BackgroundThe analysis of modular gene co-expression networks is a well-established method commonly used for discovering the systems-level functionality of genes. In addition, these studies provide a basis for the discovery of clinically relevant molecular pathways underlying different diseases and conditions.ResultsIn this paper, we present a fast and easy-to-use Bioconductor package named CEMiTool that unifies the discovery and the analysis of co-expression modules. Using the same real datasets, we demonstrate that CEMiTool outperforms existing tools, and provides unique results in a user-friendly html report with high quality graphs. Among its features, our tool evaluates whether modules contain genes that are over-represented by specific pathways or that are altered in a specific sample group, as well as it integrates transcriptomic data with interactome information, identifying the potential hubs on each network. We successfully applied CEMiTool to over 1000 transcriptome datasets, and to a new RNA-seq dataset of patients infected with Leishmania, revealing novel insights of the disease’s physiopathology.ConclusionThe CEMiTool R package provides users with an easy-to-use method to automatically implement gene co-expression network analyses, obtain key information about the discovered gene modules using additional downstream analyses and retrieve publication-ready results via a high-quality interactive report.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2053-1) contains supplementary material, which is available to authorized users.

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T cells must respond differently to antigens of varying affinity presented at different doses. Previous attempts to map peptide MHC (pMHC) affinity onto T-cell responses have produced inconsistent patterns of responses, preventing formulations of canonical models of T-cell signaling. Here, a systematic analysis of T-cell responses to 1 million-fold variations in both pMHC affinity and dose produced bell-shaped dose–response curves and different optimal pMHC affinities at different pMHC doses. Using sequential model rejection/identification algorithms, we identified a unique, minimal model of cellular signaling incorporating kinetic proofreading with limited signaling coupled to an incoherent feed-forward loop (KPL-IFF) that reproduces these observations. We show that the KPL-IFF model correctly predicts the T-cell response to antigen copresentation. Our work offers a general approach for studying cellular signaling that does not require full details of biochemical pathways.

Adoptive T cell therapies have achieved significant clinical responses, especially in hematopoietic cancers. Two types of receptor systems have been used to redirect the activity of T cells, normal heterodimeric T-cell receptors (TCRs) or synthetic chimeric antigen receptors (CARs). TCRs recognize peptide-HLA complexes whereas CARs typically use an antibody-derived scFv (single-chain fragments variable) that recognizes cancer-associated cell-surface antigens. While both receptors mediate diverse effector functions, a quantitative comparison of the sensitivity and signaling capacity of TCRs and CARs has been limited due to their differences in affinities and ligands. Here we describe their direct comparison by using TCRs that could be formatted either as conventional αβ heterodimers, or as scFv constructs linked to CD3ζ and CD28 signaling domains or to CD3ζ only. Two high-affinity TCRs (KD values of approximately 50 and 250 nM) against MART1/HLA-A2 or WT1/HLA-A2 were used, allowing MART1 or WT1 peptide titrations to easily assess the impact of antigen density. Although CARs were expressed at higher surface levels than TCRs, they were 10 to 100-fold less sensitive, even in the absence of the CD8 co-receptor. Mathematical modeling demonstrated that lower CAR sensitivity could be attributed to less efficient signaling kinetics. Furthermore, reduced cytokine secretion observed at high antigen density for both TCRs and CARs suggested a role for negative regulators in both systems. Interestingly, at high antigen density, CARs also mediated greater maximal release of some cytokines, such as IL-2 and IL-6. These results have implications for next-generation design of receptors used in adoptive T cell therapies.

The αβ T-cell coreceptor CD4 enhances immune responses more than 1 million-fold in some assays, and yet the affinity of CD4 for its ligand, peptide-major histocompatibility class II (pMHC II) on antigen-presenting cells, is so weak that it was previously unquantifiable. Here, we report that a soluble form of CD4 failed to bind detectably to pMHC II in surface plasmon resonance-based assays, establishing a new upper limit for the solution affinity at 2.5 mM. However, when presented multivalently on magnetic beads, soluble CD4 bound pMHC II-expressing B cells, confirming that it is active and allowing mapping of the native coreceptor binding site on pMHC II. Whereas binding was undetectable in solution, the affinity of the CD4/pMHC II interaction could be measured in 2D using CD4-and adhesion molecule-functionalized, supported lipid bilayers, yielding a 2D K d of ∼5,000 molecules/μm 2 . This value is two to three orders of magnitude higher than previously measured 2D K d values for interacting leukocyte surface proteins. Calculations indicated, however, that CD4/pMHC II binding would increase rates of T-cell receptor (TCR) complex phosphorylation by threefold via the recruitment of Lck, with only a small, 2-20% increase in the effective affinity of the TCR for pMHC II. The affinity of CD4/pMHC II therefore seems to be set at a value that increases T-cell sensitivity by enhancing phosphorylation, without compromising ligand discrimination. T cells with αβ T-cell receptors (TCRs) comprise functionally distinct subsets depending on which transcription factors and which of two coreceptors, CD8 or CD4, they express. CD8 + T cells respond to peptide agonists presented by major histocompatibility class I molecules (pMHC I) and are cytotoxic, whereas conventional CD4 + cells recognize peptide-MHC class II (pMHC II) and provide "help" defined by the cytokines they secrete (1). Cell adhesion assays explain this functionality insofar as CD8 and CD4 bind directly to pMHC I and pMHC II, respectively (2, 3). CD4 comprises two pairs of V-set and C2-set Ig superfamily domains, with early mutational data showing that the "top" two domains bind pMHC II (4). Crystal structures of cross-species and affinitymatured CD4/pMHC II complexes suggest that CD4 binds a pocket formed by the α2 and β2 domains of pMHC II (5, 6). The role of coreceptors in heightening T-cell responses is well established. For example, whereas CD4 + T-cells can respond to single pMHC II complexes presented by antigen-presenting cells (APCs), 30 or more complexes are required if CD4 is blocked (7).How CD4 achieves these effects, however, is incompletely understood. Coreceptors are pivotal in recruiting the kinase Lck to TCR/pMHC complexes (8, 9), but for reasons that are unclear coreceptor/pMHC interactions are extraordinarily weak. Traditionally, weak protein interactions are characterized using surface plasmon resonance (SPR) measurements, where one protein is tethered to the sensor surface and over it the other is passed at various concentrations. The SPR-based ...

