bioRxiv preprint doi: https://doi.org/10.1101/2020.02.13.943688; this version posted February 14, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Causal dynamical modelling predicts novel regulatory genes of FOXP3 in human regulatory T cells Rucha Sawlekar∗†, Stefano Magni∗†, Christophe Capelle∗‡, Alexandre Baron‡, Ni Zeng‡, Laurent Mombaerts†, Zuogong Yue§, Ye Yuan¶, Feng Q. He‖‡, and Jorge Gonçalvesk†∗∗ Abstract Keywords: regulatory T cells, FOXP3, dynami- cal modelling, humans, time-series analysis, network Regulatory T cells (Tregs), characterized as a inference, gene regulatory network, transcriptomic CD4+CD25+FOXP3+ subset of T cells, are vi- data, system identification, causal network. tal to the induction of immune tolerance and the maintenance of immune homeostasis. While target Regulatory T cells (Tregs) are key players of the genes of Treg master regulator FOXP3 have been immune system, which in turn plays a crucial role in identified, the upstream regulatory machinery of a wide range of diseases. It is well established that FOXP3 still remains largely unknown. Here we dy- the transcription factor FOXP3 is a master regula- namically model causal relationships among genes tor of Tregs. However, genes regulating FOXP3 are from available time-series genome-scale datasets, to still largely unknown and identifying them may be predict direct or indirect regulatory genes of FOXP3 vital for developing new immunotherapeutics. This in human primary Tregs. From the whole genome, paper computationally screens the whole genome for we selected five top ranked candidates for further ex- potential regulators of FOXP3, and then experimen- perimental validation. Following knockdown, three tally validates them. Overall, the paper illustrates out of the five candidates indeed showed significant how the combination of genome-wide time-series effects on the mRNA expression of FOXP3. Fur- data with dynamical modelling can identify a small ther experiments showed that one out of these three set of relevant causal interactions. predicted candidates, namely nuclear receptor bind- ing factor 2 (NRBF2), also affected FOXP3 protein Tregs perform immunosuppression of self-reactive expression. These results open new doors to iden- lymphocytes to induce immunological self-tolerance tify potential new mechanisms of immune related and maintain homeostasis [Josefowicz et al., 2012; diseases. Kenneth et al., 2012; Li et al., 2015]. Tregs are involved in different types of diseases, such as au- ∗These authors contributed equally to this work. toimmune diseases [Dejaco et al., 2006; Fehérvari †Luxembourg Centre for Systems Biomedicine, Univer- and Sakaguchi, 2004; Sakaguchi et al., 2006], cancer sity of Luxembourg, 6 Avenue Du Swing, L-4367 Belvaux, [Franchina et al., 2018; Shang et al., 2015; Tanaka Luxembourg. ‡Luxembourg Institute of Health, 29 Rue Henri Koch, and Sakaguchi, 2017], infectious diseases [Joosten L-4354 Esch- Sur-Alzette, Luxembourg. and Ottenhoff, 2008; Stephen-Victor et al., 2017], §University of New South Wales, Kensington NSW 2052, neurodegenerative diseases [Baruch et al., 2015; He Australia. and Balling, 2013] and others [Cools et al., 2007]. ¶School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan The transcription factor FOXP3 has been shown 430074, China. to play a decisive role for the development and func- ‖Co-corresponding authors of this work. Emails: [email protected], [email protected]. tion of Tregs [Ziegler, 2006]. This gene is expressed ∗∗Department of Plant Sciences, University of Cambridge, specifically in CD4+CD25+ Tregs [Hori et al., 2003; Downing Street, Cambridge CB2 3EA, United Kingdom. Rudensky, 2011]. Altered expression of FOXP3 has 1 bioRxiv preprint doi: https://doi.org/10.1101/2020.02.13.943688; this version posted February 14, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. been found in various types of autoimmune diseases 13601 transcripts left, corresponding to 7826 genes. [Liu et al., 2013] and tumors [Cunha et al., 2012; A gene can correspond to multiple transcripts, each Martin et al., 2010; Szylberg et al., 2016]. Genetic measured by a separate probe set; from now on mutations in FOXP3 result in autoimmunity and in- for simplicity we will only use the term transcripts flammatory syndromes, both in humans and in mice and omit the term probe set. The role of the com- [Fontenot et al., 2003; Khattri et al., 2003; Mayer putational modelling was to reduce this number et al., 