Introducing Bayesian thinking to high-throughput screening for false-negative rate estimation

An Automatic Quality Control Pipeline for High-Throughput Screening Hit Identification

The correction or removal of signal errors in high-throughput screening (HTS) data is critical to the identification of high-quality lead candidates. Although a number of strategies have been previously developed to correct systematic errors and to remove screening artifacts, they are not universally effective and still require fair amount of human intervention. We introduce a fully automated quality control (QC) pipeline that can correct generic interplate systematic errors and remove intraplate random artifacts. The new pipeline was first applied to ~100 large-scale historical HTS assays; in silico analysis showed auto-QC led to a noticeably stronger structure-activity relationship. The method was further tested in several independent HTS runs, where QC results were sampled for experimental validation. Significantly increased hit confirmation rates were obtained after the QC steps, confirming that the proposed method was effective in enriching true-positive hits. An implementation of the algorithm is available to the screening community.

Identification of host-targeted small molecules that restrict intracellular Mycobacterium tuberculosis growth

Mycobacterium tuberculosis remains a significant threat to global health. Macrophages are the host cell for M. tuberculosis infection, and although bacteria are able to replicate intracellularly under certain conditions, it is also clear that macrophages are capable of killing M. tuberculosis if appropriately activated. The outcome of infection is determined at least in part by the host-pathogen interaction within the macrophage; however, we lack a complete understanding of which host pathways are critical for bacterial survival and replication. To add to our understanding of the molecular processes involved in intracellular infection, we performed a chemical screen using a high-content microscopic assay to identify small molecules that restrict mycobacterial growth in macrophages by targeting host functions and pathways. The identified host-targeted inhibitors restrict bacterial growth exclusively in the context of macrophage infection and predominantly fall into five categories: G-protein coupled receptor modulators, ion channel inhibitors, membrane transport proteins, anti-inflammatories, and kinase modulators. We found that fluoxetine, a selective serotonin reuptake inhibitor, enhances secretion of pro-inflammatory cytokine TNF-α and induces autophagy in infected macrophages, and gefitinib, an inhibitor of the Epidermal Growth Factor Receptor (EGFR), also activates autophagy and restricts growth. We demonstrate that during infection signaling through EGFR activates a p38 MAPK signaling pathway that prevents macrophages from effectively responding to infection. Inhibition of this pathway using gefitinib during in vivo infection reduces growth of M. tuberculosis in the lungs of infected mice. Our results support the concept that screening for inhibitors using intracellular models results in the identification of tool compounds for probing pathways during in vivo infection and may also result in the identification of new anti-tuberculosis agents that work by modulating host pathways. Given the existing experience with some of our identified compounds for other therapeutic indications, further clinically-directed study of these compounds is merited.

Infection with the bacterial pathogen Mycobacterium tuberculosis causes the disease tuberculosis (TB) that imposes significant worldwide morbidity and mortality. Approximately 2 billion people are infected with M. tuberculosis, and almost 1.5 million people die annually from TB. With increasing drug resistance and few novel drug candidates, our inability to effectively treat all infected individuals necessitates a deeper understanding of the host-pathogen interface to facilitate new approaches to treatment. In addition, the current anti-tuberculosis regimen requires months of strict compliance to clear infection; targeting host immune function could play a strategic role in reducing the duration and complexity of treatment while effectively treating drug-resistant strains. Here we use a microscopy-based screen to identify molecules that target host pathways and inhibit the growth of M. tuberculosis in macrophages. We identified several host pathways not previously implicated in tuberculosis. The identified inhibitors prevent growth either by blocking host pathways exploited by M. tuberculosis for virulence, or by activating immune responses that target intracellular bacteria. Fluoxetine, used clinically for treating depression, induces autophagy and enhances production of TNF-α. Similarly, gefitinib, used clinically for treating cancer, inhibits M. tuberculosis growth in macrophages. Importantly, gefitinib treatment reduces bacterial replication in the lungs of M. tuberculosis-infected mice.