Despite advances in immunosuppressive prophylaxis and overall supportive care, gastrointestinal (GI) graft-versus-host disease (GVHD) remains a major, lethal side effect after allogeneic hematopoietic stem cell transplantation . It has become increasingly clear that the intestinal epithelium, in addition to being a target of transplant-related toxicity and GVHD, plays an important role in the onset of GVHD. Over the last two decades, increased understanding of the epithelial constituents and their microenvironment has led to the development of novel prophylactic and therapeutic interventions, with the potential to protect the intestinal epithelium from GVHD-associated damage and promote its recovery following insult. In this review, we will discuss intestinal epithelial injury and the role of the intestinal epithelium in GVHD pathogenesis. In addition, we will highlight possible approaches to protect the GI tract from damage posttransplant and to stimulate epithelial regeneration, in order to promote intestinal recovery. Combined treatment modalities integrating immunomodulation, epithelial protection, and induction of regeneration may hold the key to unlocking mucosal recovery and optimizing therapy for acute intestinal GVHD. Several factors are thought to contribute to this damage, including pretransplant conditioning, posttransplant activation of alloreactive T cells, and both tissue-targeted and immunomodulatory effects of the intestinal microbiota  tract is a common occurrence following allogeneic hematopoietic stem cell transplantation ta Figs. , 2. Befo5, but this approach is often ineffective. Up to 50% of patients develop steroid-refractory (SR)-GVHD and require additional treatment6. The Jak1/2-inhibitor Ruxolitinib (Rux) is currently the only FDA-approved treatment for SR-GVHD7. Other second and third-line therapies lack consistent demonstrated benefit, and are mostly based on providing additional immunosuppression. This causes most GVHD therapeutic approaches to be accompanied by an increased risk of infection and a potentially reduced graft-versus-leukemia (GVL) effect.Current GI-GVHD treatment focuses mainly on suppressing posttransplant aberrant immune responses with corticosteroids8. In this review we focus specifically on the role of the intestinal epithelium and epithelial injury in GVHD initiation, and how it can be protected from transplant-associated insult. Furthermore, strategies to promote epithelial restoration by improving regeneration and augmenting posttransplant epithelial recovery are discussed, both through regulating epithelial-intrinsic constituents as well as factors supplied by the microenvironment.An evolving understanding of intestinal homeostasis and its related epithelial constituents has led to new treatment opportunities that aim to protect the intestines peri-transplant, without impairing the recovery of physiologic immune function posttransplant. It has also become increasingly clear that the intestinal epithelium is not only a direct target of GVHD-associated damage, but in addition may take part in the development and propagation of the disease, and possibly in its resolution as well9. As such it protects the recipient from harmful gut contents, including pathogens. The barrier is formed by the plasma membranes of a single layer of IECs that are tightly connected with tight junctions (TJ). The IEC lining is covered with a protective, extracellular layer of mucus produced by epithelial Goblet cells, which inhibits direct contact between the IEC and gut luminal particles and bacteria. The mucosal immune cells are present within the epithelial compartment, as well as in the lamina propria below, and in designated lymphoid regions called Peyer’s patches. In addition to providing a physical barrier, the epithelial cell layer is essential for the absorption and transport of nutrients and water. Both integrity and functionality of the epithelial barrier are crucial elements in the course of transplant, as loss is associated with systemic infections, severe GI symptoms like anorexia/diarrhea and poor outcome in general2.The intestinal epithelial barrier forms the first line of defense between the lumen and the underlying immune system in the gut10. In both preclinical models and human studies overall aGVHD severity is associated with the intensity of the pretransplant conditioning regimen12. In mice, high intensity regimens were associated with reduced mucus layer thickness and the presence of bacterial RNA in the colon lamina propria13. Indeed, total body irradiation (TBI) and chemotherapy treatment led to significant leakage of orally administered FITC-labeled dextran into the bloodstream, indicating consequential compromise of the epithelial barrier14. In a study by Nalle et al. preconditioning-induced damage to the epithelium was even required to induce aGVHD in a MHC-matched minor-histocompatibility-antigen (miHA)-mismatched transplantation15. In humans, compromised epithelial integrity, as measured by 51Cr-EDTA absorption, has been documented in association with myeloablative regimens even 14 days after stem cell infusion17. Conditioning toxicity is also associated with the release of pro-inflammatory cytokines in the GI tract, which can contribute to GVHD development19. In particular, TBI appears to be associated with both a higher aGVHD incidence20 and treatment-related mortality21.Pretransplant conditioning is recognized as an early insult to this barrier integrity23, and the associated intestinal epithelial damage17 and mucositis24 have been found to be less severe. The occurrence of aGVHD after RIC is reduced as well25. Additionally, more favorable combinations of agents have been applied to this approach. For example, in reducing the number of combined alkylators, Bu/Flu has a more favorable toxicity profile than Bu/Cy in patients across HSCT indications, while still providing a myeloablative regimen28.The implementation of reduced intensity conditioning (RIC) regimens has decreased conditioning-associated tissue toxicity and enabled older and more fragile patients to undergo allo-HSCT. Regimens typically include an alkylating agent like busulfan (Bu), melphalan or cyclophosphamide (Cy) and a purine analog such as fludarabine (Flu), with or without low-dose TBI. Clinical GI toxicity of RIC transplants is reported to be moderate29 and efficacy30. Similar results were found in children32. Additionally, Flu exposure, as calculated by a PK-model, was recently retrospectively demonstrated to be a strong predictor of HSCT survival in adults33. In conclusion, PK-directed dosing of favorable chemotherapeutic combinations will hopefully further reduce conditioning-related toxicity and development of acute GVHD in the near future.More recent developments in conditioning regimen tolerability have focused on tailored dosing based on chemotherapy plasma levels of the individual patient to reduce exposure and consequent epithelial damage. This concept is known as reduced toxicity conditioning (RTC). The superiority of pharmacokinetic (PK)-directed dosing for intravenous Bu in adults has been established for over a decade with both enhanced safety36. As such, many DAMPs, PAMPs and corresponding PRRs have been implicated in the development of GVHD, and multiple approaches have been taken to dampen the response at this level  targeting heparin sulfate43, all of which have been shown to reduce GVHD in mouse models and AAT in addition SR-GVHD in humans44. Secondly, binding of DAMPs to target immune cells can be blocked, e.g., by P2X7R antagonists, blocking ATP binding45, or anti-TIM-1 monoclonal antibodies, inhibiting binding to phosphatidylserine on apoptotic cell debris46. Thirdly, responsiveness of immune cells to DAMPS could be modulated. MicroRNA-155 deficiency in host DCs protected against GVHD through reduced purinergic