2014; Mercer and Unutmaz, 2009]. Most of to 5 genes, reflecting our available experimental the experimental evidence indicates that FOXP3 resources for validation. deficiency is responsible for IPEX (immunodysreg- Existing models capture known regulations of ulation polyendocrinopathy enteropathy X-linked) genes by FOXP3 [Carbo et al., 2013, 2015; Hong syndrome which is a rare disease caused by dysfunc- et al., 2011; van den Ham and de Boer, 2008], so they tion of Tregs [Bennett et al., 2001]. were not useful in finding novel regulators of FOXP3. Target genes of FOXP3 have been identified Hence, we need to fit new models from data that through intensive studies [Marson et al., 2007; Zheng capture causality, i.e. that identify those genes that et al., 2007; Zheng and Rudensky, 2007]. Meanwhile, cause changes in FOXP3. Here, we take advantage progress in high-throughput technical developments, of a relatively large number of available time-series such as ChIP-seq and ChIP-Chip, have started to Tregs samples to fit dynamical models. In particular, illuminate genetic and epigenetic mechanisms reg- ordinary differential equations are mathematical ab- ulating expression or protein stability of FOXP3 stractions that capture both dynamics and causality, [Chen et al., 2013; Floess et al., 2007; Fu et al., and help build testable hypotheses in experiments. 2012; Gao et al., 2015; Miyara and Sakaguchi, 2007; Our models take as inputs the time-series expression Schmidt et al., 2012]. The majority of known up- values of the whole genome, i.e. each of the 13601 stream regulators of the expression of FOXP3 are transcripts left after pre-processing of the data, and general regulatory genes, e.g. those controlling in- FOXP3 as the single output (target). terleukin signaling pathways (IL2, IL4, IL6 and so There is always a trade-off on the choice of model on) and cell surface receptors (TGFB) [Lal and complexity. With rich data, models can be complex, Bromberg, 2009]. Those genes tend to regulate a providing detailed information of mechanisms of ac- large number of genes far beyond FOXP3, which tion. However, with limited data, complex models might cause significant unwanted side effects when can easily overfit and introduce bias, resulting in being targeted. large numbers of false negatives. In our case, with More specific upstream genes regulating FOXP3 only a single time-series experiment and limited are still largely unknown. Identifying these regula- resources for validation, we consider the simplest tors of FOXP3 may be crucial for developing new dynamical model: first-order linear time-invariant immunotherapeutics against autoimmune and other (LTI) systems, a well established modelling strat- related diseases. Unbiased experimental screening egy [Dalchau, 2012; Herrero et al., 2012; Mombaerts of the whole genome for such upstream regulators et al., 2019b, 2016; Müller et al., 2019]. Moreover, is almost impossible with current experimental ap- to scale the method to the whole genome, we built proaches. However, such tasks can be efficiently a large number of simple pairwise models (one per performed computationally. This is the goal of this transcript), testing whether each transcript on its work. own could regulate FOXP3. A model associated The models trained in this work are based on with a particular transcript is given by microarray data of the whole-genome mRNA ex- dFOXP3(t) pression in isolated human Tregs [He et al., 2012]. = a · FOXP3(t)+ b · transcript(t) . Tregs from two different donors were stimulated at dt time zero with anti-CD3/-CD28/IL2, and measure- The left-hand side of the equation is the derivative ments were taken at time zero followed by sampling of FOXP3 expression over time, i.e. the rate of every twenty minutes over a period of six hours (19 change of the concentration of FOXP3. Finally, time points in total). After the pre-processing of we searched for parameters a, b that best fitted the the data, as outlined in column 1 of Fig. 1a and data. This procedure was repeated for all 13601 described in Supplementary Section 1.1, there were transcripts. Given the simplicity of the models, 2 bioRxiv preprint doi: https://doi.org/10.1101/2020.02.13.943688; this version posted February 14, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. a System Identification: Pre-processing 2 Time delay and 1 of data All-2-one 3 ranking Time-series data All-2-one All-2-one with input time delays, d FOXP t [ 3]( ) a FOXP t b u t