Shunsuke Teraguchi

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.

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

Significance Correlation of expression between genes can offer useful hints regarding their function or underlying regulatory mechanism. Today, large amounts of expression data are publicly available, allowing researchers to estimate expression correlation over thousands of samples. However, extracting information from correlation data is not straightforward, because underlying expression data are generated by different laboratories working on different cell types and under different conditions. Here we present Immuno-Navigator, a database for correlation of expression in cells of the immune system, which addresses these issues. We present examples of ways our database can be used for generating hypotheses for further experimental analysis. We demonstrate how it recapitulates known facts in immunology and successfully predicts key regulators in naturally occurring regulatory T cells. 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 β8 (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

BackgroundImmune 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.MethodsTo 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.ResultsThe 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.ConclusionsThe 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.

Systems biology approaches to toll-like receptor signaling

Toll‐like receptor (TLR) signaling pathways constitute an evolutionarily conserved host defense system that protects against a broad range of infectious agents. Modeling of TLR signaling has been carried out at several levels. Structural models of TLRs and their adaptors, which utilize a small number of structural domains to recognize a diverse range of pathogens, provide a starting point for understanding how pathogens are recognized and signaling events initiated. Various experimental and computational techniques have been used to construct models of downstream signal transduction networks from the measurements of gene expression and chromatin structure under resting and perturbed conditions along with predicted regulatory sequence motifs. Although a complete and accurate mathematical model of all TLR signaling pathways has yet to be derived, many important modules have been identified and investigated, enhancing our understanding of innate immune responses. Extensions of these models based on emerging experimental techniques are discussed. WIREs Syst Biol Med 2012 doi: 10.1002/wsbm.1178

Intrinsically disordered domains deviate significantly from random sequences in mammalian proteins

BackgroundIn 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.ResultsOverall, 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.ConclusionsOur 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.

Stochastic binary modeling of cells in continuous time as an alternative to biochemical reaction equations.

We have developed a coarse-grained formulation for modeling the dynamic behavior of cells quantitatively, based on stochasticity and heterogeneity, rather than on biochemical reactions. We treat each reaction as a continuous-time stochastic process, while reducing each biochemical quantity to a binary value at the level of individual cells. The system can be analytically represented by a finite set of ordinary linear differential equations, which provides a continuous time course prediction of each molecular state. Here we introduce our formalism and demonstrate it with several examples.

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.

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

BackgroundIdentification 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.ResultsWe 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.ConclusionsOur 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/.

Dynamics of enhancers in myeloid antigen presenting cells upon LPS stimulation

BackgroundRecent 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.ResultsWe 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/EBPβ, 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.ConclusionsThe results presented in this study reveal the complexity and dynamics of TF- enhancer binding before and after stimulation in myeloid APCs.

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.