Transcriptional programming of dendritic cells for enhanced MHC class II antigen presentation.

CD11b(+) dendritic cells (DCs) seem to be specialized for presenting antigens via major histocompatibility (MHC) class II complexes to stimulate helper T cells, but the genetic and regulatory basis for this is not established. Conditional deletion of Irf4 resulted in loss of CD11b(+) DCs, impaired formation of peptide-MHC class II complexes and defective priming of helper T cells but not of cytotoxic T lymphocyte (CTL) responses. Gene expression and chromatin immunoprecipitation followed by deep sequencing (ChIP-Seq) analyses delineated an IRF4-dependent regulatory module that programs enhanced MHC class II antigen presentation. Expression of the transcription factor IRF4 but not of IRF8 restored the ability of IRF4-deficient DCs to efficiently process and present antigen to MHC class II-restricted T cells and promote helper T cell responses. We propose that the evolutionary divergence of IRF4 and IRF8 facilitated the specialization of DC subsets for distinct modes of antigen presentation and priming of helper T cell versus CTL responses.

Neutralizing Antibody Responses to Viral Infections Are Linked to the Non-classical MHC Class II Gene H2-Ob.

Select humans and animals control persistent viral infections via adaptive immune responses that include production of neutralizing antibodies. The precise genetic basis for the control remains enigmatic. Here, we report positional cloning of the gene responsible for production of retrovirus-neutralizing antibodies in mice of the I/LnJ strain. It encodes the beta subunit of the non-classical major histocompatibility complex class II (MHC-II)-like molecule H2-O, a negative regulator of antigen presentation. The recessive and functionally null I/LnJ H2-Ob allele supported the production of virus-neutralizing antibodies independently of the classical MHC haplotype. Subsequent bioinformatics and functional analyses of the human H2-Ob homolog, HLA-DOB, revealed both loss- and gain-of-function alleles, which could affect the ability of their carriers to control infections with human hepatitis B (HBV) and C (HCV) viruses. Thus, understanding of the previously unappreciated role of H2-O (HLA-DO) in immunity to infections may suggest new approaches in achieving neutralizing immunity to viruses.

Specification of type 2 innate lymphocytes by the transcriptional determinant Gfi1.

Type 2 innate lymphoid cells (ILC2 cells) participate in host defense against helminth parasites and in allergic inflammation. Given their functional relatedness to type 2 helper T cells (T(H)2 cells), we explored whether Gfi1 acts as a shared transcriptional determinant in ILC2 cells. Gfi1 promoted the development of ILC2 cells and controlled their responsiveness during infection with Nippostrongylus brasiliensis and protease allergen-induced lung inflammation. Gfi1 'preferentially' regulated the responsiveness of ILC2 cells to interleukin 33 (IL-33) by directly activating Il1rl1, which encodes the IL-33 receptor (ST2). Loss of Gfi1 in activated ILC2 cells resulted in impaired expression of the transcription factor GATA-3 and a dysregulated genome-wide effector state characterized by coexpression of IL-13 and IL-17. Our findings establish Gfi1 as a shared determinant that reciprocally regulates the type 2 and IL-17 effector states in cells of the innate and adaptive immune systems.

Computational prediction of protein interactions in single cells by proximity sequencing.

Proximity sequencing (Prox-seq) simultaneously measures gene expression, protein expression and protein complexes on single cells. Using information from dual-antibody binding events, Prox-seq infers surface protein dimers at the single-cell level. Prox-seq provides multi-dimensional phenotyping of single cells in high throughput, and was recently used to track the formation of receptor complexes during cell signaling and discovered a novel interaction between CD9 and CD8 in naïve T cells. The distribution of protein abundance can affect identification of protein complexes in a complicated manner in dual-binding assays like Prox-seq. These effects are difficult to explore with experiments, yet important for accurate quantification of protein complexes. Here, we introduce a physical model of Prox-seq and computationally evaluate several different methods for reducing background noise when quantifying protein complexes. Furthermore, we developed an improved method for analysis of Prox-seq data, which resulted in more accurate and robust quantification of protein complexes. Finally, our Prox-seq model offers a simple way to investigate the behavior of Prox-seq data under various biological conditions and guide users toward selecting the best analysis method for their data.

Privacy preserving validation for multiomic prediction models.

