Rapid perturbation of protein function permits the ability to define primary molecular responses while avoiding downstream cumulative effects of protein dysregulation. The auxin-inducible degron (AID) system was developed as a tool to achieve rapid and inducible protein degradation in nonplant systems. However, tagging proteins at their endogenous loci results in chronic auxin-independent degradation by the proteasome. To correct this deficiency, we expressed the auxin response transcription factor (ARF) in an improved inducible degron system. ARF is absent from previously engineered AID systems but is a critical component of native auxin signaling. In plants, ARF directly interacts with AID in the absence of auxin, and we found that expression of the ARF PB1 (Phox and Bem1) domain suppresses constitutive degradation of AID-tagged proteins. Moreover, the rate of auxin-induced AID degradation is substantially faster in the ARF-AID system. To test the ARF-AID system in a quantitative and sensitive manner, we measured genome-wide changes in nascent transcription after rapidly depleting the ZNF143 transcription factor. Transcriptional profiling indicates that ZNF143 activates transcription in cis and regulates promoter-proximal paused RNA polymerase density. Rapidly inducible degradation systems that preserve the target protein's native expression levels and patterns will revolutionize the study of biological systems by enabling specific and temporally defined protein dysregulation.

Coupling molecular biology to high-throughput sequencing has revolutionized the study of biology. Molecular genomics techniques are continually refined to provide higher resolution mapping of nucleic acid interactions and structure. Sequence preferences of enzymes can interfere with the accurate interpretation of these data. We developed seqOutBias to characterize enzymatic sequence bias from experimental data and scale individual sequence reads to correct intrinsic enzymatic sequence biases. SeqOutBias efficiently corrects DNase-seq, TACh-seq, ATAC-seq, MNase-seq and PRO-seq data. We show that seqOutBias correction facilitates identification of true molecular signatures resulting from transcription factors and RNA polymerase interacting with DNA.

Inhalational general anesthesia results from the poorly understood interactions of haloethers with multiple protein targets, which prominently includes ion channels in the nervous system. Previously, we reported that the commonly used inhaled anesthetic sevoflurane potentiates the activity of voltage-gated K+ (Kv) channels, specifically, several mammalian Kv1 channels and the Drosophila K-Shaw2 channel. Also, previous work suggested that the S4-S5 linker of K-Shaw2 plays a role in the inhibition of this Kv channel by n-alcohols and inhaled anesthetics. Here, we hypothesized that the S4-S5 linker is also a determinant of the potentiation of Kv1.2 and K-Shaw2 by sevoflurane. Following functional expression of these Kv channels in Xenopus oocytes, we found that converse mutations in Kv1.2 (G329T) and K-Shaw2 (T330G) dramatically enhance and inhibit the potentiation of the corresponding conductances by sevoflurane, respectively. Additionally, Kv1.2-G329T impairs voltage-dependent gating, which suggests that Kv1.2 modulation by sevoflurane is tied to gating in a state-dependent manner. Toward creating a minimal Kv1.2 structural model displaying the putative sevoflurane binding sites, we also found that the positive modulations of Kv1.2 and Kv1.2-G329T by sevoflurane and other general anesthetics are T1-independent. In contrast, the positive sevoflurane modulation of K-Shaw2 is T1-dependent. In silico docking and molecular dynamics-based free-energy calculations suggest that sevoflurane occupies distinct sites near the S4-S5 linker, the pore domain and around the external selectivity filter. We conclude that the positive allosteric modulation of the Kv channels by sevoflurane involves separable processes and multiple sites within regions intimately involved in channel gating.

We applied a set of in silico and in vitro assays, compliant with the CiPA (Comprehensive In Vitro Proarrhythmia Assay) paradigm, to assess the risk of chloroquine or hydroxychloroquine‐mediated QT prolongation and Torsades de Pointes (TdP), alone and combined with erythromycin and azithromycin, drugs repurposed during the first wave of COVID‐19. Each drug or drug combination was tested in patch clamp assays on 7 cardiac ion channels, in in silico models of human ventricular electrophysiology (Virtual Assay ® ) using control (healthy) or high‐risk cell populations, and in human induced pluripotent stem cell (hiPSC)‐derived cardiomyocytes. In each assay, concentration‐response curves encompassing and exceeding therapeutic free plasma levels were generated. Both chloroquine and hydroxychloroquine showed blocking activity against some potassium, sodium and calcium currents. Chloroquine and hydroxychloroquine inhibited I Kr (IC 50 : 1µM and 3‐7µM, respectively) and I K1 currents (IC 50 : 5 and 44µM, respectively). When combining hydroxychloroquine with azithromycin, no synergistic effects were observed. The two macrolides had no or very weak effects on the ion currents (IC 50 >300‐1000µM). Using Virtual Assay ® , both antimalarials affected several TdP indicators, chloroquine being more potent than hydroxychloroquine. Effects were more pronounced in the high‐risk cell population. In hiPSC‐derived cardiomyocytes, all drugs showed early‐after‐depolarizations, except azithromycin. Combining chloroquine or hydroxychloroquine with a macrolide did not aggravate their effects. In conclusion, our integrated nonclinical CiPA dataset confirmed that, at therapeutic plasma concentrations relevant for malaria or off‐label use in COVID‐19, chloroquine and hydroxychloroquine use is associated with a proarrhythmia risk, which is higher in populations carrying predisposing factors but not worsened with macrolide combination.

Neuroinflammation due to glial activation has been linked to many CNS diseases. We developed a computational model of a microglial cytokine interaction network to study the regulatory mechanisms of microglia-mediated neuroinflammation. We established a literature-based cytokine network, including TNFα, TGFβ, and IL-10, and fitted a mathematical model to published data from LPS-treated microglia. The addition of a previously unreported TGFβ autoregulation loop to our model was required to account for experimental data. Global sensitivity analysis revealed that TGFβ- and IL-10-mediated inhibition of TNFα was critical for regulating network behavior. We assessed the sensitivity of the LPS-induced TNFα response profile to the initial TGFβ and IL-10 levels. The analysis showed two relatively shifted TNFα response profiles within separate domains of initial condition space. Further analysis revealed that TNFα exhibited adaptation to sustained LPS stimulation. We simulated the effects of functionally inhibiting TGFβ and IL-10 on TNFα adaptation. Our analysis showed that TGFβ and IL-10 knockouts (TGFβ KO and IL-10 KO) exert divergent effects on adaptation. TFGβ KO attenuated TNFα adaptation whereas IL-10 KO enhanced TNFα adaptation. We experimentally tested the hypothesis that IL-10 KO enhances TNFα adaptation in murine macrophages and found supporting evidence. These opposing effects could be explained by differential kinetics of negative feedback. Inhibition of IL-10 reduced early negative feedback that results in enhanced TNFα-mediated TGFβ expression. We propose that differential kinetics in parallel negative feedback loops constitute a novel mechanism underlying the complex and non-intuitive pro- versus anti-inflammatory effects of individual cytokine perturbations.