Jesper Tegnér

IL-1β promotes Th17 differentiation by inducing alternative splicing of FOXP3.

CD4+FOXP3+ regulatory T (Treg) cells are essential for maintaining immunological self-tolerance. Treg cell development and function depend on the transcription factor FOXP3, which is present in several distinct isoforms due to alternative splicing. Despite the importance of FOXP3 in the proper maintenance of Treg cells, the regulation and functional consequences of FOXP3 isoform expression remains poorly understood. Here, we show that in human Treg cells IL-1β promotes excision of FOXP3 exon 7. FOXP3 is not only expressed by Treg cells but is also transiently expressed when naïve T cells differentiate into Th17 cells. Forced splicing of FOXP3 into FOXP3Δ2Δ7 strongly favored Th17 differentiation in vitro. We also found that patients with Crohn’s disease express increased levels of FOXP3 transcripts lacking exon 7, which correlate with disease severity and IL-17 production. Our results demonstrate that alternative splicing of FOXP3 modulates T cell differentiation. These results highlight the importance of characterizing FOXP3 expression on an isoform basis and suggest that immune responses may be manipulated by modulating the expression of FOXP3 isoforms, which has broad implications for the treatment of autoimmune diseases.

Functional genomics analysis of vitamin D effects on CD4+ T cells in vivo in experimental autoimmune encephalomyelitis

Significance Vitamin D has been suggested to be associated with beneficial immunomodulation in autoimmune diseases. We demonstrate that the protective effect of vitamin D in an animal model of multiple sclerosis (MS) is linked to multiple signaling and metabolic pathways critical for T-cell activation and differentiation into pathogenic T helper (Th) 1 and Th17 subsets in vivo. This effect is mediated by epigenetic mechanisms as reflected by genome-wide reduction of DNA methylation and upregulation of microRNAs, with concomitant downregulation of their protein-coding target genes. Our data support the role of vitamin D in modulating risk for human disease, because orthologues of nearly 50% of MS candidate risk genes changed their expression in vivo in CD4+ T cells upon vitamin D supplementation. Vitamin D exerts multiple immunomodulatory functions and has been implicated in the etiology and treatment of several autoimmune diseases, including multiple sclerosis (MS). We have previously reported that in juvenile/adolescent rats, vitamin D supplementation protects from experimental autoimmune encephalomyelitis (EAE), a model of MS. Here we demonstrate that this protective effect associates with decreased proliferation of CD4+ T cells and lower frequency of pathogenic T helper (Th) 17 cells. Using transcriptome, methylome, and pathway analyses in CD4+ T cells, we show that vitamin D affects multiple signaling and metabolic pathways critical for T-cell activation and differentiation into Th1 and Th17 subsets in vivo. Namely, Jak/Stat, Erk/Mapk, and Pi3K/Akt/mTor signaling pathway genes were down-regulated upon vitamin D supplementation. The protective effect associated with epigenetic mechanisms, such as (i) changed levels of enzymes involved in establishment and maintenance of epigenetic marks, i.e., DNA methylation and histone modifications; (ii) genome-wide reduction of DNA methylation, and (iii) up-regulation of noncoding RNAs, including microRNAs, with concomitant down-regulation of their protein-coding target RNAs involved in T-cell activation and differentiation. We further demonstrate that treatment of myelin-specific T cells with vitamin D reduces frequency of Th1 and Th17 cells, down-regulates genes in key signaling pathways and epigenetic machinery, and impairs their ability to transfer EAE. Finally, orthologs of nearly 50% of candidate MS risk genes and 40% of signature genes of myelin-reactive T cells in MS changed their expression in vivo in EAE upon supplementation, supporting the hypothesis that vitamin D may modulate risk for developing MS.

An Algorithmic Information Calculus for Causal Discovery and Reprogramming Systems.

We introduce a new conceptual framework and a model-based interventional calculus to steer, manipulate, and reconstruct the dynamics and generating mechanisms of non-linear dynamical systems from partial and disordered observations based on the contributions of each of the systems, by exploiting first principles from the theory of computability and algorithmic information. This calculus entails finding and applying controlled interventions to an evolving object to estimate how its algorithmic information content is affected in terms of positive or negative shifts towards and away from randomness in connection to causation. The approach is an alternative to statistical approaches for inferring causal relationships and formulating theoretical expectations from perturbation analysis. We find that the algorithmic information landscape of a system runs parallel to its dynamic attractor landscape, affording an avenue for moving systems on one plane so they can be controlled on the other plane. Based on these methods, we advance tools for reprogramming a system that do not require full knowledge or access to the system’s actual kinetic equations or to probability distributions. This new approach yields a suite of universal parameter-free algorithms of wide applicability, ranging from the discovery of causality, dimension reduction, feature selection, model generation, a maximal algorithmic-randomness principle and a system’s (re)programmability index. We apply these methods to static (e.coli Transcription Factor network) and to evolving genetic regulatory networks (differentiating naïve from Th17 cells, and the CellNet database). We highlight their ability to pinpoint key elements (genes) related to cell function and cell development, conforming to biological knowledge from experimentally validated data and the literature, and demonstrate how the method can reshape a system’s dynamics in a controlled manner through algorithmic causal mechanisms.

