PairGP: Gaussian process modeling of longitudinal data from paired multi-condition studies.

High-throughput technologies produce gene expression time-series data that need fast and specialized algorithms to be processed. While current methods already deal with different aspects, such as the non-stationarity of the process and the temporal correlation, they often fail to take into account the pairing among replicates. We propose PairGP, a non-stationary Gaussian process method to compare gene expression time-series across several conditions that can account for paired longitudinal study designs and can identify groups of conditions that have different gene expression dynamics. We demonstrate the method on both simulated data and previously unpublished RNA sequencing (RNA-seq) time-series with five conditions. The results show the advantage of modeling the pairing effect to better identify groups of conditions with different dynamics. The pairing effect model displays good capabilities of selecting the most probable grouping of conditions even in the presence of a high number of conditions. The developed method is of general application and can be applied to any gene expression time series dataset. The model can identify common replicate effects among the samples coming from the same biological replicates and model those as separate components. Learning the pairing effect as a separate component, not only allows us to exclude it from the model to get better estimates of the condition effects, but also to improve the precision of the model selection process. The pairing effect that was accounted before as noise, is now identified as a separate component, resulting in more accurate and explanatory models of the data.

Time-resolved transcriptome and proteome landscape of human regulatory T cell (Treg) differentiation reveals novel regulators of FOXP3.

BACKGROUND: Regulatory T cells (Tregs) expressing the transcription factor FOXP3 are crucial mediators of self-tolerance, preventing autoimmune diseases but possibly hampering tumor rejection. Clinical manipulation of Tregs is of great interest, and first-in-man trials of Treg transfer have achieved promising outcomes. Yet, the mechanisms governing induced Treg (iTreg) differentiation and the regulation of FOXP3 are incompletely understood. RESULTS: To gain a comprehensive and unbiased molecular understanding of FOXP3 induction, we performed time-series RNA sequencing (RNA-Seq) and proteomics profiling on the same samples during human iTreg differentiation. To enable the broad analysis of universal FOXP3-inducing pathways, we used five differentiation protocols in parallel. Integrative analysis of the transcriptome and proteome confirmed involvement of specific molecular processes, as well as overlap of a novel iTreg subnetwork with known Treg regulators and autoimmunity-associated genes. Importantly, we propose 37 novel molecules putatively involved in iTreg differentiation. Their relevance was validated by a targeted shRNA screen confirming a functional role in FOXP3 induction, discriminant analyses classifying iTregs accordingly, and comparable expression in an independent novel iTreg RNA-Seq dataset. CONCLUSION: The data generated by this novel approach facilitates understanding of the molecular mechanisms underlying iTreg generation as well as of the concomitant changes in the transcriptome and proteome. Our results provide a reference map exploitable for future discovery of markers and drug candidates governing control of Tregs, which has important implications for the treatment of cancer, autoimmune, and inflammatory diseases.

Transcriptional Repressor HIC1 Contributes to Suppressive Function of Human Induced Regulatory T Cells.

Regulatory T (Treg) cells are critical in regulating the immune response. In vitro induced Treg (iTreg) cells have significant potential in clinical medicine. However, applying iTreg cells as therapeutics is complicated by the poor stability of human iTreg cells and their variable suppressive activity. Therefore, it is important to understand the molecular mechanisms of human iTreg cell specification. We identified hypermethylated in cancer 1 (HIC1) as a transcription factor upregulated early during the differentiation of human iTreg cells. Although FOXP3 expression was unaffected, HIC1 deficiency led to a considerable loss of suppression by iTreg cells with a concomitant increase in the expression of effector T cell associated genes. SNPs linked to several immune-mediated disorders were enriched around HIC1 binding sites, and in vitro binding assays indicated that these SNPs may alter the binding of HIC1. Our results suggest that HIC1 is an important contributor to iTreg cell development and function.

TET proteins regulate the lineage specification and TCR-mediated expansion of iNKT cells.

