The IκB kinase complex regulates the stability of cytokine-encoding mRNA induced by TLR-IL-1R by controlling degradation of regnase-1.

Toll-like receptor (TLR) signaling activates the inhibitor of transcription factor NF-κB (IκB) kinase (IKK) complex, which governs NF-κB-mediated transcription during inflammation. The RNase regnase-1 serves a critical role in preventing autoimmunity by controlling the stability of mRNAs that encode cytokines. Here we show that the IKK complex controlled the stability of mRNA for interleukin 6 (IL-6) by phosphorylating regnase-1 in response to stimulation via the IL-1 receptor (IL-1R) or TLR. Phosphorylated regnase-1 underwent ubiquitination and degradation. Regnase-1 was reexpressed in IL-1R- or TLR-activated cells after a period of lower expression. Regnase-1 mRNA was negatively regulated by regnase-1 itself via a stem-loop region present in the regnase-1 3' untranslated region. Our data demonstrate that the IKK complex phosphorylates not only IκBα, thereby activating transcription, but also regnase-1, thereby releasing a 'brake' on IL-6 mRNA expression.

Improved Antibody-Specific Epitope Prediction Using AlphaFold and AbAdapt.

Antibodies recognize their cognate antigens with high affinity and specificity, but the prediction of binding sites on the antigen (epitope) corresponding to a specific antibody remains a challenging problem. To address this problem, we developed AbAdapt, a pipeline that integrates antibody and antigen structural modeling with rigid docking in order to derive antibody-antigen specific features for epitope prediction. In this study, we systematically assessed the impact of integrating the state-of-the-art protein modeling method AlphaFold with the AbAdapt pipeline. By incorporating more accurate antibody models, we observed improvement in docking, paratope prediction, and prediction of antibody-specific epitopes. We further applied AbAdapt-AF in an anti-receptor binding domain (RBD) antibody complex benchmark and found AbAdapt-AF outperformed three alternative docking methods. Also, AbAdapt-AF demonstrated higher epitope prediction accuracy than other tested epitope prediction tools in the anti-RBD antibody complex benchmark. We anticipate that AbAdapt-AF will facilitate prediction of antigen-antibody interactions in a wide range of applications.

Theoretical modeling reveals that regulatory T cells increase T-cell interaction with antigen-presenting cells for stable immune tolerance.

The immune system in tolerance maintains cell diversity without responding to self-antigens. Foxp3-expressing CD25+CD4+ regulatory T cells (Tregs) inhibit T-cell activation through various molecular mechanisms. However, several key questions are still not resolved, including how Tregs control the immune response on the basis of their self-skewed T-cell receptor repertoire and how Tregs avoid impeding relevant immunity against pathogens. Here, we show that Tregs promote the proliferation of conventional T cells in the presence of excessive co-stimulation when murine T cells are stimulated in vitro with allogeneic antigen-presenting cells (APCs). Antigen-specific Tregs increase the number of cells interacting with dendritic cells (DCs) by increasing the number of viable DCs and the expression of adhesion molecules on DCs. Theoretical simulations and mathematical models representing the dynamics of T-APC interaction and T-cell numbers in a lymph node indicate that Tregs reduce the dissociation probability of T cells from APCs and increase the new association. These functions contribute to tolerance by enhancing the interaction of low-affinity T cells with APCs. Supporting the theoretical analyses, we found that reducing the T-cell numbers in mice increases the ratio of specific T cells among CD4+ T cells after immunization and effectively induces autoimmune diabetes in non obese diabetes mice. Thus, as a critical function, antigen-specific Tregs stabilize the immune state, irrespective of it being tolerant or responsive, by augmenting T-APC interaction. We propose a novel regulation model in which stable tolerance with large heterogeneous populations proceeds to a specific immune response through a transient state with few populations.

Immuno-Navigator, a batch-corrected coexpression database, reveals cell type-specific gene networks in the immune system.