receptor and inflammasome-associated gene expression, and concurrent reduced IL-1β release47. Inhibiting microRNA-155 with antagomir could be a promising new approach. Contrarily, Siglecs play a crucial role in mitigating specifically DAMP-induced immune responses49. Following conditioning-mediated tissue damage, the interaction of Siglec-G on host APCs with the glycoprotein CD24 on T cells was essential for GVHD protection in both a MHC-matched and mismatched mouse model50. Enhancing the Siglec-CD24 interaction with a CD24-Fc fusion protein mitigated GVHD in experimental GVHD51. The results of a phase II trial testing the safety of CD24-Fc for the prevention of aGVHD following myeloablative allo-HSCT are expected shortly.Upon intestinal barrier breach, translocating pathogen-associated molecular patterns (PAMPs) and tissue-released damage-associated molecular patterns (DAMPs) are recognized by pattern recognition receptors (PRRs), which activate the innate immune system. Concurrently, the development of alloreactive responses can be initiatedel Table . Firstly56. Interestingly, host TLR deficiency was found to be protective against GVHD in murine studies54, but inhibition of TLR and inflammasome pathway signaling (via MyD88 and TRIF) only in host hematopoietic cells did not reduce GVHD55. This suggests a GVHD-promoting role of non-hematopoietic tissue signaling. More importantly, deficiency of TLR954 and NLRP6 inflammasome52 was protective only when it was restricted to the non-hematopoietic compartment, indicating that TLR signaling at the tissue level, which includes the intestinal epithelial compartment, may be a future target for GVHD reduction. Despite the numerous possibilities of molecules and pathways to block, only very few damage-modulating agents have led to successful clinical trial results. Probably, the concurrent involvement of many different damage molecules as well as redundant downstream signaling pathways, make achieving significant clinical improvements by targeting just one molecule unlikely. Alternatively, involved molecules and signaling pathways may have concurrent roles in the resolution of GVHD, and targeting them would abrogate this, giving no net improvement.Additionally, targeting innate signaling pathways downstream of PRRs might be a future therapeutic approach59. Olfm4 is another marker identifying ISCs60 in humans and in mouse small intestine. ISCs are maintained by both secreted and membrane-bound molecules of surrounding cells, together constituting the ISC niche61. These niche cells include Paneth cells (PCs), which lie interspersed between CBC ISCs at the bottom of the crypt and promote stemness through the release of Wnt3 and EGF, which bind to their respective receptors Frizzled–LRP5–LRP6 complex and ERBB1 on CBCs63. PCs also express Notch ligands DLL1 and DLL4 on the cell-surface that directly interact with ISC Notch receptors such as NOTCH1 to maintain stemness and inhibit differentiation into secretory-cell lineages62. Crypt-adjacent stromal cells also promote ISC maintenance through the secretion of Wnts64 and R-spondins (Rspo)66. Rspo-binding of LGR5 on the ISC potentiates Wnt signaling by phosphorylation and stabilization of β-catenin in the cytoplasm, thus promoting subsequent translocation to the nucleus67. To protect ISCs from mesenchymal-derived epithelium-maturating BMP2 and BMP4 signals69, myofibroblasts and smooth muscle cells around the crypt bottom secrete BMP-inhibitor proteins such as Gremlin 1 and Gremlin 2 that sequester BMPs before they can bind the BMP receptors71. As epithelial precursors continue to proliferate and move up the crypt, they give rise to the highly proliferative transit amplifying (TA) cell compartment. Finally, differentiation into the destined cell type of for instance the absorptive or secretory lineage occurs under the influence of both environmental and intrinsically programmed factor dynamics63.Under homeostatic conditions, the intestinal epithelium is continuously regenerated by stem and progenitor cells that are present within the crypt region Fig. . While a73. In addition to crypt loss, the number of CBC ISCs per crypt is reduced in experimental GVHD77. Severe colonic crypt loss at the time of GVHD has been associated with delayed recovery, persistence of symptoms, and the development of SR-GVHD78, suggesting an impaired capacity to recover beyond the initial insult. In addition, PCs are reduced in GVHD83, which may hamper their ability to provide the indispensable niche factors Wnt3, EGF and membrane-bound Notch ligands for the maintenance of ISC integrity. Besides their role as regulators of ISC proliferation, PCs play an important physiologic role in the production of antimicrobial (AMP) and the release of immunomodulatory proteins , both key components of host defense in the gut84. Α-defensins are a major class of AMPs produced by PCs, and their production is markedly reduced in experimental GVHD81, as well as in GVHD patients83. Loss of PC α-defensins has been associated with decreased bacterial diversity and domination of bacterial species such as Proteobacteria at the phylum level, Enterobacteriales at the order level, and Escherichia and Bacteroides at the genus level, some of which pathogenic81. Interestingly, increased plasma levels of the AMP REG3α act as a biomarker of GI-GVHD, predictive of response to therapy, non-relapse mortality and survival85. Murine transplant recipients deficient in Reg3γ, the claimed mouse ortholog of REG3α, developed more severe GI-GVHD with increased crypt apoptosis86. As such, PC deficiency may contribute to GVHD pathology due to the impairment of ISC niche functions as well as through a reduction in bacterial containment.When aGVHD of the gut develops, the histopathology characteristically demonstrates epithelial apoptosis within intestinal crypts87. The potential of allo-T cells to damage the intestinal crypt compartment was studied using so-called intestinal organoid cultures; self-organizing, 3D mini-guts, that form from isolated crypts or purified ISCs when cultured in the presence of defined ISC niche growth factors EGF, Rspo-1 and BMP-inhibitor Noggin58. Intestinal organoids contain multiple epithelial cell types, including Lgr5+ ISCs, and ex vivo culture models with allogeneic T cells recapitulated in vivo ISC damage77. Interferon (IFN)-γ secreted by allo-T cells directly caused ISC apoptosis through signaling via the IFNγ receptor (IFNγR) expressed by ISCs77. Interestingly, blocking IFNγR signaling with the JAK1/2-specific inhibitor Ruxolinitib early after allo-BMT protected ISCs from IFNγ-induced damage77. Ruxolitinib has recently been approved by the FDA for treatment of SR-GVHD7. The rationale for its use in GVHD is based on the suppression of allo-T cell activation, proliferation, cytokine production, and promoting a more favorable regulatory T cell to conventional T cell ratio. The findings of this study indicate that earlier posttransplant use might have a beneficial, target-tissue-protective effect in patients developing GI-GVHD88.Up until recently it was uncertain whether crypt loss in GVHD was directly caused by the effector mechanisms of allo-T cells. Using 3D microscopy, donor T cells were shown to primarily invade the intestinal crypt region early after allo-BMT38. HSP90 is released during tissue damage and induces the intracellular response to ER stress, which PCs are particularly sensitive to89. Administration of 17AAG was found to decrease the ER stress actor expression and increase the level of spliced XBP1 important for the regulation of the unfolded-protein response, and preserved both PCs and ISCs in two MHC-mismatched models38. A second example concerns serine protease inhibitor 6 (Spi6), the only known endogenous inhibitor of the cytolytic serine protease Granzyme B (GzmB) which protects immune cells