Autoimmune limbic encephalitis (ALE) is the most common type of autoimmune encephalitis (AIE). Subacute memory disturbance, temporal lobe seizures, and psychiatric symptoms are clinical hallmarks of ...the disease. However, little is known on the factors contributing to cognitive functioning in ALE. Hence, we here investigate major determinants of cognitive functioning in ALE. In a retrospective analysis of 102 patients with ALE, we first compared verbal learning capacity, nonverbal learning capacity, and attentional and executive functioning by absence or presence of different types of neural autoantibodies (AABs). Subsequently we established three linear regression models including 63, 38, and 61 patients, respectively to investigate how cognitive functioning in these domains may depend on common markers of ALE such as intrathecal inflammation, blood‐cerebrospinal fluid (CSF)‐barrier function, mesiotemporal epileptiform discharges and slowing, determined by electroencephalography (EEG) and structural mesiotemporal changes, measured with magnetic resonance imaging (MRI). We also accounted for possible effects of cancer‐ and immunotherapy and other centrally effective medication. There was no effect of AAB status on cognitive functioning. Although the regression models could not predict verbal and nonverbal learning capacity, structural mesiotemporal neural network alterations on T2‐/fluid attenuated inversion recovery (FLAIR)‐signal‐weighted MRI and mesiotemporal epileptiform discharges or slowing on EEG exerted a significant impact on memory functions. In contrast, the regression model significantly predicted attentional and executive functioning with CSF white blood cell count and centrally effective medication being significant determinants. In this cohort, cognitive functioning in ALE does not depend on the AAB status. Common markers of ALE cannot predict memory functioning that only partially depends on structural and functional alterations of mesiotemporal neural networks. Common markers of ALE significantly predict attentional and executive functioning that is significantly related to centrally effective medication and CSF white blood cell count, which may point toward inflammation affecting brain regions beyond the limbic system.
Although CSF analysis routinely enables the diagnosis of neurological diseases, it is mainly used for the gross distinction between infectious, autoimmune inflammatory, and degenerative disorders of ...the CNS. To investigate, whether a multi-dimensional cellular blood and CSF characterization can support the diagnosis of clinically similar neurological diseases, we analysed 546 patients with autoimmune neuroinflammatory, degenerative, or vascular conditions in a cross-sectional retrospective study. By combining feature selection with dimensionality reduction and machine learning approaches we identified pan-disease parameters that were altered across all autoimmune neuroinflammatory CNS diseases and differentiated them from other neurological conditions and inter-autoimmunity classifiers that subdifferentiate variants of CNS-directed autoimmunity. Pan-disease as well as diseases-specific changes formed a continuum, reflecting clinical disease evolution. A validation cohort of 231 independent patients confirmed that combining multiple parameters into composite scores can assist the classification of neurological patients. Overall, we showed that the integrated analysis of blood and CSF parameters improves the differential diagnosis of neurological diseases, thereby facilitating early treatment decisions.
Inclusion body myositis (IBM) is unique across the spectrum of idiopathic inflammatory myopathies (IIM) due to its distinct clinical presentation and refractoriness to current treatment approaches. ...One explanation for this resistance may be the engagement of cell-autonomous mechanisms that sustain or promote disease progression of IBM independent of inflammatory activity. In this study, we focused on senescence of tissue-resident cells as potential driver of disease. For this purpose, we compared IBM patients to non-diseased controls and immune-mediated necrotizing myopathy patients. Histopathological analysis suggested that cellular senescence is a prominent feature of IBM, primarily affecting non-myogenic cells. In-depth analysis by single nuclei RNA sequencing allowed for the deconvolution and study of muscle-resident cell populations. Among these, we identified a specific cluster of fibro-adipogenic progenitors (FAPs) that demonstrated key hallmarks of senescence, including a pro-inflammatory secretome, expression of p21, increased β-galactosidase activity, and engagement of senescence pathways. FAP function is required for muscle cell health with changes to their phenotype potentially proving detrimental. In this respect, the transcriptomic landscape of IBM was also characterized by changes to the myogenic compartment demonstrating a pronounced loss of type 2A myofibers and a rarefication of acetylcholine receptor expressing myofibers. IBM muscle cells also engaged a specific pro-inflammatory phenotype defined by intracellular complement activity and the expression of immunogenic surface molecules. Skeletal muscle cell dysfunction may be linked to FAP senescence by a change in the collagen composition of the latter. Senescent FAPs lose collagen type XV expression, which is required to support myofibers’ structural integrity and neuromuscular junction formation in vitro. Taken together, this study demonstrates an altered phenotypical landscape of muscle-resident cells and that FAPs, and not myofibers, are the primary senescent cell type in IBM.
