Predicting bioprocess targets of chemical compounds through integration of chemical-genetic and genetic interactions

Dereplication of Natural Product Antifungals via Liquid Chromatography-Tandem Mass Spectrometry and Chemical Genomics

Recently expanded reports of multidrug-resistant fungal infections underscore the need to develop new and more efficient methods for antifungal drug discovery. A ubiquitous problem in natural product drug discovery campaigns is the rediscovery of known compounds or their relatives; accordingly, we have integrated Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) for structural dereplication and Yeast Chemical Genomics for bioprocess evaluation into a screening platform to identify such compounds early in the screening process. We identified 450 fractions inhibiting Candida albicans and the resistant strains of C. auris and C. glabrata among more than 40,000 natural product fractions. LC-MS/MS and chemical genomics were then used to identify those with known chemistry and mechanisms of action. The parallel deployment of these orthogonal methods improved the detection of unwanted compound classes over the methods applied individually.

BIONIC: biological network integration using convolutions

Biological networks constructed from varied data can be used to map cellular function, but each data type has limitations. Network integration promises to address these limitations by combining and automatically weighting input information to obtain a more accurate and comprehensive representation of the underlying biology. We developed a deep learning-based network integration algorithm that incorporates a graph convolutional network framework. Our method, BIONIC (Biological Network Integration using Convolutions), learns features that contain substantially more functional information compared to existing approaches. BIONIC has unsupervised and semisupervised learning modes, making use of available gene function annotations. BIONIC is scalable in both size and quantity of the input networks, making it feasible to integrate numerous networks on the scale of the human genome. To demonstrate the use of BIONIC in identifying new biology, we predicted and experimentally validated essential gene chemical-genetic interactions from nonessential gene profiles in yeast.

Gradients in gene essentiality reshape antibacterial research

Essential genes encode the processes that are necessary for life. Until recently, commonly applied binary classifications left no space between essential and non-essential genes. In this review, we frame bacterial gene essentiality in the context of genetic networks. We explore how the quantitative properties of gene essentiality are influenced by the nature of the encoded process, environmental conditions and genetic background, including a strain's distinct evolutionary history. The covered topics have important consequences for antibacterials, which inhibit essential processes. We argue that the quantitative properties of essentiality can thus be used to prioritize antibacterial cellular targets and desired spectrum of activity in specific infection settings. We summarize our points with a case study on the core essential genome of the cystic fibrosis pathobiome and highlight avenues for targeted antibacterial development.

Analysis of combinatorial CRISPR screens with the Orthrus scoring pipeline

The continued improvement of combinatorial CRISPR screening platforms necessitates the development of new computational pipelines for scoring combinatorial screening data. Unlike for single-guide RNA (sgRNA) pooled screening platforms, combinatorial scoring for multiplexed systems is confounded by guide design parameters such as the number of gRNAs per construct, the position of gRNAs along constructs, and additional features that may impact gRNA expression, processing or capture. In this protocol we describe Orthrus, an R package for processing, scoring and analyzing combinatorial CRISPR screening data that addresses these challenges. This protocol walks through the application of Orthrus to previously published combinatorial screening data from the CHyMErA experimental system, a platform we recently developed that pairs Cas9 with Cas12a gRNAs and enables programmed targeting of multiple genomic sites. We demonstrate Orthrus' features for screen quality assessment and two distinct scoring modes for dual guide RNAs (dgRNAs) that target the same gene twice or dgRNAs that target two different genes. Running Orthrus requires basic R programming experience, ~5-10 min of computational time and 15-60 min total.