High-throughput gene expression data are one of the primary resources for exploring complex intracellular dynamics in modern biology. The integration of large amounts of public data may allow us to examine general dynamical relationships between regulators and target genes. However, obstacles for such analyses are study-specific biases or batch effects in the original data. Here we present Immuno-Navigator, a batch-corrected gene expression and coexpression database for 24 cell types of the mouse immune system. We systematically removed batch effects from the underlying gene expression data and showed that this removal considerably improved the consistency between inferred correlations and prior knowledge. The data revealed widespread cell type-specific correlation of expression. Integrated analysis tools allow users to use this correlation of expression for the generation of hypotheses about biological networks and candidate regulators in specific cell types. We show several applications of Immuno-Navigator as examples. In one application we successfully predicted known regulators of importance in naturally occurring Treg cells from their expression correlation with a set of Treg-specific genes. For one high-scoring gene, integrin ß8 (Itgb8), we confirmed an association between Itgb8 expression in forkhead box P3 (Foxp3)-positive T cells and Treg-specific epigenetic remodeling. Our results also suggest that the regulation of Treg-specific genes within Treg cells is relatively independent of Foxp3 expression, supporting recent results pointing to a Foxp3-independent component in the development of Treg cells.

Genome-wide map of RNA degradation kinetics patterns in dendritic cells after LPS stimulation facilitates identification of primary sequence and secondary structure motifs in mRNAs.

BACKGROUND: Immune cells have to change their gene expression patterns dynamically in response to external stimuli such as lipopolysaccharide (LPS). The gene expression is regulated at multiple steps in eukaryotic cells, in which control of RNA levels at both the transcriptional level and the post-transcriptional level plays important role. Impairment of the control leads to aberrant immune responses such as excessive or impaired production of cytokines. However, genome-wide studies focusing on the post-transcriptional control were relatively rare until recently. Moreover, several RNA cis elements and RNA-binding proteins have been found to be involved in the process, but our general understanding remains poor, partly because identification of regulatory RNA motifs is very challenging in spite of its importance. We took advantage of genome-wide measurement of RNA degradation in combination with estimation of degradation kinetics by qualitative approach, and performed de novo prediction of RNA sequence and structure motifs. METHODS: To classify genes by their RNA degradation kinetics, we first measured RNA degradation time course in mouse dendritic cells after LPS stimulation and the time courses were clustered to estimate degradation kinetics and to find patterns in the kinetics. Then genes were clustered by their similarity in degradation kinetics patterns. The 3' UTR sequences of a cluster was subjected to de novo sequence or structure motif prediction. RESULTS: The quick degradation kinetics was found to be strongly associated with lower gene expression level, immediate regulation (both induction and repression) of gene expression level, and longer 3' UTR length. De novo sequence motif prediction found AU-rich element-like and TTP-binding sequence-like motifs which are enriched in quickly degrading genes. De novo structure motif prediction found a known functional motif, namely stem-loop structure containing sequence bound by RNA-binding protein Roquin and Regnase-1, as well as unknown motifs. CONCLUSIONS: The current study indicated that degradation kinetics patterns lead to classification different from that by gene expression and the differential classification facilitates identification of functional motifs. Identification of novel motif candidates implied post-transcriptional controls different from that by known pairs of RNA-binding protein and RNA motif.

Dynamics of enhancers in myeloid antigen presenting cells upon LPS stimulation.

BACKGROUND: Recent studies have underscored the role of enhancers in defining cell type-specific transcriptomes. Cell type-specific enhancers are bound by combinations of shared and cell type-specific transcription factors (TFs). However, little is known about combinatorial binding of TFs to enhancers, dynamics of TF binding following stimulation, or the downstream effects on gene expression. Here, we address these questions in two types of myeloid antigen presenting cells (APCs), macrophages and dendritic cells (DCs), before and after stimulation with lipopolysaccharide (LPS), a potent stimulator of the innate immune response. RESULTS: We classified enhancers according to the combination of TFs binding them. There were significant correlations between the sets of TFs bound to enhancers prior to stimulation and expression changes of nearby genes after stimulation. Importantly, a set of enhancers pre-bound by PU.1, C/EBPß, ATF3, IRF4, and JunB was strongly associated with induced genes and binding by stimulus-activated regulators. Our classification suggests that transient loss of ATF3 binding to a subset of these enhancers is important for regulation of early-induced genes. Changes in TF-enhancer binding after stimulation were correlated with binding by additional activated TFs and with the presence of proximally located enhancers. CONCLUSIONS: The results presented in this study reveal the complexity and dynamics of TF- enhancer binding before and after stimulation in myeloid APCs.

