Altered and allele-specific open chromatin landscape reveals epigenetic and genetic regulators of innate immunity in COVID-19.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection causes severe COVID-19 in some patients and mild COVID-19 in others. Dysfunctional innate immune responses have been identified to contribute to COVID-19 severity, but the key regulators are still unknown. Here, we present an integrative single-cell multi-omics analysis of peripheral blood mononuclear cells from hospitalized and convalescent COVID-19 patients.

Cluster-independent marker feature identification from single-cell omics data using SEMITONES.

Identification of cell identity markers is an essential step in single-cell omics data analysis. Current marker identification strategies typically rely on cluster assignments of cells. However, cluster assignment, particularly for developmental data, is nontrivial, potentially arbitrary, and commonly relies on prior knowledge.

A single-cell Arabidopsis root atlas reveals developmental trajectories in wild-type and cell identity mutants.

In all multicellular organisms, transcriptional networks orchestrate organ development. The Arabidopsis root, with its simple structure and indeterminate growth, is an ideal model for investigating the spatiotemporal transcriptional signatures underlying developmental trajectories. To map gene expression dynamics across root cell types and developmental time, we built a comprehensive, organ-scale atlas at single-cell resolution.

Intricacies of single-cell multi-omics data integration.

A wealth of single-cell protocols makes it possible to characterize different molecular layers at unprecedented resolution. Integrating the resulting multimodal single-cell data to find cell-to-cell correspondences remains a challenge. We argue that data integration needs to happen at a meaningful biological level of abstraction and that it is necessary to consider the inherent discrepancies between modalities to strike a balance between biological discovery and noise removal.

Applying synergy metrics to combination screening data: agreements, disagreements and pitfalls.

Synergistic drug combinations are commonly sought to overcome monotherapy resistance in cancer treatment. To identify such combinations, high-throughput cancer cell line combination screens are performed; and synergy is quantified using competing models based on fundamentally different assumptions. Here, we compare the behaviour of four synergy models, namely Loewe additivity, Bliss independence, highest single agent and zero interaction potency, using the Merck oncology combination screen.

Target and Tissue Selectivity Prediction by Integrated Mechanistic Pharmacokinetic-Target Binding and Quantitative Structure Activity Modeling.

Selectivity is an important attribute of effective and safe drugs, and prediction of in vivo target and tissue selectivity would likely improve drug development success rates. However, a lack of understanding of the underlying (pharmacological) mechanisms and availability of directly applicable predictive methods complicates the prediction of selectivity. We explore the value of combining physiologically based pharmacokinetic (PBPK) modeling with quantitative structure-activity relationship (QSAR) modeling to predict the influence of the target dissociation constant (K ) and the target dissociation rate constant on target and tissue selectivity.

Identification of likely orthologs of tobacco salicylic acid-binding protein 2 and their role in systemic acquired resistance in Arabidopsis thaliana.

Salicylic acid-binding protein 2 (SABP2) is essential for the establishment of systemic acquired resistance (SAR) in tobacco; SABP2's methyl salicylate (MeSA) esterase activity is required in healthy systemic tissues of infected plants to release the active defense phytohormone SA from MeSA, which serves as a long-distance signal for SAR. In the current study, we characterize a new gene family from Arabidopsis thaliana encoding 18 potentially active alpha/beta fold hydrolases that share 32-57% identity with SABP2. Of 14 recombinant AtMES (MES for methyl esterase) proteins tested, five showed preference for MeSA as a substrate and displayed SA inhibition of MeSA esterase activity in vitro (AtMES1, -2, -4, -7, and -9).