TET proteins oxidize 5-methylcytosine in DNA to 5-hydroxymethylcytosine and other oxidation products. We found that simultaneous deletion of Tet2 and Tet3 in mouse CD4+CD8+ double-positive thymocytes resulted in dysregulated development and proliferation of invariant natural killer T cells (iNKT cells). Tet2-Tet3 double-knockout (DKO) iNKT cells displayed pronounced skewing toward the NKT17 lineage, with increased DNA methylation and impaired expression of genes encoding the key lineage-specifying factors T-bet and ThPOK. Transfer of purified Tet2-Tet3 DKO iNKT cells into immunocompetent recipient mice resulted in an uncontrolled expansion that was dependent on the nonclassical major histocompatibility complex (MHC) protein CD1d, which presents lipid antigens to iNKT cells. Our data indicate that TET proteins regulate iNKT cell fate by ensuring their proper development and maturation and by suppressing aberrant proliferation mediated by the T cell antigen receptor (TCR).

A subpopulation model to analyze heterogeneous cell differentiation dynamics.

MOTIVATION: Cell differentiation is steered by extracellular signals that activate a cell type specific transcriptional program. Molecular mechanisms that drive the differentiation can be analyzed by combining mathematical modeling with population average data. For standard mathematical models, the population average data is informative only if the measurements come from a homogeneous cell culture. In practice, however, the differentiation efficiencies are always imperfect. Consequently, cell cultures are inherently mixtures of several cell types, which have different molecular mechanisms and exhibit quantitatively different dynamics. There is an urgent need for data-driven mathematical modeling approaches that can detect possible heterogeneity and, further, recover the molecular mechanisms from heterogeneous data. RESULTS: We develop a novel method that models a heterogeneous population using homogeneous subpopulations that evolve in parallel. Different subpopulations can represent different cell types and each subpopulation can have cell type specific molecular mechanisms. We present statistical methodology that can be used to quantify the effect of heterogeneity and to infer the subpopulation specific molecular interactions. After a proof of principle study with simulated data, we apply our methodology to analyze the differentiation of human Th17 cells using time-course RNA sequencing data. We construct putative molecular networks driving the T cell activation and Th17 differentiation and allow the cell populations to be split into two subpopulations in the case of heterogeneous samples. Our analysis shows that the heterogeneity indeed has a statistically significant effect on observed dynamics and, furthermore, our statistical methodology can infer both the subpopulation specific molecular mechanisms and the effect of heterogeneity. AVAILABILITY AND IMPLEMENTATION: An implementation of the method is available at http://research.ics.aalto.fi/csb/software/subpop/ CONTACT: jukka.intosalmi@aalto.fi or harri.lahdesmaki@aalto.fiSupplementary information: Supplementary data are available at Bioinformatics online.

Comparative analysis of human and mouse transcriptomes of Th17 cell priming.

Uncontrolled Th17 cell activity is associated with cancer and autoimmune and inflammatory diseases. To validate the potential relevance of mouse models of targeting the Th17 pathway in human diseases we used RNA sequencing to compare the expression of coding and non-coding transcripts during the priming of Th17 cell differentiation in both human and mouse. In addition to already known targets, several transcripts not previously linked to Th17 cell polarization were found in both species. Moreover, a considerable number of human-specific long non-coding RNAs were identified that responded to cytokines stimulating Th17 cell differentiation. We integrated our transcriptomics data with known disease-associated polymorphisms and show that conserved regulation pinpoints genes that are relevant to Th17 cell-mediated human diseases and that can be modelled in mouse. Substantial differences observed in non-coding transcriptomes between the two species as well as increased overlap between Th17 cell-specific gene expression and disease-associated polymorphisms underline the need of parallel analysis of human and mouse models. Comprehensive analysis of genes regulated during Th17 cell priming and their classification to conserved and non-conserved between human and mouse facilitates translational research, pointing out which candidate targets identified in human are worth studying by using in vivo mouse models.

