Brain exposure to SARS-CoV-2 virions perturbs synaptic homeostasis.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is associated with short- and long-term neurological complications. The variety of symptoms makes it difficult to unravel molecular mechanisms underlying neurological sequalae after coronavirus disease 2019 (COVID-19). Here we show that SARS-CoV-2 triggers the up-regulation of synaptic components and perturbs local electrical field potential. Using cerebral organoids, organotypic culture of human brain explants from individuals without COVID-19 and post-mortem brain samples from individuals with COVID-19, we find that neural cells are permissive to SARS-CoV-2 to a low extent. SARS-CoV-2 induces aberrant presynaptic morphology and increases expression of the synaptic components Bassoon, latrophilin-3 (LPHN3) and fibronectin leucine-rich transmembrane protein-3 (FLRT3). Furthermore, we find that LPHN3-agonist treatment with Stachel partially restored organoid electrical activity and reverted SARS-CoV-2-induced aberrant presynaptic morphology. Finally, we observe accumulation of relatively static virions at LPHN3-FLRT3 synapses, suggesting that local hindrance can contribute to synaptic perturbations. Together, our study provides molecular insights into SARS-CoV-2-brain interactions, which may contribute to COVID-19-related neurological disorders.

lesSDRF is more: maximizing the value of proteomics data through streamlined metadata annotation.

Public proteomics data often lack essential metadata, limiting its potential. To address this, we present lesSDRF, a tool to simplify the process of metadata annotation, thereby ensuring that data leave a lasting, impactful legacy well beyond its initial publication.

Machine Learning on Large-Scale Proteomics Data Identifies Tissue and Cell-Type Specific Proteins.

Using data from 183 public human data sets from PRIDE, a machine learning model was trained to identify tissue and cell-type specific protein patterns. PRIDE projects were searched with ionbot and tissue/cell type annotation was manually added. Data from physiological samples were used to train a Random Forest model on protein abundances to classify samples into tissues and cell types. Subsequently, a one-vs-all classification and feature importance were used to analyze the most discriminating protein abundances per class. Based on protein abundance alone, the model was able to predict tissues with 98% accuracy, and cell types with 99% accuracy. The F-scores describe a clear view on tissue-specific proteins and tissue-specific protein expression patterns. In-depth feature analysis shows slight confusion between physiologically similar tissues, demonstrating the capacity of the algorithm to detect biologically relevant patterns. These results can in turn inform downstream uses, from identification of the tissue of origin of proteins in complex samples such as liquid biopsies, to studying the proteome of tissue-like samples such as organoids and cell lines.

Does performance management affect nurses' well-being?

AIM: This article focuses on employee performance-management practices in the healthcare sector. We specifically aim to contribute to a better understanding of the impact of employee performance-management practices on affective well-being of nurses in hospitals. Theory suggests that the features of employee-performance management (planning and evaluation of individual performances) predict affective well-being (in this study: job satisfaction and affective commitment). METHODS: Performance-management planning and evaluation and affective well-being were drawn from a survey of nurses at a Flemish hospital. Separate estimations were performed for different aspects of affective well-being. RESULTS: Performance planning has a negative effect on job satisfaction of nurses. Both vertical alignment and satisfaction with the employee performance-management system increase the affective well-being of nurses; however, the impact of vertical alignment differs for different aspects of affective well-being (i.e. job satisfaction and affective commitment). CONCLUSION: Performance-management planning and evaluation of nurses are associated with attitudinal outcomes. The results indicate that employee performance-management features have different impacts on different aspects of well-being.