At a high level, the tidyverse is a language for solving data science challenges with R code. Its primary goal is to facilitate a conversation between a human and a computer about data. Less abstractly, the tidyverse is a collection of R packages that share a high-level design philosophy and low-level grammar and data structures, so that learning one package makes it easier to learn the next.

The sole human cathelicidin peptide, LL-37, has been demonstrated to protect animals against endotoxemia/sepsis. Low, physiological concentrations of LL-37 (≤1 μg/ml) were able to modulate inflammatory responses by inhibiting the release of the proinflammatory cytokine TNF-α in LPS-stimulated human monocytic cells. Microarray studies established a temporal transcriptional profile and identified differentially expressed genes in LPS-stimulated monocytes in the presence or absence of LL-37. LL-37 significantly inhibited the expression of specific proinflammatory genes up-regulated by NF-κB in the presence of LPS, including NFκB1 (p105/p50) and TNF-α-induced protein 2 (TNFAIP2). In contrast, LL-37 did not significantly inhibit LPS-induced genes that antagonize inflammation, such as TNF-α-induced protein 3 (TNFAIP3) and the NF-κB inhibitor, NFκBIA, or certain chemokine genes that are classically considered proinflammatory. Nuclear translocation, in LPS-treated cells, of the NF-κB subunits p50 and p65 was reduced ≥50% in the presence of LL-37, demonstrating that the peptide altered gene expression in part by acting directly on the TLR-to-NF-κB pathway. LL-37 almost completely prevented the release of TNF-α and other cytokines by human PBMC following stimulation with LPS and other TLR2/4 and TLR9 agonists, but not with cytokines TNF-α or IL-1β. Biochemical and inhibitor studies were consistent with a model whereby LL-37 modulated the inflammatory response to LPS/endotoxin and other agonists of TLR by a complex mechanism involving multiple points of intervention. We propose that the natural human host defense peptide LL-37 plays roles in the delicate balancing of inflammatory responses in homeostasis as well as in combating sepsis induced by certain TLR agonists.

Neuroactive small molecules are indispensable tools for treating mental illnesses and dissecting nervous system function. However, it has been difficult to discover novel neuroactive drugs. Here, we describe a high—throughput (HT) behavior—based approach to neuroactive small molecule discovery in the zebrafish. We use automated screening assays to evaluate thousands of chemical compounds and find that diverse classes of neuroactive molecules cause distinct patterns of behavior. These `behavioral barcodes' can be used to rapidly identify novel psychotropic chemicals and to predict their molecular targets. For example, we identify novel acetylcholinesterase and monoamine oxidase inhibitors using phenotypic comparisons and computational techniques. By combining HT screening technologies with behavioral phenotyping in vivo, behavior—based chemical screens may accelerate the pace of neuroactive drug discovery and provide small—molecule tools for understanding vertebrate behavior.