Myasthenia gravis is a chronic antibody-mediated autoimmune disease disrupting neuromuscular synaptic transmission. Informative biomarkers remain an unmet need to stratify patients with active ...disease requiring intensified monitoring and therapy; their identification is the primary objective of this study. We applied mass spectrometry-based proteomic serum profiling for biomarker discovery. We studied an exploration and a prospective validation cohort consisting of 114 and 140 anti-acetylcholine receptor antibody (AChR-Ab)-positive myasthenia gravis patients, respectively. For downstream analysis, we applied a machine learning approach. Protein expression levels were confirmed by ELISA and compared to other myasthenic cohorts, in addition to myositis and neuropathy patients. Anti-AChR-Ab levels were determined by a radio receptor assay. Immunohistochemistry and immunofluorescence of intercostal muscle biopsies were employed for validation in addition to interactome studies of inter-alpha-trypsin inhibitor heavy chain H3 (ITIH3). Machine learning identified ITIH3 as potential serum biomarker reflective of disease activity. Serum levels correlated with disease activity scores in the exploration and validation cohort and were confirmed by ELISA. Lack of correlation between anti-AChR-Ab levels and clinical scores underlined the need for biomarkers. In a subgroup analysis, ITIH3 was indicative of treatment responses. Immunostaining of muscle specimens from these patients demonstrated ITIH3 localization at the neuromuscular endplates in myasthenia gravis but not in controls, thus providing a structural equivalent for our serological findings. Immunoprecipitation of ITIH3 and subsequent proteomics lead to identification of its interaction partners playing crucial roles in neuromuscular transmission. This study provides data on ITIH3 as a potential pathophysiological-relevant biomarker of disease activity in myasthenia gravis. Future studies are required to facilitate translation into clinical practice.
Abstract
Objective
Autoimmune limbic encephalitis (ALE) is characterized by memory impairment, psychiatric symptoms, and epileptic seizures. Though, the neuropsychological profile of ALE is not yet ...well defined. However, there is some evidence that neuropsychological impairments might exceed those related to the limbic system and that different autoantibodies (AABs) are associated with distinguishable pattern of neuropsychological impairments. We provide a comprehensive presentation of neuropsychological performance of ALE in an immune therapy-naïve sample.
Methods
We retrospectively analyzed 69 immunotherapy-naïve ALE-patients (26 seropositive—8 LGI1-, 4 CASPR2-, 2 GABAB-R-, 3 Hu-, 4 GAD65-, 2 Ma2-, 2 unknown antigen, and 1 Yo-AABs and 43 seronegative patients, mean age 56.0 years 21.9–78.2, mean disease duration 88 weeks 0–572). Neuropsychological evaluations comprised of the domains memory, attention, praxis, executive functions, language, social cognition, and psychological symptoms. We compared these functions between seronegative −, seropositive patients with AABs against intracellular neural antigens and seropositive patients with AABs against surface membrane neural antigens.
Results
No effect of AAB group on neuropsychological performance could be detected. Overall, ALE predominantly presents with deficits in long-term memory and memory recognition, autobiographical-episodic memory loss, impairment of emotion recognition, and depressed mood. Furthermore, deficits in praxis of pantomimes and imitations, visuo-construction, and flexibility may occur.
Conclusion
ALE shows a wide spectrum of neuropsychological impairments, which might exceed the limbic system, with no evidence of differences between AAB groups. Neuropsychological assessment for diagnosing ALE should include long-term memory, memory recognition, autobiographical-episodic memory, emotion recognition, and a detailed investigation of depression.
Multiple sclerosis (MS) is an autoimmune disease of the central nervous system (CNS) of still unclear etiology. In recent years, the search for biomarkers facilitating its diagnosis, prognosis, ...therapy response, and other parameters has gained increasing attention. In this regard, in a previous meta-analysis comprising 22 studies, we found that MS is associated with higher nitrite/nitrate (NOx) levels in the cerebrospinal fluid (CSF) compared to patients with non-inflammatory other neurological diseases (NIOND). However, many of the included studies did not distinguish between the different clinical subtypes of MS, included pre-treated patients, and inclusion criteria varied. As a follow-up to our meta-analysis, we therefore aimed to analyze the serum and CSF NOx levels in clinically well-defined cohorts of treatment-naïve MS patients compared to patients with somatic symptom disorder. To this end, we analyzed the serum and CSF levels of NOx in 117 patients (71 relapsing-remitting (RR) MS, 16 primary progressive (PP) MS, and 30 somatic symptom disorder). We found that RRMS and PPMS patients had higher serum NOx levels compared to somatic symptom disorder patients. This difference remained significant in the subgroup of MRZ-negative RRMS patients. In conclusion, the measurement of NOx in the serum might indeed be a valuable tool in supporting MS diagnosis.
