Biology subjects

Paul, F.

Publications and source records attributed to Paul, F..

At least 19 recordsLinked to original sources

bioRxiv2026

Intrathecal antibodies cross-react with EBV BRRF2 and human antigens in multiple sclerosis

Intrathecal antibody synthesis is a hallmark of multiple sclerosis (MS). Although some intrathecally synthesized antibodies in MS are known to target viral antigens, the spectrum of their antigenic specificities remains incompletely defined. We combined proteome-wide antibody profiling by Phage ImmunoPrecipitation Sequencing (PhIP-Seq) with cross-compartment analytics (MICAR) to study intrathecal antibody synthesis at peptide resolution in paired CSF and serum samples from individuals with MS (n = 40) and non-MS controls (n = 83). While intrathecal antibody responses in MS were polyspecific and included reactivities to various viruses, we identified a subset of individuals with a convergent intrathecal antibody reactivity to a previously described motif within the Epstein-Barr virus (EBV) protein BRRF2 (BRRF2408-415). This motif-directed response showed co-reactivity with multiple CNS-expressed human antigens. In an independent cohort of n = 909 individuals with MS and n = 311 controls, including individuals with NMOSD and MOGAD, serum antibodies to BRRF2408-415 were detected in 8.36% of MS individuals and in 0.64% of non-MS controls, corresponding to an odds ratio for MS of 14.1 (95% CI: 4.4-86.04). Within MS individuals, BRRF2408-415 seropositivity was associated with increased intrathecal IgG synthesis. Cross-reactivity of antibodies to BRRF2408-415 with human targets, including TRIM71 and RTN2, was confirmed by competition ELISA and cell-based assays. Together, these data define an intrathecal EBV BRRF2-linked antibody signature with human target cross-reactivity in a subset of MS individuals. This signature identifies a highly specific serological marker in MS and may support future stratification of the heterogeneous MS spectrum.

immunology↗

bioRxiv2026

Physiological basis of photosynthetic hydrogen production in the cyanobacterium Synechocystis

Photosynthetic hydrogen (photoH2) production by the cyanobacterium Synechocystis sp. PCC 6803 is an attractive means for storing solar energy. However, photoH2 yields remain limited by competing electron flux pathways. Recent in vitro characterization suggests that photoH2 production requires electrons from both carbohydrate oxidation and photosynthesis. Engineered fusions between photosystem I (PSI) and hydrogenase (PSI-H2ase) aim to divert electrons toward H2 production and rely exclusively on photosynthesis. Thus, photoH2 production differs fundamentally between wildtype (WT) and PSI-H2ase fusion mutants. Here, we show that photoH2 production in WT is enhanced by supplemented glucose, consistent with the recently reported confurcating nature of HoxEFUYH H2ases. PhotoH2 production was further studied in the new psaE-hoxUYH mutant by simultaneously monitoring electron flux through PSI alongside with turnover rates of O2, CO2 and H2. PsaE-hoxUYH achieved the highest photoH2 yield and longest production period among the currently available PSI-H2ase mutants in Synechocystis, prolonged by removing O2. Upon illumination, psaE-hoxUYH exhibited high initial photoH2 production rates, which decreased in parallel with CO2 fixation and ceased immediately in the presence of O2. In absence of O2, photoH2 production still declined slowly. Therefore, in addition to CO2 fixation and O2, other yet unknown factors might limit photoH2 production under these conditions. Moreover, we traced a previously observed high H2 production phase of unclear origin in psaD-hoxYH cultures to contaminating [FeFe]-H2ases from Clostridium intestinale rather than genuine photoH2 production by the mutant. Together, these findings indicate a complex metabolic interplay tuning photoH2 production in Synechocystis WT and PSI-H2ase fusion mutants.

microbiology↗

bioRxiv2026

Aquaporin-4-Specific T Cell Responses in NMOSD Revealed by mRNA-Engineered Dendritic Cells

