Three million images and morphological profiles of cells treated with matched chemical and genetic perturbations.

The identification of genetic and chemical perturbations with similar impacts on cell morphology can elucidate compounds' mechanisms of action or novel regulators of genetic pathways. Research on methods for identifying such similarities has lagged due to a lack of carefully designed and well-annotated image sets of cells treated with chemical and genetic perturbations. Here we create such a Resource dataset, CPJUMP1, in which each perturbed gene's product is a known target of at least two chemical compounds in the dataset. We systematically explore the directionality of correlations among perturbations that target the same protein encoded by a given gene, and we find that identifying matches between chemical and genetic perturbations is a challenging task. Our dataset and baseline analyses provide a benchmark for evaluating methods that measure perturbation similarities and impact, and more generally, learn effective representations of cellular state from microscopy images. Such advancements would accelerate the applications of image-based profiling of cellular states, such as uncovering drug mode of action or probing functional genomics.

Expanding therapeutic opportunities for neurodegenerative diseases: A perspective on the important role of phenotypic screening.

Over the last 20 years, there have been remarkably few FDA-approved first-in-class drugs for neurodegenerative diseases. Debilitating conditions such as Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis have no effective disease-modifying therapeutics on the market, signifying an area of high unmet medical need where novel approaches are needed. Using a phenotypic screening approach, two separate groups discovered small molecule non-antisense oligonucleotide splice modulators for spinal muscular atrophy, a severe monogenetic disease that causes the degeneration ofalpha motor neuronsin the spinal cord. These compounds function by a novel mechanism: selective stabilization of the interaction of U1 small nuclear ribonucleic protein (snRNP), a core component of the spliceosome, with the 5' splice site of a pre-mRNA. The ability of the phenotypic screening approach to uncover a previously unknown mechanism and reveal a new druggable target class has broader implications for other neurodegenerative diseases.

SMN2 splice modulators enhance U1-pre-mRNA association and rescue SMA mice.

Spinal muscular atrophy (SMA), which results from the loss of expression of the survival of motor neuron-1 (SMN1) gene, represents the most common genetic cause of pediatric mortality. A duplicate copy (SMN2) is inefficiently spliced, producing a truncated and unstable protein. We describe herein a potent, orally active, small-molecule enhancer of SMN2 splicing that elevates full-length SMN protein and extends survival in a severe SMA mouse model. We demonstrate that the molecular mechanism of action is via stabilization of the transient double-strand RNA structure formed by the SMN2 pre-mRNA and U1 small nuclear ribonucleic protein (snRNP) complex. The binding affinity of U1 snRNP to the 5' splice site is increased in a sequence-selective manner, discrete from constitutive recognition. This new mechanism demonstrates the feasibility of small molecule-mediated, sequence-selective splice modulation and the potential for leveraging this strategy in other splicing diseases.

Optimizing the Cell Painting assay for image-based profiling.

In image-based profiling, software extracts thousands of morphological features of cells from multi-channel fluorescence microscopy images, yielding single-cell profiles that can be used for basic research and drug discovery. Powerful applications have been proven, including clustering chemical and genetic perturbations on the basis of their similar morphological impact, identifying disease phenotypes by observing differences in profiles between healthy and diseased cells and predicting assay outcomes by using machine learning, among many others. Here, we provide an updated protocol for the most popular assay for image-based profiling, Cell Painting. Introduced in 2013, it uses six stains imaged in five channels and labels eight diverse components of the cell: DNA, cytoplasmic RNA, nucleoli, actin, Golgi apparatus, plasma membrane, endoplasmic reticulum and mitochondria. The original protocol was updated in 2016 on the basis of several years' experience running it at two sites, after optimizing it by visual stain quality. Here, we describe the work of the Joint Undertaking for Morphological Profiling Cell Painting Consortium, to improve upon the assay via quantitative optimization by measuring the assay's ability to detect morphological phenotypes and group similar perturbations together. The assay gives very robust outputs despite various changes to the protocol, and two vendors' dyes work equivalently well. We present Cell Painting version 3, in which some steps are simplified and several stain concentrations can be reduced, saving costs. Cell culture and image acquisition take 1-2 weeks for typically sized batches of ≤20 plates; feature extraction and data analysis take an additional 1-2 weeks.This protocol is an update to Nat. Protoc. 11, 1757-1774 (2016): https://doi.org/10.1038/nprot.2016.105.