MixChIP: a probabilistic method for cell type specific protein-DNA binding analysis.

BACKGROUND: Transcription factors (TFs) are proteins that bind to DNA and regulate gene expression. To understand details of gene regulation, characterizing TF binding sites in different cell types, diseases and among individuals is essential. However, sometimes TF binding can only be measured from biological samples that contain multiple cell or tissue types. Sample heterogeneity can have a considerable effect on TF binding site detection. While manual separation techniques can be used to isolate a cell type of interest from heterogeneous samples, such techniques are challenging and can change intra-cellular interactions, including protein-DNA binding. Computational deconvolution methods have emerged as an alternative strategy to study heterogeneous samples and numerous methods have been proposed to analyze gene expression. However, no computational method exists to deconvolve cell type specific TF binding from heterogeneous samples. RESULTS: We present a probabilistic method, MixChIP, to identify cell type specific TF binding sites from heterogeneous chromatin immunoprecipitation sequencing (ChIP-seq) data. Our method simultaneously estimates the binding strength in different cell types as well as the proportions of different cell types in each sample when only partial prior information about cell type composition is available. We demonstrate the utility of MixChIP by analyzing ChIP-seq data from two cell lines which we artificially mix to generate (simulated) heterogeneous samples and by analyzing ChIP-seq data from breast cancer patients measuring oestrogen receptor (ER) binding in primary breast cancer tissues. We show that MixChIP is more accurate in detecting TF binding sites from multiple heterogeneous ChIP-seq samples than the standard methods which do not account for sample heterogeneity. CONCLUSIONS: Our results show that MixChIP can estimate cell-type proportions and identify cell type specific TF binding sites from heterogeneous ChIP-seq samples. Thus, MixChIP can be an invaluable tool in analyzing heterogeneous ChIP-seq samples, such as those originating from cancer studies. R implementation is available at http://research.ics.aalto.fi/csb/software/mixchip/ .

Analyzing Th17 cell differentiation dynamics using a novel integrative modeling framework for time-course RNA sequencing data.

BACKGROUND: The differentiation of naive CD 4(+) helper T (Th) cells into effector Th17 cells is steered by extracellular cytokines that activate and control the lineage specific transcriptional program. While the inducing cytokine signals and core transcription factors driving the differentiation towards Th17 lineage are well known, detailed mechanistic interactions between the key components are poorly understood. RESULTS: We develop an integrative modeling framework which combines RNA sequencing data with mathematical modeling and enables us to construct a mechanistic model for the core Th17 regulatory network in a data-driven manner. CONCLUSIONS: Our results show significant evidence, for instance, for inhibitory mechanisms between the transcription factors and reveal a previously unknown dependency between the dosage of the inducing cytokine TGF ß and the expression of the master regulator of competing (induced) regulatory T cell lineage. Further, our experimental validation approves this dependency in Th17 polarizing conditions.

GIMAP GTPase family genes: potential modifiers in autoimmune diabetes, asthma, and allergy.