Neuromyelitis optica spectrum disorder (NMOSD) is an autoimmune disease of the central nervous system characterized by loss of immune tolerance to the water channel aquaporin-4 (AQP4). Current therapies do not specifically restore AQP4 tolerance or selectively suppress antigen-specific immune responses. Reprogramming patients own dendritic cells (DCs) with mRNA to induce tolerance represents a promising therapeutic strategy. A key prerequisite for this approach is generating mRNAs encoding disease-relevant autoantigens recognized by the patients immune system. Here, we generated recombinant mRNAs encoding human AQP4 and evaluated T cell responses to autologous DCs transfected with AQP4 mRNA in eight NMOSD patients and ten healthy controls. Transfected DCs were co-cultured with autologous T cells and stimulated twice. The assay detected robust AQP4-specific T cell response in a patient with recent disease activity who was not receiving immunosuppressive therapy, demonstrating its ability to identify clinically relevant autoreactive T cell responses. These findings establish the feasibility of an mRNA-based antigen-specific T cell assay for studying NMOSD pathogenesis, stratifying patients by T cell involvement, and supporting development of personalized tolerance-inducing therapies. Summary SentenceRecombinant mRNA encoding human aquaporin-4 (AQP4) was introduced into dendritic cells from NMOSD patients, enabling the detection of antigen-specific T cell responses, with the strongest activation observed in a patient with recent disease activity, highlighting the potential of this approach for immune monitoring and for understanding the role of T cells in the pathogenesis of NMOSD.

immunology↗

bioRxiv2026

Easy-to-use whole-genome sequencing workflows and standardized practices to uncover hidden genetic variation in Synechocystis PCC 6803 wild-type and knock-out strains

Knock-out mutants are often used to study gene function by disrupting a specific gene and comparing the mutant to a wild-type strain. Reliable interpretation, however, requires that the two strains differ only by the intended mutation and that the observed phenotype is caused specifically by the deleted gene. In the highly polyploid cyanobacterium Synechocystis sp. PCC 6803, this is particularly challenging because incomplete segregation can mask genetic heterogeneity or secondary suppressor mutations. The genetic variation among laboratory wild-type lines can further confound phenotypic analyses. We show that these challenges can be addressed by routine strain validation via whole-genome sequencing (WGS). To this end, we developed and tested user friendly workflows for short-read (Illumina), long-read (Oxford Nanopore Technologies; ONT), and hybrid data, providing standardized quality control, variant calling, and structural variant detection. We benchmarked their performance in detecting single-nucleotide polymorphisms (SNPs), small indels, and structural variants using simulated datasets across different coverages and mixed populations. Applying the workflows to three Synechocystis sp. PCC 6803 wild-type lines revealed multiple sequence and structural differences relative to the reference genome, including previously undescribed genetic variants, underscoring the importance of documenting the strain background and the value of long-read sequencing. Characterization of two independent 6-phosphogluconate dehydrogenase (gnd) knock-out mutants and their complemented strains highlighted how a failed rescue can reveal a phenotype unrelated to the intended knock-out. An automated literature analysis revealed that only a minority of the investigated Synechocystis studies that used knock-out mutants included complementation as a control (39%), whereas this practice is more common in studies involving Escherichia coli (63%) and Saccharomyces cerevisiae (55%). Based on these results, we propose a practical guide for standardizing knock-out phenotyping in Synechocystis PCC 6803. Combined with accessible workflows for routine whole-genome validation, this framework aims to support more robust and reproducible knock-out studies in the future.

microbiology↗

bioRxiv2025

Memory dysfunction and psychiatric outcomes in anti-NMDA receptor encephalitis are linked to altered structural brain complexity

