Assessing the Impact of Sample Heterogeneity on Transcriptome Analysis of Human Diseases Using MDP Webtool.

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 http://mdp.sysbio.tools.

CEMiTool: a Bioconductor package for performing comprehensive modular co-expression analyses.

BACKGROUND: The 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. RESULTS: In 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. CONCLUSION: The 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.

Comparison of T Cell Activities Mediated by Human TCRs and CARs That Use the Same Recognition Domains.

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 TCRs or synthetic chimeric Ag receptors (CARs). TCRs recognize peptide-HLA complexes whereas CARs typically use an Ab-derived single-chain fragments variable that recognizes cancer-associated cell-surface Ags. Although 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. In this study we describe their direct comparison by using TCRs that could be formatted either as conventional αß heterodimers, or as single-chain fragments variable constructs linked to CD3ζ and CD28 signaling domains or to CD3ζ alone. Two high-affinity TCRs (KD values of ∼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 Ag density. Although CARs were expressed at higher surface levels than TCRs, they were 10-100-fold less sensitive, even in the absence of the CD8 coreceptor. Mathematical modeling demonstrated that lower CAR sensitivity could be attributed to less efficient signaling kinetics. Furthermore, reduced cytokine secretion observed at high Ag density for both TCRs and CARs suggested a role for negative regulators in both systems. Interestingly, at high Ag density, CARs also mediated greater maximal release of some cytokines, such as IL-2 and IL-6. These results have implications for the next-generation design of receptors used in adoptive T cell therapies.

Systems Vaccinology Applied to DNA Vaccines: Perspective and Challenges.

DNA vaccination represents a new milestone in our technological efforts to avoid infectious diseases. Although this method of vaccination has had success in providing protection in animals, these vaccines suffer from low immunogenicity in humans. Questions remain over the molecular mechanism of DNA vaccination, the best ways in which to safely increase vaccine reactogenecity, and what biomarkers can be used as correlates of protection. Systems vaccinology, which utilizes modern experimental and computational approaches to provide an integrated view of the vaccination process, offers the potential to answer these questions. In this review we discuss the current tools utilized in systems vaccinology, the ways in which they have and can be applied to DNA vaccinology, and challenges faced in the field.

Architecture of a minimal signaling pathway explains the T-cell response to a 1 million-fold variation in antigen affinity and dose.

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.

Remarkably low affinity of CD4/peptide-major histocompatibility complex class II protein interactions.

The αß 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 Kd of ∼5,000 molecules/µm(2) This value is two to three orders of magnitude higher than previously measured 2D Kd 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.

SpyAvidin hubs enable precise and ultrastable orthogonal nanoassembly.

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:ß2-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.

Phenotypic models of T cell activation.

T cell activation is a crucial checkpoint in adaptive immunity, and this activation depends on the binding parameters that govern the interactions between T cell receptors (TCRs) and peptide-MHC complexes (pMHC complexes). Despite extensive experimental studies, the relationship between the TCR-pMHC binding parameters and T cell activation remains controversial. To make sense of conflicting experimental data, a variety of verbal and mathematical models have been proposed. However, it is currently unclear which model or models are consistent or inconsistent with experimental data. A key problem is that a direct comparison between the models has not been carried out, in part because they have been formulated in different frameworks. For this Analysis article, we reformulated published models of T cell activation into phenotypic models, which allowed us to directly compare them. We find that a kinetic proofreading model that is modified to include limited signalling is consistent with the majority of published data. This model makes the intriguing prediction that the stimulation hierarchy of two different pMHC complexes (or two different TCRs that are specific for the same pMHC complex) may reverse at different pMHC concentrations.