SLE serum induces altered goblet cell differentiation and leakiness in human intestinal organoids.

Human intestinal epithelial cells are the interface between luminal content and basally residing immune cells. They form a tight monolayer that constantly secretes mucus creating a multilayered protective barrier. Alterations in this barrier can lead to increased permeability which is common in systemic lupus erythematosus (SLE) patients. However, it remains unexplored how the barrier is affected. Here, we present an in vitro model specifically designed to examine the effects of SLE on epithelial cells. We utilize human colon organoids that are stimulated with serum from SLE patients. Combining transcriptomic with functional analyses revealed that SLE serum induced an expression profile marked by a reduction of goblet cell markers and changed mucus composition. In addition, organoids exhibited imbalanced cellular composition along with enhanced permeability, altered mitochondrial function, and an interferon gene signature. Similarly, transcriptomic analysis of SLE colon biopsies revealed a downregulation of secretory markers. Our work uncovers a crucial connection between SLE and intestinal homeostasis that might be promoted in vivo through the blood, offering insights into the causal connection of barrier dysfunction and autoimmune diseases.

Prediction of polyspecificity from antibody sequence data by machine learning.

Antibodies are generated with great diversity in nature resulting in a set of molecules, each optimized to bind a specific target. Taking advantage of their diversity and specificity, antibodies make up for a large part of recently developed biologic drugs. For therapeutic use antibodies need to fulfill several criteria to be safe and efficient. Polyspecific antibodies can bind structurally unrelated molecules in addition to their main target, which can lead to side effects and decreased efficacy in a therapeutic setting, for example via reduction of effective drug levels. Therefore, we created a neural-network-based model to predict polyspecificity of antibodies using the heavy chain variable region sequence as input. We devised a strategy for enriching antibodies from an immunization campaign either for antigen-specific or polyspecific binding properties, followed by generation of a large sequencing data set for training and cross-validation of the model. We identified important physico-chemical features influencing polyspecificity by investigating the behaviour of this model. This work is a machine-learning-based approach to polyspecificity prediction and, besides increasing our understanding of polyspecificity, it might contribute to therapeutic antibody development.

Gene Regulatory Network of Human GM-CSF-Secreting T Helper Cells.

GM-CSF produced by autoreactive CD4-positive T helper cells is involved in the pathogenesis of autoimmune diseases, such as multiple sclerosis. However, the molecular regulators that establish and maintain the features of GM-CSF-positive CD4 T cells are unknown. In order to identify these regulators, we isolated human GM-CSF-producing CD4 T cells from human peripheral blood by using a cytokine capture assay. We compared these cells to the corresponding GM-CSF-negative fraction, and furthermore, we studied naïve CD4 T cells, memory CD4 T cells, and bulk CD4 T cells from the same individuals as additional control cell populations. As a result, we provide a rich resource of integrated chromatin accessibility (ATAC-seq) and transcriptome (RNA-seq) data from these primary human CD4 T cell subsets and we show that the identified signatures are associated with human autoimmune diseases, especially multiple sclerosis. By combining information about mRNA expression, DNA accessibility, and predicted transcription factor binding, we reconstructed directed gene regulatory networks connecting transcription factors to their targets, which comprise putative key regulators of human GM-CSF-positive CD4 T cells as well as memory CD4 T cells. Our results suggest potential therapeutic targets to be investigated in the future in human autoimmune disease.

An Algorithmic Information Calculus for Causal Discovery and Reprogramming Systems.

We introduce and develop a method that demonstrates that the algorithmic information content of a system can be used as a steering handle in the dynamical phase space, thus affording an avenue for controlling and reprogramming systems. The method consists of applying a series of controlled interventions to a networked system while estimating how the algorithmic information content is affected. We demonstrate the method by reconstructing the phase space and their generative rules of some discrete dynamical systems (cellular automata) serving as controlled case studies. Next, the model-based interventional or causal calculus is evaluated and validated using (1) a huge large set of small graphs, (2) a number of larger networks with different topologies, and finally (3) biological networks derived from a widely studied and validated genetic network (E. coli) as well as on a significant number of differentiating (Th17) and differentiated human cells from a curated biological network 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.

SCENERY: a web application for (causal) network reconstruction from cytometry data.

Flow and mass cytometry technologies can probe proteins as biological markers in thousands of individual cells simultaneously, providing unprecedented opportunities for reconstructing networks of protein interactions through machine learning algorithms. The network reconstruction (NR) problem has been well-studied by the machine learning community. However, the potentials of available methods remain largely unknown to the cytometry community, mainly due to their intrinsic complexity and the lack of comprehensive, powerful and easy-to-use NR software implementations specific for cytometry data. To bridge this gap, we present Single CEll NEtwork Reconstruction sYstem (SCENERY), a web server featuring several standard and advanced cytometry data analysis methods coupled with NR algorithms in a user-friendly, on-line environment. In SCENERY, users may upload their data and set their own study design. The server offers several data analysis options categorized into three classes of methods: data (pre)processing, statistical analysis and NR. The server also provides interactive visualization and download of results as ready-to-publish images or multimedia reports. Its core is modular and based on the widely-used and robust R platform allowing power users to extend its functionalities by submitting their own NR methods. SCENERY is available at scenery.csd.uoc.gr or http://mensxmachina.org/en/software/.