IntroductionMost patients with anti-N-methyl-D-aspartate receptor encephalitis (NMDARE) experience long-term neuropsychiatric sequelae despite immunotherapy. However, how these residual symptoms relate to structural brain changes in the post-acute phase remains unclear. Recently, fractal dimensionality (FD) has emerged as a sensitive imaging marker of structural brain complexity in related conditions but has not been explored in autoimmune encephalitis. MethodsThis cross-sectional study combined clinical, cognitive, and neuroimaging analyses in 70 patients with post-acute NMDARE (median time from onset: 22 months), and 70 healthy controls matched for age (t=-0.57, p=0.57) and sex ({chi}2=0, p=1). High-resolution T1-weighted magnetic resonance imaging (MRI) data were analyzed with FreeSurfer and computational fractal analysis. Clinical outcomes were assessed with the modified Rankin Scale (mRS) and Clinical Assessment Scale in Autoimmune Encephalitis (CASE). Psychiatric manifestations underwent phenotypical analyses, and memory performance was assessed with standardized neuropsychological tests. ResultsPatients with NMDARE were severely affected at peak illness but showed substantial overall improvement in the post-acute stage (median mRS at peak=5, post-acute=1, z=-7.2, p<0.001; median CASE at peak=11, post-acute=1, z=-7.2, p<0.001). However, 67% of patients showed residual CASE symptoms, with memory dysfunction (61%) and psychiatric symptoms (36%) representing the most prevalent domains. Therein, psychiatric symptoms showed a phenotypical shift from a schizophrenia-like phenotype at peak illness to an affective phenotype in the post-acute stage. Neuroimaging uncovered a characteristic pattern of reduced structural brain complexity, including the hippocampus bilaterally and a fronto-cingulo-temporal cluster in cortical gray matter and cerebral white matter. Importantly, normative modeling revealed that patients with residual symptoms showed stronger alterations of brain complexity than those without (psychiatric: t=-2.65, p=0.010; memory: t=-3.98, p<0.001; both vs. no symptoms: t=-5.46, p<0.001). Similarly, patients with stronger alterations of brain complexity showed lower scores of visuospatial and verbal memory (all pFDR<0.015). DiscussionPost-acute NMDARE is characterized by systematic reductions in structural brain complexity, consistently involving previously implicated regions while identifying changes in the cingulate cortex as a new morphological correlate. These changes are linked to residual psychiatric symptoms and memory dysfunction, highlighting FD as a promising new imaging marker of long-term outcomes. Our findings suggest that current treatment strategies may be insufficient to fully address the residual symptom burden after the acute phase of NMDARE.

neuroscience↗

bioRxiv2025

Functional connectivity is linked to symbolic BOLD patterns: replication, extension, and clinical application of the human 'complexome'

Functional connectivity (FC) quantifies the temporal coherence of blood-oxygen-level-dependent (BOLD) signals across brain regions. Recently, the information-theoretic "complexome" framework has linked FC to coinciding complexity drops: transient moments in which regional BOLD signals simultaneously become regular. Here, we replicate this relationship in an independent dataset and extend the framework by (i) integrating it with signal cofluctuation analysis through edge-timeseries, (ii) extending the previous binary concept of simultaneous complexity drops to a continuous, threshold-free calculation, (iii) providing evidence of clinical relevance in the model disease of anti-N-methyl-D-aspartate-receptor encephalitis, and (iv) deriving a novel measure of pairwise dissimilarity in local BOLD patterns. This index of pattern incongruency (IPI) explains clinically relevant FC reductions and maps onto novel associations with cognition. These findings show that global FC is closely related to local patterns within underlying BOLD signals, strengthening the link between complexity dynamics and the brains functional organization as a large-scale network.

neuroscience↗

bioRxiv2024

Minimally and vaguely informative priors to combat practical parameter non-identifiability of hidden Markov models exemplified by ion channel data

Hidden Markov models (HMMs) for biomolecules suffer from various forms of parameter non-identifiability. This poses severe challenges to both maximum likelihood and Bayesian inference. However, Bayesian inference offers effective means of overcoming these pathologies. We study the role of prior distributions in the face of practical parameter non-identifiability in Bayesian inference applied to prototypical patch clamp data of ligand-gated ion channels. We advocate the use of minimally informative priors, as they increase the accuracy and decrease the uncertainty of the inference. For complex HMMs, stronger prior assumptions are needed to render the posterior sufficiently proper. This can be achieved by confining the parameter space to physically motivated limits. Another beneficial assumption is finite cooperativity of ligand-binding and unbinding events, which introduces a bias towards non-cooperativity but still allows for a non-vanishing degree of cooperativity that is inferred from the data. Despite its vagueness, our prior renders the posterior sufficiently proper for all datasets that we considered without imposing the assumption of non-cooperativity. Combining all prior factors allows for meaningful inferences with a dataset of a thousand times lower quality.

bioinformatics↗

bioRxiv2024

Persistent cognitive deficits in anti-LGI1 encephalitis are linked to a reorganization of structural brain networks

