A Perspective on the Role of Computational Models in Immunology.

This is an exciting time for immunology because the future promises to be replete with exciting new discoveries that can be translated to improve health and treat disease in novel ways. Immunologists are attempting to answer increasingly complex questions concerning phenomena that range from the genetic, molecular, and cellular scales to that of organs, whole animals or humans, and populations of humans and pathogens. An important goal is to understand how the many different components involved interact with each other within and across these scales for immune responses to emerge, and how aberrant regulation of these processes causes disease. To aid this quest, large amounts of data can be collected using high-throughput instrumentation. The nonlinear, cooperative, and stochastic character of the interactions between components of the immune system as well as the overwhelming amounts of data can make it difficult to intuit patterns in the data or a mechanistic understanding of the phenomena being studied. Computational models are increasingly important in confronting and overcoming these challenges. I first describe an iterative paradigm of research that integrates laboratory experiments, clinical data, computational inference, and mechanistic computational models. I then illustrate this paradigm with a few examples from the recent literature that make vivid the power of bringing together diverse types of computational models with experimental and clinical studies to fruitfully interrogate the immune system.

Structural Insights Accelerate the Discovery of Opioid Alternatives.

Opioids such as morphine and oxycodone are analgesics frequently prescribed for the treatment of moderate or severe pain. Unfortunately, these medications are associated with exceptionally high abuse potentials and often cause fatal side effects, mainly through the µ-opioid receptor (MOR). Efforts to discover novel, safer, and more efficacious analgesics targeting MOR have encountered challenges. In this review, we summarize alternative strategies and targets that could be used to develop safer nonopioid analgesics. A molecular understanding of G protein-coupled receptor activation and signaling has illuminated not only the complexities of receptor pharmacology but also the potential for pathway-selective agonists and allosteric modulators as safer medications. The availability of structures of pain-related receptors, in combination with high-throughput computational tools, has accelerated the discovery of multitarget ligands with promising pharmacological profiles. Emerging clinical evidence also supports the notion that drugs targeting peripheral opioid receptors have potential as improved analgesic agents.

Quantifying Target Occupancy of Small Molecules Within Living Cells.

The binding affinity and kinetics of target engagement are fundamental to establishing structure-activity relationships (SARs) for prospective therapeutic agents. Enhancing these binding parameters for operative targets, while minimizing binding to off-target sites, can translate to improved drug efficacy and a widened therapeutic window. Compound activity is typically assessed through modulation of an observed phenotype in cultured cells. Quantifying the corresponding binding properties under common cellular conditions can provide more meaningful interpretation of the cellular SAR analysis. Consequently, methods for assessing drug binding in living cells have advanced and are now integral to medicinal chemistry workflows. In this review, we survey key technological advancements that support quantitative assessments of target occupancy in cultured cells, emphasizing generalizable methodologies able to deliver analytical precision that heretofore required reductionist biochemical approaches.

Comprehensive In Vivo Interrogation Reveals Phenotypic Impact of Human Enhancer Variants.

Establishing causal links between non-coding variants and human phenotypes is an increasing challenge. Here, we introduce a high-throughput mouse reporter assay for assessing the pathogenic potential of human enhancer variants in vivo and examine nearly a thousand variants in an enhancer repeatedly linked to polydactyly. We show that 71% of all rare non-coding variants previously proposed as causal lead to reporter gene expression in a pattern consistent with their pathogenic role. Variants observed to alter enhancer activity were further confirmed to cause polydactyly in knockin mice. We also used combinatorial and single-nucleotide mutagenesis to evaluate the in vivo impact of mutations affecting all positions of the enhancer and identified additional functional substitutions, including potentially pathogenic variants hitherto not observed in humans. Our results uncover the functional consequences of hundreds of mutations in a phenotype-associated enhancer and establish a widely applicable strategy for systematic in vivo evaluation of human enhancer variants.

High-Throughput Discovery and Characterization of Human Transcriptional Effectors.

Thousands of proteins localize to the nucleus; however, it remains unclear which contain transcriptional effectors. Here, we develop HT-recruit, a pooled assay where protein libraries are recruited to a reporter, and their transcriptional effects are measured by sequencing. Using this approach, we measure gene silencing and activation for thousands of domains. We find a relationship between repressor function and evolutionary age for the KRAB domains, discover that Homeodomain repressor strength is collinear with Hox genetic organization, and identify activities for several domains of unknown function. Deep mutational scanning of the CRISPRi KRAB maps the co-repressor binding surface and identifies substitutions that improve stability/silencing. By tiling 238 proteins, we find repressors as short as ten amino acids. Finally, we report new activator domains, including a divergent KRAB. These results provide a resource of 600 human proteins containing effectors and demonstrate a scalable strategy for assigning functions to protein domains.