from GzmB-mediated damage. In a GVHD model, host Spi6 expression in the non-hematopoietic compartment played a prominent role in GVHD protection, independently of donor-derived GzmB, and Spi6 was upregulated in the intestinal epithelium upon irradiation and subsequent GVHD induction90. A third example is found in the inhibitors of apoptosis proteins (IAPs), which are classically involved in the inhibition of cell death proteases such as caspase 3. IAP inhibition was found to exacerbate GVHD, but not when IAP1/XIAP deficiency was limited to the immune system. This suggests intact tissue IAPs are relevant to tissue protection in GVHD91. Patients with XIAP deficiency undergoing allo-HSCT after myeloablative conditioning appear to have a poor overall outcome and extra protection against GVHD may be crucial for successful transplantation of these recipients92. A fourth example suggests manipulating epithelial integrity regulators to protect the epithelium in GVHD93. The expression of MLCK210, an established factor in epithelial tight junction regulation, was found to be increased in the intestinal epithelium of GVHD patients and mice93. MLCK210 deficiency in the host led to decreased barrier dysfunction, lower clinical GVHD sores and increased survival in multiple mouse GVHD models. Target-tissue allo-T cell numbers were lower in MLCK210-deficient hosts, and the GzmB-expressing CD8 T cell fraction in mesenteric lymph nodes (MLNs) was reduced93. Multiple mechanisms can attribute to this however, including activation, proliferation and homing of the allo-T cells, as well as epithelial–T cell interaction specific factors. Lastly, a recent study describes the disruption of oxidative phosphorylation in IECs exposed to allo-T cells, caused by a reduction in succinate dehydrogenase A (SDHA), a component of mitochondrial complex II94. In colonic biopsies from confirmed GI-GVHD patients the amount of SDHA was reduced in comparison to patients suspected of GI-GVHD that could not be histopathological confirmed. Genetically increasing SDHA levels in an experimental GVHD model reduced clinical and histopathological intestinal GVHD severity. Further study of all four approaches is required to establish usefulness in the clinical setting.Finally, other protective mechanisms downstream of T cell-induced cytotoxicity have recently been described, that could protect epithelial cells in preclinical models. For example, inhibition of HSP90 by 17AAG after allo-HSCT protected the ISC niche96. Blocking α4β7 binding to its constitutively expressed receptor MAdCAM-1 on intestinal endothelium with anti-MAdCAM-1 antibody after GVHD induction96, or using α4β7-deficient donor T cells95, selectively reduced CD8 T cell infiltration in the gut96 and led to less GI-GVHD95. Interestingly, it was recently discovered that MAdCAM-1 expression in the small intestine vasculature localizes predominantly to vessels located in the lower crypt region, offering a possible explanation for the observed pattern of allo-T cells invading the crypt region early posttransplant. As such, inhibition of the α4β7-integrin/MAdCAM-1 axis reduced T cell infiltrate into the crypt base region of the mucosa and protected the ISC compartment from GVHD87. In a clinical setting, patients with GI-GVHD had a significantly higher percentage of α4β7-expressing memory T cell subsets than patients with skin-only GVHD or patients with no evidence of GVHD97. Retrospective studies indicated potential efficacy of vedolizumab, an anti-α4β7 antibody, for reduction of GI-GVHD severity99. Results of a prospective, dose-finding trial of vedolizumab starting 1 day prior to transplant are promising; the treatment was well tolerated, and the incidence of subsequent GI-GVHD development was low100. Blocking the α4β7 integrin pathway with monoclonal antibodies is therefore a promising strategy for protecting the crypt compartment following allo-HSCT. Antibodies that block only the β7 subunit may hold promise as well. These, in addition to antagonizing the α4β7-MAdCAM-1 mediated T cell influx, also target the αEβ7-E-cadherin interaction, believed to be important for T cell retention in the intraepithelial compartment101. A phase II clinical trial of the recombinant human anti-β7 etrolizumab for inflammatory bowel disease had promising results102. Future study will have to show if it can be useful in the treatment of GVHD as well.Given the epithelial damage caused by allo-T cells, blocking their entry into the intestines provides a promising strategy for tissue protection without increasing global immunosuppression and associated risks of relapse or infection elsewhere. Expression of α4β7 integrin is an important contributor to T cell homing to the GI tract, and plays a major role in the homing of allo-T cells to the GI tract as well103. Despite the dominant role of hematopoietic APCs in propagating MHC-I-restricted/CD8 T cell-dependent GVHD104, radio-resistant non-hematopoietic APCs could contribute to the initiation of MHC-I-dependent GVHD as well105. Furthermore, profound deletion of professional host APCs did not decrease CD4-dependent GVHD in both a MHC-matched miHA-mismatched and a MHC-II-mismatched mouse model106. The action of recipient non-hematopoietic, non-professional APCs was sufficient to induce lethal GVHD107. MHC-II expression on IECs specifically could thus initiate lethal GVH immune responses, even in the presence of other types of APCs108. As such, approaches to reduce intestinal epithelial MHC-II expression, for instance through the initiation of a high fat diet in mice109, may reduce the development of experimental GVHD. Nonetheless, preventing donor T cells from reaching the intestinal epithelium with agents such as vedolizumab may be the most promising approach for reducing intestinal epithelial antigen presentation at the present time.In addition to protecting the intestinal epithelium from the effector phase of aGVHD, prophylactic inhibition of T cell entry to the gut may also protect against epithelium-dependent contributions to GVHD development. It was recently reported that intestinal epithelial antigen presentation can propagate alloreactive T cell responses. This is contrary the notion that host and donor professional antigen presenting cells (APCs) are the principle APC populations contributing to the activation of alloreactive donor T cells111, and aGVHD is a well-defined risk factor for cGVHD12. While certain aspects of acute GVHD pathophysiology may be shared with cGVHD, such as the involvement of Th17/Tc17113, there is a paucity in research data studying the links between intestinal epithelial injury and the development of consequent cGVHD of the gut. Most recent insight in the pathobiology include an allogeneic ‘auto-immune’-like course of events, with defective thymic deletion of self-reactive T cells and aberrant B cell activation and production of antibodies114. Therefore, new targets of therapy include T and B cell-signaling pathways that are operational during cGVHD115. As aberrant tissue repair mechanisms, an inflammatory local milieu and continuous antigen exposure are also contributors in the development of cGVHD116, approaches discussed above to prevent intestinal epithelial injury and the development of acute GVHD are in essence applicable in the prevention of cGVHD as well.In some cases acute GVHD can progress or contribute to the development of chronic GVHD (cGVHD)8, evidence exists that regeneration does take place, but may not be enough to overcome the continuous insult. This is for instance illustrated by the fact that intestinal epithelial crypts that survive allo-T cell insult in fact proliferate more than crypts of matched controls77. In addition, enterocytes of patients suffering from refractory GI-GVHD showed significant telomere shortening, which is associated with compensatory