Structured AbstractO_ST_ABSImportanceC_ST_ABSDespite immunotherapy, most patients with anti-leucine-rich, glioma-inactivated 1 encephalitis (LGI1-E) develop long-term cognitive deficits that persist for years after peak illness. However, the structural brain changes that underlie these deficits remain poorly understood. ObjectiveTo study the relationship between cognitive outcomes and white matter (WM) networks in LGI1-E. DesignCross-sectional study. SettingGerman university center (Charite - Universitatsmedizin Berlin). Participants25 patients with LGI1-E (19/25 male [76%], mean age: 63 {+/-} 12 years) and 25 age- and sex-matched healthy controls (HC), recruited between January 2013 and April 2019. Main Outcomes and MeasuresClinical assessments including the modified Rankin Scale (mRS) and Clinical Assessment Scale in Autoimmune Encephalitis (CASE); comprehensive cognitive testing; WM tractography using diffusion-weighted MRI. ResultsAll patients had received first-line immunotherapy, and two-thirds underwent second-line immunotherapy. Patients showed a significant reduction in mRS scores from peak illness to post-acute follow-up (z = -3.8, p < 0.001, n = 20), with 85% presenting "good" functional outcomes (post-acute mRS [≤] 2), paralleled by a significant reduction in CASE scores (z = -3.5, p < 0.001, n = 20). Despite this overall improvement, however, cognitive symptoms were highly prevalent at peak illness (95% of patients affected) and strongly persisted into the post-acute disease stage (85% affected). Neuroimaging at post-acute follow-up (median: 12 months from onset) revealed that LGI1-E is characterized by (i) significantly reduced whole-brain structural connectivity (t = -2.16, p = 0.036, d = -0.61), (ii) a cortico-subcortical hypoconnectivity cluster that strongly affects the hippocampus but also severely impacts extra-limbic brain systems, (iii) systematic limbic and extra-limbic decreases in node degree -- a graph-theoretical measure of overall connectedness, and (iv) a "topological reorganization" of structural brain networks, marked by a bidirectional shift in the relative importance of individual brain regions in the network. Importantly, the extent of this network reorganization was significantly related to persistent cognitive deficits in the domains of verbal memory (r = -0.57, p = 0.007, n = 21), attention (r = -0.47, p = 0.030, n = 21), and executive functions (r = -0.60, p = 0.010, n = 17). Conclusion and RelevanceThis study characterizes LGI1-E as a network disease that affects both limbic and extra-limbic brain systems and shows that a reorganization of WM networks is linked to multi-domain cognitive deficits in the post-acute disease stage - despite immunotherapy and good overall recovery. These findings highlight the need for extended treatment strategies to improve long-term cognitive outcomes and propose a sensitive new neuroimaging marker to include in prospective clinical trials. Key PointsO_ST_ABSQuestionC_ST_ABSWhat structural brain changes underlie the persistent cognitive deficits observed in patients with anti-leucine-rich, glioma-inactivated 1 encephalitis (LGI1-E)? FindingsThis cross-sectional study shows that LGI1-E is characterized by a structural reorganization of white matter networks that affects both limbic and extra-limbic brain systems and correlates with persistent deficits in verbal memory, attention, and executive functions at post-acute follow-up - despite immunotherapy and good overall clinical recovery. MeaningThis study characterizes LGI1-E as a network disease -beyond focal damage to the limbic system- and shows that persistent cognitive deficits relate to immunotherapy-resistant changes in structural brain networks, highlighting the need for extended treatment strategies to improve long-term cognitive outcomes.

neuroscience↗

bioRxiv2023

Hippocampal hub failure is linked to long-term memory impairment in anti-NMDA-receptor encephalitis -Insights from structural connectome graph theoretical network analysis

IntroductionAnti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is characterized by distinct structural and functional brain alterations, predominantly affecting the medial temporal lobes and the hippocampus. Structural connectome analysis with graph-based investigations of network properties allows for an in-depth characterization of global and local network changes and their relationship with clinical deficits in NMDAR encephalitis. ObjectiveTo investigate changes in structural connectivity and network efficiency in NMDAR encephalitis by use of probabilistic whole-brain tractography and graph theoretical analysis of structural brain networks. MethodsStructural networks from sixty-one NMDAR encephalitis patients in the post-acute stage (median time from acute hospital discharge: 18 months) and sixty-one age- and sex-matched healthy controls (HC) were analyzed using diffusion-weighted imaging (DWI)-based probabilistic anatomically-constrained tractography and spherical deconvolution-informed filtering of tractograms. We calculated global, modular, and nodal graph measures indicative of structural connectivity and network reorganization with special focus on default-mode network, medial temporal lobe, and hippocampus. Pathologically altered metrics were included in multiple regression analyses to investigate their potential association with clinical course, disease severity, and cognitive outcome. ResultsPatients with NMDAR encephalitis showed regular global graph metrics, but bilateral reductions of hippocampal node strength (left: p=0.049; right: p=0.013) and increased node strength of right precuneus (p=0.013) compared to HC. Betweenness centrality was decreased for left-sided entorhinal cortex (p=0.042) and left caudal middle frontal gyrus (p = 0.037). Correlation analyses showed a significant association between reduced left hippocampal node strength and verbal long-term memory impairment (p=0.021) ConclusionFocal network property changes of the medial temporal lobes indicate hippocampal hub failure that is associated with memory impairment in NMDAR encephalitis at the post-acute stage, while global structural network properties remain unaltered. Graph theory analysis provides new pathophysiological insight into structural network changes and their association with persistent cognitive deficits in NMDAR encephalitis.

