Biology subjects

Spreafico, R.

Publications and source records attributed to Spreafico, R..

At least 19 recordsLinked to original sources

bioRxiv2026

RNA evidence supports clinical stage antibody drug targets in oncology

Background: The selection of efficacious and safe antibody-based oncology targets depends on tumor specificity and normal-tissue expression, and remains challenging. Bulk RNA-seq can identify tumor-enriched surface genes, but may obscure tumor specificity and normal-cell expression patterns that are relevant to the therapeutic window. We evaluated whether single-cell RNA-seq-derived expression features improve retrospective prioritization of clinically pursued antibody targets. Methods: We developed a bulk and single-cell RNA expression-based ranking framework across six solid tumor indications. 4,532 candidate surfaceome targets were scored using tumor Specificity, normal Safety, and blood Sink features derived from bulk RNA-seq, single-cell RNA-seq, or a combined modality. Ranking performance was evaluated against a curated validation set of tumor-associated antibody-based oncology targets using logistic regression, with odds ratios (ORs) reported per one-standard-deviation increase in score. Results: Higher Specificity scores were associated with clinical-stage target selection across all modalities. In cross-indication meta-analysis, single-cell Specificity showed higher ORs than bulk Specificity across clinical phase thresholds, including Phase [≥] 1 (OR 2.56 versus 1.81), Phase [≥] 2 (OR 2.77 versus 1.88), Phase [≥] 3 (OR 3.53 versus 2.04) and Approved targets (OR 3.51 versus 1.74). Combined bulk-and-single-cell Specificity showed intermediate enrichment. Specificity score alone did not consistently show a significant single-cell advantage over bulk in paired indication-level comparisons, except for Phase [≥] 3 targets. After adding normal-cell Safety and blood Sink features, the single-cell general score showed the strongest enrichment across thresholds (Phase [≥] 1 OR 3.61, Phase [≥] 2 OR 4.16, Phase [≥] 3 OR 5.57, and Approved targets OR 4.25). This general score also showed consistently significant single-cell advantage over bulk in paired indication-level comparisons. Safety contributed most strongly in the single-cell modality, increasing meta-analytic ORs by an average of ~36% across thresholds compared to Specificity alone. Performance varied by indication, with breast cancer showing weaker Specificity-based enrichment. Conclusion: Expression-derived ranking features are retrospectively enriched among clinically pursued tumor-associated antibody targets. Single-cell evidence is most informative when tumor Specificity is combined with normal-cell Safety information, supporting its use as an interpretable component of early antibody target prioritization. These findings are in alignment with recent evidence that single-cell RNA-based cell-type specificity supports clinical-stage targets and demonstrate that expression-driven ranking can systematically recover clinically relevant oncology targets.

bioinformatics↗

bioRxiv2026

EpiRanha: Hunting for Epitope Similarity with a Structure- and Residue-Aware Graph Neural Network

AO_SCPLOWBSTRACTC_SCPLOWPrecise epitope recognition underpins the efficacy and safety of therapeutic antibodies, yet existing approaches to epitope similarity scoring rely largely on sequence identity or rigid structural superposition, limiting their ability to robustly assess cross-reactivity and potential off-target interactions. We introduce EpiRanha, a multimodal framework that integrates residue-level ESM-2 sequence embeddings with an E(n)-equivariant graph neural network operating on three-dimensional protein structure. EpiRanha produces per-residue "fingerprints" that jointly encode sequence-level context and spatial organization, and then applies a beam-search strategy to identify and rank multiple high-confidence epitope candidates across protein surfaces that are similar to a given query epitope. We evaluate EpiRanha against TM-align on nanobody-antigen complexes from SAbDab-nano and a set of AlphaFold-predicted proteins. EpiRanha consistently recovers the query epitope on its cognate antigen, including highly discontiguous conformational epitopes that rigid alignment methods such as TM-align often fail to capture, achieving lower structural loss and fewer false negatives through flexible residue-level mapping. Beyond these self-matches, EpiRanha also identifies biologically plausible epitope-level similarities on other proteins. Overall, EpiRanha advances epitope characterization beyond sequence or geometry alone, enabling more robust off-target risk assessment, informing training-set construction for predictive models, and more selective antibody design.