Genome-Scale Imaging of the 3D Organization and Transcriptional Activity of Chromatin.

The 3D organization of chromatin regulates many genome functions. Our understanding of 3D genome organization requires tools to directly visualize chromatin conformation in its native context. Here we report an imaging technology for visualizing chromatin organization across multiple scales in single cells with high genomic throughput. First we demonstrate multiplexed imaging of hundreds of genomic loci by sequential hybridization, which allows high-resolution conformation tracing of whole chromosomes. Next we report a multiplexed error-robust fluorescence in situ hybridization (MERFISH)-based method for genome-scale chromatin tracing and demonstrate simultaneous imaging of more than 1,000 genomic loci and nascent transcripts of more than 1,000 genes together with landmark nuclear structures. Using this technology, we characterize chromatin domains, compartments, and trans-chromosomal interactions and their relationship to transcription in single cells. We envision broad application of this high-throughput, multi-scale, and multi-modal imaging technology, which provides an integrated view of chromatin organization in its native structural and functional context.

A High-Throughput Platform to Identify Small-Molecule Inhibitors of CRISPR-Cas9.

The precise control of CRISPR-Cas9 activity is required for a number of genome engineering technologies. Here, we report a generalizable platform that provided the first synthetic small-molecule inhibitors of Streptococcus pyogenes Cas9 (SpCas9) that weigh <500 Da and are cell permeable, reversible, and stable under physiological conditions. We developed a suite of high-throughput assays for SpCas9 functions, including a primary screening assay for SpCas9 binding to the protospacer adjacent motif, and used these assays to screen a structurally diverse collection of natural-product-like small molecules to ultimately identify compounds that disrupt the SpCas9-DNA interaction. Using these synthetic anti-CRISPR small molecules, we demonstrated dose and temporal control of SpCas9 and catalytically impaired SpCas9 technologies, including transcription activation, and identified a pharmacophore for SpCas9 inhibition using structure-activity relationships. These studies establish a platform for rapidly identifying synthetic, miniature, cell-permeable, and reversible inhibitors against both SpCas9 and next-generation CRISPR-associated nucleases.

High-Throughput Mapping of B Cell Receptor Sequences to Antigen Specificity.

B cell receptor (BCR) sequencing is a powerful tool for interrogating immune responses to infection and vaccination, but it provides limited information about the antigen specificity of the sequenced BCRs. Here, we present LIBRA-seq (linking B cell receptor to antigen specificity through sequencing), a technology for high-throughput mapping of paired heavy- and light-chain BCR sequences to their cognate antigen specificities. B cells are mixed with a panel of DNA-barcoded antigens so that both the antigen barcode(s) and BCR sequence are recovered via single-cell next-generation sequencing. Using LIBRA-seq, we mapped the antigen specificity of thousands of B cells from two HIV-infected subjects. The predicted specificities were confirmed for a number of HIV- and influenza-specific antibodies, including known and novel broadly neutralizing antibodies. LIBRA-seq will be an integral tool for antibody discovery and vaccine development efforts against a wide range of antigen targets.

Directed Evolution of Protein Catalysts.

Directed evolution is a powerful technique for generating tailor-made enzymes for a wide range of biocatalytic applications. Following the principles of natural evolution, iterative cycles of mutagenesis and screening or selection are applied to modify protein properties, enhance catalytic activities, or develop completely new protein catalysts for non-natural chemical transformations. This review briefly surveys the experimental methods used to generate genetic diversity and screen or select for improved enzyme variants. Emphasis is placed on a key challenge, namely how to generate novel catalytic activities that expand the scope of natural reactions. Two particularly effective strategies, exploiting catalytic promiscuity and rational design, are illustrated by representative examples of successfully evolved enzymes. Opportunities for extending these approaches to more complex biocatalytic systems are also considered.

Intelligent Image-Activated Cell Sorting.

A fundamental challenge of biology is to understand the vast heterogeneity of cells, particularly how cellular composition, structure, and morphology are linked to cellular physiology. Unfortunately, conventional technologies are limited in uncovering these relations. We present a machine-intelligence technology based on a radically different architecture that realizes real-time image-based intelligent cell sorting at an unprecedented rate. This technology, which we refer to as intelligent image-activated cell sorting, integrates high-throughput cell microscopy, focusing, and sorting on a hybrid software-hardware data-management infrastructure, enabling real-time automated operation for data acquisition, data processing, decision-making, and actuation. We use it to demonstrate real-time sorting of microalgal and blood cells based on intracellular protein localization and cell-cell interaction from large heterogeneous populations for studying photosynthesis and atherothrombosis, respectively. The technology is highly versatile and expected to enable machine-based scientific discovery in biological, pharmaceutical, and medical sciences.

Discovery of Next-Generation Antimicrobials through Bacterial Self-Screening of Surface-Displayed Peptide Libraries.

