STING Polymer Structure Reveals Mechanisms for Activation, Hyperactivation, and Inhibition.

How the central innate immune protein, STING, is activated by its ligands remains unknown. Here, using structural biology and biochemistry, we report that the metazoan second messenger 2'3'-cGAMP induces closing of the human STING homodimer and release of the STING C-terminal tail, which exposes a polymerization interface on the STING dimer and leads to the formation of disulfide-linked polymers via cysteine residue 148. Disease-causing hyperactive STING mutations either flank C148 and depend on disulfide formation or reside in the C-terminal tail binding site and cause constitutive C-terminal tail release and polymerization. Finally, bacterial cyclic-di-GMP induces an alternative active STING conformation, activates STING in a cooperative manner, and acts as a partial antagonist of 2'3'-cGAMP signaling. Our insights explain the tight control of STING signaling given varying background activation signals and provide a therapeutic hypothesis for autoimmune syndrome treatment.

Clonally expanded B cells in multiple sclerosis bind EBV EBNA1 and GlialCAM.

Multiple sclerosis (MS) is a heterogenous autoimmune disease in which autoreactive lymphocytes attack the myelin sheath of the central nervous system. B lymphocytes in the cerebrospinal fluid (CSF) of patients with MS contribute to inflammation and secrete oligoclonal immunoglobulins1,2. Epstein-Barr virus (EBV) infection has been epidemiologically linked to MS, but its pathological role remains unclear3. Here we demonstrate high-affinity molecular mimicry between the EBV transcription factor EBV nuclear antigen 1 (EBNA1) and the central nervous system protein glial cell adhesion molecule (GlialCAM) and provide structural and in vivo functional evidence for its relevance. A cross-reactive CSF-derived antibody was initially identified by single-cell sequencing of the paired-chain B cell repertoire of MS blood and CSF, followed by protein microarray-based testing of recombinantly expressed CSF-derived antibodies against MS-associated viruses. Sequence analysis, affinity measurements and the crystal structure of the EBNA1-peptide epitope in complex with the autoreactive Fab fragment enabled tracking of the development of the naive EBNA1-restricted antibody to a mature EBNA1-GlialCAM cross-reactive antibody. Molecular mimicry is facilitated by a post-translational modification of GlialCAM. EBNA1 immunization exacerbates disease in a mouse model of MS, and anti-EBNA1 and anti-GlialCAM antibodies are prevalent in patients with MS. Our results provide a mechanistic link for the association between MS and EBV and could guide the development of new MS therapies.

Bump-and-Hole Engineering Identifies Specific Substrates of Glycosyltransferases in Living Cells.

Studying posttranslational modifications classically relies on experimental strategies that oversimplify the complex biosynthetic machineries of living cells. Protein glycosylation contributes to essential biological processes, but correlating glycan structure, underlying protein, and disease-relevant biosynthetic regulation is currently elusive. Here, we engineer living cells to tag glycans with editable chemical functionalities while providing information on biosynthesis, physiological context, and glycan fine structure. We introduce a non-natural substrate biosynthetic pathway and use engineered glycosyltransferases to incorporate chemically tagged sugars into the cell surface glycome of the living cell. We apply the strategy to a particularly redundant yet disease-relevant human glycosyltransferase family, the polypeptide N-acetylgalactosaminyl transferases. This approach bestows a gain-of-chemical-functionality modification on cells, where the products of individual glycosyltransferases can be selectively characterized or manipulated to understand glycan contribution to major physiological processes.

Inter-cellular CRISPR screens reveal regulators of cancer cell phagocytosis.