proliferation117. Below we will discuss regenerative approaches that hold promise to support the epithelium in the context of GVHD, either by stimulating epithelial constituents to promote recovery of the lining itself or by influencing the mucosal microenvironment  in the lamina propria. LECS were found to be reduced in number and their Rspo-3 production impaired in experimental GVHD138. In a phase I study, patients receiving urinary-derived human chorionic gonadotropin (uhCG) that could supplement EGF as supportive care in aGVHD had a promising biochemical response correlating with day 28 clinical response139. Also Notch ligands like Jag1 and DLL3 have been implicated in intestinal epithelial reconstitution and proliferation during inflammation, downstream of YAP pathway activation140, but have thus far not been studied in the context of GVHD.Also other stem cell niche factors are important for crypt regeneration and intestinal epithelial integrity after damaging or inflammatory insults. For instance, mice with impaired EGF-receptor signaling in the gut were more susceptible to inflammation in DSS colitis, due to impaired IEC regeneration and consequent barrier compromise141. The IL-10-type cytokine IL-22142 is produced by a variety of immune cells and is involved in antimicrobial immunity and in both induction and resolution of inflammation in the intestine145. In addition, IL-22 has been implicated in the maintenance of the intestinal barrier and epithelial repair, due to its influence on mucus production145 and IEC proliferation146. IL-22 derived from group 3 ILCs (ILC3s) was shown to be protective in the GI tracts of transplant recipients in experimental GVHD74. However, the pathophysiological process of gut GVHD leads to loss of intestinal ILC3s and their protective IL-22 production74. Furthermore, patients with low numbers of ILCs in circulation prior to transplant had an increased risk of developing GVHD147. Interestingly, in vivo treatment of transplanted mice with the recombinant human IL-22 dimer/Fc fusion molecule F-652  reduced GVHD-related clinical scoring and mortality in a MHC-matched GVHD model76. IL-22 activated STAT3 phosphorylation in small intestine ISCs and organoids, promoting ISC survival and expansion as well as overall epithelial regeneration and recovery76. Findings of the role of the IL-22-STAT3 axis in crypt regeneration have been validated by the fact that STAT3 was required for damage-induced crypt regeneration after radiation injury148. In addition, IL-22 was required for an effective DNA damage response in protecting ISCs from genotoxic stress149. The influence of IL-22 on crypt regeneration under homeostatic conditions in vivo has not yet been studied. A Phase II trial for treatment of newly diagnosed GI-GVHD with a combination of corticosteroids and a recombinant human IL-22 dimer has recently been performed to investigate the safety potential of this novel tissue-regenerative approach to GVHD treatment (NCT02406651).In addition to epithelial and stromal contributions to the ISC niche, there is a growing appreciation that the local immune system can regulate the ISC compartment and its regeneration150. The authors proposed a dichotomy between stem cell maintenance, which could be ILC3/IL-22/STAT3 dependent, and crypt proliferation, which they found to occur in a ILC3-dependent but IL-22 independent manner150. As such, the application of an ILC3-based cell therapy, instead of only administering IL-22, may have additional benefits for GVHD patients.While therapy with IL-22 appears promising, additional immune-mediated pathways of regeneration may hold translational potential as well. IL-22-independent effects of ILC3s on epithelial regeneration involving the Hippo-YAP1 pathway have been described after methotrexate-induced GI damage151. Goblet cells are important for barrier function by secreting mucus that shields the intestinal epithelium from gut contents and microbes, and were found to be reduced in GVHD in mice and patients13. As mentioned earlier, ILCs are lost in GVHD, but pretransplant administration of IL-25 led to protective Goblet cell induction, decreased bacterial translocation, and ameliorated GVHD, increasing survival in a haploidentical and MHC-mismatched model13. In addition, ILC2-derived IL-13 may have a direct regenerative effect through binding the IL-13R expressed on ISCs. IL-13 increased ISC self-renewal and β-catenin signaling152.Also another type of innate lymphocyte cells, ILC2s residing in MLNs and Peyer’s patches, are known to support the intestinal barrier function, by inducing Goblet cell expansion through IL-13 secretion in response to Tuft-cell-derived IL-25153. In vivo treatment of naïve mice with recombinant IFN-λ in the form of PEGylated (PEG-)IL-29, which has been tested in phase I-III clinical trials as an adjunctive treatment for hepatitis C virus, increased ISC numbers and led to more efficient ISC-derived organoid growth ex vivo. In experimental GVHD, prophylactic PEG-IL-29 administration prolonged survival, reduced GVHD severity and increased epithelial proliferation153.Recently, it was reported that type III IFNs (IFN-λ), known for their role in epithelial viral defense, have an epithelial protective effect in experimental GVHD154, but also has epithelial-supportive effects in vitro. Low-dose TNFα increased the number of human fetal intestinal organoids, while higher doses impaired organoid formation155. Mechanistically, TNF treatment directly promoted Wnt/B-catenin signaling156 and increased the expression of several stem cell markers in murine intestinal156 and human fetal intestinal organoids155, including Acl2. This epithelial-supportive mechanism provides insights as to why TNFα-blockade has had inconsistent results in the treatment of GI-GVHD157. A similar paradox can be found with the pro-inflammatory cytokine IL-6. IL-6 inhibition through blockade of IL6R-signaling with tocilizumab has had some promising results in both experimental and clinical GVHD, and has been associated with induction of allo-T cell suppressive Tregs159. However, IL-6 administration in healthy mice has been associated with STAT3-induced epithelial regenerative effects such as increased intestinal villus height, elongated enterocyte lifespan and a concurrent decrease in pro-apoptotic caspase activity160. Accordingly, in a GI damage model of mechanical wound injury, IL-6 inhibition resulted in impaired healing due to decreased proliferation161. As such, care should be taken with IL-6-blocking therapeutic approaches in GI-GVHD.Some other, classic pro-inflammatory cytokines have been shown to also play a role in maintaining epithelial integrity. Many of these cytokines may be inhibited by immunosuppressive GVHD therapies. For instance, TNFα has long been implicated in GI-GVHD pathogenesis162, and disruption of the IL-10 signaling pathway resulted in exacerbation of experimental GVHD163. Nonetheless, treatment of IL-10 or co-culture with peripherally induced Tregs led to the expansion of ISC numbers in murine organoids, and increased clonogenicity after passage164. In addition, human recombinant IL-10 was shown to promote intestinal epithelial proliferation by activation of CREB signaling165. Despite the fact that treatment of GVHD by exogenous IL-10 does not seem to be clinically feasible due to its pleiotropic and divergent effects163, there may be a crypt-protective effect if epithelial-targeted administration would be possible.An additional example of cytokine-mediated restoration can be found in the regulatory cytokine IL-10. IL-10- and IL10R deficiency are known to cause severe intestinal disease in both mice and humans166. While initially described as a growth factor for skin epithelium, KGF can enhance intestinal epithelial proliferation167, and crypt cell survival after irradiation168. In experimental GVHD, KGF