Peptides have great potential to combat antibiotic resistance. While many platforms can screen peptides for their ability to bind to target cells, there are virtually no platforms that directly assess the functionality of peptides. This limitation is exacerbated when identifying antimicrobial peptides because the phenotype, death, selects against itself and has caused a scientific bottleneck that confines research to a few naturally occurring classes of antimicrobial peptides. We have used this seeming dissonance to develop Surface Localized Antimicrobial Display (SLAY), a platform that allows screening of unlimited numbers of peptides of any length, composition, and structure in a single tube for antimicrobial activity. Using SLAY, we screened ∼800,000 random peptide sequences for antimicrobial function and identified thousands of active sequences, dramatically increasing the number of known antimicrobial sequences. SLAY hits present with different potential mechanisms of peptide action and access to areas of antimicrobial physicochemical space beyond what nature has evolved. VIDEO ABSTRACT.

High-throughput phenotyping reveals expansive genetic and structural underpinnings of immune variation.

By developing a high-density murine immunophenotyping platform compatible with high-throughput genetic screening, we have established profound contributions of genetics and structure to immune variation (http://www.immunophenotype.org). Specifically, high-throughput phenotyping of 530 unique mouse gene knockouts identified 140 monogenic 'hits', of which most had no previous immunologic association. Furthermore, hits were collectively enriched in genes for which humans show poor tolerance to loss of function. The immunophenotyping platform also exposed dense correlation networks linking immune parameters with each other and with specific physiologic traits. Such linkages limit freedom of movement for individual immune parameters, thereby imposing genetically regulated 'immunologic structures', the integrity of which was associated with immunocompetence. Hence, we provide an expanded genetic resource and structural perspective for understanding and monitoring immune variation in health and disease.

Transcriptional profiling and therapeutic targeting of oxidative stress in neuroinflammation.

Oxidative stress is a central part of innate immune-induced neurodegeneration. However, the transcriptomic landscape of central nervous system (CNS) innate immune cells contributing to oxidative stress is unknown, and therapies to target their neurotoxic functions are not widely available. Here, we provide the oxidative stress innate immune cell atlas in neuroinflammatory disease and report the discovery of new druggable pathways. Transcriptional profiling of oxidative stress-producing CNS innate immune cells identified a core oxidative stress gene signature coupled to coagulation and glutathione-pathway genes shared between a microglia cluster and infiltrating macrophages. Tox-seq followed by a microglia high-throughput screen and oxidative stress gene network analysis identified the glutathione-regulating compound acivicin, with potent therapeutic effects that decrease oxidative stress and axonal damage in chronic and relapsing multiple sclerosis models. Thus, oxidative stress transcriptomics identified neurotoxic CNS innate immune populations and may enable discovery of selective neuroprotective strategies.

FDA-approved disulfiram inhibits pyroptosis by blocking gasdermin D pore formation.

Cytosolic sensing of pathogens and damage by myeloid and barrier epithelial cells assembles large complexes called inflammasomes, which activate inflammatory caspases to process cytokines (IL-1ß) and gasdermin D (GSDMD). Cleaved GSDMD forms membrane pores, leading to cytokine release and inflammatory cell death (pyroptosis). Inhibiting GSDMD is an attractive strategy to curb inflammation. Here we identify disulfiram, a drug for treating alcohol addiction, as an inhibitor of pore formation by GSDMD but not other members of the GSDM family. Disulfiram blocks pyroptosis and cytokine release in cells and lipopolysaccharide-induced septic death in mice. At nanomolar concentration, disulfiram covalently modifies human/mouse Cys191/Cys192 in GSDMD to block pore formation. Disulfiram still allows IL-1ß and GSDMD processing, but abrogates pore formation, thereby preventing IL-1ß release and pyroptosis. The role of disulfiram in inhibiting GSDMD provides new therapeutic indications for repurposing this safe drug to counteract inflammation, which contributes to many human diseases.

A genomic approach to human autoimmune diseases.

The past decade has seen an explosion in the use of DNA-based microarrays. These techniques permit assessment of RNA abundance on a genome-wide scale. Medical applications emerged in the field of cancer, with studies of both solid tumors and hematological malignancies leading to the development of tests that are now used to personalize therapeutic options. Microarrays have also been used to analyze the blood transcriptome in a wide range of diseases. In human autoimmune diseases, these studies are showing potential for identifying therapeutic targets as well as biomarkers for diagnosis, assessment of disease activity, and response to treatment. More quantitative and sensitive high-throughput RNA profiling methods are starting to be available and will be necessary for transcriptome analyses to become routine tests in the clinical setting. We expect this to crystallize within the coming decade, as these methods become part of the personalized medicine armamentarium.

Ligand and Target Discovery by Fragment-Based Screening in Human Cells.