neuroscience↗

bioRxiv2023

Multiscale networks in multiple sclerosis

Complex diseases such as Multiple Sclerosis (MS) cover a wide range of biological scales, from genes and proteins to cells and tissues, up to the full organism. We conducted a multilayer network analysis and deep phenotyping with multi-omics data (genomics, phosphoproteomics and cytomics), brain and retinal imaging, and clinical data, obtained from a multicenter prospective cohort of 328 patients and 90 healthy controls. Multilayer networks were constructed using mutual information, and Boolean simulations identified paths within and among all layers. The path more commonly found from the boolean simulations connects MP2K, with Th17 cells, the retinal nerve fiber layer (RNFL) thickness and the age related MS severity score (ARMSS). Combinations of several proteins (HSPB1, MP2K1, SR6, KS6B1, SRC, MK03, LCK and STAT6)) and immune cells (Th17, Th1 non-classic, CD8, CD8 Treg, CD56 neg, and B memory) were part of the paths explaining the clinical phenotype. Specific paths identified were subsequently analyzed by flow cytometry at the single-cell level. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=94 SRC="FIGDIR/small/530153v1_ufig1.gif" ALT="Figure 1"> View larger version (48K): org.highwire.dtl.DTLVardef@7dd26org.highwire.dtl.DTLVardef@482239org.highwire.dtl.DTLVardef@1bef03borg.highwire.dtl.DTLVardef@8dc6e6_HPS_FORMAT_FIGEXP M_FIG C_FIG Author SummaryComplex diseases such as Multiple Sclerosis (MS) involve the contribution of a wide range of biological processes. We conducted a systems biology study of MS based on network analysis and deep phenotyping in a prospective cohort of patients with clinical, imaging, genetics, and omics assessments. The gene, proteins and cell paths explained variation in central nervous system damage, and in metrics of disease severity. Such multilayer paths explain the different phenotypes of the disease and can be developed as biomarkers of MS.

systems biology↗

bioRxiv2022

Comparable neural pathways link psychological stress and brain health in health and multiple sclerosis

Clinical and neuroscientific studies suggest a link between psychological stress and reduced brain health - in healthy humans and patients with neurological disorders. However, it is unclear which neural pathways mediate between stress and brain health and whether these pathways are similar in health and disease. Here, we applied an Arterial-Spin-Labeling MRI stress task in 42 healthy persons and 56 with multiple sclerosis. We tested whether brain-predicted age differences ("brain-PAD"), a highly sensitive structural brain health biomarker derived from machine learning, mirror functional connectivity between stress-responsive regions. We found that regional neural stress responsivity did not differ between groups. Although elevated brain-PAD indicated worse brain health in patients, anterior insula-occipital functional connectivity correlated with brain-PAD in both groups. Grey matter variations contributed similarly to brain-PAD in both groups. These findings suggest a generic connection between stress and brain health whose impact is amplified in multiple sclerosis by disease-specific vulnerability factors.

neuroscience↗

bioRxiv2022

Functional connectivity dynamics reflect disability and multi-domain clinical impairment in patients with relapsing-remitting multiple sclerosis

BackgroundFunctional neuroimaging studies have revealed complex and heterogeneous patterns of aberrant functional connectivity (FC) in multiple sclerosis (MS), yet it remains unclear how time-resolved FC relates to variance in clinical disease severity. ObjectivesTo characterize brain activity in MS patients with time-resolved FC analysis and explore the relationship between disease severity, multi-domain impairments, and altered network dynamics. MethodsResting-state functional MRI data were acquired from 101 MS patients and 101 age- and sex-matched healthy controls (HC). Dynamic FC analysis identified five connectivity states that were compared between HC and patients with high vs. low disability. ResultsPatients with higher disease severity exhibited a more widespread spatiotemporal pattern of altered FC and spent more time in a high-connectivity, low-occurrence state compared to patients with lower disease severity and HC. Depressive symptom severity was positively related to functional dynamics on global and network scales in patients, while fatigue and motor impairment were inversely related to frontoparietal network connectivity with the basal ganglia. ConclusionsTime-resolved FC analysis uncovered alterations in network dynamics and clinical correlations that remained undetected with a static account of brain activity. Such time-varying approaches are thus crucial for disentangling the relationship between brain dynamics, disease severity, and symptoms in MS.