A Parzen window-based approach for the detection of locally enriched transcription factor binding sites.

BACKGROUND: Identification of cis- and trans-acting factors regulating gene expression remains an important problem in biology. Bioinformatics analyses of regulatory regions are hampered by several difficulties. One is that binding sites for regulatory proteins are often not significantly over-represented in the set of DNA sequences of interest, because of high levels of false positive predictions, and because of positional restrictions on functional binding sites with regard to the transcription start site. RESULTS: We have developed a novel method for the detection of regulatory motifs based on their local over-representation in sets of regulatory regions. The method makes use of a Parzen window-based approach for scoring local enrichment, and during evaluation of significance it takes into account GC content of sequences. We show that the accuracy of our method compares favourably to that of other methods, and that our method is capable of detecting not only generally over-represented regulatory motifs, but also locally over-represented motifs that are often missed by standard motif detection approaches. Using a number of examples we illustrate the validity of our approach and suggest applications, such as the analysis of weaker binding sites. CONCLUSIONS: Our approach can be used to suggest testable hypotheses for wet-lab experiments. It has potential for future analyses, such as the prediction of weaker binding sites. An online application of our approach, called LocaMo Finder (Local Motif Finder), is available at http://sysimm.ifrec.osaka-u.ac.jp/tfbs/locamo/.

Unbiased integration of single cell transcriptome replicates.

Single cell transcriptomic approaches are becoming mainstream, with replicate experiments commonly performed with the same single cell technology. Methods that enable integration of these datasets by removing batch effects while preserving biological information are required for unbiased data interpretation. Here, we introduce Canek for this purpose. Canek leverages information from mutual nearest neighbor to combine local linear corrections with cell-specific non-linear corrections within a fuzzy logic framework. Using a combination of real and synthetic datasets, we show that Canek corrects batch effects while introducing the least amount of bias compared with competing methods. Canek is computationally efficient and can easily integrate thousands of single-cell transcriptomes from replicated experiments.

AbAdapt: an adaptive approach to predicting antibody-antigen complex structures from sequence.

Motivation: The scoring of antibody-antigen docked poses starting from unbound homology models has not been systematically optimized for a large and diverse set of input sequences. Results: To address this need, we have developed AbAdapt, a webserver that accepts antibody and antigen sequences, models their 3D structures, predicts epitope and paratope, and then docks the modeled structures using two established docking engines (Piper and Hex). Each of the key steps has been optimized by developing and training new machine-learning models. The sequences from a diverse set of 622 antibody-antigen pairs with known structure were used as inputs for leave-one-out cross-validation. The final set of cluster representatives included at least one 'Adequate' pose for 550/622 (88.4%) of the queries. The median (interquartile range) ranks of these 'Adequate' poses were 22 (5-77). Similar results were obtained on a holdout set of 100 unrelated antibody-antigen pairs. When epitopes were repredicted using docking-derived features for specific antibodies, the median ROC AUC increased from 0.679 to 0.720 in cross-validation and from 0.694 to 0.730 in the holdout set. Availability and implementation: AbAdapt and related data are available at https://sysimm.org/abadapt/. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Intrinsically disordered domains deviate significantly from random sequences in mammalian proteins.

BACKGROUND: In order to characterize mammalian intrinsically disordered domains (IDDs) we examined the patterns in their amino acid abundance as well as overrepresented local sequence motifs. We considered IDDs from mouse proteins associated with innate immune responses as well as a set of generic human genes. These sets were compared with artificially generated random sequences with the same overall amino acid abundance and length distributions. IDDs were then clustered by amino acid abundance, and further analyzed in terms of co-occurrence of clusters with functionally characterized Pfam domains. RESULTS: Overall, IDDs were very different from randomly generated sequences. The deviation from random distributions was at least as great as that for ordered domains, for which the deviation can be rationalized in terms of strong evolutionary pressure for structure and function. The co-occurrence of certain Pfam domains with specific IDD clusters was found to be significant (p-value < 0.01). Local sequence motifs that were over-represented in the innate immune set consisted mostly of low complexity fragments, primarily characterized by amino acid repeats, and could not be assigned an obvious functional role. CONCLUSIONS: Our results suggest that IDDs are constrained within a narrow subset of possible sequences. This is most likely a result of biophysical restraints that have yet to be elucidated. More detailed examination of the functional relationship between the IDDs and associated Pfam domains is one possible avenue of investigation.