GTPase of the immunity-associated protein (GIMAP) family members are differentially regulated during human Th cell differentiation and have been previously connected to immune-mediated disorders in animal studies. GIMAP4 is believed to contribute to the Th cell subtype-driven immunological balance via its role in T cell survival. GIMAP5 has a key role in BB-DR rat and NOD mouse lymphopenia. To elucidate GIMAP4 and GIMAP5 function and role in human immunity, we conducted a study combining genetic association in different immunological diseases and complementing functional analyses. Single nucleotide polymorphisms tagging the GIMAP haplotype variation were genotyped in Finnish type 1 diabetes (T1D) families and in a prospective Swedish asthma and allergic sensitization birth cohort. Initially, GIMAP5 rs6965571 was associated with risk for asthma and allergic sensitization (odds ratio [OR] 3.74, p = 0.00072, and OR 2.70, p = 0.0063, respectively) and protection from T1D (OR 0.64, p = 0.0058); GIMAP4 rs13222905 was associated with asthma (OR 1.28, p = 0.035) and allergic sensitization (OR 1.27, p = 0.0068). However, after false discovery rate correction for multiple testing, only the associations of GIMAP4 with allergic sensitization and GIMAP5 with asthma remained significant. In addition, transcription factor binding sites surrounding the associated loci were predicted. A gene-gene interaction in the T1D data were observed between the IL2RA rs2104286 and GIMAP4 rs9640279 (OR 1.52, p = 0.0064) and indicated between INS rs689 and GIMAP5 rs2286899. The follow-up functional analyses revealed lower IL-2RA expression upon GIMAP4 knockdown and an effect of GIMAP5 rs2286899 genotype on protein expression. Thus, the potential role of GIMAP4 and GIMAP5 as modifiers of immune-mediated diseases cannot be discarded.

Ca2+-calmodulin-dependent protein kinase II represses cardiac transcription of the L-type calcium channel alpha(1C)-subunit gene (Cacna1c) by DREAM translocation.

Recent studies have demonstrated that changes in the activity of calcium-calmodulin-dependent protein kinase II (CaMKII) induce a unique cardiomyocyte phenotype through the regulation of specific genes involved in excitation-contraction (E-C)-coupling. To explain the transcriptional effects of CaMKII we identified a novel CaMKII-dependent pathway for controlling the expression of the pore-forming α-subunit (Cav1.2) of the L-type calcium channel (LTCC) in cardiac myocytes. We show that overexpression of either cytosolic (δC) or nuclear (δB) CaMKII isoforms selectively downregulate the expression of the Cav1.2. Pharmacological inhibition of CaMKII activity induced measurable changes in LTCC current density and subsequent changes in cardiomyocyte calcium signalling in less than 24 h. The effect of CaMKII on the α1C-subunit gene (Cacna1c) promoter was abolished by deletion of the downstream regulatory element (DRE), which binds transcriptional repressor DREAM/calsenilin/KChIP3. Imaging DREAM-GFP (green fluorescent protein)-expressing cardiomyocytes showed that CaMKII potentiates the calcium-induced nuclear translocation of DREAM. Thereby CaMKII increases DREAM binding to the DRE consensus sequence of the endogenous Cacna1c gene. By mathematical modelling we demonstrate that the LTCC downregulation through the Ca2+-CaMKII-DREAM cascade constitutes a physiological feedback mechanism enabling cardiomyocytes to adjust the calcium intrusion through LTCCs to the amount of intracellular calcium detected by CaMKII.

Functional characterization of ERp18, a new endoplasmic reticulum-located thioredoxin superfamily member.

Native disulfide bond formation in the endoplasmic reticulum is a critical process in the maturation of many secreted and outer membrane proteins. Although a large number of proteins have been implicated in this process, it is clear that our current understanding is far from complete. Here we describe the functional characterization of a new 18-kDa protein (ERp18) related to protein-disulfide isomerase. We show that ERp18 is located in the endoplasmic reticulum and that it contains a single catalytic domain with an unusual CGAC active site motif and a probable insertion between beta3 and alpha3 of the thioredoxin fold. From circular dichroism and NMR measurements, ERp18 is well structured and undergoes only a minor conformational change upon dithioldisulfide exchange in the active site. Guanidinium chloride denaturation curves indicate that the reduced form of the protein is more stable than the oxidized form, suggesting that it is involved in disulfide bond formation. Furthermore, in vitro ERp18 possesses significant peptide thiol-disulfide oxidase activity, which is dependent on the presence of both active site cysteine residues. This activity differs from that of the human PDI family in that under standard assay conditions it is limited by substrate oxidation and not by enzyme reoxidation. A putative physiological role for Erp18 in native disulfide bond formation is discussed.