Clionamines stimulate autophagy, inhibit Mycobacterium tuberculosis survival in macrophages, and target Pik1

The pathogen Mycobacterium tuberculosis (Mtb) evades the innate immune system by interfering with autophagy and phagosomal maturation in macrophages, and, as a result, small molecule stimulation of autophagy represents a host-directed therapeutics (HDTs) approach for treatment of tuberculosis (TB). Here we show the marine natural product clionamines activate autophagy and inhibit Mtb survival in macrophages. A yeast chemical-genetics approach identified Pik1 as target protein of the clionamines. Biotinylated clionamine B pulled down Pik1 from yeast cell lysates and a clionamine analog inhibited phosphatidyl 4-phosphate (PI4P) production in yeast Golgi membranes. Chemical-genetic profiles of clionamines and cationic amphiphilic drugs (CADs) are closely related, linking the clionamine mode of action to co-localization with PI4P in a vesicular compartment. Small interfering RNA (siRNA) knockdown of PI4KB, a human homolog of Pik1, inhibited the survival of Mtb in macrophages, identifying PI4KB as an unexploited molecular target for efforts to develop HDT drugs for treatment of TB.

A marine microbiome antifungal targets urgent-threat drug-resistant fungi

New antifungal drugs are urgently needed to address the emergence and transcontinental spread of fungal infectious diseases, such as pandrug-resistant Candida auris. Leveraging the microbiomes of marine animals and cutting-edge metabolomics and genomic tools, we identified encouraging lead antifungal molecules with in vivo efficacy. The most promising lead, turbinmicin, displays potent in vitro and mouse-model efficacy toward multiple-drug-resistant fungal pathogens, exhibits a wide safety index, and functions through a fungal-specific mode of action, targeting Sec14 of the vesicular trafficking pathway. The efficacy, safety, and mode of action distinct from other antifungal drugs make turbinmicin a highly promising antifungal drug lead to help address devastating global fungal pathogens such as C. auris.

Drug antagonism and single-agent dominance result from differences in death kinetics

Cancer treatment generally involves drugs used in combinations. Most previous work has focused on identifying and understanding synergistic drug-drug interactions; however, understanding antagonistic interactions remains an important and understudied issue. To enrich for antagonism and reveal common features of these combinations, we screened all pairwise combinations of drugs characterized as activators of regulated cell death. This network is strongly enriched for antagonism, particularly a form of antagonism that we call 'single-agent dominance'. Single-agent dominance refers to antagonisms in which a two-drug combination phenocopies one of the two agents. Dominance results from differences in cell death onset time, with dominant drugs acting earlier than their suppressed counterparts. We explored mechanisms by which parthanatotic agents dominate apoptotic agents, finding that dominance in this scenario is caused by mutually exclusive and conflicting use of Poly(ADP-ribose) polymerase 1 (PARP1). Taken together, our study reveals death kinetics as a predictive feature of antagonism, due to inhibitory crosstalk between cell death pathways.

Retro-2 protects cells from ricin toxicity by inhibiting ASNA1-mediated ER targeting and insertion of tail-anchored proteins

The small molecule Retro-2 prevents ricin toxicity through a poorly-defined mechanism of action (MOA), which involves halting retrograde vesicle transport to the endoplasmic reticulum (ER). CRISPRi genetic interaction analysis revealed Retro-2 activity resembles disruption of the transmembrane domain recognition complex (TRC) pathway, which mediates post-translational ER-targeting and insertion of tail-anchored (TA) proteins, including SNAREs required for retrograde transport. Cell-based and in vitro assays show that Retro-2 blocks delivery of newly-synthesized TA-proteins to the ER-targeting factor ASNA1 (TRC40). An ASNA1 point mutant identified using CRISPR-mediated mutagenesis abolishes both the cytoprotective effect of Retro-2 against ricin and its inhibitory effect on ASNA1-mediated ER-targeting. Together, our work explains how Retro-2 prevents retrograde trafficking of toxins by inhibiting TA-protein targeting, describes a general CRISPR strategy for predicting the MOA of small molecules, and paves the way for drugging the TRC pathway to treat broad classes of viruses known to be inhibited by Retro-2.