bioinformatics↗

bioRxiv2026

Effects of protein interface mutations on protein quality and affinity

Accurately modeling antibody-antigen interactions requires distinguishing intrinsic binding affinity ("protein-interaction") from protein biophysical properties ("protein-quality"), including folding, stability, and expression. However, high-throughput mutational measurements commonly used to train and benchmark computational models often conflate these effects, obscuring the true determinants of molecular recognition. Here, we present an experimental and analytical framework to disentangle protein-interaction effects from protein-quality effects in single-domain antibody (VHH)-antigen binding. Using a large-scale deep mutational scanning (DMS) dataset spanning four VHH-antigen complexes, with single and double mutations in both partners, we introduce control binders to quantify protein-quality changes independently of protein-interaction. This enables decomposition of experimentally measured affinity into protein-interaction and protein-quality components at scale. Leveraging the disentangled dataset, we evaluated state-of-the-art structure- and sequence-based models for protein-quality and protein-interaction prediction and show that their performance largely reflects protein-quality rather than protein-interaction effects. Our results highlight a major confounder in current datasets and suggest that accounting for protein-quality will be essential for training next-generation affinity-prediction models. Nomenclature Antibody related termsO_LIPrimary VHH: The VHH of a VHH-antigen complex for which the paratope and the epitope weremutated. C_LIO_LIControl VHH: A second VHH that binds to the same antigen as the primary VHH but has non-overlapping epitope positions and therefore does not bind to any of the mutated antigen positions. C_LI Affinity-related termsO_LIReal Affinity: "The strength of the interaction between two [...] molecules that bind reversibly (interact)" 1. In the context of antibody-antigen binding, it quantifies interactions between active proteins (which are expressed and correctly folded 2 and are therefore functionally and biologically active (see below). It is commonly quantified by the equilibrium dissociation constant, KD. C_LIO_LIObserved affinity ({degrees}KD): The interaction strength experimentally measured between two molecules. Unlike real affinity, this value is confounded by the biophysical properties of the individual binding partners, specifically their folding, stability, and expression levels. Consequently, the observed affinity often differs from the real/intrinsic affinity if a significant fraction of the protein population is inactive 3. NOTE: Unless otherwise specified, {degrees}KD is reported in - log10 space. For example, a {degrees}KD of -9 corresponds to 10-9M or 1nM. C_LIO_LIChange in observed affinity ({Delta}{degrees}KD): The shift in the observed affinity between two proteins upon mutation, reported as the log10-transformed fold change. A value of 1 reflects a 10-fold difference, a value of 2 a 100-fold difference, etc. This aggregate change resolves into two distinct biophysical components 2, 4: O_LIProtein-interaction change: The change in the intrinsic thermodynamic affinity between the two binding partners, each in its active state (i.e., the specific change in interface Gibbs free energy because both enthalpy and entropy are considered). C_LIO_LIProtein-quality change: The change in the fraction of the mutated protein population that is biologically active - meaning it is expressed, correctly folded, and stable 2, 5. O_LIFolding: The process that guides the polypeptide chain toward its native conformation, which is a prerequisite for forming a functional binding site. C_LIO_LIStability: The thermodynamic capacity to maintain the folded structure over time and under physiological conditions. Stability (decrease in Gibbs free energy from the unfolded to the folded state) ensures the binding interface remains intact and prevents competing processes such as aggregation 6. C_LIO_LIExpression: The steady-state abundance of the protein. This is largely dependent on proper folding and stability, as cellular quality control mechanisms degrade proteins that fail to fold or remain stable at functional concentrations. C_LI C_LI C_LIO_LIChange in relative affinity ({Delta}{Delta}{degrees}KD): the difference between the {Delta}{degrees}KD of the primary VHH compared to the control VHH for a given epitope mutation. C_LI Model-related termsO_LIESM-IF1 sc: Single-chain (sc) structure-conditioned inverse folding model (ESM-IF1), using the isolated monomer structure of the mutated protein: either the VHH or the antigen 7. C_LIO_LIESM-IF1 mc: Multi-chain (mc) structure-conditioned model (ESM-IF1), using the full complex structure (both antibody and antigen) 7. C_LIO_LIStability prediction score: Score that represents the predicted change in stability based on a single mutation, normally represented as {Delta}{Delta}G. C_LI