Reproducibility of results obtained using ribonucleic acid (RNA) data across labs remains a major hurdle in cancer research. Often, molecular predictors trained on one dataset cannot be applied to another due to differences in RNA library preparation and quantification, which inhibits the validation of predictors across labs. While current RNA correction algorithms reduce these differences, they require simultaneous access to patient-level data from all datasets, which necessitates the sharing of training data for predictors when sharing predictors. Here, we describe SpinAdapt, an unsupervised RNA correction algorithm that enables the transfer of molecular models without requiring access to patient-level data. It computes data corrections only via aggregate statistics of each dataset, thereby maintaining patient data privacy. Despite an inherent trade-off between privacy and performance, SpinAdapt outperforms current correction methods, like Seurat and ComBat, on publicly available cancer studies, including TCGA and ICGC. Furthermore, SpinAdapt can correct new samples, thereby enabling unbiased evaluation on validation cohorts. We expect this novel correction paradigm to enhance research reproducibility and to preserve patient privacy.

Gender bias in autoimmunity is influenced by microbiota.

Gender bias and the role of sex hormones in autoimmune diseases are well established. In specific pathogen-free nonobese diabetic (NOD) mice, females have 1.3-4.4 times higher incidence of type 1 diabetes (T1D). Germ-free (GF) mice lost the gender bias (female-to-male ratio 1.1-1.2). Gut microbiota differed in males and females, a trend reversed by male castration, confirming that androgens influence gut microbiota. Colonization of GF NOD mice with defined microbiota revealed that some, but not all, lineages overrepresented in male mice supported a gender bias in T1D. Although protection of males did not correlate with blood androgen concentration, hormone-supported expansion of selected microbial lineages may work as a positive-feedback mechanism contributing to the sexual dimorphism of autoimmune diseases. Gene-expression analysis suggested pathways involved in protection of males from T1D by microbiota. Our results favor a two-signal model of gender bias, in which hormones and microbes together trigger protective pathways.

Pseudocell Tracer-A method for inferring dynamic trajectories using scRNAseq and its application to B cells undergoing immunoglobulin class switch recombination.

Single cell RNA sequencing (scRNAseq) can be used to infer a temporal ordering of cellular states. Current methods for the inference of cellular trajectories rely on unbiased dimensionality reduction techniques. However, such biologically agnostic ordering can prove difficult for modeling complex developmental or differentiation processes. The cellular heterogeneity of dynamic biological compartments can result in sparse sampling of key intermediate cell states. To overcome these limitations, we develop a supervised machine learning framework, called Pseudocell Tracer, which infers trajectories in pseudospace rather than in pseudotime. The method uses a supervised encoder, trained with adjacent biological information, to project scRNAseq data into a low-dimensional manifold that maps the transcriptional states a cell can occupy. Then a generative adversarial network (GAN) is used to simulate pesudocells at regular intervals along a virtual cell-state axis. We demonstrate the utility of Pseudocell Tracer by modeling B cells undergoing immunoglobulin class switch recombination (CSR) during a prototypic antigen-induced antibody response. Our results revealed an ordering of key transcription factors regulating CSR to the IgG1 isotype, including the concomitant expression of Nfkb1 and Stat6 prior to the upregulation of Bach2 expression. Furthermore, the expression dynamics of genes encoding cytokine receptors suggest a poised IL-4 signaling state that preceeds CSR to the IgG1 isotype.

Human Hepatitis B Viral Infection Outcomes Are Linked to Naturally Occurring Variants of HLA-DOA That Have Altered Function.

HLA molecules of the MHC class II (MHCII) bind and present pathogen-derived peptides for CD4 T cell activation. Peptide loading of MHCII in the endosomes of cells is controlled by the interplay of the nonclassical MHCII molecules, HLA-DM (DM) and HLA-DO (DO). DM catalyzes peptide loading, whereas DO, an MHCII substrate mimic, prevents DM from interacting with MHCII, resulting in an altered MHCII-peptide repertoire and increased MHCII-CLIP. Although the two genes encoding DO (DOA and DOB) are considered nonpolymorphic, there are rare natural variants. Our previous work identified DOB variants that altered DO function. In this study, we show that natural variation in the DOA gene also impacts DO function. Using the 1000 Genomes Project database, we show that ∼98% of individuals express the canonical DOA*0101 allele, and the remaining individuals mostly express DOA*0102, which we found was a gain-of-function allele. Analysis of 25 natural occurring DOα variants, which included the common alleles, identified three null variants and one variant with reduced and nine with increased ability to modulate DM activity. Unexpectedly, several of the variants produced reduced DO protein levels yet efficiently inhibited DM activity. Finally, analysis of associated single-nucleotide polymorphisms genetically linked the DOA*0102 common allele, a gain-of-function variant, with human hepatitis B viral persistence. In contrast, we found that the DOα F114L null allele was linked with viral clearance. Collectively, these studies show that natural variation occurring in the human DOA gene impacts DO function and can be linked to specific outcomes of viral infections.

