Graph-pMHC: graph neural network approach to MHC class II peptide presentation and antibody immunogenicity.

Antigen presentation on MHC class II (pMHCII presentation) plays an essential role in the adaptive immune response to extracellular pathogens and cancerous cells. But it can also reduce the efficacy of large-molecule drugs by triggering an anti-drug response. Significant progress has been made in pMHCII presentation modeling due to the collection of large-scale pMHC mass spectrometry datasets (ligandomes) and advances in machine learning. Here, we develop graph-pMHC, a graph neural network approach to predict pMHCII presentation. We derive adjacency matrices for pMHCII using Alphafold2-multimer and address the peptide-MHC binding groove alignment problem with a simple graph enumeration strategy. We demonstrate that graph-pMHC dramatically outperforms methods with suboptimal inductive biases, such as the multilayer-perceptron-based NetMHCIIpan-4.0 (+20.17% absolute average precision). Finally, we create an antibody drug immunogenicity dataset from clinical trial data and develop a method for measuring anti-antibody immunogenicity risk using pMHCII presentation models. Our model increases receiver operating characteristic curve (ROC)-area under the ROC curve (AUC) by 2.57% compared to just filtering peptides by hits in OASis alone for predicting antibody drug immunogenicity.

SIGNAL: A web-based iterative analysis platform integrating pathway and network approaches optimizes hit selection from genome-scale assays.

Hit selection from high-throughput assays remains a critical bottleneck in realizing the potential of omic-scale studies in biology. Widely used methods such as setting of cutoffs, prioritizing pathway enrichments, or incorporating predicted network interactions offer divergent solutions yet are associated with critical analytical trade-offs. The specific limitations of these individual approaches and the lack of a systematic way by which to integrate their rankings have contributed to limited overlap in the reported results from comparable genome-wide studies and costly inefficiencies in secondary validation efforts. Using comparative analysis of parallel independent studies as a benchmark, we characterize the specific complementary contributions of each approach and demonstrate an optimal framework to integrate these methods. We describe selection by iterative pathway group and network analysis looping (SIGNAL), an integrated, iterative approach that uses both pathway and network methods to optimize gene prioritization. SIGNAL is accessible as a rapid user-friendly web-based application (https://signal.niaid.nih.gov). A record of this paper's transparent peer review is included in the Supplemental information.

A small sustained increase in NOD1 abundance promotes ligand-independent inflammatory and oncogene transcriptional responses.

Small, genetically determined differences in transcription [expression quantitative trait loci (eQTLs)] are implicated in complex diseases through unknown molecular mechanisms. Here, we showed that a small, persistent increase in the abundance of the innate pathogen sensor NOD1 precipitated large changes in the transcriptional state of monocytes. A ~1.2- to 1.3-fold increase in NOD1 protein abundance resulting from loss of regulation by the microRNA cluster miR-15b/16 lowered the threshold for ligand-induced activation of the transcription factor NF-κB and the MAPK p38. An additional sustained increase in NOD1 abundance to 1.5-fold over basal amounts bypassed this low ligand concentration requirement, resulting in robust ligand-independent induction of proinflammatory genes and oncogenes. These findings reveal that tight regulation of NOD1 abundance prevents this sensor from exceeding a physiological switching checkpoint that promotes persistent inflammation and oncogene expression. Furthermore, our data provide insight into how a quantitatively small change in protein abundance can produce marked changes in cell state that can serve as the initiator of disease.

Dual Roles for Ikaros in Regulation of Macrophage Chromatin State and Inflammatory Gene Expression.

Macrophage activation by bacterial LPS leads to induction of a complex inflammatory gene program dependent on numerous transcription factor families. The transcription factor Ikaros has been shown to play a critical role in lymphoid cell development and differentiation; however, its function in myeloid cells and innate immune responses is less appreciated. Using comprehensive genomic analysis of Ikaros-dependent transcription, DNA binding, and chromatin accessibility, we describe unexpected dual repressor and activator functions for Ikaros in the LPS response of murine macrophages. Consistent with the described function of Ikaros as transcriptional repressor, Ikzf1-/- macrophages showed enhanced induction for select responses. In contrast, we observed a dramatic defect in expression of many delayed LPS response genes, and chromatin immunoprecipitation sequencing analyses support a key role for Ikaros in sustained NF-κB chromatin binding. Decreased Ikaros expression in Ikzf1+/- mice and human cells dampens these Ikaros-enhanced inflammatory responses, highlighting the importance of quantitative control of Ikaros protein level for its activator function. In the absence of Ikaros, a constitutively open chromatin state was coincident with dysregulation of LPS-induced chromatin remodeling, gene expression, and cytokine responses. Together, our data suggest a central role for Ikaros in coordinating the complex macrophage transcriptional program in response to pathogen challenge.

Comprehensive RNAi-based screening of human and mouse TLR pathways identifies species-specific preferences in signaling protein use.

Toll-like receptors (TLRs) are a major class of pattern recognition receptors, which mediate the responses of innate immune cells to microbial stimuli. To systematically determine the roles of proteins in canonical TLR signaling pathways, we conducted an RNA interference (RNAi)-based screen in human and mouse macrophages. We observed a pattern of conserved signaling module dependencies across species, but found notable species-specific requirements at the level of individual proteins. Among these, we identified unexpected differences in the involvement of members of the interleukin-1 receptor-associated kinase (IRAK) family between the human and mouse TLR pathways. Whereas TLR signaling in mouse macrophages depended primarily on IRAK4 and IRAK2, with little or no role for IRAK1, TLR signaling and proinflammatory cytokine production in human macrophages depended on IRAK1, with knockdown of IRAK4 or IRAK2 having less of an effect. Consistent with species-specific roles for these kinases, IRAK4 orthologs failed to rescue signaling in IRAK4-deficient macrophages from the other species, and only mouse macrophages required the kinase activity of IRAK4 to mediate TLR responses. The identification of a critical role for IRAK1 in TLR signaling in humans could potentially explain the association of IRAK1 with several autoimmune diseases. Furthermore, this study demonstrated how systematic screening can be used to identify important characteristics of innate immune responses across species, which could optimize therapeutic targeting to manipulate human TLR-dependent outputs.