High-throughput testing of drugs across molecular-characterised cell lines can identify candidate treatments and discover biomarkers. However, the cells' response to a drug is typically quantified by a summary statistic from a best-fit dose-response curve, whilst neglecting the uncertainty of the curve fit and the potential variability in the raw readouts. Here, we model the experimental variance using Gaussian Processes, and subsequently, leverage uncertainty estimates to identify associated biomarkers with a new Bayesian framework. Applied to in vitro screening data on 265 compounds across 1,074 cancer cell lines, our models identified 24 clinically established drug response biomarkers, and provided evidence for 6 novel biomarkers by accounting for association with low uncertainty. We validated our uncertainty estimates with an additional drug screen of 26 drugs, 10 cell lines with 8 to 9 replicates. Our method is applicable to any dose-response data without replicates, and improves biomarker discovery for precision medicine.

Background Parkinson's disease (PD) is the second most common neurodegenerative disorder whose prevalence is rapidly increasing worldwide. The molecular mechanisms underpinning the pathophysiology of sporadic PD remain incompletely understood. Therefore, causative therapies are still elusive. To obtain a more integrative view of disease‐mediated alterations, we investigated the molecular landscape of PD in human post‐mortem midbrains, a region that is highly affected during the disease process. Methods Tissue from 19 PD patients and 12 controls were obtained from the Parkinson's UK Brain Bank and subjected to multi‐omic analyses: small and total RNA sequencing was performed on an Illumina's HiSeq4000, while proteomics experiments were performed in a hybrid triple quadrupole‐time of flight mass spectrometer (TripleTOF5600+) following quantitative sequential window acquisition of all theoretical mass spectra. Differential expression analyses were performed with customized frameworks based on DESeq2 (for RNA sequencing) and with Perseus v.1.5.6.0 (for proteomics). Custom pipelines in R were used for integrative studies. Results Our analyses revealed multiple deregulated molecular targets linked to known disease mechanisms in PD as well as to novel processes. We have identified and experimentally validated (quantitative real‐time polymerase chain reaction/western blotting) several PD‐deregulated molecular candidates, including miR‐539‐3p, miR‐376a‐5p, miR‐218‐5p and miR‐369‐3p, the valid miRNA‐mRNA interacting pairs miR‐218‐5p/RAB6C and miR‐369‐3p/GTF2H3, as well as multiple proteins, such as CHI3L1, HSPA1B, FNIP2 and TH. Vertical integration of multi‐omic analyses allowed validating disease‐mediated alterations across different molecular layers. Next to the identification of individual molecular targets in all explored omics layers, functional annotation of differentially expressed molecules showed an enrichment of pathways related to neuroinflammation, mitochondrial dysfunction and defects in synaptic function. Conclusions This comprehensive assessment of PD‐affected and control human midbrains revealed multiple molecular targets and networks that are relevant to the disease mechanism of advanced PD. The integrative analyses of multiple omics layers underscore the importance of neuroinflammation, immune response activation, mitochondrial and synaptic dysfunction as putative therapeutic targets for advanced PD.

Hospital staff are at high risk for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection during the coronavirus disease (COVID-19) pandemic. This cross-sectional study aimed to determine the prevalence of SARS-CoV-2 infection in hospital staff at the University Hospital rechts der Isar in Munich, Germany, and identify modulating factors. Overall seroprevalence of SARS-CoV-2-IgG in 4,554 participants was 2.4%. Staff engaged in direct patient care, including those working in COVID-19 units, had a similar probability of being seropositive as non–patient-facing staff. Increased probability of infection was observed in staff reporting interactions with SARS-CoV-2‒infected coworkers or private contacts or exposure to COVID-19 patients without appropriate personal protective equipment. Analysis of spatiotemporal trajectories identified that distinct hotspots for SARS-CoV-2‒positive staff and patients only partially overlap. Patient-facing work in a healthcare facility during the SARS-CoV-2 pandemic might be safe as long as adequate personal protective equipment is used and infection prevention practices are followed inside and outside the hospital