Analysis of FOXP3+ regulatory T cell subpopulations in peripheral blood and tissue of patients with systemic lupus erythematosus.

Regulatory T cells (Tregs) are critical mediators of immune tolerance, yet their involvement in the autoimmune disease systemic lupus erythematosus (SLE) is incompletely understood. We analyzed CD4+ T cell subpopulations with Treg-related phenotypes and their association with disease activity in peripheral blood (PB) and tissues of patients with SLE. In detail, we quantified subpopulations regarding CD25, FOXP3, CD62L, CCR6, CD27, CD45RA, and CD45RO expression in PB from 31 patients with SLE divided into two disease activity groups and 32 healthy controls using flow cytometry. CD4+ and FOXP3+ T cells in skin and kidney biopsies of patients with SLE were quantified by immunohistochemistry. CD4+CD25+/++FOXP3+ and CD4+CD25+CD45RA-/CD45RO+ T cell frequencies were significantly higher in PB from patients with active compared to inactive SLE. The fraction of CD4+CD25++FOXP3+ Tregs and CD4+CD25+CD45RA+/CD45RO- naïve Tregs was not significantly different between these groups. CD4+CD25++ Tregs from active SLE patients comprised significantly less CD27+ cells and more CCR6+ cells compared to patients with inactive SLE. The percentage of CD4+FOXP3+ T cells among inflammatory infiltrates in skin and kidney biopsies of SLE patients was not different from other inflammatory skin/kidney diseases. In conclusion, although CD4+FOXP3+ T cell frequencies in the inflamed tissues of SLE patients were comparable to other inflammatory diseases, distinct T cell subpopulations appeared misbalanced in PB of patients with active SLE. Here, cells phenotypically resembling activated T cells, but not Tregs, were increased compared to patients with inactive SLE. Within Tregs of patients with active SLE, markers related to Treg function and homing were altered.

In Vitro Differentiation of Human CD4+FOXP3+ Induced Regulatory T Cells (iTregs) from Naïve CD4+ T Cells Using a TGF-ß-containing Protocol.

Regulatory T cells (Tregs) are an integral part of peripheral tolerance, suppressing immune reactions against self-structures and thus preventing autoimmune diseases. Clinical approaches to adoptively transfer Tregs, or to deplete Tregs in cancer, are underway with promising first outcomes. Because the number of naturally occurring Tregs (nTregs) is very limited, studying certain Treg features using in vitro induced Tregs (iTregs) can be advantageous. To date, the best although not absolutely specific protein marker to delineate Tregs is the transcription factor FOXP3. Despite the importance of Tregs including non-redundant roles of peripherally induced Tregs, the protocols to generate iTregs are currently controversial, particularly for human cells. This protocol therefore describes the in vitro differentiation of human CD4+FOXP3+ iTregs from human naïve T cells using a range of Treg-inducing factors (TGF-ß plus IL-2 only, or their combination with retinoic acid, rapamycin or butyrate) in parallel. It also describes the phenotyping of these cells by flow cytometry and qRT-PCR. These protocols result in reproducible expression of FOXP3 and other Treg signature genes and enable the study of general FOXP3-regulatory mechanisms as well as protocol-specific effects to delineate the impact of certain factors. iTregs can be utilized to study various phenotypic aspects as well as molecular mechanisms of Treg induction. Detailed molecular studies are facilitated by relatively large cell numbers that can be obtained. A limitation for the application of iTregs is the relative instability of FOXP3 expression in these cells compared to nTregs. iTregs generated by these protocols can also be used for functional assays such as studying their suppressive function, in which iTregs induced by TGF-ß plus retinoic acid and rapamycin display superior suppressive activity. However, the suppressive capacity of iTregs can differ from nTregs and the use of appropriate controls is crucial.

TGF-ß Affects the Differentiation of Human GM-CSF+ CD4+ T Cells in an Activation- and Sodium-Dependent Manner.

The cytokine granulocyte-macrophage colony-stimulating factor (GM-CSF) is involved in the pathogenesis of chronic inflammatory diseases such as multiple sclerosis. However, the environmental cues promoting differentiation of GM-CSF producing T cells are unclear. Herein, we performed a broad experimental screening of cytokines and data-driven analysis assessing their ability to induce human GM-CSF+ CD4+ T cells and their subpopulations. TGF-ß was discovered to induce GM-CSF production independently of proliferation and IL-2 signaling including STAT5. In contrast, IL-6 and IL-23 decreased GM-CSF production. On the population level, GM-CSF induction was highly correlated with expression of FOXP3 across cytokine stimulations but not with that of IL-17. However, on single-cell level GM-CSF and IFN-γ expression were most correlated, independently of the cytokine environment. Importantly, under low sodium conditions in the medium or upon stimulation with plate-bound instead of bead-bound anti-CD3 and anti-CD28 antibodies, the effects of TGF-ß on GM-CSF, but not on FOXP3, were reversed. Our analysis indicates a novel role for TGF-ß in generating GM-CSF+ subsets of human CD4+ T cells. These results are important for understanding of autoimmune disease and therapeutic considerations.

IL-1ß 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-1ß 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.