The Information-Theoretic and Algorithmic Approach to Human, Animal, and Artificial Cognition

We survey concepts at the frontier of research connecting artificial, animal, and human cognition to computation and information processing—from the Turing test to Searle’s Chinese room argument, from integrated information theory to computational and algorithmic complexity. We start by arguing that passing the Turing test is a trivial computational problem and that its pragmatic difficulty sheds light on the computational nature of the human mind more than it does on the challenge of artificial intelligence. We then review our proposed algorithmic information-theoretic measures for quantifying and characterizing cognition in various forms. These are capable of accounting for known biases in human behavior, thus vindicating a computational algorithmic view of cognition as first suggested by Turing, but this time rooted in the concept of algorithmic probability, which in turn is based on computational universality while being independent of computational model, and which has the virtue of being predictive and testable as a model theory of cognitive behavior.

Dynamics and heterogeneity of brain damage in multiple sclerosis

Multiple Sclerosis (MS) is an autoimmune disease driving inflammatory and degenerative processes that damage the central nervous system (CNS). However, it is not well understood how these events interact and evolve to evoke such a highly dynamic and heterogeneous disease. We established a hypothesis whereby the variability in the course of MS is driven by the very same pathogenic mechanisms responsible for the disease, the autoimmune attack on the CNS that leads to chronic inflammation, neuroaxonal degeneration and remyelination. We propose that each of these processes acts more or less severely and at different times in each of the clinical subgroups. To test this hypothesis, we developed a mathematical model that was constrained by experimental data (the expanded disability status scale [EDSS] time series) obtained from a retrospective longitudinal cohort of 66 MS patients with a long-term follow-up (up to 20 years). Moreover, we validated this model in a second prospective cohort of 120 MS patients with a three-year follow-up, for which EDSS data and brain volume time series were available. The clinical heterogeneity in the datasets was reduced by grouping the EDSS time series using an unsupervised clustering analysis. We found that by adjusting certain parameters, albeit within their biological range, the mathematical model reproduced the different disease courses, supporting the dynamic CNS damage hypothesis to explain MS heterogeneity. Our analysis suggests that the irreversible axon degeneration produced in the early stages of progressive MS is mainly due to the higher rate of myelinated axon degeneration, coupled to the lower capacity for remyelination. However, and in agreement with recent pathological studies, degeneration of chronically demyelinated axons is not a key feature that distinguishes this phenotype. Moreover, the model reveals that lower rates of axon degeneration and more rapid remyelination make relapsing MS more resilient than the progressive subtype. Therefore, our results support the hypothesis of a common pathogenesis for the different MS subtypes, even in the presence of genetic and environmental heterogeneity. Hence, MS can be considered as a single disease in which specific dynamics can provoke a variety of clinical outcomes in different patient groups. These results have important implications for the design of therapeutic interventions for MS at different stages of the disease.

Hypermethylation of MIR21 in CD4+ T cells from patients with relapsing-remitting multiple sclerosis associates with lower miRNA-21 levels and concomitant up-regulation of its target genes

Background: Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system caused by genetic and environmental factors. DNA methylation, an epigenetic mechanism that controls genome activity, may provide a link between genetic and environmental risk factors. Objective: We sought to identify DNA methylation changes in CD4+ T cells in patients with relapsing-remitting (RR-MS) and secondary-progressive (SP-MS) disease and healthy controls (HC). Methods: We performed DNA methylation analysis in CD4+ T cells from RR-MS, SP-MS, and HC and associated identified changes with the nearby risk allele, smoking, age, and gene expression. Results: We observed significant methylation differences in the VMP1/MIR21 locus, with RR-MS displaying higher methylation compared to SP-MS and HC. VMP1/MIR21 methylation did not correlate with a known MS risk variant in VMP1 or smoking but displayed a significant negative correlation with age and the levels of mature miR-21 in CD4+ T cells. Accordingly, RR-MS displayed lower levels of miR-21 compared to SP-MS, which might reflect differences in age between the groups, and healthy individuals and a significant enrichment of up-regulated miR-21 target genes. Conclusion: Disease-related changes in epigenetic marking of MIR21 in RR-MS lead to differences in miR-21 expression with a consequence on miR-21 target genes.