neuroscience↗

bioRxiv2022

Arginine-dependent hypusination of the eukaryotic translation initiation factor (eIF)5A drives erythroid lineage differentiation

Metabolic programs contribute to hematopoietic stem and progenitor cell (HSPC) fate but it is not known whether the metabolic regulation of protein synthesis controls HSPC differentiation. We discovered that SLC7A1/CAT1-dependent arginine uptake and its catabolism to spermidine control the erythroid specification of HSPCs via activation of eukaryotic translation initiation factor 5A (eIF5A). eIF5A activity is dependent on the metabolism of spermidine to hypusine and inhibiting hypusine synthesis abrogates erythropoiesis and diverts EPO-stimulated HSPCs to a myeloid fate. Proteomic profiling reveals mitochondrial translation to be a critical target of hypusinated eIF5A and induction of mitochondrial function partially rescues erythropoiesis in the absence of hypusine. Within the hypusine network, ribosomal proteins are highly enriched and we identify defective eIF5A hypusination in erythroid pathologies caused by abnormal ribosome biogenesis. Thus, eIF5A-dependent protein synthesis is critical in the branching of erythro-myeloid differentiation and attenuated eIF5A activity characterizes ribosomal protein-linked disorders of ineffective erythropoiesis.

physiology↗

bioRxiv2021

MYCN mediates cysteine addiction and sensitizes to ferroptosis

Aberrant expression of MYC family members predicts poor clinical outcome in many human cancers. Oncogenic MYC profoundly alters metabolism and mediates an antioxidant response to maintain redox balance. Here we show that MYC induces massive lipid peroxidation upon depletion of cysteine, the rate-limiting amino acid for glutathione biosynthesis and sensitizes cells to ferroptosis, an oxidative, non-apoptotic and irondependent type of cell death. In MYCN-amplified childhood neuroblastoma, MYCN mediates resistance to ferroptosis by activating transsulfuration of methionine to cysteine. MYCN may contribute to spontaneous tumor regression in low-risk neuroblastomas by promoting ferroptosis in cells with epigenetically silenced cystathionine-beta-synthase, the rate-limiting enzyme for transsulfuration. We identified enzymes and antiporter proteins crucial to ferroptotic escape, providing multiple previously unknown sites that may be acted on therapeutically.

cancer biology↗

bioRxiv2020

State-dependent signatures of Anti-NMDA-Receptor Encephalitis: a dynamic functional connectivity study

ObjectiveTraditional static functional connectivity (FC) analyses have shown functional network alterations in patients with anti-NMDA receptor encephalitis (NMDARE). Here, we use a dynamic FC approach that increases the temporal resolution of connectivity analyses from minutes to seconds. We hereby explore the spatiotemporal variability of large-scale brain network activity in NMDARE and assess the discriminatory power of functional brain states in a supervised classification approach. MethodsWe included resting-state fMRI data from 57 patients and 61 controls to extract four discrete connectivity states and assess state-wise group differences in FC, dwell time, transition frequency, fraction time and occurrence rate. Additionally, for each state, logistic regression models with embedded feature selection were trained to predict group status in a leave-one-out cross-validation scheme. ResultsCompared to controls, patients exhibited diverging dynamic FC patterns in three out of four states mainly encompassing the default-mode network and frontal areas. This was accompanied by a characteristic shift in the dwell time pattern and higher volatility of state transitions in patients. Moreover, dynamic FC measures were associated with disease severity, disease duration and positive and negative schizophrenia-like symptoms. Predictive power was highest in dynamic FC models and outperformed static analyses, reaching up to 78.6% classification accuracy. ConclusionsBy applying time-resolved analyses, we disentangle state-specific FC impairments and characteristic changes in temporal dynamics not detected in static analyses, offering new perspectives on functional reorganization underlying NMDARE. Correlation of dynamic FC measures with disease symptoms and severity indicates their clinical relevance and potential as prognostic biomarkers in NMDARE.