administration started prior to and continued after the transplant reduced GVHD mortality and severity in the GI tract170. However, administration of palifermin, a recombinant human KGF, did not reduce GVHD incidence or improve overall survival in allo-transplant patients in two randomized controlled trials172, although it did reduce mucositis incidence and severity in a subgroup of patients171. Another EGF-like growth factor in preclinical development is the potent liver mitogen Hepatocyte Growth Factor (HGF) produced by intestinal fibroblasts and macrophages173. Using a human HGF expression vector injected into muscle at the time of transplant, stable expression of hHGF in HSCT recipient mice reduced GVHD histopathology and crypt apoptosis174. Interestingly, HGF was found to be a possible substitute for EGF in intestinal organoid cultures. Mice lacking the receptor for HGF in their epithelium had reduced numbers of proliferating crypts and ISCs after irradiation175. Perhaps the protective effect of HGF in experimental GVHD is a result of directly targeting the intestinal epithelium. Lastly, it has been postulated that amphiregulin (AREG), a weak EGF-receptor agonist produced by a multitude of immune, stromal and epithelial cells, may have intestinal epithelial regenerative effects in the GVHD setting. It has been implicated as a possible plasma biomarker for risk stratification and steroid response in aGVHD176. Genetic disruption of Areg significantly impaired intestinal regeneration after radiation injury in full knockout mice177. Nonetheless, the beneficial effects of AREG on experimental GVHD incidence and mortality observed thus far do not directly implicate epithelial regeneration as the main mechanism and could still be ascribed to allo-immune suppression, such as through Treg function enhancement180. Taken together, the intestinal regenerative effect of growth factor substituents may be promising, but seems to have limited application in the clinic thus far.In addition to previously discussed niche factors, stromal cells surrounding the epithelial crypts are important sources of EGF-like growth factors for the intestinal epithelium. Keratinocyte growth factor (KGF) is one of the most well-studied for its protective role during conditioning-induced damage and oral mucositis181 in the context of GI-GVHD. Glucagon-like peptide (GLP)-2 is produced by intestinal L-cells, which are a subset of enteroendocrine cells. L-cells are reduced in mice and patients that develop GVHD182. In vivo, GLP-2 agonism acutely increased the proportion of Lgr5+ ISCs in S-phase and prolonged treatment increased numbers of Olfm4+ ISCs per crypt183. GLP-2 stimulation of intestinal organoids led to increased organoid size182. Prophylactic treatment with a GLP-2 agonist injected subcutaneously in a MHC-mismatched mouse model, improved survival, decreased gut GVHD histopathology scores, and restored ISC loss, even when applied as an additive to steroids182. Future clinical studies will have to investigate its utility in clinical GVHD patients.In addition to growth factors, enteroendocrine hormones may have intestinal epithelial protective effects185. In a MHC-mismatched model, treatment with 3pRNA or DNA prior to allo-HSCT protected mice from conditioning-induced intestinal damage and GVHD without diminishing the GVL activity of allo-T cells. Mechanistically, activation of the pathways led to the expression of protective type I IFNs (IFN-Is), which were indispensable for the maintenance of gut epithelial barrier integrity, but only when they were induced prior to the TBI insult. Treatment of intestinal organoids with 3pRNA and DNA confirmed the direct epithelial effects with increased IFN-I-dependent proliferation184. Intestinal epithelial IFN-I signaling was recently implicated in the regulation of stemness and differentiation into secretory-cell lineages. Mice lacking IEC Interferon regulatory factor 2, which downregulates IFN-signaling, had fewer ISCs, accumulation of immature PCs and impaired regeneration after damage186. In clinical studies, treatment with IFN-α before HSCT187 or after relapse post-HSCT188 was associated with a higher incidence of overall acute GVHD. Tight regulation of IFN-signaling induction during injury thus appears to be crucial.Despite the pro-inflammatory effects of some innate immune signaling pathways, it was demonstrated that specific innate pattern recognition pathways can exert a protective effect on the intestinal epithelium during GVHD in mice. The RIG-I/MAVS pathway is involved in the sensing of dsRNA during infection, while the cGAS/STING signaling pathway is involved in the recognition of DNA. Perturbation of these innate pathways with genetic STING knockouts, changed the sensitivity to GVHD with contrasting effects on outcomes depending on the donor/recipient disparity and the specifics of the transplant models utilized189. Similarly, in mouse BMT models, host NOD2 expression in the hematopoietic compartment is protective against the development of GVHD190. Nevertheless, MDP was shown to directly increase organoid-forming potential of intestinal crypts and to protect ISCs from oxidative-stress-induced cell death191. NOD2 also supported intestinal crypt survival and regeneration after irradiation, both in organoid cultures of NOD2 knockout mice and in vivo192. Given these findings, a non-hematopoietic protective role of NOD2 signaling in GVHD protection may also be possible. Nonetheless, more study is required to appreciate whether these mechanisms can also be exploited in GVHD patients to promote regeneration.Another cytosolic innate immune pathway implicated in the protection against intestinal epithelial injury is NOD2. It binds to the peptidoglycan muramyl dipeptide (MDP), which is produced by most bacteria. In a T cell-induced enteropathy model, NOD2 deficiency outside the intestinal epithelial compartment led to more severe crypt damage, apoptosis and delayed epithelial regeneration195. It was found to directly increase epithelial regeneration in 3D organoid cultures196 and improve wound healing through tight junction protein upregulation197. In the setting of GVHD, intragastric administration of butyrate to allogeneic recipients improved IEC junctional integrity, decreased expression of apoptotic proteins in IECs, and led to decreased GVHD, independent of the induction of Tregs196. Clostridia commensals are known butyrate producers, and administration of a microbial cocktail including 17 strains of Clostridiales elevated intraluminal butyrate concentrations, decreased GVHD clinical scores, and increased survival in a mouse GVHD model196. This could explain why the presence of Clostridiales198 and its protection in the microbiome by selective antibiotic use was found to be associated with reduced GVHD-related mortality in clinical studies200. It also provides a rationale for the use of fecal microbiota transplants for the treatment of GVHD203. Recently, it was demonstrated that signaling through non-hematopoietic GPR43, a metabolite sensor, is critical for the GVHD treatment effects of SCFAs, independent of baseline microbiota constitution204. Given the clear associations of microbial constituents and GVHD outcomes, manipulation of the enteric flora or associated metabolites represent promising approaches for clinical prevention and treatment of GVHD.Over the past decade, several bacterial metabolic products have been associated with gut barrier integrity, including in the context of GVHD. Many studies have focused on short chain fatty acids (SCFAs), such as butyrate and propionate. Butyrate contributes to intestinal health in multiple waysLactobacillus-derived indole-3-aldhehyde, for instance, engages the aryl hydrocarbon receptor (AHR), an environmental sensor and crucial transcription factor for ILC3s in the gut. As such, it