Single-Cell Genomics: Approaches and Utility in Immunology.

Single-cell genomics offers powerful tools for studying immune cells, which make it possible to observe rare and intermediate cell states that cannot be resolved at the population level. Advances in computer science and single-cell sequencing technology have created a data-driven revolution in immunology. The challenge for immunologists is to harness computing and turn an avalanche of quantitative data into meaningful discovery of immunological principles, predictive models, and strategies for therapeutics. Here, we review the current literature on computational analysis of single-cell RNA-sequencing data and discuss underlying assumptions, methods, and applications in immunology, and highlight important directions for future research.

Transcriptome-wide miR-155 binding map reveals widespread noncanonical microRNA targeting.

MicroRNAs (miRNAs) are essential components of gene regulation, but identification of miRNA targets remains a major challenge. Most target prediction and discovery relies on perfect complementarity of the miRNA seed to the 3' untranslated region (UTR). However, it is unclear to what extent miRNAs target sites without seed matches. Here, we performed a transcriptome-wide identification of the endogenous targets of a single miRNA-miR-155-in a genetically controlled manner. We found that approximately 40% of miR-155-dependent Argonaute binding occurs at sites without perfect seed matches. The majority of these noncanonical sites feature extensive complementarity to the miRNA seed with one mismatch. These noncanonical sites confer regulation of gene expression, albeit less potently than canonical sites. Thus, noncanonical miRNA binding sites are widespread, often contain seed-like motifs, and can regulate gene expression, generating a continuum of targeting and regulation.

Predicting protein-protein interactions through sequence-based deep learning.

Motivation: High-throughput experimental techniques have produced a large amount of protein-protein interaction (PPI) data, but their coverage is still low and the PPI data is also very noisy. Computational prediction of PPIs can be used to discover new PPIs and identify errors in the experimental PPI data. Results: We present a novel deep learning framework, DPPI, to model and predict PPIs from sequence information alone. Our model efficiently applies a deep, Siamese-like convolutional neural network combined with random projection and data augmentation to predict PPIs, leveraging existing high-quality experimental PPI data and evolutionary information of a protein pair under prediction. Our experimental results show that DPPI outperforms the state-of-the-art methods on several benchmarks in terms of area under precision-recall curve (auPR), and computationally is more efficient. We also show that DPPI is able to predict homodimeric interactions where other methods fail to work accurately, and the effectiveness of DPPI in specific applications such as predicting cytokine-receptor binding affinities. Availability and implementation: Predicting protein-protein interactions through sequence-based deep learning): https://github.com/hashemifar/DPPI/. Supplementary information: Supplementary data are available at Bioinformatics online.

BASIC: BCR assembly from single cells.

Motivation: The B-cell receptor enables individual B cells to identify diverse antigens, including bacterial and viral proteins. While advances in RNA-sequencing (RNA-seq) have enabled high throughput profiling of transcript expression in single cells, the unique task of assembling the full-length heavy and light chain sequences from single cell RNA-seq (scRNA-seq) in B cells has been largely unstudied. Results: We developed a new software tool, BASIC, which allows investigators to use scRNA-seq for assembling BCR sequences at single-cell resolution. To demonstrate the utility of our software, we subjected nearly 200 single human B cells to scRNA-seq, assembled the full-length heavy and the light chains, and experimentally confirmed these results by using single-cell primer-based nested PCRs and Sanger sequencing. Availability and Implementation: http://ttic.uchicago.edu/∼aakhan/BASIC Contact: aakhan@ttic.edu Supplementary Information: Supplementary data are available at Bioinformatics online.

Gene regulatory networks in the immune system.