molecular biology↗

bioRxiv2025

ASD: Antigen-Specific Antibody Database

The development of computational models addressing therapeutic antibodies faces significant challenges due to the scarcity of data. A critical data element is the set of antibody-antigen interaction pairs associated with sequences. To address this issue, we developed the Antigen Specific Antibody Database (ASD, https://naturalantibody.com/agab/), a database aggregating antibody-antigen interaction data from multiple studies with standardized formatting and annotations. Our dataset compilation strategy resulted in data from 15 distinct sources, resulting in 1,097,946 unique antibody-antigen interactions (with 9,575 unique antigens). The ASD database captures diverse affinity measures and qualitative binding assessment, along with metadata including UniProt and PDB identifiers, target protein names, confidence levels, and experimental conditions. Through this integration drive, we make available an ample resource of interaction data gathered from the public domain to act as a foundation for model development and further data generation.

bioinformatics↗

bioRxiv2025

Chronic sensing of host-derived lipids is an all-in-one signal that primes and activates NLRP3.

Activation of the NLRP3 inflammasome leads to the production of bioactive interleukin (IL)-1{beta} fostering atherosclerosis. The current dogma is that NLRP3 must be first primed by microbial stimuli, known as pathogen-associated molecular patterns (PAMPs), and then activated by either microbial or host-derived inflammatory cues. The mechanism that controls NLRP3 functioning in the context of non-communicable diseases lacking overt microbial infections remains debated. Here, we show that chronic exposure to atherosclerosis-associated oxidized phospholipids (oxPLs) simultaneously primes and activates NLRP3 independently of microbial cues. Mechanistically, chronic exposure to host-derived oxPLs activate the transcription factor NRF2, which is necessary and sufficient to prime and activate NLRP3 in a PAMP-independent manner. NRF2 chronic activation drives oxidized mitochondrial DNA to activate NLRP3. Ex vivo analyses of atherosclerotic plaques in mice and humans identify a population of monocytes-derived macrophages which activates NRF2 and expresses IL-1{beta}. Overall, our data point to oxPL-dependent NRF2 activation as an all-in-one signal necessary and sufficient to prime and activate NLRP3, sustaining atherogenesis.

immunology↗

bioRxiv2025

NAStructuralDB : Structural database to facilitate computational studies of molecular modeling and recognition of proteins with special focus on antibody-antigen interactions.

Studying the interactions between antibodies and antigens is fundamental to the development of novel therapeutic biologics. Predictions of such interactions start with data collection. Though there exist reliable resources to identify antibody structures in the Protein Data Bank (PDB), such data still requires substantial processing to be usable in predictive tasks. Redundancy in sequences needs to be removed to avoid data leakages between train, test and validation sets. Descriptors such as surface accessibility, secondary structure and antibody region information need to be additionally annotated. Information on inter- and intra-molecular contacts, which is crucial to studying paratope/epitope information, needs to be collected. The specialized immunoglobulin format of Nanobodies(R) requires a separate dataset mirroring that of antibodies, given that their structure contains only a single VHH chain. Because antibody-antigen structures account for a small amount of all protein-protein contacts, having a molecular contact reference from other proteins is also desired. To address these issues, we introduce NAStructuralDB (https://naturalantibody.com/na-structural/), a dataset of processed structures of antibodies, Nanobodies(R), proteins and their complexes with molecular contact information and associated annotations. We use the opportunity of having collected the contact data to provide a reference of binding propensities of different residues across distinct contact types. We anticipate that this dataset will accelerate a broad range of predictive tasks by standardizing common, time-consuming data preparation steps in antibody and protein design.

bioinformatics↗

bioRxiv2025

AbDesign: Database of point mutants of antibodies with associated structures reveals poor generalization of binding predictions from machine learning models.

Antibodies are naturally evolved molecular recognition scaffolds that can bind a variety of surfaces. Their designability is crucial to the development of biologics, with computational methods holding promise in accelerating the delivery of medicines to the clinic. Modeling antibody-antigen recognition is prohibitively difficult, with data paucity being one of the biggest hurdles. Current affinity datasets comprise a small number of experimental measurements, which are often not standardized between molecules. Here, we address these issues by creating a dataset of seven antigens with two antibodies each, for which we introduce a heterogeneous set of mutations to the CDR-H3 measured by ELISA. Each of the parental complexes has a known crystal structure. We perform benchmarking of state-of-the-art affinity prediction algorithms to gauge their effectiveness. Current computational methods exhibit significant limitations in accurately predicting the effects of single-point mutations. In contrast, the older empirical, physics-based method FoldX, performs well in identifying mutants that retain binding. These findings highlight the need for more resources like the one presented here -- large, molecularly diverse, and experimentally consistent datasets.