neuroscience↗

bioRxiv2020

Bayesian Hidden Markov modeling and model selection by Kalman filtering applied to multi-dimensional data of ion channels

Inferring adequate kinetic schemes for ion channel gating from ensemble currents is a daunting task due to limited information in the data. We address this problem by using a parallelized Bayesian filter to specify hidden Markov models for current and fluorescence data. We demonstrate the flexibility of this algorithm by including different noise distributions. Our generalized Kalman filter outperforms both a classical Kalman filter and a rate equation approach when applied to patch-clamp data exhibiting realistic open-channel noise. The derived generalization also enables inclusion of orthogonal fluorescence data, making unidentifiable parameters identifiable and increasing the accuracy of the parameter estimates by an order of magnitude. By using Bayesian highest credibility volumes, we found that our approach, in contrast to the rate equation approach, yields a realistic quantification of uncertainty. Furthermore, the Bayesian filter delivers unbiased estimates for a wider range of data quality and identifies parameters which the rate equation approach does not identify. These results also demonstrate the power of assessing the validity of algorithms by Bayesian credibility volumes in general. Finally, we show that our Bayesian filter is more robust against errors induced by either analog filtering before analog-to-digital conversion or by limited time resolution of fluorescence data than a rate equation approach.

bioinformatics↗

bioRxiv2019

Low-Density Granulocytes are a novel immunopathological feature in both Multiple Sclerosis and Neuromyelitis optica spectrum disorder.

ObjectiveTo investigate whether low-density granulocytes (LDGs) are a immunophenotypic feature of patients with multiple sclerosis (MS) and/or neuromyelitis optica spectrum disorder (NMOSD).\n\nMethodsBlood samples were collected from 26 patients with NMOSD and 20 patients with MS, as well as from 18 patients with Systemic Lupus Erythematosus (SLE) and 23 Healthy Donors (HD). We isolated peripheral blood mononuclear cells (PBMCs) with density gradient separation and stained the cells with antibodies against CD14, CD15, CD16, and CD45, and analysed the cells by flow cytometry or imaging flow cytometry. We defined LDGs as CD14-CD15high and calculated their share in total PBMC leukocytes (CD45+) as well as the share of CD16hi LDGs. Clinical data on disease course, medication, and antibody status were obtained.\n\nResultsLDGs were significantly more common in MS and NMOSD than in HDs, comparable to SLE samples (median values HD 0.2%, MS 0.9%, NMOSD 2.1%, SLE 4.3%). 0/23 of the HDs, but 17/20 NMOSD and 11/17 MS samples as well as 13/15 SLE samples had at least 0.7 % LDGs. NMOSD patients without continuous immunosuppressive treatment had significantly more LDGs compared to their treated counterparts. LDG nuclear morphology ranged from segmented to rounded, suggesting a heterogeneity within the group.\n\nConclusionLDGs are a feature of the immunophenotype in some patients with MS and NMOSD.

immunology↗

bioRxiv2019

Prediction of combination therapies based on topological modeling of the immune signaling network in Multiple Sclerosis

Signal transduction deregulation is a hallmark of many complex diseases, including Multiple Sclerosis (MS). Here, we performed ex vivo multiplexed phosphoproteomic assays in PBMCs from 180 MS patients either untreated or treated with fingolimod, natalizumab, interferon-beta, glatiramer acetate or the experimental therapy epigallocatechin gallate (EGCG), and from 60 matched healthy controls. Fitting a bespoke literature-derived network of MS-related pathways using logic modeling yielded a signaling network specific for each patient. Patient models were merged to characterize healthy-, disease- and drug-specific signaling networks. We defined a co-druggability score based on the topology for each drugs network. We used this score to identify kinase interactions whose activity could be reverted to a "healthy-like" status by combination therapy. We predicted several combinations with approved MS drugs. Specifically, TAK1 kinase, involved in TGF-B, toll-like receptor, B-cell receptor and response to inflammation pathways was found to be highly deregulated and co-druggable with four MS drugs. One of these predicted combinations, Fingolimod with a TAK1 inhibitor, was validated in an animal model of MS. Our approach based on patient-specific signaling networks enables prediction of targets for combination therapy for MS and other complex diseases. One sentence summaryA new approach to predict combination therapies based on modeling signaling architecture using phosphoproteomics from patients with Multiple Sclerosis characterizes deregulated signaling pathways and reveals new therapeutic targets and drug combinations.

systems biology↗