Advances in the synthesis and screening of small-molecule libraries have accelerated the discovery of chemical probes for studying biological processes. Still, only a small fraction of the human proteome has chemical ligands. Here, we describe a platform that marries fragment-based ligand discovery with quantitative chemical proteomics to map thousands of reversible small molecule-protein interactions directly in human cells, many of which can be site-specifically determined. We show that fragment hits can be advanced to furnish selective ligands that affect the activity of proteins heretofore lacking chemical probes. We further combine fragment-based chemical proteomics with phenotypic screening to identify small molecules that promote adipocyte differentiation by engaging the poorly characterized membrane protein PGRMC2. Fragment-based screening in human cells thus provides an extensive proteome-wide map of protein ligandability and facilitates the coordinated discovery of bioactive small molecules and their molecular targets.

Selective Chemical Inhibition of PGC-1α Gluconeogenic Activity Ameliorates Type 2 Diabetes.

Type 2 diabetes (T2D) is a worldwide epidemic with a medical need for additional targeted therapies. Suppression of hepatic glucose production (HGP) effectively ameliorates diabetes and can be exploited for its treatment. We hypothesized that targeting PGC-1α acetylation in the liver, a chemical modification known to inhibit hepatic gluconeogenesis, could be potentially used for treatment of T2D. Thus, we designed a high-throughput chemical screen platform to quantify PGC-1α acetylation in cells and identified small molecules that increase PGC-1α acetylation, suppress gluconeogenic gene expression, and reduce glucose production in hepatocytes. On the basis of potency and bioavailability, we selected a small molecule, SR-18292, that reduces blood glucose, strongly increases hepatic insulin sensitivity, and improves glucose homeostasis in dietary and genetic mouse models of T2D. These studies have important implications for understanding the regulatory mechanisms of glucose metabolism and treatment of T2D.

A high-throughput synthetic biology approach for studying combinatorial chromatin-based transcriptional regulation.

The construction of synthetic gene circuits requires the rational combination of multiple regulatory components, but predicting their behavior can be challenging due to poorly understood component interactions and unexpected emergent behaviors. In eukaryotes, chromatin regulators (CRs) are essential regulatory components that orchestrate gene expression. Here, we develop a screening platform to investigate the impact of CR pairs on transcriptional activity in yeast. We construct a combinatorial library consisting of over 1,900 CR pairs and use a high-throughput workflow to characterize the impact of CR co-recruitment on gene expression. We recapitulate known interactions and discover several instances of CR pairs with emergent behaviors. We also demonstrate that supervised machine learning models trained with low-dimensional amino acid embeddings accurately predict the impact of CR co-recruitment on transcriptional activity. This work introduces a scalable platform and machine learning approach that can be used to study how networks of regulatory components impact gene expression.

A Biobank of Breast Cancer Explants with Preserved Intra-tumor Heterogeneity to Screen Anticancer Compounds.

The inter- and intra-tumor heterogeneity of breast cancer needs to be adequately captured in pre-clinical models. We have created a large collection of breast cancer patient-derived tumor xenografts (PDTXs), in which the morphological and molecular characteristics of the originating tumor are preserved through passaging in the mouse. An integrated platform combining in vivo maintenance of these PDTXs along with short-term cultures of PDTX-derived tumor cells (PDTCs) was optimized. Remarkably, the intra-tumor genomic clonal architecture present in the originating breast cancers was mostly preserved upon serial passaging in xenografts and in short-term cultured PDTCs. We assessed drug responses in PDTCs on a high-throughput platform and validated several ex vivo responses in vivo. The biobank represents a powerful resource for pre-clinical breast cancer pharmacogenomic studies (http://caldaslab.cruk.cam.ac.uk/bcape), including identification of biomarkers of response or resistance.

Inhibition of Dihydroorotate Dehydrogenase Overcomes Differentiation Blockade in Acute Myeloid Leukemia.

While acute myeloid leukemia (AML) comprises many disparate genetic subtypes, one shared hallmark is the arrest of leukemic myeloblasts at an immature and self-renewing stage of development. Therapies that overcome differentiation arrest represent a powerful treatment strategy. We leveraged the observation that the majority of AML, despite their genetically heterogeneity, share in the expression of HoxA9, a gene normally downregulated during myeloid differentiation. Using a conditional HoxA9 model system, we performed a high-throughput phenotypic screen and defined compounds that overcame differentiation blockade. Target identification led to the unanticipated discovery that inhibition of the enzyme dihydroorotate dehydrogenase (DHODH) enables myeloid differentiation in human and mouse AML models. In vivo, DHODH inhibitors reduced leukemic cell burden, decreased levels of leukemia-initiating cells, and improved survival. These data demonstrate the role of DHODH as a metabolic regulator of differentiation and point to its inhibition as a strategy for overcoming differentiation blockade in AML.