Quantitative Systems Pharmacology for Neuroscience Drug Discovery and Development: Current Status, Opportunities, and Challenges.

The substantial progress made in the basic sciences of the brain has yet to be adequately translated to successful clinical therapeutics to treat central nervous system (CNS) diseases. Possible explanations include the lack of quantitative and validated biomarkers, the subjective nature of many clinical endpoints, and complex pharmacokinetic/pharmacodynamic relationships, but also the possibility that highly selective drugs in the CNS do not reflect the complex interactions of different brain circuits. Although computational systems pharmacology modeling designed to capture essential components of complex biological systems has been increasingly accepted in pharmaceutical research and development for oncology, inflammation, and metabolic disorders, the uptake in the CNS field has been very modest. In this article, a cross-disciplinary group with representatives from academia, pharma, regulatory, and funding agencies make the case that the identification and exploitation of CNS therapeutic targets for drug discovery and development can benefit greatly from a system and network approach that can span the gap between molecular pathways and the neuronal circuits that ultimately regulate brain activity and behavior. The National Institute of Neurological Disorders and Stroke (NINDS), in collaboration with the National Institute on Aging (NIA), National Institute of Mental Health (NIMH), National Institute on Drug Abuse (NIDA), and National Center for Advancing Translational Sciences (NCATS), convened a workshop to explore and evaluate the potential of a quantitative systems pharmacology (QSP) approach to CNS drug discovery and development. The objective of the workshop was to identify the challenges and opportunities of QSP as an approach to accelerate drug discovery and development in the field of CNS disorders. In particular, the workshop examined the potential for computational neuroscience to perform QSP-based interrogation of the mechanism of action for CNS diseases, along with a more accurate and comprehensive method for evaluating drug effects and optimizing the design of clinical trials. Following up on an earlier white paper on the use of QSP in general disease mechanism of action and drug discovery, this report focuses on new applications, opportunities, and the accompanying limitations of QSP as an approach to drug development in the CNS therapeutic area based on the discussions in the workshop with various stakeholders.

Designing experiments for high-throughput protein expression.

The advent of high-throughput protein production and the vast amount of data it is capable of generating has created both new opportunities and problems. Automation and miniaturization allow experimentation to be performed more efficiently, justifying the cost involved in establishing a high-throughput platform. These changes have also magnified the need for effective statistical methods to identify trends and relationships in the data. The application of quantitative management tools to this process provides the means of ensuring maximum efficiency and productivity.

E. coli and insect cell expression, automated purification and quantitative analysis.

The production of recombinant proteins usually involves the exploration of a wide variety of expression and purification methodologies in the pursuit of a strategy tailored to a particular protein. The methods applied are reliant on exploiting individual differences between expression systems or the variations in specific protein properties. These bespoke strategies have not lent themselves to high-throughput methodologies. Ultimately the development of robust generic methods capable of simplifying and stabilizing the process, allowing automation, was necessary to increase throughput. This chapter describes a series of high-throughput methods used to express, purify, and quantify recombinant protein produced in E. coli or insect cells.

Development of a protease production platform for structure-based drug design.