Monoclonal antibody therapies targeting tumour antigens drive cancer cell elimination in large part by triggering macrophage phagocytosis of cancer cells1-7. However, cancer cells evade phagocytosis using mechanisms that are incompletely understood. Here we develop a platform for unbiased identification of factors that impede antibody-dependent cellular phagocytosis (ADCP) using complementary genome-wide CRISPR knockout and overexpression screens in both cancer cells and macrophages. In cancer cells, beyond known factors such as CD47, we identify many regulators of susceptibility to ADCP, including the poorly characterized enzyme adipocyte plasma membrane-associated protein (APMAP). We find that loss of APMAP synergizes with tumour antigen-targeting monoclonal antibodies and/or CD47-blocking monoclonal antibodies to drive markedly increased phagocytosis across a wide range of cancer cell types, including those that are otherwise resistant to ADCP. Additionally, we show that APMAP loss synergizes with several different tumour-targeting monoclonal antibodies to inhibit tumour growth in mice. Using genome-wide counterscreens in macrophages, we find that the G-protein-coupled receptor GPR84 mediates enhanced phagocytosis of APMAP-deficient cancer cells. This work reveals a cancer-intrinsic regulator of susceptibility to antibody-driven phagocytosis and, more broadly, expands our knowledge of the mechanisms governing cancer resistance to macrophage phagocytosis.

Structural insights reveal interplay between LAG-3 homodimerization, ligand binding, and function.

Lymphocyte activation gene-3 (LAG-3) is an inhibitory receptor expressed on activated T cells and an emerging immunotherapy target. Domain 1 (D1) of LAG-3, which has been purported to directly interact with major histocompatibility complex class II (MHCII) and fibrinogen-like protein 1 (FGL1), has been the major focus for the development of therapeutic antibodies that inhibit LAG-3 receptor-ligand interactions and restore T cell function. Here, we present a high-resolution structure of glycosylated mouse LAG-3 ectodomain, identifying that cis-homodimerization, mediated through a network of hydrophobic residues within domain 2 (D2), is critically required for LAG-3 function. Additionally, we found a previously unidentified key protein-glycan interaction in the dimer interface that affects the spatial orientation of the neighboring D1 domain. Mutation of LAG-3 D2 residues reduced dimer formation, dramatically abolished LAG-3 binding to both MHCII and FGL1 ligands, and consequentially inhibited the role of LAG-3 in suppressing T cell responses. Intriguingly, we showed that antibodies directed against D1, D2, and D3 domains are all capable of blocking LAG-3 dimer formation and MHCII and FGL-1 ligand binding, suggesting a potential allosteric model of LAG-3 function tightly regulated by dimerization. Furthermore, our work reveals unique epitopes, in addition to D1, that can be targeted for immunotherapy of cancer and other human diseases.

Expression, purification, and characterization of diacylated Lipo-YcjN from Escherichia coli.

YcjN is a putative substrate binding protein expressed from a cluster of genes involved in carbohydrate import and metabolism in Escherichia coli. Here, we determine the crystal structure of YcjN to a resolution of 1.95 Å, revealing that its three-dimensional structure is similar to substrate binding proteins in subcluster D-I, which includes the well-characterized maltose binding protein (MBP). Furthermore, we found that recombinant overexpression of YcjN results in the formation of a lipidated form of YcjN that is posttranslationally diacylated at cysteine 21. Comparisons of size-exclusion chromatography profiles and dynamic light scattering measurements of lipidated and non-lipidated YcjN proteins suggest that lipidated YcjN aggregates in solution via its lipid moiety. Additionally, bioinformatic analysis indicates that YcjN-like proteins may exist in both Bacteria and Archaea, potentially in both lipidated and non-lipidated forms. Together, our results provide a better understanding of the aggregation properties of recombinantly expressed bacterial lipoproteins in solution and establish a foundation for future studies that aim to elucidate the role of these proteins in bacterial physiology.

ENPP1's regulation of extracellular cGAMP is a ubiquitous mechanism of attenuating STING signaling.

The metazoan innate immune second messenger 2'3'-cGAMP is present both inside and outside cells. However, only extracellular cGAMP can be negatively regulated by the extracellular hydrolase ENPP1. Here, we determine whether ENPP1's regulation of extracellular cGAMP is a ubiquitous mechanism of attenuating stimulator of interferon genes (STING) signaling. We identified ENPP1H362A, a point mutation that cannot degrade the 2'-5' linkage in cGAMP while maintaining otherwise normal function. The selectivity of this histidine is conserved down to bacterial nucleotide pyrophosphatase/phosphodiesterase (NPP), allowing structural analysis and suggesting an unexplored ancient history of 2'-5' cyclic dinucleotides. Enpp1H362A mice demonstrated that extracellular cGAMP is not responsible for the devastating phenotype in ENPP1-null humans and mice but is responsible for antiviral immunity and systemic inflammation. Our data define extracellular cGAMP as a pivotal STING activator, identify an evolutionarily critical role for ENPP1 in regulating inflammation, and suggest a therapeutic strategy for viral and inflammatory conditions by manipulating ENPP1 activity.