We describe examples of genomic control circuits that underlie developmental transitions and cellular activation states within the immune system. The architectures of simple gene regulatory networks (GRNs) are highlighted to emphasize conservation of regulatory motifs. The regulatory logic and the cell fate dynamics of each simple GRN, the latter revealed by mathematical and computational modeling, are elaborated. This framework is being expanded to enable the assembly and analysis of complex GRNs using genomic, computational, and high-throughput experimental methodologies. The paradigm will provide new insights into immune cell development and function, and into the pathophysiology of autoimmune and inflammatory disorders, as well as immune malignancies.

Inferring transcriptional and microRNA-mediated regulatory programs in glioblastoma.

Large-scale cancer genomics projects are profiling hundreds of tumors at multiple molecular layers, including copy number, mRNA and miRNA expression, but the mechanistic relationships between these layers are often excluded from computational models. We developed a supervised learning framework for integrating molecular profiles with regulatory sequence information to reveal regulatory programs in cancer, including miRNA-mediated regulation. We applied our approach to 320 glioblastoma profiles and identified key miRNAs and transcription factors as common or subtype-specific drivers of expression changes. We confirmed that predicted gene expression signatures for proneural subtype regulators were consistent with in vivo expression changes in a PDGF-driven mouse model. We tested two predicted proneural drivers, miR-124 and miR-132, both underexpressed in proneural tumors, by overexpression in neurospheres and observed a partial reversal of corresponding tumor expression changes. Computationally dissecting the role of miRNAs in cancer may ultimately lead to small RNA therapeutics tailored to subtype or individual.

TRIBAL: Tree Inference of B cell Clonal Lineages.

B cells are a critical component of the adaptive immune system, responsible for producing antibodies that help protect the body from infections and foreign substances. Single cell RNA-sequencing (scRNA-seq) has allowed for both profiling of B cell receptor (BCR) sequences and gene expression. However, understanding the adaptive and evolutionary mechanisms of B cells in response to specific stimuli remains a significant challenge in the field of immunology. We introduce a new method, TRIBAL, which aims to infer the evolutionary history of clonally related B cells from scRNA-seq data. The key insight of TRIBAL is that inclusion of isotype data into the B cell lineage inference problem is valuable for reducing phylogenetic uncertainty that arises when only considering the receptor sequences. Consequently, the TRIBAL inferred B cell lineage trees jointly capture the somatic mutations introduced to the B cell receptor during affinity maturation and isotype transitions during class switch recombination. In addition, TRIBAL infers isotype transition probabilities that are valuable for gaining insight into the dynamics of class switching. Via in silico experiments, we demonstrate that TRIBAL infers isotype transition probabilities with the ability to distinguish between direct versus sequential switching in a B cell population. This results in more accurate B cell lineage trees and corresponding ancestral sequence and class switch reconstruction compared to competing methods. Using real-world scRNA-seq datasets, we show that TRIBAL recapitulates expected biological trends in a model affinity maturation system. Furthermore, the B cell lineage trees inferred by TRIBAL were equally plausible for the BCR sequences as those inferred by competing methods but yielded lower entropic partitions for the isotypes of the sequenced B cell. Thus, our method holds the potential to further advance our understanding of vaccine responses, disease progression, and the identification of therapeutic antibodies.

Computational prediction of protein interactions on single cells by proximity sequencing.

Proximity sequencing (Prox-seq) measures gene expression, protein expression, and protein complexes at the single cell level, using information from dual-antibody binding events and a single cell sequencing readout. Prox-seq provides multi-dimensional phenotyping of single cells and was recently used to track the formation of receptor complexes during inflammatory signaling in macrophages and to discover a new interaction between CD9/CD8 proteins on naïve T cells. The distribution of protein abundance affects identification of protein complexes in a complicated manner in dual-binding assays like Prox-seq. These effects are difficult to explore with experiments, yet important for accurate quantification of protein complexes. Here, we introduce a physical model for protein dimer formation on single cells and computationally evaluate several different methods for reducing background noise when quantifying protein complexes. Furthermore, we developed an improved method for analysis of Prox-seq single-cell data, which resulted in more accurate and robust quantification of protein complexes. Finally, our model offers a simple way to investigate the behavior of Prox-seq under various biological conditions and guide users toward selecting the best analysis method for their data.

