Antibodies to the inositol 1,4,5-trisphosphate receptor type 1 (ITPR1) in cerebellar ataxia

We report on a serum autoantibody associated with cerebellar ataxia. Immunohistochemical studies of sera from four patients referred for autoantibody testing revealed binding of high-titer (up to 1:5,000) IgG antibodies, mainly IgG1, to the molecular layer, Purkinje cell layer, and white matter on mouse, rat, porcine, and monkey cerebellum sections. The antibody bound to PC somata, dendrites, and axons, resulting in a binding pattern similar to that reported for anti-Ca/anti-ARHGAP26, but did not react with recombinant ARHGAP26. Extensive control studies were performed to rule out a broad panel of previously described paraneoplastic and non-paraneoplastic anti-neural autoantibodies. The characteristic binding pattern as well as double staining experiments suggested inositol 1,4,5-trisphosphate receptor type 1 (ITPR1) as the target antigen. Verification of the antigen included specific neutralization of the tissue reaction following preadsorption with ITPR1 (but not ARHGAP26) and a dot-blot assay with purified ITPR1 protein. By contrast, anti-ARHGAP26-positive sera did not bind to ITPR1. In a parallel approach, a combination of histoimmunoprecipitation and mass spectrometry also identified ITPR1 as the target antigen. Finally, a recombinant cell-based immunofluorescence assay using HEK293 cells expressing ITPR1 and ARHGAP26, respectively, confirmed the identification of ITPR1. Mutations of ITPR1 have previously been implicated in spinocerebellar ataxia with and without cognitive decline. Our findings suggest a role of autoimmunity against ITPR1 in the pathogenesis of autoimmune cerebellitis and extend the panel of diagnostic markers for this disease

R.ROSETTA: an interpretable machine learning framework.

Funder: Uppsala Universitet; doi: http://dx.doi.org/10.13039/501100007051Funder: Polska Akademia Nauk; doi: http://dx.doi.org/10.13039/501100004382Funder: Uppsala UniversityBACKGROUND: Machine learning involves strategies and algorithms that may assist bioinformatics analyses in terms of data mining and knowledge discovery. In several applications, viz. in Life Sciences, it is often more important to understand how a prediction was obtained rather than knowing what prediction was made. To this end so-called interpretable machine learning has been recently advocated. In this study, we implemented an interpretable machine learning package based on the rough set theory. An important aim of our work was provision of statistical properties of the models and their components. RESULTS: We present the R.ROSETTA package, which is an R wrapper of ROSETTA framework. The original ROSETTA functions have been improved and adapted to the R programming environment. The package allows for building and analyzing non-linear interpretable machine learning models. R.ROSETTA gathers combinatorial statistics via rule-based modelling for accessible and transparent results, well-suited for adoption within the greater scientific community. The package also provides statistics and visualization tools that facilitate minimization of analysis bias and noise. The R.ROSETTA package is freely available at https://github.com/komorowskilab/R.ROSETTA . To illustrate the usage of the package, we applied it to a transcriptome dataset from an autism case-control study. Our tool provided hypotheses for potential co-predictive mechanisms among features that discerned phenotype classes. These co-predictors represented neurodevelopmental and autism-related genes. CONCLUSIONS: R.ROSETTA provides new insights for interpretable machine learning analyses and knowledge-based systems. We demonstrated that our package facilitated detection of dependencies for autism-related genes. Although the sample application of R.ROSETTA illustrates transcriptome data analysis, the package can be used to analyze any data organized in decision tables

R.ROSETTA: an interpretable machine learning framework.

Funder: Uppsala Universitet; doi: http://dx.doi.org/10.13039/501100007051Funder: Polska Akademia Nauk; doi: http://dx.doi.org/10.13039/501100004382Funder: Uppsala UniversityBACKGROUND: Machine learning involves strategies and algorithms that may assist bioinformatics analyses in terms of data mining and knowledge discovery. In several applications, viz. in Life Sciences, it is often more important to understand how a prediction was obtained rather than knowing what prediction was made. To this end so-called interpretable machine learning has been recently advocated. In this study, we implemented an interpretable machine learning package based on the rough set theory. An important aim of our work was provision of statistical properties of the models and their components. RESULTS: We present the R.ROSETTA package, which is an R wrapper of ROSETTA framework. The original ROSETTA functions have been improved and adapted to the R programming environment. The package allows for building and analyzing non-linear interpretable machine learning models. R.ROSETTA gathers combinatorial statistics via rule-based modelling for accessible and transparent results, well-suited for adoption within the greater scientific community. The package also provides statistics and visualization tools that facilitate minimization of analysis bias and noise. The R.ROSETTA package is freely available at https://github.com/komorowskilab/R.ROSETTA . To illustrate the usage of the package, we applied it to a transcriptome dataset from an autism case-control study. Our tool provided hypotheses for potential co-predictive mechanisms among features that discerned phenotype classes. These co-predictors represented neurodevelopmental and autism-related genes. CONCLUSIONS: R.ROSETTA provides new insights for interpretable machine learning analyses and knowledge-based systems. We demonstrated that our package facilitated detection of dependencies for autism-related genes. Although the sample application of R.ROSETTA illustrates transcriptome data analysis, the package can be used to analyze any data organized in decision tables

