019

Feasibility of spiral fMRI based on an LTI gradient model

Spiral imaging is very well suited for functional MRI, however its use has been limited by the fact that artifacts caused by gradient imperfections and B0 inhomogeneity are more difficult to correct compared to EPI. Effective correction requires accurate knowledge of the traversed k-space trajectory. With the goal of making spiral fMRI more accessible, we have evaluated image reconstruction using trajectories predicted by the gradient impulse response function (GIRF), which can be determined in a one-time calibration step. GIRF-predicted reconstruction was tested for high-resolution (0.8 mm) fMRI at 7T. Image quality and functional results of the reconstructions using GIRF-prediction were compared to reconstructions using the delay-corrected nominal trajectory and concurrent field monitoring. The reconstructions using nominal spiral trajectories contain substantial artifacts and the activation maps contain misplaced activation. Image artifacts are substantially reduced when using the GIRF-predicted reconstruction, and the activation maps for the GIRF-predicted and monitored reconstructions largely overlap. The GIRF reconstruction provides a large increase in the spatial specificity of the activation compared to the nominal reconstruction. The GIRF-reconstruction generates image quality and fMRI results similar to using a concurrently monitored trajectory. The presented approach does not prolong or complicate the fMRI acquisition. Using GIRF-predicted trajectories has the potential to enable high-quality spiral fMRI in situations where concurrent trajectory monitoring is not available. HighlightsO_LIThis work investigates the feasibility of using a one-time system calibration to account for k-space trajectory deviations in spiral fMRI. C_LIO_LIThis versatile calibration is based on a linear time-invariant gradient model, the gradient impulse response function (GIRF). C_LIO_LIWe show that the image quality and the spatial specificity of the fMRI activation are substantially improved when using the GIRF-predicted trajectories. C_LIO_LIBasing reconstructions on nominal gradient inputs, on the other hand, induces image artifacts and misplaced fMRI activation. C_LIO_LIWe demonstrate that system characterization via the GIRF can enable spiral fMRI in situations where concurrent trajectory monitoring is unavailable. C_LI

neuroscience↗

bioRxiv2018

Trace-based correction of breathing-induced field fluctuations in T2*-weighted imaging of the spinal cord

PurposeSpinal cord MRI at ultra-high field is hampered by time-varying magnetic fields associated with the breathing cycle, giving rise to ghosting artifacts in multi-shot acquisitions. Here, we suggest a correction approach based on linking the signal from a respiratory bellows to field changes inside the spinal cord. The information is used to correct the data at the image reconstruction level.\n\nMethodsThe correction was demonstrated in the context of multi-shot T2*-weighted imaging of the cervical spinal cord at 7T. A respiratory trace was acquired during a high-resolution multi-echo gradient-echo sequence, used for structural imaging and quantitative T2* mapping, and a multi-shot EPI time series, as would be suitable for fMRI. The coupling between the trace and the breathing-induced fields was determined by a short calibration scan in each individual. Images were reconstructed with and without trace-based correction.\n\nResultsIn the multi-echo GRE, breathing-induced fields caused severe ghosting in the long echo time images, which led to a systematic underestimation of T2* in the spinal cord. The trace-based correction reduced the ghosting and increased the estimated T2* values. Breathing-related ghosting was also observed in the multi-shot EPI images. The correction largely removed the ghosting, thereby improving the temporal signal-to-noise ratio of the time series.\n\nConclusionsTrace-based retrospective correction of breathing-induced field variations can reduce ghosting and improve quantitative metrics in multi-shot structural and functional T2*-weighted imaging of the spinal cord. The method is straightforward to implement and does not rely on sequence modifications or additional hardware beyond a respiratory bellows.

neuroscience↗