bioinformatics↗

bioRxiv2024

Distinct components of nucleoside-modified messenger RNA vaccines cooperate to instruct efficient germinal center responses

Nucleoside-modified mRNA vaccines elicit protective antibodies through their ability to promote T follicular helper (Tfh) cells. The lipid nanoparticle (LNP) component of mRNA vaccines possesses inherent adjuvant activity. However, to what extent the nucleoside-modified mRNA can be sensed and contribute to Tfh cell responses remains largely undefined. Herein, we deconvoluted the signals induced by LNP and mRNA that instruct dendritic cells (DCs) to promote Tfh cell differentiation. We demonstrated that the nucleoside-modified mRNA drives the production of type I interferons that act on DCs to induce their maturation and the induction of Th1-biased Tfh responses. Conversely, LNP favors the acquisition of a Tfh cell-inducing program in DCs, a stronger Th2 polarization in Tfh cells, and allows for rapid mRNA translation by DCs within the draining lymph node. Our work unravels distinct adjuvant features of mRNA and LNP necessary for the induction of Tfh cells, with implications for vaccine design.

immunology↗

bioRxiv2023

Host-derived oxidized phospholipids initiate effector-triggered immunity fostering lethality upon microbial encounter.

Macrophages detect invading microorganisms via pattern recognition receptors that recognize pathogen-associated molecular patterns, or via sensing the activity of virulence factors that initiates effector-triggered immunity (ETI). Tissue damage that follows pathogen encounter leads to the release of host-derived factors that participate to inflammation. How these self-derived molecules are sensed by macrophages and their impact on immunity remain poorly understood. Here we demonstrate that, in mice and humans, host-derived oxidized phospholipids (oxPLs) are formed upon microbial encounter. oxPL blockade restricts inflammation and prevents the death of the host, without affecting pathogen burden. Mechanistically, oxPLs bind and inhibit AKT, a master regulator of immunity and metabolism. AKT inhibition potentiates the methionine cycle, and epigenetically dampens Il10, a pluripotent anti-inflammatory cytokine. Overall, we found that host-derived inflammatory cues act as "self" virulence factors that initiate ETI and that their activity can be targeted to protect the host against excessive inflammation upon microbial encounter.

immunology↗

bioRxiv2022

Universal Amplicon Sequencing of North Imperial Valley Wetlands Microbiomes

DNA sequencing of complex microbial communities allows for the classification and quantification of thousands of distinct organisms in diverse environmental niches. We present a three domain "Universal Amplicon" (UA) method to simultaneously amplify DNA from the ribosomal small subunit locus from bacteria, archaea, and eukaryotes (and their organelles) using a single pair of amplification primers. We demonstrate the amenability of the UA to multiplexed Illumina library preparation and MiSeq-based sequencing. We validate the UA by sequencing a commercially available microbial community of known quantitative composition and through direct comparison to a shotgun metagenomics dataset. Following validation, we apply the UA to a time-course study of the wetlands of the Northern Imperial Valley in California and show substantial and variable microbial life in the Salton Sea and nearby waters. We demonstrate that the microbial ecology of the Salton Sea varies on at least a monthly basis and is distinct from the surrounding area. Finally, we contribute an open-source Shiny app for real-time analysis of complex metagenomic communities, with application to this study and far beyond.

ecology↗

bioRxiv2022

Antibody optimization enabled by artificial intelligence predictions of binding affinity and naturalness

Traditional antibody optimization approaches involve screening a small subset of the available sequence space, often resulting in drug candidates with suboptimal binding affinity, developability or immunogenicity. Based on two distinct antibodies, we demonstrate that deep contextual language models trained on high-throughput affinity data can quantitatively predict binding of unseen antibody sequence variants. These variants span a KD range of three orders of magnitude over a large mutational space. Our models reveal strong epistatic effects, which highlight the need for intelligent screening approaches. In addition, we introduce the modeling of "naturalness", a metric that scores antibody variants for similarity to natural immunoglobulins. We show that naturalness is associated with measures of drug developability and immunogenicity, and that it can be optimized alongside binding affinity using a genetic algorithm. This approach promises to accelerate and improve antibody engineering, and may increase the success rate in developing novel antibody and related drug candidates.