Genomic Heritabilities and Correlations of 17 Traits Related to Obesity and Associated Conditions in the Japanese Population.

Over the past few decades, obesity has become a public health issue of global concern. Even though disparities exist between human populations, e.g., the higher liver fat content of the Japanese despite a lower body mass index (BMI), studies on the genetics of obesity still largely focus on populations of European descent, leading to a dearth of genetic data on non-European populations. In this context, this study aimed to establish a broad picture of the genetic attributes of the Japanese population, by examining a representative sample of 18,889 individuals participating in the Tohoku Medical Megabank Project cohort. We applied linear mixed model methods to 17 traits related to obesity and associated diseases to estimate the heritabilities explained by common genetic variants and the genetic correlations between each pair of traits. These analyses allowed us to quantify the SNP heritability of health indicators such as BMI (0.248 ± 0.032) and HDL cholesterol (0.324 ± 0.031), and to provide one of the few estimates of the SNP heritability of cystatin C in unrelated individuals (0.260 ± 0.025). We discuss potential differences between the Japanese and people of European ancestry with respect to the genetic correlations between urinary biomarkers and adiposity traits, for which large estimates were obtained. For instance, the genetic correlations between urine potassium level and the values for weight, BMI, waist circumference, and waist-to-height ratio ranged from 0.290 to 0.559, much higher than the corresponding estimates in the UK Biobank.

Flexible, Functional, and Familiar: Characteristics of SARS-CoV-2 Spike Protein Evolution.

The SARS-CoV-2 S protein is a major point of interaction between the virus and the human immune system. As a consequence, the S protein is not a static target but undergoes rapid molecular evolution. In order to more fully understand the selection pressure during evolution, we examined residue positions in the S protein that vary greatly across closely related viruses but are conserved in the subset of viruses that infect humans. These "evolutionarily important" residues were not distributed evenly across the S protein but were concentrated in two domains: the N-terminal domain and the receptor-binding domain, both of which play a role in host cell binding in a number of related viruses. In addition to being localized in these two domains, evolutionary importance correlated with structural flexibility and inversely correlated with distance from known or predicted host receptor-binding residues. Finally, we observed a bias in the composition of the amino acids that make up such residues toward more human-like, rather than virus-like, sequence motifs.

Methods for sequence and structural analysis of B and T cell receptor repertoires.

B cell receptors (BCRs) and T cell receptors (TCRs) make up an essential network of defense molecules that, collectively, can distinguish self from non-self and facilitate destruction of antigen-bearing cells such as pathogens or tumors. The analysis of BCR and TCR repertoires plays an important role in both basic immunology as well as in biotechnology. Because the repertoires are highly diverse, specialized software methods are needed to extract meaningful information from BCR and TCR sequence data. Here, we review recent developments in bioinformatics tools for analysis of BCR and TCR repertoires, with an emphasis on those that incorporate structural features. After describing the recent sequencing technologies for immune receptor repertoires, we survey structural modeling methods for BCR and TCRs, along with methods for clustering such models. We review downstream analyses, including BCR and TCR epitope prediction, antibody-antigen docking and TCR-peptide-MHC Modeling. We also briefly discuss molecular dynamics in this context.

Structural Modeling of Lymphocyte Receptors and Their Antigens.

Structural modeling plays a key role in protein function prediction on a genome-wide scale. For B and T lymphocyte receptors, the critical functional question is: which antigens and epitopes are targeted? With emerging B cell receptor (BCR) and T cell receptor (TCR) sequencing methods improving in both breadth and depth, there is a growing need for methods that can help answer this question. Since lymphocyte-antigen recognition depends on complementarity, structural modeling is likely to play an important role in understanding antigen specificity and affinity. In the case of BCRs, such modeling methods have a long history in the study and design of antibodies. However, for TCRs there are relatively few publicly available modeling tools, and, to our knowledge, none that incorporate interaction between TCRs and peptide-MHC (pMHC) complexes. Here, we provide a web-based tool, ImmuneScape ( https://sysimm.org/immune-scape/ ), to carry out TCR-pMHC modeling as a first step toward structure-based function prediction.