Integrating yeast chemical genomics and mammalian cell pathway analysis

Chemical genomics has been applied extensively to evaluate small molecules that modulate biological processes in Saccharomyces cerevisiae. Here, we use yeast as a surrogate system for studying compounds that are active against metazoan targets. Large-scale chemical-genetic profiling of thousands of synthetic and natural compounds from the Chinese National Compound Library identified those with high-confidence bioprocess target predictions. To discover compounds that have the potential to function like therapeutic agents with known targets, we also analyzed a reference library of approved drugs. Previously uncharacterized compounds with chemical-genetic profiles resembling existing drugs that modulate autophagy and Wnt/β-catenin signal transduction were further examined in mammalian cells, and new modulators with specific modes of action were validated. This analysis exploits yeast as a general platform for predicting compound bioactivity in mammalian cells.

Using BEAN-counter to quantify genetic interactions from multiplexed barcode sequencing experiments

The construction of genome-wide mutant collections has enabled high-throughput, high-dimensional quantitative characterization of gene and chemical function, particularly via genetic and chemical-genetic interaction experiments. As the throughput of such experiments increases with improvements in sequencing technology and sample multiplexing, appropriate tools must be developed to handle the large volume of data produced. Here, we describe how to apply our approach to high-throughput, fitness-based profiling of pooled mutant yeast collections using the BEAN-counter software pipeline (Barcoded Experiment Analysis for Next-generation sequencing) for analysis. The software has also successfully processed data from Schizosaccharomyces pombe, Escherichia coli, and Zymomonas mobilis mutant collections. We provide general recommendations for the design of large-scale, multiplexed barcode sequencing experiments. The procedure outlined here was used to score interactions for ~4 million chemical-by-mutant combinations in our recently published chemical-genetic interaction screen of nearly 14,000 chemical compounds across seven diverse compound collections. Here we selected a representative subset of these data on which to demonstrate our analysis pipeline. BEAN-counter is open source, written in Python, and freely available for academic use. Users should be proficient at the command line; advanced users who wish to analyze larger datasets with hundreds or more conditions should also be familiar with concepts in analysis of high-throughput biological data. BEAN-counter encapsulates the knowledge we have accumulated from, and successfully applied to, our multiplexed, pooled barcode sequencing experiments. This protocol will be useful to those interested in generating their own high-dimensional, quantitative characterizations of gene or chemical function in a high-throughput manner.

From Yeast to Humans: Leveraging New Approaches in Yeast to Accelerate Discovery of Therapeutic Targets for Synucleinopathies

Neurodegenerative diseases (ND) represent a growing, global health crisis, one that lacks any disease-modifying therapeutic strategy. This critical need for new therapies must be met with an exhaustive approach to exploit all tools available. A yeast (Saccharomyces cerevisiae) model of α-synuclein toxicity-the protein causally linked to Parkinson's disease and other synucleinopathies-offers a powerful approach that takes advantage of the unique offerings of this system: tractable genetics, robust high-throughput screening strategies, unparalleled data repositories, powerful computational tools, and extensive evolutionary conservation of fundamental biological pathways. These attributes have enabled genetic and small molecule screens that have revealed toxic phenotypes and drug targets that translate directly to patient-derived iPSC neurons. Extending these insights, recent advances in genetic network analyses have generated the first "humanized" α-synuclein network, which has identified druggable proteins and led to validation of the toxic phenotypes in patient-derived cells. Unbiased phenotypic small molecule screens can identify compounds targeting critical proteins within α-synuclein networks. While identification of direct drug targets for phenotypic screen hits represents a bottleneck, high-throughput chemical genetic methods provide a means to uncover cellular targets and pathways for large numbers of compounds in parallel. Taken together, the yeast α-synuclein model and associated tools can reveal insights into underlying cellular pathologies, lead molecules and their cognate targets, and strategies to translate mechanisms of toxicity and cytoprotection into complex neuronal systems.