ATAT: Automated Tissue Alignment and Traversal in Spatial Transcriptomics with Self-Supervised Learning.

Spatial transcriptomics (ST) has enhanced RNA analysis in tissue biopsies, but interpreting these data is challenging without expert input. We present Automated Tissue Alignment and Traversal (ATAT), a novel computational framework designed to enhance ST analysis in the context of multiple and complex tissue architectures and morphologies, such as those found in biopsies of the gastrointestinal tract. ATAT utilizes self-supervised contrastive learning on hematoxylin and eosin (H&E) stained images to automate the alignment and traversal of ST data. This approach addresses a critical gap in current ST analysis methodologies, which rely heavily on manual annotation and pathologist expertise to delineate regions of interest for accurate gene expression modeling. Our framework not only streamlines the alignment of multiple ST samples, but also demonstrates robustness in modeling gene expression transitions across specific regions. Additionally, we highlight the ability of ATAT to traverse complex tissue topologies in real-world cases from various individuals and conditions. Our method successfully elucidates differences in immune infiltration patterns across the intestinal wall, enabling the modeling of transcriptional changes across histological layers. We show that ATAT achieves comparable performance to the state-of-the-art method, while alleviating the burden of manual annotation and enabling alignment of tissue samples with complex morphologies.

Deploying synthetic coevolution and machine learning to engineer protein-protein interactions.

Fine-tuning of protein-protein interactions occurs naturally through coevolution, but this process is difficult to recapitulate in the laboratory. We describe a platform for synthetic protein-protein coevolution that can isolate matched pairs of interacting muteins from complex libraries. This large dataset of coevolved complexes drove a systems-level analysis of molecular recognition between Z domain-affibody pairs spanning a wide range of structures, affinities, cross-reactivities, and orthogonalities, and captured a broad spectrum of coevolutionary networks. Furthermore, we harnessed pretrained protein language models to expand, in silico, the amino acid diversity of our coevolution screen, predicting remodeled interfaces beyond the reach of the experimental library. The integration of these approaches provides a means of simulating protein coevolution and generating protein complexes with diverse molecular recognition properties for biotechnology and synthetic biology.

Microbiota-dependent proteolysis of gluten subverts diet-mediated protection against type 1 diabetes.

Diet and commensals can affect the development of autoimmune diseases like type 1 diabetes (T1D). However, whether dietary interventions are microbe-mediated was unclear. We found that a diet based on hydrolyzed casein (HC) as a protein source protects non-obese diabetic (NOD) mice in conventional and germ-free (GF) conditions via improvement in the physiology of insulin-producing cells to reduce autoimmune activation. The addition of gluten (a cereal protein complex associated with celiac disease) facilitates autoimmunity dependent on microbial proteolysis of gluten: T1D develops in GF animals monocolonized with Enterococcus faecalis harboring secreted gluten-digesting proteases but not in mice colonized with protease deficient bacteria. Gluten digestion by E. faecalis generates T cell-activating peptides and promotes innate immunity by enhancing macrophage reactivity to lipopolysaccharide (LPS). Gnotobiotic NOD Toll4-negative mice monocolonized with E. faecalis on an HC + gluten diet are resistant to T1D. These findings provide insights into strategies to develop dietary interventions to help protect humans against autoimmunity.

Integration of tumor extrinsic and intrinsic features associates with immunotherapy response in non-small cell lung cancer.

The efficacy of immune checkpoint blockade (ICB) varies greatly among metastatic non-small cell lung cancer (NSCLC) patients. Loss of heterozygosity at the HLA-I locus (HLA-LOH) has been identified as an important immune escape mechanism. However, despite HLA-I disruptions in their tumor, many patients have durable ICB responses. Here we seek to identify HLA-I-independent features associated with ICB response in NSCLC. We use single-cell profiling to identify tumor-infiltrating, clonally expanded CD4+ T cells that express a canonical cytotoxic gene program and NSCLC cells with elevated HLA-II expression. We postulate cytotoxic CD4+ T cells mediate anti-tumor activity via HLA-II on tumor cells and augment HLA-I-dependent cytotoxic CD8+ T cell interactions to drive ICB response in NSCLC. We show that integrating tumor extrinsic cytotoxic gene expression with tumor mutational burden is associated with longer time to progression in a real-world cohort of 123 NSCLC patients treated with ICB regimens, including those with HLA-LOH.