Bayesian inference of biochemical kinetic parameters using the linear noise approximation

Background Fluorescent and luminescent gene reporters allow us to dynamically quantify changes in molecular species concentration over time on the single cell level. The mathematical modeling of their interaction through multivariate dynamical models requires the deveopment of effective statistical methods to calibrate such models against available data. Given the prevalence of stochasticity and noise in biochemical systems inference for stochastic models is of special interest. In this paper we present a simple and computationally efficient algorithm for the estimation of biochemical kinetic parameters from gene reporter data. Results We use the linear noise approximation to model biochemical reactions through a stochastic dynamic model which essentially approximates a diffusion model by an ordinary differential equation model with an appropriately defined noise process. An explicit formula for the likelihood function can be derived allowing for computationally efficient parameter estimation. The proposed algorithm is embedded in a Bayesian framework and inference is performed using Markov chain Monte Carlo. Conclusion The major advantage of the method is that in contrast to the more established diffusion approximation based methods the computationally costly methods of data augmentation are not necessary. Our approach also allows for unobserved variables and measurement error. The application of the method to both simulated and experimental data shows that the proposed methodology provides a useful alternative to diffusion approximation based methods

Аналіз вибіркових даних при оцінюванні наукового потенціалу і характер статистичних властивостей вербальних моделей

OBJECTIVE: To determine sensitivity and specificity of a standardized recombinant cell-based indirect immunofluorescence assay (RC-IFA) for anti-Tr antibodies in comparison to a reference procedure. METHODS: Delta/Notch-like epidermal growth factor-related receptor (DNER) was expressed in HEK293 and used as a substrate for RC-IFA. HEK293 control cells expressing CDR2/Yo and CDR2L as well as mock-transfected HEK293 cells were used as controls. Serum samples from 38 patients with anti-Tr antibodies (33 with paraneoplastic cerebellar degeneration [PCD] and Hodgkin lymphoma), 66 patients with anti-Tr-negative PCD, 53 patients with Hodgkin lymphoma without neurologic symptoms, 40 patients with rheumatic diseases, and 42 healthy blood donors were tested for anti-DNER reactivity in the RC-IFA. In addition, RC-IFA results were compared to those from a commercial tissue-based IFA using monkey cerebellum. RESULTS: Using the RC-IFA, anti-DNER was detected in all anti-Tr-positive patients but in none of the controls (sensitivity 100%, 95% confidence interval [CI] 92.8%-100%; specificity 100%, 95% CI 98.7%-100%). In comparison, anti-Tr was not detected in 4 samples with low-titer autoantibodies using the commercial tissue-based assay. Preadsorption of sera with either recombinant full-length DNER or its extracellular domain selectively abolished anti-Tr reactivity. CONCLUSION: Anti-Tr antibodies bind to the extracellular domain of DNER and can be detected by RC-IFA using HEK293 cells expressing the recombinant receptor. The new method performs better than a frequently used commercial tissue-based indirect immunofluorescence assay (IFA) in samples with low-titer antibodies. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that RC-IFA accurately detects anti-Tr as compared to conventional IFA

Pathogenic Activation and Therapeutic Blockage of FcαR-Expressing Polymorphonuclear Leukocytes in IgA Pemphigus

Pathomechanisms in IgA pemphigus are assumed to rely on Fc-dependent cellular activation by antigen-specific IgA autoantibodies; however, models for the disease and more detailed pathophysiologic data are lacking. In this study, we aimed to establish in vitro models of disease for IgA pemphigus, allowing us to study the effects of the interaction of anti-keratinocyte IgA with cell surface FcαRs. Employing multiple in vitro assays, such as a skin cryosection assay and a human skin organ culture model, in this study, we present mechanistic data for the pathogenesis of IgA pemphigus, mediated by anti–desmoglein 3 IgA autoantibodies. Our results reveal that this disease is dependent on FcαR-mediated activation of leukocytes in the epidermis. Importantly, this cell-dependent pathology can be dose-dependently abrogated by peptide-mediated inhibition of FcαR:IgA-Fc interaction, as confirmed in an additional model for IgA-dependent disease, that is, IgA vasculitis. These data suggest that IgA pemphigus can be modeled in vitro and that IgA pemphigus and IgA vasculitis are FcαR-dependent disease entities that can be specifically targeted in these experimental systems