bioinformatics↗

bioRxiv2022

Robust and durable prophylactic protection conferred by RNA interference in preclinical models of SARS-CoV-2

RNA interference is a natural antiviral mechanism that could be harnessed to combat SARS-CoV-2 infection by targeting and destroying the viral genome. We screened lipophilic small-interfering RNA (siRNA) conjugates targeting highly conserved regions of the SARS-CoV-2 genome and identified leads targeting outside of the spike-encoding region capable of achieving [≥]3-log viral reduction. Serial passaging studies demonstrated that a two-siRNA combination prevented development of resistance compared to a single-siRNA approach. A two-siRNA combination delivered intranasally protected Syrian hamsters from weight loss and lung pathology by viral infection upon prophylactic administration but not following onset of infection. Together, the data support potential utility of RNAi as a prophylactic approach to limit SARS-CoV-2 infection that may help combat emergent variants, complement existing interventions, or protect populations where vaccines are less effective. Most importantly, this strategy has implications for developing medicines that may be valuable in protecting against future coronavirus pandemics.

pharmacology and toxicology↗

bioRxiv2021

Maturation signatures of conventional dendritic cells in COVID-19 reflect direct viral sensing

Growing evidence suggests that conventional dendritic cells (cDCs) undergo aberrant maturation in COVID-19 and this negatively affects T cell activation. The presence of functional effector T cells in mild patients and dysfunctional T cells in severely ill patients suggests that adequate T cell responses are needed to limit disease severity. Therefore, understanding how cDCs cope with SARS-CoV-2 infections can help elucidate the mechanism of generation of protective immune responses. Here, we report that cDC2 subtypes exhibit similar infection-induced gene signatures with the up-regulation of interferon-stimulated genes and IL-6 signaling pathways. The main difference observed between DC2s and DC3s is the up-regulation of anti-apoptotic genes in DC3s, which explains their accumulation during infection. Furthermore, comparing cDCs between severe and mild patients, we find in the former a profound down-regulation of genes encoding molecules involved in antigen presentation, such as major histocompatibility complex class II (MHCII) molecules, {beta}2 microglobulin, TAP and costimulatory proteins, while an opposite trend is observed for proinflammatory molecules, such as complement and coagulation factors. Therefore, as the severity of the disease increases, cDC2s enhance their inflammatory properties and lose their main function, which is the antigen presentation capacity. In vitro, direct exposure of cDC2s to the virus recapitulates the type of activation observed in vivo. Our findings provide evidence that SARS-CoV-2 can interact directly with cDC2s and, by inducing the down-regulation of crucial molecules required for T cell activation, implements an efficient immune escape mechanism that correlates with disease severity.

immunology↗

bioRxiv2020

The circulating SARS-CoV-2 spike variant N439K maintains fitness while evading antibody-mediated immunity

SARS-CoV-2 can mutate to evade immunity, with consequences for the efficacy of emerging vaccines and antibody therapeutics. Herein we demonstrate that the immunodominant SARS-CoV-2 spike (S) receptor binding motif (RBM) is the most divergent region of S, and provide epidemiological, clinical, and molecular characterization of a prevalent RBM variant, N439K. We demonstrate that N439K S protein has enhanced binding affinity to the hACE2 receptor, and that N439K virus has similar clinical outcomes and in vitro replication fitness as compared to wild- type. We observed that the N439K mutation resulted in immune escape from a panel of neutralizing monoclonal antibodies, including one in clinical trials, as well as from polyclonal sera from a sizeable fraction of persons recovered from infection. Immune evasion mutations that maintain virulence and fitness such as N439K can emerge within SARS-CoV-2 S, highlighting the need for ongoing molecular surveillance to guide development and usage of vaccines and therapeutics.

microbiology↗

bioRxiv2020

Type III interferons disrupt the lung epithelial barrier upon viral recognition.

