Open Access

Poster presentation: Introduction The ability of neurons to emit different firing patterns is considered relevant for neuronal information processing. In dopaminergic neurons, prominent patterns include highly regular pacemakers with separate spikes and stereotyped intervals, processes with repetitive bursts and partial regularity, and irregular spike trains with nonstationary properties. In order to model and quantify these processes and the variability of their patterns with respect to pharmacological and cellular properties, we aim to describe the two dimensions of burstiness and regularity in a single model framework. Methods We present a stochastic spike train model in which the degree of burstiness and the regularity of the oscillation are described independently and with two simple parameters. In this model, a background oscillation with independent and normally distributed intervals gives rise to Poissonian spike packets with a Gaussian firing intensity. The variability of inter-burst intervals and the average number of spikes in each burst indicate regularity and burstiness, respectively. These parameters can be estimated by fitting the model to the autocorrelograms. This allows to assign every spike train a position in the two-dimensional space described by regularity and burstiness and thus, to investigate the dependence of the firing patterns on different experimental conditions. Finally, burst detection in single spike trains is possible within the model because the parameter estimates determine the appropriate bandwidth that should be used for burst identification. Results and Discussion We applied the model to a sample data set obtained from dopaminergic substantia nigra and ventral tegmental area neurons recorded extracellularly in vivo and studied differences between the firing activity of dopaminergic neurons in wildtype and K-ATP channel knock-out mice. The model is able to represent a variety of discharge patterns and to describe changes induced pharmacologically. It provides a simple and objective classification scheme for the observed spike trains into pacemaker, irregular and bursty processes. In addition to the simple classification, changes in the parameters can be studied quantitatively, also including the properties related to bursting behavior. Interestingly, the proposed algorithm for burst detection may be applicable also to spike trains with nonstationary firing rates if the remaining parameters are unaffected. Thus, the proposed model and its burst detection algorithm can be useful for the description and investigation of neuronal firing patterns and their variability with cellular and experimental conditions.

Bipolar affective disorder (BD) is a severe psychiatric illness, for which lithium (Li) is the gold standard for acute and maintenance therapies. The therapeutic response to Li in BD is heterogeneous and reliable biomarkers allowing patients stratification are still needed. A GWAS performed by the International Consortium on Lithium Genetics (ConLiGen) has recently identified genetic markers associated with treatment responses to Li in the human leukocyte antigens (HLA) region. To better understand the molecular mechanisms underlying this association, we have genetically imputed the classical alleles of the HLA region in the European patients of the ConLiGen cohort. We found our best signal for amino-acid variants belonging to the HLA-DRB1*11:01 classical allele, associated with a better response to Li (p < 1 × 10−3; FDR < 0.09 in the recessive model). Alanine or Leucine at position 74 of the HLA-DRB1 heavy chain was associated with a good response while Arginine or Glutamic acid with a poor response. As these variants have been implicated in common inflammatory/autoimmune processes, our findings strongly suggest that HLA-mediated low inflammatory background may contribute to the efficient response to Li in BD patients, while an inflammatory status overriding Li anti-inflammatory properties would favor a weak response.

CXCL12-CXCR4 signaling controls multiple physiological processes and its dysregulation is associated with cancers and inflammatory diseases. To discover as-yet-unknown endogenous ligands of CXCR4, we screened a blood-derived peptide library for inhibitors of CXCR4-tropic HIV-1 strains. This approach identified a 16 amino acid fragment of serum albumin as an effective and highly specific CXCR4 antagonist. The endogenous peptide, termed EPI-X4, is evolutionarily conserved and generated from the highly abundant albumin precursor by pH-regulated proteases. EPI-X4 forms an unusual lasso-like structure and antagonizes CXCL12-induced tumor cell migration, mobilizes stem cells, and suppresses inflammatory responses in mice. Furthermore, the peptide is abundant in the urine of patients with inflammatory kidney diseases and may serve as a biomarker. Our results identify EPI-X4 as a key regulator of CXCR4 signaling and introduce proteolysis of an abundant precursor protein as an alternative concept for chemokine receptor regulation.

Background: Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine learning model, based on retrospective & prospective clinical data, to stratify patient risk and predict ICU survival and outcomes. Methods: A Germany-wide electronic registry was established to pseudonymously collect admission, therapeutic and discharge information of SARS-CoV-2 ICU patients retrospectively and prospectively. Machine learning approaches were evaluated for the accuracy and interpretability of predictions. The Explainable Boosting Machine approach was selected as the most suitable method. Individual, non-linear shape functions for predictive parameters and parameter interactions are reported. Results: 1039 patients were included in the Explainable Boosting Machine model, 596 patients retrospectively collected, and 443 patients prospectively collected. The model for prediction of general ICU outcome was shown to be more reliable to predict “survival”. Age, inflammatory and thrombotic activity, and severity of ARDS at ICU admission were shown to be predictive of ICU survival. Patients’ age, pulmonary dysfunction and transfer from an external institution were predictors for ECMO therapy. The interaction of patient age with D-dimer levels on admission and creatinine levels with SOFA score without GCS were predictors for renal replacement therapy. Conclusions: Using Explainable Boosting Machine analysis, we confirmed and weighed previously reported and identified novel predictors for outcome in critically ill COVID-19 patients. Using this strategy, predictive modeling of COVID-19 ICU patient outcomes can be performed overcoming the limitations of linear regression models. Trial registration “ClinicalTrials” (clinicaltrials.gov) under NCT04455451.

Aims: Pompe disease is caused by pathogenic mutations in the alpha 1,4‐glucosidase (GAA) gene and in patients with late onset Pome disease (LOPD), genotype–phenotype correlations are unpredictable. Skeletal muscle pathology includes glycogen accumulation and altered autophagy of various degrees. A correlation of the muscle morphology with clinical features and the genetic background in GAA may contribute to the understanding of the phenotypic variability. Methods: Muscle biopsies taken before enzyme replacement therapy were analysed from 53 patients with LOPD. On resin sections, glycogen accumulation, fibrosis, autophagic vacuoles and the degree of muscle damage (morphology‐score) were analysed and the results were compared with clinical findings. Additional autophagy markers microtubule‐associated protein 1A/1B‐light chain 3, p62 and Bcl2‐associated athanogene 3 were analysed on cryosections from 22 LOPD biopsies. Results: The myopathology showed a high variability with, in most patients, a moderate glycogen accumulation and a low morphology‐score. High morphology‐scores were associated with increased fibrosis and autophagy highlighting the role of autophagy in severe stages of skeletal muscle damage. The morphology‐score did not correlate with the patient's age at biopsy, disease duration, nor with the residual GAA enzyme activity or creatine‐kinase levels. In 37 patients with LOPD, genetic analysis identified the most frequent mutation, c.‐32‐13T>G, in 95%, most commonly in combination with c.525delT (19%). No significant correlation was found between the different GAA genotypes and muscle morphology type. Conclusions: Muscle morphology in LOPD patients shows a high variability with, in most cases, moderate pathology. Increased pathology is associated with more fibrosis and autophagy.