can expand ILC3s and their IL-22 production in the intestinal mucosa205, as well as influence the immune response via many other immune cell types206. AHR ligation however also has a direct epithelial effect, as it was shown to regulate ISC differentiation and thereby maintain barrier integrity207. In a GVHD mouse model, administration of indole-3-aldehyde reduced GVHD severity, intestinal epithelial damage, and gut bacterial translocation. The effects were mediated through an IFN-I response observed at the transcriptional level in whole gut samples208. In allo-HSCT patients, higher levels of urine 3‐indoxyl sulfate, an indole-derived metabolite, correlated with lower treatment-related mortality and higher overall survival209. Therefore, indole-3-aldehyde administration represents another potential interventional approach of interest for treatment of GVHD.The tryptophan catabolite indole and its derivatives are other product of commensal bacteria with gut immunomodulatory effects. The intestinal epithelium experiences substantial toxicity during the course of allogeneic transplantation. Given the pivotal role of alloreactive T cells in GVHD pathogenesis, effective immunosuppression is the cornerstone of GVHD treatment strategies. However, in addition to control of the alloreactive immune response, development of target-organ-focused strategies that can protect the epithelium and stimulate its regeneration is important for further progress in improving clinical outcomes for transplant patients. Given advancements in both experimental and clinical research, it is possible that this hope may be realized in the near future.210 or carriers such as nanoparticles213, might make additional GI-targeted approaches more feasible in the future, similar to the wide use of oral budesonide for more targeted administration of corticosteroids to the GI tract. On an even smaller scale, a better understanding of the structural design of factors involved may enable the decoupling of protective functions from pro-inflammatory effects. In a recent study authors were able to design a STAT3-biased IL-22 receptor agonist, which elicited tissue selective STAT3 activation in vivo214.Most epithelial-targeted factors and pathways discussed here have pleiotropic effects with complex feedback mechanisms in multiple tissues and different cell types. The mere stimulation or inhibition on the systemic level therefore may not result in the intended outcome. New ways to specifically target the intestine, for example through intestine-directed genetically engineered cells33 in combination with early initiation of Jak1/2 inhibition to shield the epithelium from allo-T cell-derived IFNy and protect ISCs77 may represent a currently attainable approach to improve GVHD prophylaxis. Should GVHD develop, pro-regenerative therapies such as IL-22 could be administered in the front-line setting along with corticosteroids to promote epithelial recovery76. Attention must also be paid to maintaining a supportive enteric microbial environment, including preservation of healthy anaerobic commensals such as butyrate producers200. A comprehensive approach involving these strategies as well as the implementation of additional tissue-targeted modalities currently in development will be necessary to fully incorporate epithelial biology into GVHD treatment strategies and optimize outcomes for HSCT patients.A combinatory approach of factors that hold promise in preliminary trials at different levels of epithelial support could be considered Table . To have


Rheumatoid arthritis (RA) is an immune-mediated disease affecting diarthrodial joints that remains an unmet medical need despite improved therapy. This limitation likely reflects the diversity of pathogenic pathways in RA, with individual patients demonstrating variable responses to targeted therapies. Better understanding of RA pathogenesis would be aided by a more complete characterization of the disease. To tackle this challenge, we develop and apply a systems biology approach to identify important transcription factors (TFs) in individual RA fibroblast-like synoviocyte (FLS) cell lines by integrating transcriptomic and epigenomic information. Based on the relative importance of the identified TFs, we stratify the RA FLS cell lines into two subtypes with distinct phenotypes and predicted active pathways. We biologically validate these predictions for the top subtype-specific TF RARα and demonstrate differential regulation of TGFβ signaling in the two subtypes. This study characterizes clusters of RA cell lines with distinctive TF biology by integrating transcriptomic and epigenomic data, which could pave the way towards a greater understanding of disease heterogeneity. Fibroblast-like synoviocytes (FLS) are used as a model of rheumatoid arthritis synoviocytes, although cell lines derived from individual patients can have heterogeneous biology. Here the authors use a Taiji computational approach to analyze gene expression, chromatin accessibility and functional differences between individual patient-derived RA FLS lines. A variety of targeted therapies, such as TNF blockers, have improved outcomes for RA patients, but a significant percentage have persistent inflammation. Treatment typically follows a trial and error method until a drug that decreases disease activity is identified2. Variable responses to therapy from patient to patient indicates diverse mechanisms of the disease, despite similar clinical phenotypes in RA.Rheumatoid arthritis (RA) is a systemic inflammatory disease that causes joint inflammation and destruction3. A variety of approaches have used diverse technologies to predict therapeutic response, including histology4, immunostaining for cytokines like TNF, and composite scoring systems5. None of these, to date, have been successfully translated to clinical practice, although a recent biopsy study suggests possible correlations between the transcriptome and response to a targeted therapy6. Wide variability in synovial histology and infiltration by various innate and adaptive cell lineages in the joint further suggests that the important individual cell types can vary from patient to patient.There is considerable evidence of synovial tissue heterogeneity in RA7 that contributes to disease pathogenesis. Mesenchymal cell precursors in the peripheral blood of patients with RA have been associated with disease flares. We recently mapped the epigenomic landscape of cultured primary RA FLS, which demonstrated inter-patient heterogeneity but a stable repertoire of imprinted marks that distinguish RA from controls8. These differentially modified epigenetic regions identify diverse pathogenic pathways and potential therapeutic targets involved in immunity as well as unexpected processes such as the Huntington Disease Pathway.Fibroblast-like synoviocytes (FLS) form the synovial lining and have a major function in joint destruction in RA. Cultured FLS derived from rheumatoid synovium are epigenetically imprinted in RA and exhibit an aggressive phenotype with aberrant signaling9 to perform individual analysis on primary human cells. Our central hypothesis was that this algorithm could be applied to individual samples and that individual TFs can have different functions in primary cells derived from RA patients. This algorithm unexpectedly enabled clustering of FLS lines derived from individual RA patients into at least two groups that predicted context-specific TF function. Each cluster has distinct TFs that are predicted to have opposite effects on FLS function. Biologic validation experiments confirmed this hypothesis for the highly ranked retinoic acid receptor alpha (RARα) and demonstrated that it regulates distinct functions of the TGFß pathway in the two clusters. This dichotomy could explain the diverse effects of retinoids in RA animal models