Structure-guided stabilization improves the ability of the HIV-1 gp41 hydrophobic pocket to elicit neutralizing antibodies.

The hydrophobic pocket found in the N-heptad repeat (NHR) region of HIV-1 gp41 is a highly conserved epitope that is the target of various HIV-1-neutralizing monoclonal antibodies. Although the high conservation of the pocket makes it an attractive vaccine candidate, it has been challenging to elicit potent anti-NHR antibodies via immunization. Here, we solved a high-resolution structure of the NHR mimetic IQN17, and, consistent with previous ligand-bound gp41 pocket structures, we observed remarkable conformational plasticity of the pocket. The high malleability of this pocket led us to test whether we could improve the immunogenicity of the gp41 pocket by stabilizing its conformation. We show that the addition of five amino acids at the C terminus of IQN17, to generate IQN22, introduces a stabilizing salt bridge at the base of the peptide that rigidifies the pocket. Mice immunized with IQN22 elicited higher avidity antibodies against the gp41 pocket and a more potent, albeit still weak, neutralizing response against HIV-1 compared with IQN17. Stabilized epitope-focused immunogens could serve as the basis for future HIV-1 fusion-inhibiting vaccines.

On the influence of several factors on pathway enrichment analysis.

Pathway enrichment analysis has become a widely used knowledge-based approach for the interpretation of biomedical data. Its popularity has led to an explosion of both enrichment methods and pathway databases. While the elegance of pathway enrichment lies in its simplicity, multiple factors can impact the results of such an analysis, which may not be accounted for. Researchers may fail to give influential aspects their due, resorting instead to popular methods and gene set collections, or default settings. Despite ongoing efforts to establish set guidelines, meaningful results are still hampered by a lack of consensus or gold standards around how enrichment analysis should be conducted. Nonetheless, such concerns have prompted a series of benchmark studies specifically focused on evaluating the influence of various factors on pathway enrichment results. In this review, we organize and summarize the findings of these benchmarks to provide a comprehensive overview on the influence of these factors. Our work covers a broad spectrum of factors, spanning from methodological assumptions to those related to prior biological knowledge, such as pathway definitions and database choice. In doing so, we aim to shed light on how these aspects can lead to insignificant, uninteresting or even contradictory results. Finally, we conclude the review by proposing future benchmarks as well as solutions to overcome some of the challenges, which originate from the outlined factors.

Ensembles of knowledge graph embedding models improve predictions for drug discovery.

Recent advances in Knowledge Graphs (KGs) and Knowledge Graph Embedding Models (KGEMs) have led to their adoption in a broad range of fields and applications. The current publishing system in machine learning requires newly introduced KGEMs to achieve state-of-the-art performance, surpassing at least one benchmark in order to be published. Despite this, dozens of novel architectures are published every year, making it challenging for users, even within the field, to deduce the most suitable configuration for a given application. A typical biomedical application of KGEMs is drug-disease prediction in the context of drug discovery, in which a KGEM is trained to predict triples linking drugs and diseases. These predictions can be later tested in clinical trials following extensive experimental validation. However, given the infeasibility of evaluating each of these predictions and that only a minimal number of candidates can be experimentally tested, models that yield higher precision on the top prioritized triples are preferred. In this paper, we apply the concept of ensemble learning on KGEMs for drug discovery to assess whether combining the predictions of several models can lead to an overall improvement in predictive performance. First, we trained and benchmarked 10 KGEMs to predict drug-disease triples on two independent biomedical KGs designed for drug discovery. Following, we applied different ensemble methods that aggregate the predictions of these models by leveraging the distribution or the position of the predicted triple scores. We then demonstrate how the ensemble models can achieve better results than the original KGEMs by benchmarking the precision (i.e., number of true positives prioritized) of their top predictions. Lastly, we released the source code presented in this work at https://github.com/enveda/kgem-ensembles-in-drug-discovery.