Estimation of diffusion constants from single molecular measurement without explicit tracking.

BACKGROUND: Time course measurement of single molecules on a cell surface provides detailed information about the dynamics of the molecules that would otherwise be inaccessible. To extract the quantitative information, single particle tracking (SPT) is typically performed. However, trajectories extracted by SPT inevitably have linking errors when the diffusion speed of single molecules is high compared to the scale of the particle density. METHODS: To circumvent this problem, we develop an algorithm to estimate diffusion constants without relying on SPT. The proposed algorithm is based on a probabilistic model of the distance to the nearest point in subsequent frames. This probabilistic model generalizes the model of single particle Brownian motion under an isolated environment into the one surrounded by indistinguishable multiple particles, with a mean field approximation. RESULTS: We demonstrate that the proposed algorithm provides reasonable estimation of diffusion constants, even when other methods suffer due to high particle density or inhomogeneous particle distribution. In addition, our algorithm can be used for visualization of time course data from single molecular measurements. CONCLUSIONS: The proposed algorithm based on the probabilistic model of indistinguishable Brownian particles provide accurate estimation of diffusion constants even in the regime where the traditional SPT methods underestimate them due to linking errors.

Identification of a two-SNP PLA2R1 Haplotype and HLA-DRB1 Alleles as Primary Risk Associations in Idiopathic Membranous Nephropathy.

The associations of single nucleotide polymorphisms (SNPs) in PLA2R1 and HLA-DQA1, as well as HLA-DRB1*15:01-DQB1*06:02 haplotype with idiopathic membranous nephropathy (IMN) is well known. However, the primary associations of these loci still need to be determined. We used Japanese-specific SNP genotyping array and imputation using 2,048 sequenced Japanese samples to fine-map PLA2R1 region in 98 patients and 413 controls. The most significant SNPs were replicated in a separate sample set of 130 patients and 288 controls. A two-SNP haplotype of intronic and missense SNPs showed the strongest association. The intronic SNP is strongly associated with PLA2R1 expression in the Genotype-Tissue Expression (GTEx) database, and the missense SNP is predicted to alter peptide binding with HLA-DRB1*15:01 by the Immune Epitope Database (IEDB). In HLA region, we performed relative predispositional effect (RPE) tests and identified additional risk alleles in both HLA-DRB1 and HLA-DQB1. We collapsed the risk alleles in each of HLA-DRB1 and HLA-DQB1 into single risk alleles. Reciprocal conditioning of these collapsed risk alleles showed more residual significance for HLA-DRB1 collapsed risk than HLA-DQB1 collapsed risk. These results indicate that changes in the expression levels of structurally different PLA2R protein confer risk for IMN in the presence of risk HLA-DRB1 alleles.

Mapping circulating serum miRNAs to their immune-related target mRNAs.

PURPOSE: Evidence suggests that circulating serum microRNAs (miRNAs) might preferentially target immune-related mRNAs. If this were the case, we hypothesized that immune-related mRNAs would have more predicted serum miRNA binding sites than other mRNAs and, reciprocally, that serum miRNAs would have more immune-related mRNA targets than non-serum miRNAs. MATERIALS AND METHODS: We developed a consensus target predictor using the random forest framework and calculated the number of predicted miRNA-mRNA interactions in various subsets of miRNAs (serum, non-serum) and mRNAs (immune related, nonimmune related). RESULTS: Immune-related mRNAs were predicted to be targeted by serum miRNA more than other mRNAs. Moreover, serum miRNAs were predicted to target many more immune-related mRNA targets than non-serum miRNAs; however, these two biases in immune-related mRNAs and serum miRNAs appear to be completely independent. CONCLUSION: Immune-related mRNAs have more miRNA binding sites in general, not just for serum miRNAs; likewise, serum miRNAs target many more mRNAs than non-serum miRNAs overall, regardless of whether they are immune related or not. Nevertheless, these two independent phenomena result in a significantly larger number of predicted serum miRNA-immune mRNA interactions than would be expected by chance.