Lower respiratory tract infections are a leading cause of mortality driven by infectious agents. RNA viruses such as influenza virus, respiratory syncytial virus and the new pandemic coronavirus SARS-CoV-2 can be highly pathogenic. Clinical and experimental evidence indicate that most severe and lethal cases do not depend on the viral burden and are, instead, characterized by an aberrant immune response. In this work we assessed how the innate immune response contributes to the pathogenesis of RNA virus infections. We demonstrate that type III interferons produced by dendritic cells in the lung in response to viral recognition cause barrier damage and compromise the host tissue tolerance. In particular, type III interferons inhibit tissue repair and lung epithelial cell proliferation, causing susceptibility to lethal bacterial superinfections. Overall, our data give a strong mandate to rethink the pathophysiological roles of this group of interferons and their possible use in the clinical practice against endemic as well as emerging viral infections.

immunology↗

bioRxiv2020

Structural and functional analysis of a potent sarbecovirus neutralizing antibody

SARS-CoV-2 is a newly emerged coronavirus responsible for the current COVID-19 pandemic that has resulted in more than one million infections and 73,000 deaths1,2. Vaccine and therapeutic discovery efforts are paramount to curb the pandemic spread of this zoonotic virus. The SARS-CoV-2 spike (S) glycoprotein promotes entry into host cells and is the main target of neutralizing antibodies. Here we describe multiple monoclonal antibodies targeting SARS-CoV-2 S identified from memory B cells of a SARS survivor infected in 2003. One antibody, named S309, potently neutralizes SARS-CoV-2 and SARS-CoV pseudoviruses as well as authentic SARS-CoV-2 by engaging the S receptor-binding domain. Using cryo-electron microscopy and binding assays, we show that S309 recognizes a glycan-containing epitope that is conserved within the sarbecovirus subgenus, without competing with receptor attachment. Antibody cocktails including S309 along with other antibodies identified here further enhanced SARS-CoV-2 neutralization and may limit the emergence of neutralization-escape mutants. These results pave the way for using S309 and S309-containing antibody cocktails for prophylaxis in individuals at high risk of exposure or as a post-exposure therapy to limit or treat severe disease.

immunology↗

bioRxiv2020

The physical form of microbial ligands bypasses the need for dendritic cell migration to stimulate adaptive immunity

A central paradigm of immunology is that the innate immune system first detects infectious agents in peripheral tissues, shortly after a pathogen has breached an epithelial barrier. This detection event is mediated by pattern recognition receptors in phagocytes, which then migrate to draining lymph nodes (dLNs), where information of a microbial encounter is conveyed to T and B lymphocytes to generate adaptive immunity. Through the study of fungal moieties, we present data that challenge this model. We found that soluble fungal polysaccharides are immunosilent in the periphery, but become potent immunogens in the dLN. These ligands completely bypass the need of phagocyte migration and, instead, directly activate an immune response that is most similar to those that typify viral infections. These data establish a class of microbial products that violate a central tenet of the immunological lexicon and illustrate that the physical form (not just the chemical structure) impacts innate and adaptive immunity.

immunology↗

bioRxiv2020

NFκB dynamics determine stimulus-responsive epigenomic reprogramming in macrophages

The epigenome defines the cell type, but also shows plasticity that enables cells to tune their gene expression potential to the context of extracellular cues. This is evident in immune sentinel cells such as macrophages, which can respond to pathogens and cytokines with phenotypic shifts that are driven by epigenomic reprogramming1. Recent studies indicate that this reprogramming arises from the activity of transcription factors such as nuclear factor kappa-light-chain-enhancer of activated B cells (NF{kappa}B), which binds not only to available enhancers but may produce de novo enhancers in previously silent areas of the genome2. Here, we show that NF{kappa}B reprograms the macrophage epigenome in a stimulus-specific manner, in response only to a subset of pathogen-derived stimuli. The basis for these surprising differences lies in the stimulus-specific temporal dynamics of NF{kappa}B activity. Testing predictions of a mathematical model of nucleosome interactions, we demonstrate through live cell imaging and genetic perturbations that NF{kappa}B promotes open chromatin and formation of de novo enhancers most strongly when its activity is non-oscillatory. These de novo enhancers result in the activation of additional response genes. Our study demonstrates that the temporal dynamics of NF{kappa}B activity, which encode ligand identity3, can be decoded by the epigenome through de novo enhancer formation. We propose a mechanistic paradigm in which the temporal dynamics of transcription factors are a key determinant of their capacity to control epigenomic reprogramming, thus enabling the formation of stimulus-specific memory in innate immune sentinel cells.

molecular biology↗