Structure-based drug design (SBDD) has played an integral role in the development of highly specific, potent protease inhibitors resulting in a number of drugs in clinical trials and on the market. Possessing biochemical assays and structural information on both the target protease and homologous family members helps ensure compound selectivity. We have redesigned the path from clone to protein eliminating many of the traditional bottlenecks associated with protein production to ensure a constant supply to feed many diverse protease drug discovery programs. The process was initiated with the design of a multi-system vector, capable of expression in both eukaryotic and prokaryotic hosts; this vector also facilitated high-throughput cloning, expression and purification. When combined into an expression screen, supplemented with salvage screens for detergent extraction and refolding, a route for protein production was established rapidly. Using this process-orientated approach we have successfully expressed and purified all mechanistic classes of active human and viral proteases for enzymatic assays and crystallization studies. While exploiting recent developments in high-throughput biochemistry, we still employ classical biophysical techniques such as light-scattering and analytical ultracentrifugation, to ensure the highest quality protein enters crystallization trials. We have drawn on examples from our own research programs to illustrate how these strategies have been successfully used in the production of proteases for SBDD.

GDF11 Increases with Age and Inhibits Skeletal Muscle Regeneration.

Age-related frailty may be due to decreased skeletal muscle regeneration. The role of TGF-ß molecules myostatin and GDF11 in regeneration is unclear. Recent studies showed an age-related decrease in GDF11 and that GDF11 treatment improves muscle regeneration, which were contrary to prior studies. We now show that these recent claims are not reproducible and the reagents previously used to detect GDF11 are not GDF11 specific. We develop a GDF11-specific immunoassay and show a trend toward increased GDF11 levels in sera of aged rats and humans. GDF11 mRNA increases in rat muscle with age. Mechanistically, GDF11 and myostatin both induce SMAD2/3 phosphorylation, inhibit myoblast differentiation, and regulate identical downstream signaling. GDF11 significantly inhibited muscle regeneration and decreased satellite cell expansion in mice. Given early data in humans showing a trend for an age-related increase, GDF11 could be a target for pharmacologic blockade to treat age-related sarcopenia.

Screening factors effecting a response in soluble protein expression: formalized approach using design of experiments.

We have integrated high-throughput expression and purification with quantitative protein analysis to identify factors influencing protein production. Application of high-throughput expression and purification, combined with automated gel capillary electrophoresis, allowed the quantitative analysis of multiple expression variables in a single experiment. An experimental design utilizing multiple factorial screens was employed to identify single factors and interactions having a significant impact on expression. As a test case, expression of the nonstructural protein NS3 from different hepatitis C virus genotypes (1b, 2a, and 3a) was examined in Escherichia coli. The 1b genotype of NS3 produced the highest level of expression, which was then further optimized using response surface modeling to give a four-fold increase in soluble protein levels. The quantitative and statistical approach presented has the capability of rapidly identifying interactions among experimental variables, leading to more reliable prediction of protein expression. We propose that this technique has universal application in the production of recombinant proteins, providing a powerful tool for the optimization of protein expression.

Full-length influenza hemagglutinin HA2 refolds into the trimeric low-pH-induced conformation.

The influenza virus uses hemagglutinin (HA) to fuse the viral and cellular membranes. As part of an effort to study the membrane-interacting elements of HA, the fusion peptide, and the C-terminal transmembrane anchor, we have expressed in Escherichia coli the full-length HA(2) chain with maltose-binding protein fused at its N-terminus. The chimeric protein can be refolded in vitro in the presence of specific detergents to yield stable, homogeneous trimers, as determined by analytical ultracentrifugation. The trimers have the so-called "low pH" conformation-the rearranged HA(2) conformation obtained when intact HA(1)/HA(2) is induced to refold by exposure to low pH-as detected by electron microscopy and monoclonalantibody reactivity. These results provide further evidence for the notion that the neutral-pH structure of intact HA is metastable and that binding of protons lowers the kinetic barriers that prevent rearrangement to the minimum-free-energy conformation. The refolded chimeric protein described here is a suitable species for undertaking studies of how the fusion peptide inserts into membranes and assessing the nature of possible intermediates in the fusion process.