A rare subset of skin-tropic regulatory T cells expressing Il10/Gzmb inhibits the cutaneous immune response.

Foxp3+ regulatory T cells (Tregs) migrating from the skin to the draining lymph node (dLN) have a strong immunosuppressive effect on the cutaneous immune response. However, the subpopulations responsible for their inhibitory function remain unclear. We investigated single-cell gene expression heterogeneity in Tregs from the dLN of inflamed skin in a contact hypersensitivity model. The immunosuppressive genes Ctla4 and Tgfb1 were expressed in the majority of Tregs. Although Il10-expressing Tregs were rare, unexpectedly, the majority of Il10-expressing Tregs co-expressed Gzmb and displayed Th1-skewing. Single-cell profiling revealed that CD43+ CCR5+ Tregs represented the main subset within the Il10/Gzmb-expressing cell population in the dLN. Moreover, CD43+ CCR5+ CXCR3- Tregs expressed skin-tropic chemokine receptors, were preferentially retained in inflamed skin and downregulated the cutaneous immune response. The identification of a rare Treg subset co-expressing multiple immunosuppressive molecules and having tissue-remaining capacity offers a novel strategy for the control of skin inflammatory responses.

Intrinsic disorder mediates cooperative signal transduction in STIM1.

Intrinsically disordered domains have been reported to play important roles in signal transduction networks by introducing cooperativity into protein-protein interactions. Unlike intrinsically disordered domains that become ordered upon binding, the EF-SAM domain in the stromal interaction molecule (STIM) 1 is distinct in that it is ordered in the monomeric state and partially unfolded in its oligomeric state, with the population of the two states depending on the local Ca(2+) concentration. The oligomerization of STIM1, which triggers extracellular Ca(2+) influx, exhibits cooperativity with respect to the local endoplasmic reticulum Ca(2+) concentration. Although the physiological importance of the oligomerization reaction is well established, the mechanism of the observed cooperativity is not known. Here, we examine the response of the STIM1 EF-SAM domain to changes in Ca(2+) concentration using mathematical modeling based on in vitro experiments. We find that the EF-SAM domain partially unfolds and dimerizes cooperatively with respect to Ca(2+) concentration, with Hill coefficients and half-maximal activation concentrations very close to the values observed in vivo for STIM1 redistribution and extracellular Ca(2+) influx. Our mathematical model of the dimerization reaction agrees quantitatively with our analytical ultracentrifugation-based measurements and previously published free energies of unfolding. A simple interpretation of these results is that Ca(2+) loss effectively acts as a denaturant, enabling cooperative dimerization and robust signal transduction. We present a structural model of the Ca(2+)-unbound EF-SAM domain that is consistent with a wide range of evidence, including resistance to proteolytic cleavage of the putative dimerization portion.

Functional annotation of intrinsically disordered domains by their amino acid content using IDD Navigator.

Function prediction of intrinsically disordered domains (IDDs) using sequence similarity methods is limited by their high mutability and prevalence of low complexity regions. We describe a novel method for identifying similar IDDs by a similarity metric based on amino acid composition and identify significantly overrepresented Gene Ontology (GO) and Pfam domain annotations within highly similar IDDs. Applications and extensions of the proposed method are discussed, in particular with respect to protein functional annotation. We test the predicted annotations in a large-scale survey of IDDs in mouse and find that the proposed method provides significantly greater protein coverage in terms of function prediction than traditional sequence alignment methods like BLAST. As a proof of concept we examined several disorder-containing proteins: GRA15 and ROP16, both encoded in the parasitic protozoa T. gondii; Cyclon, a mostly uncharacterized protein involved in the regulation of immune cell death; STIM1, a protein essential for regulating calcium levels in the endoplasmic reticulum. We show that the overrepresented GO terms are consistent with recently-reported biological functions. We implemented the method in the web server IDD Navigator. IDD Navigator is available at http://sysimm.ifrec.osaka-u.ac.jp/disorder/beta.php.