A Spectrum of Neural Autoantigens, Newly Identified by Histo-Immunoprecipitation, Mass Spectrometry, and Recombinant Cell-Based Indirect Immunofluorescence

BackgroundA plurality of neurological syndromes is associated with autoantibodies against neural antigens relevant for diagnosis and therapy. Identification of these antigens is crucial to understand the pathogenesis and to develop specific immunoassays. Using an indirect immunofluorescence assay (IFA)-based approach and applying different immunoprecipitation (IP), chromatographic and mass spectrometric protocols was possible to isolate and identify a spectrum of autoantigens from brain tissue.MethodsSera and CSF of 320 patients suspected of suffering from an autoimmune neurological syndrome were comprehensively investigated for the presence of anti-neural IgG autoantibodies by IFA using mosaics of biochips with brain tissue cryosections and established cell-based recombinant antigen substrates as well as immunoblots. Samples containing unknown brain tissue-specific autoantibodies were subjected to IP with cryosections of cerebellum and hippocampus (rat, pig, and monkey) immobilized to glass slides or with lysates produced from homogenized tissue, followed by sodium dodecyl sulfate-polyacrylamide gel electrophoresis, tryptic digestion, and matrix-assisted laser desorption/ionization–time of flight mass spectrometry analysis. Identifications were confirmed by IFA with recombinant HEK293 cells and by neutralizing the patients’ autoantibodies with the respective recombinantly expressed antigens in the tissue-based immunofluorescence test.ResultsMost samples used in this study produced speckled, granular, or homogenous stainings of the hippocampal and cerebellar molecular and/or granular layers. Others exclusively stained the Purkinje cells. Up to now, more than 20 different autoantigens could be identified by this approach, among them ATP1A3, CPT1C, Flotillin1/2, ITPR1, NBCe1, NCDN, RGS8, ROCK2, and Syntaxin-1B as novel autoantigens.DiscussionThe presented antigen identification strategy offers an opportunity for identifying up to now unknown neural autoantigens. Recombinant cell substrates containing the newly identified antigens can be used in serology and the clinical relevance of the autoantibodies can be rapidly evaluated in cohort studies

Inositol 1,4,5-trisphosphate receptor type 1 autoantibodies in paraneoplastic and non-paraneoplastic peripheral neuropathy

Background: Recently, we described a novel autoantibody, anti-Sj/ITPR1-IgG, that targets the inositol 1,4,5-trisphosphate receptor type 1 (ITPR1) in patients with cerebellar ataxia. However, ITPR1 is expressed not only by Purkinje cells but also in the anterior horn of the spinal cord, in the substantia gelatinosa and in the motor, sensory (including the dorsal root ganglia) and autonomic peripheral nervous system, suggesting that the clinical spectrum associated with autoimmunity to ITPR1 may be broader than initially thought. Here we report on serum autoantibodies to ITPR1 (up to 1:15,000) in three patients with (radiculo)polyneuropathy, which in two cases was associated with cancer (ITPR1-expressing adenocarcinoma of the lung, multiple myeloma), suggesting a paraneoplastic aetiology. Methods: Serological and other immunological studies, and retrospective analysis of patient records. Results: The clinical findings comprised motor, sensory (including severe pain) and autonomic symptoms. While one patient presented with subacute symptoms mimicking Guillain-Barré syndrome (GBS), the symptoms progressed slowly in two other patients. Electrophysiology revealed delayed F waves; a decrease in motor and sensory action potentials and conduction velocities; delayed motor latencies; signs of denervation, indicating sensorimotor radiculopolyneuropathy of the mixed type; and no conduction blocks. ITPR1-IgG belonged to the complement-activating IgG1 subclass in the severely affected patient but exclusively to the IgG2 subclass in the two more mildly affected patients. Cerebrospinal fluid ITPR1-IgG was found to be of predominantly extrathecal origin. A 3H-thymidine-based proliferation assay confirmed the presence of ITPR1-reactive lymphocytes among peripheral blood mononuclear cells (PBMCs). Immunophenotypic profiling of PBMCs protein demonstrated predominant proliferation of B cells, CD4 T cells and CD8 memory T cells following stimulation with purified ITPR1 protein. Patient ITPR1-IgG bound both to peripheral nervous tissue and to lung tumour tissue. A nerve biopsy showed lymphocyte infiltration (including cytotoxic CD8 cells), oedema, marked axonal loss and myelin-positive macrophages, indicating florid inflammation. ITPR1-IgG serum titres declined following tumour removal, paralleled by clinical stabilization. Conclusions: Our findings expand the spectrum of clinical syndromes associated with ITPR1-IgG and suggest that autoimmunity to ITPR1 may underlie peripheral nervous system diseases (including GBS) in some patients and may be of paraneoplastic origin in a subset of cases