•Even among MS patients considered mildly affected, most showed disease activity•Driven by MRI activity, loss of NEDA-3 was the most frequent marker of disease activity•PIRA occurred in 50% of ...patients and was often not accompanied by loss of NEDA-3•MRI and clinical measurements often did not show disease activity simultaneously•Measuring different disease activity outcome measures could improve monitoring
The current range of disease-modifying treatments (DMTs) for relapsing-remitting multiple sclerosis (RRMS) has placed more importance on the accurate monitoring of disease progression for timely and appropriate treatment decisions. With a rising number of measurements for disease progression, it is currently unclear how well these measurements or combinations of them can monitor more mildly affected RRMS patients.
To investigate several composite measures for monitoring disease activity and their potential relation to the biomarker neurofilament light chain (NfL) in a clearly defined early RRMS patient cohort with a milder disease course.
From a total of 301 RRMS patients, a subset of 46 patients being treated with a continuous first-line therapy was analyzed for loss of no evidence of disease activity (lo-NEDA-3) status, relapse-associated worsening (RAW) and progression independent of relapse activity (PIRA), up to seven years after treatment initialization. Kaplan-Meier estimates were used for time-to-event analysis. Additionally, a Cox regression model was used to analyze the effect of NfL levels on outcome measures in this cohort.
In this mildly affected cohort, both lo-NEDA-3 and PIRA frequently occurred over a median observational period of 67.2 months and were observed in 39 (84.8%) and 23 (50.0%) patients, respectively. Additionally, 12 out of 26 PIRA manifestations (46.2%) were observed without a corresponding lo-NEDA-3 status. Jointly, either PIRA or lo-NEDA-3 showed disease activity in all patients followed-up for at least the median duration (67.2 months). NfL values demonstrated an association with the occurrence of relapses and RAW.
The complementary use of different disease progression measures helps mirror ongoing disease activity in mildly affected early RRMS patients being treated with continuous first-line therapy.
Given its wide availability and cost-effectiveness, multidimensional flow cytometry (mFC) became a core method in the field of immunology allowing for the analysis of a broad range of individual ...cells providing insights into cell subset composition, cellular behavior, and cell-to-cell interactions. Formerly, the analysis of mFC data solely relied on manual gating strategies. With the advent of novel computational approaches, (semi-)automated gating strategies and analysis tools complemented manual approaches.
Using Bayesian network analysis, we developed a mathematical model for the dependencies of different obtained mFC markers. The algorithm creates a Bayesian network that is a HC tree when including raw, ungated mFC data of a randomly selected healthy control cohort (HC). The HC tree is used to classify whether the observed marker distribution (either patients with amyotrophic lateral sclerosis (ALS) or HC) is predicted. The relative number of cells where the probability q is equal to zero is calculated reflecting the similarity in the marker distribution between a randomly chosen mFC file (ALS or HC) and the HC tree.
Including peripheral blood mFC data from 68 ALS and 35 HC, the algorithm could correctly identify 64/68 ALS cases. Tuning of parameters revealed that the combination of 7 markers, 200 bins, and 20 patients achieved the highest AUC on a significance level of p < 0.0001. The markers CD4 and CD38 showed the highest zero probability. We successfully validated our approach by including a second, independent ALS and HC cohort (55 ALS and 30 HC). In this case, all ALS were correctly identified and side scatter and CD20 yielded the highest zero probability. Finally, both datasets were analyzed by the commercially available algorithm 'Citrus', which indicated superior ability of Bayesian network analysis when including raw, ungated mFC data.
Bayesian network analysis might present a novel approach for classifying mFC data, which does not rely on reduction techniques, thus, allowing to retain information on the entire dataset. Future studies will have to assess the performance when discriminating clinically relevant differential diagnoses to evaluate the complementary diagnostic benefit of Bayesian network analysis to the clinical routine workup.
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