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

BackgroundRegulatory 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.ResultsTo 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.ConclusionThe 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.

A minimal unified model of disease trajectories captures hallmarks of multiple sclerosis

Multiple Sclerosis (MS) is an autoimmune disease targeting the central nervous system (CNS) causing demyelination and neurodegeneration leading to accumulation of neurological disability. Here we present a minimal, computational model involving the immune system and CNS that generates the principal subtypes of the disease observed in patients. The model captures several key features of MS, especially those that distinguish the chronic progressive phase from that of the relapse-remitting. In addition, a rare subtype of the disease, progressive relapsing MS naturally emerges from the model. The model posits the existence of two key thresholds, one in the immune system and the other in the CNS, that separate dynamically distinct behavior of the model. Exploring the two-dimensional space of these thresholds, we obtain multiple phases of disease evolution and these shows greater variation than the clinical classification of MS, thus capturing the heterogeneity that is manifested in patients.

Comment on “Epigenetics in the pathogenesis of RA”

Dear Editors, Rheumatoid arthritis (RA) is a chronic autoimmune disorder that affects between 0.5 and 1% of individuals in the more economically developed countries [1]. The cause of the disease is yet to be determined, but environmental and genetic factors have been shown to be associated. For this reason, investigating epigenetic mechanisms in RA has received serious attention over the last decade. The recent review published in Seminars in Immunopathology, written by Ospelt and colleagues [2], aimed to provide a timely overview of this rapidly developing field. In the abovementioned review, different epigenetic mechanisms and existing studies and results of relevance for RA were described. In the context of DNA methylation, being a key epigenetic marker, the respective quantification—and dissociation—of genetic and epigenetic contributions is a major question, and twin studies are necessary to discern them. It was concluded that in non-twin studies, mainly the major histocompatibility complex (MHC) regions have been identified as differentially methylated, while no differences have been found in twin studies. Regarding the latter, work on monozygotic twins discordant for RA by Gomez-Gabrero et al. was discussed and concluded that this study also did not present significant differential methylation between twins discordant for RA [3]. However, after discussions between the authors of the two mentioned papers, we came to the conclusion that the view presented in the mentioned review should be revised and the data should be described in greater detail. When performing differential analysis of arrays, it is indeed possible to characterize differential methylated probes (DMPs) and sets of consecutive (regions) differentially methylated probes (DMRs). While it is true that in the discussed study [3] no significant DMPs were observed within healthy and RA twin pairs, the existence of DMPs cannot be excluded because of the small number of twin pairs considered and the correction for multiple testing (approximately 2 million probes). However, statistically significant DMRswere discovered, which were validated and replicated by pyrosequencing. The changes in the pyrosequencing experiments were directionally consistent but without statistical significance. Importantly, the array methodology used in the study (CHARM) has been designed to identify DMRs rather than DMPs, and this is one reason why evidence of high DNA methylation profile correlation between close CpG sites was obtained [4]. This explains how it was possible to obtain sufficient statistical power for the identification of DMRs associated with the EXOSC1 gene, despite the small number of samples. Importantly, to gain enough power and to obtain robust results, the DMR search protocol was updated as described in [4], which was another achievement of the study, besides novel DMRs. These results are important for two reasons. Firstly, from the identified candidates, none was located within an MHC region, which was expected considering the genomic background, but it stressed the idea of the existence of epigenetically driven changes. Secondly, DMRs in ACPA+ pre-RA twin pairs were also studied and the PCDHB14 gene was identified, which was found to be of relevance also in RA This article is a contribution to the special issue on Immunopathology of Rheumatoid Arthritis Guest Editors: Cem Gabay and Paul Hasler

Predicting Causal Relationships from Biological Data: Applying Automated Causal Discovery on Mass Cytometry Data of Human Immune Cells

Learning the causal relationships that define a molecular system allows us to predict how the system will respond to different interventions. Distinguishing causality from mere association typically requires randomized experiments. Methods for automatedxa0 causal discovery from limited experiments exist, but have so far rarely been tested in systems biology applications. In this work, we apply state-of-the art causal discovery methods on a large collection of public mass cytometry data sets, measuring intra-cellular signaling proteins of the human immune system and their response to several perturbations. We show how different experimental conditions can be used to facilitate causal discovery, and apply two fundamental methods that produce context-specific causal predictions. Causal predictions were reproducible across independent data sets from two different studies, but often disagree with the KEGG pathway databases. Within this context, we discuss the caveats we need to overcome for automated causal discovery to become a part of the routine data analysis in systems biology.