and why this has limited their development as a target for RA13. Our method provides a possible mechanistic explanation for the diversity of clinical responses to targeted agents in diseases like RA.We now report the application of the systems biology method TaijiTo identify key driver TFs in rheumatoid arthritis we have applied Taiji to integrate genomic information from individual RA FLS lines into transcriptional regulatory networks. TFs were then ranked on their regulatory influence and cell lines clustered according to driver TF profiles. Computational assessment of the differences between these cell line clusters was conducted to identify important signaling pathways and define phenotypes. To validate these predictions, the following experiments were performed for the top ranked differential TF: ChIP-qPCR was conducted on cell lines from each cluster to assess predicted differential binding followed by siRNA knockdown of the top TF and RT-qPCR to assess downstream effects on gene expression, Western blot to assess downstream effects on protein, and MTT proliferation and Matrigel invasion assays to assess functional effects .A unique feature of Taiji is that it can be applied to individual primary cell lines derived from RA patients. We built the genetic network in each RA cell line, based on which the PageRank score for each TF in each cell line was calculated. Using the top 350 TFs ranked by variance that were expressed in at least one cell line, the 11 cell lines from RA patients clustered into two groups using hierarchical clustering with the Euclidean distance measure and the complete agglomeration method, named cluster 1  and cluster 2  Fig. . For comz-scores from CL1 and CL2. Among the top 200 TFs, we selected the top 100 TFs whose “regulatees” (child nodes) also showed significantly different PageRank z-scores between the two clusters . From this list, we obtained 28 TFs  was predicted based on the ratio of expression levels of their specific parent TFs (features). We then validated the network by confirming the predicted gene expression differences. A Pearson correlation of 0.72 on the test sets using a 10-fold cross validation Fig.  was signTCIRG1, a gene that has a function in bone remodeling and cancer metastasis16. Taiji predicted a regulatory interaction between RARα and TCIRG1 in CL1 but not in CL2  whereas CL2 lines exhibited no change  in the promoter of CL2 Fig. . ChIP-qPnge Fig. .”  highlighting this. Notably, E2F1 regulates the tumor suppressor and inhibitor of cell cycle, CDKN1C 17.We investigated the patterns of pathways and regulatory function of top cluster-specific TFs, RARα, a cell cycle inhibitor, and E2F1, a cell cycle activator. We collated each regulatee for each cell line and then compared them across all RA FLS networks to define patterns of regulatees for each TF. Strikingly, the top CL1-specific RARα and CL2-specific E2F1 together regulate 74% (434/589) of the >2-fold DEGs, showing their importance in differentiating the two clusters. In particular, they regulate cell cycle pathways including cell cycle checkpoints, homology directed repair, G0/G1 and mitotic phases. Cell cycle activators are broadly overexpressed in CL2 and repressed in CL1, such as the CL2 overexpressed cyclin-dependent kinase CDK1 that drives cells through mitosis Fig. . RARα-spp-value < 5.0 × 10−2 by two-sided Student t-test) . In contrast, the CL2 TFs are involved in “SMAD2/SMAD3:SMAD4 heterotrimer regulates transcription” .TF-TF subnetworks hold crucial transcriptional information. Therefore, we focused on the regulatees that are also TFs of the top ranked cluster-specific TFs  and vascular endothelial growth factor (VEGFA) are higher in CL2 with the latter being an AP1 target along with MMP1 and TGFβ1 . Non-canonical pathways such as the MKK7-JNK-AP1 axis represent TGFβ activated MAPK signaling casades18 that mediate RA FLS invasiveness19.Analysis of transcriptomic signatures of repressors, such as CL2 Fig. . Consist8 that identifies genomic regions with similar epigenomic profiles across 6 histone marks . Ten clusters displayed high inter-patient variance in H3K4me1 signal and were enriched in the “SMAD binding” pathway   and CL2 , respectively. This was confirmed at the protein level by Western Blot showing a decrease of at least 53 to 58%  ) (CL1 vs CL2 p < 0.05 by two-tailed Student’s t-test)  Fig. .Fig. 4RATGFβ1 canonical signaling pathway, which initiates a series of SMAD-mediated steps, were differentially regulated in CL1 and CL2. The propagation of the upstream divergent effects were consistent through the pathway for CL1 and CL2. For example, we observed a significant 21% decrease in CDKN2B for CL1  compared with a significant 13% increase in RARα deficient CL2 cells  (CL1 vs CL2 p < 0.05 by two-tailed Student’s t-test).We then confirmed that the pathway and genes downstream in the 20 and the functional effects require activation from the latent to the active form. We noted that latent TGFß1 protein levels were similar between CL1 and CL2  and no significant change for CL2 (CL1 vs CL2 p < 0.05 by two-tailed Student’s t-test)  Fig. . To evalst) Fig. . SMAD ac21, we analyzed the effects of RARα siRNA on the non-canonical RHO-ROCK-MAPK1-CCND1 axis  , which correlated with the change in ROCK1 transcription (Pearson R = 0.94). To confirm that these observations had functional relevance and predicted differential responses to RARα ligation, we stimulated cells with RARα ligand all-trans retinoic acid and observed cluster-specific effects on cell growth  Fig. . Followip-value = 4.2 × 10−2 by two-tailed Student’s t-test). Furthermore, after confirming RARα’s differential regulation of TGFβ regulatees, we evaluated proliferation of the same cell lines after RARα knockdown. Figure CDKN2B) or stimulate (CCND1) proliferation. Therefore, the mechanism cannot be ascribed to a single gene but rather the integration of RARA regulatees leading to a net increase in proliferation after RARα knockdown in CL2  and H3K36me3  in CL1. The related enriched pathways include “Degradation of extracellular matrix” , including fibronectin (FN1), and “Integrin cell surface interactions” . To assess a functional correlate of these observations, we evaluated the regulation matrix genes FN1 and vimentin (VIM) in CL1 and CL223. FN1 is transcriptionally regulated by TGFβ and RARα directly via DR5 RARE in the FN1 promoter. RARα depletion in CL1 leads to a divergent trend toward inhibition of FN1 compared to a 27% increase of FN1 in CL2 (CL1 vs CL2 p < 0.05 by Wilcoxon Rank Sum Test)  and increased by 40% vs siRNA control in CL2  (CL1 vs CL2 p < 0.05 by two-tailed Student’s t-test)  Fig.  in agreest) Fig. .Fig. 5RAp-value = 7.73 × 10−3 by paired two-tailed Student’s t-test) but there was no statistically significant change in CL1 lines  algorithm. Taiji provides biological insights into mouse embryonic developmentTwo groups, or clusters, were identified by Taiji, namely CL1 and CL2, that are characterized by the differential expression of cell cycle genes, cell proliferation, and EMT markers. Even with the relatively small number of patients in this study, the power of Taiji is its ability to leverage small differences between hundreds of genes (expression or open chromatin), subsequently integrating and quantifying them using complex transcriptional networks to identify the master TFs that regulate them. Hundreds of weak predictors are combined into dozens of strong predictors with the goal of identifying TFs with opposing regulatory functions via Taiji. Genes that encode TFs such as RARA, ETV7, E2F7 and E2F1 regulate cluster-specific differences in the core transcriptional machinery enriched in growth factor signaling (CL1) and SMAD-dependent TGFβ signaling TFs (CL2) highlighting differences in TGFβ growth factor signaling associated with a CL2 activated signature. Strikingly, we