The capture of biotin by streptavidin is an inspiration for supramolecular chemistry and a central tool for biological chemistry and nanotechnology, because of the rapid and exceptionally stable interaction. However, there is no robust orthogonal interaction to this hub, limiting the size and complexity of molecular assemblies that can be created. Here we combined traptavidin (a streptavidin variant maximizing biotin binding strength) with an orthogonal irreversible interaction. SpyTag is a peptide engineered to form a spontaneous isopeptide bond to its protein partner SpyCatcher. SpyTag or SpyCatcher was successfully fused to the C-terminus of Dead streptavidin subunits. We were able to generate chimeric tetramers with n (0 ≤ n ≤ 4) biotin binding sites and 4-n SpyTag or SpyCatcher binding sites. Chimeric SpyAvidin tetramers bound precise numbers of ligands fused to biotin or SpyTag/SpyCatcher. Mixing chimeric tetramers enabled assembly of SpyAvidin octamers (8 subunits) or eicosamers (20 subunits). We validated assemblies using electrophoresis and native mass spectrometry. Eicosameric SpyAvidin was used to cluster trimeric major histocompatibility complex (MHC) class I:β2-microglobulin:peptide complexes, generating an assembly with up to 56 components. MHC eicosamers surpassed the conventional MHC tetramers in acting as a powerful stimulus to T cell signaling. Combining ultrastable noncovalent with irreversible covalent interaction, SpyAvidins enable a simple route to create robust nanoarchitectures.

T cells must respond differently to antigens of varying affinity presented at different doses. Previous attempts to map pMHC affinity onto T cell responses have produced inconsistent patterns of responses preventing formulations of canonical models of T cell signalling. Here, a systematic analysis of T cell responses to 1,000,000-fold variations in both pMHC affinity and dose produced bell-shaped dose-response curves and different optimal pMHC affinities at different pMHC doses. Using sequential model rejection/identification algorithms, we identified a unique, minimal model of cellular signalling incorporating kinetic proofreading with limited signalling coupled to an incoherent feed forward loop (KPL-IFF), that reproduces these observations. We show that the KPL-IFF model correctly predicts the T cell response to antigen co-presentation. Our work offers a general approach for studying cellular signalling that does not require full details of biochemical pathways. Significance statementT cells initiate and regulate adaptive immune responses when their T cell antigen receptors recognise antigens. The T cell response is known to depend on the antigen affinity/dose but the precise relationship, and the mechanisms underlying it, are debated. To resolve the debate, we stimulated T cells with antigens spanning a 1,000,000-fold range in affinity/dose. We found that a different antigen (and hence different affinity) produced the largest T cell response at different doses. Using model identification algorithms, we report a simple mechanistic model that can predict the T cell response from the physiological low affinity regime into the high affinity regime applicable to therapeutic receptors.

Transcriptome analyses have increased our understanding of the molecular mechanisms underlying human diseases. Most approaches aim to identify significant genes by comparing their expression values between healthy subjects and a group of patients with a certain disease. Given that studies normally contain few samples, the heterogeneity among individuals caused by environmental factors or undetected illnesses can impact gene expression analyses. We present a systematic analysis of sample heterogeneity in a variety of gene expression studies relating to inflammatory and infectious diseases and show that novel immunological insights may arise once heterogeneity is addressed. The perturbation score of samples is quantified using nonperturbed subjects (i.e., healthy subjects) as a reference group. Such a score allows us to detect outlying samples and subgroups of diseased patients and even assess the molecular perturbation of single cells infected with viruses. We also show how removal of outlying samples can improve the “signal” of the disease and impact detection of differentially expressed genes. The method is made available via the mdp Bioconductor R package and as a user-friendly webtool, webMDP, available at .