were able to identify TFs that were predicted to have a differential functional effects in CL1 and CL2.31. RARα, in conjunction with the heterodimeric partner RXRα, has ligand-specific transcriptional effects, and can act as a transcriptional activator or repressor dependent on the cellular context32. RARα knockdown in CL1 and CL2 cell lines confirmed the TFs predicted differential regulatory function with a decrease in TGFβ mRNA expression in CL1 and an increase in CL2. These effects were transcriptionally propagated downstream through SMAD-dependent signaling in both clusters and via SMAD-independent mechanisms in CL2. Functional assays showed that RARα depletion increases the proliferation and invasion of CL2 FLS but has little to no effect in CL1 FLS.We then biologically validated the predictions by highlighting the use-case TF RARA, a cluster-specific TF, whose regulatees show functional enrichment in growth factor signaling, mitotic cell cycle, cell cycle checkpoint, DNA repair, and also include EMT markersThe biologic validation studies do not identify individual genes that are solely responsible for the differential effects of RARA. TF knockdown and activation engage the entire network and integrate the functions of many genes that can increase or decrease cell growth. Taken together, the data indicate that pro-proliferative effects of RARA deficiency in CL2 outweigh any anti-proliferative effects. It is the combination of all RARA regulatees, including CKDN2B (anti-proliferative) and cyclin D (pro-proliferative), that result in the internally consistent findings shown in Fig. 28. The use of Taiji builds of this concept of distinct pathogenic mechanisms, identifying a variety of TFs with differential functions like RARA, which interacts with TGFβ pathways that control proliferation and invasion.Dysregulation of the balance between FLS proliferative and apoptotic processes is critical to synovial lining hyperplasia and inflammation in RA. Subsets of RA patients exhibit increased synovial tissue inflammation and FLS from these tissues display gene profiles characteristic of myofibroblasts33. However, cultured FLS and their pathogenic behavior are stable, can readily distinguish RA from non-RA, and have provided insights into disease mechanisms34. We were unable to analyze individual fibroblast phenotypes observed in situ using single cell technology but this methodology is rapidly advancing and could be feasible in follow on studies35. Furthermore, it would be important in future studies, to replicate these findings in a different cohort of RA FLS.Although the number of cell lines is relatively limited in the present analysis, the Taiji integrated systems approach compensates by increasing the statistical power through simultaneous assessments of hundreds of TFs and regulatees. One potential limitation is the focus on cultured RA FLS cell lines which are homogenous but do not necessarily reflect the diversity of fibroblast phenotypes in situ described by othersIn conclusion, we present a method to stratify cell lines from individual patients with RA based on a global ranking of regulators in the genetic network constructed for each cell line. Hundreds of weak predictors are combined into dozens of strong predictors that can effectively characterize the distinct TF-patterns of individual cell lines and further provide a general tool for studying rheumatoid arthritis. The use-case of one TF, namely RARα, provides a foundation for the divergent response to retinoids in RA animal models which is highly variable including model-specific disease improvement or exacerbation.36. Cells were therefore not exposed to drugs for over 2 months prior to analysis. FLS were stained with Human TruStain FcX  at 5 μL/million cells, then live cells were selected with ZombieRed diluted at 1 µL/million cells  and detected with antibodies against CD34 , CD90 (Biolegend: PE- anti-human CD90 (Thy1) – clone: 5E10, cat#: 328110, lot: B301003), CD14 , Podoplanin  and FAPa , all at a concentration of 5 µL/million cells, on a ZE5 Cell Analyzer (BioRad). Analysis was performed with FlowJo, V10 (BD Biosciences)  were isolated and total RNA was extracted with raw read quality evaluated using FastQC. Adapter and low quality bases were trimmed from the raw RNA-seq reads and further trimmed with reads less than 30 bp being discarded as previously described8. In this study, the reads from these data were aligned, assembled, and quantified by RSEM. ATAC-seq sample preparation followed the protocol found in Buenrostro et al.37 Peaks were called for each replicate experiment using the Irreproducibility Discovery Rate (IDR) framework to identify reproducible peaks. Peaks from individual replicates as well as pooled data from two replicates were called with MACS2 and a relaxed threshold . The 3 sets of peaks were input for IDR analysis with a threshold of p-value = 5.0 × 10−2 for high confidence peaks as per Yu et al.14.This study was approved by the Institutional Review Board of University of California, San Diego, and informed consent was obtained from all participants (protocol 14 - 0175). Synovial tissue was previously obtained from female patients (11 with RA and 11 with OA) at the time of total joint replacement or synovectomy with a prior two week discontinuation of any biologics, methotrexate, and leflunomide as previously described38. Using FIMO’s algorithm39, 745 TFs were identified as having binding sites within 150 bp regions centered around ATAC-seq peak summits. 11 unique network topologies were thus constructed by forming directed edges between TF and their regulatees, if the TF had a predicted binding site in the child gene’s promoter or linked enhancer.Active regulatory elements were first identified via the overlap of high confidence peaks from ATAC-seq with known gene promoter regions (5 Kbp upstream and 1 kbp downstream of the transcription start sites), Fig. . The diszi is the z-score of the expression of the gene i in each cell line (expression quantified by RNA-seq). Edge weights were determined according to the expression level of the parent node TF and the pooled ATAC-seq peak intensity (strength of the TF-gene association) as outlined in Zhang et al.9. The normalized node weights were then used as the seed vector for the PPR calculation. Thus, if s is the vector containing node weights and W is the edge weight matrix then the PPR score vector v is calculated by solving a system of linear equations (Eq. d is the damping factor (d = 0.85 by default). Solving iteratively  algorithm was run to measure the global influence of each node Fig. . PPR is mentclass2pt{minim2 of the fold change in expression values (transcripts per kilobase million (TPM)) was calculated . If the TF to gene edge was not present for a given patient topology the expression value was set to 1 × 10−2 before taking the ratio and performing the log2 operation. Subsequently for each patient pair, each gene contained 745 features (for each pair of TFs), thus generating a 28,645 × 745 feature matrix. The response variable was calculated as the log2 of the fold change in expression for the gene . Using the feature matrix and response variable we performed 10-fold cross-validated random forest (RF) regression and subsequently calculated the average Pearson R correlation for the predicted values of each test set vs the actual response values. For randomized topologies, each row of features were randomly shuffled in the feature matrix, for the given response variable, and the same 10-fold cross validated RF regression performed.Network topologies were validated according to the following protocol. The 56,269 transcripts, for which expression data were available, were ranked according to expression in each patient. For each of the 55 unique patient pair combinations of 11 patients, the unique union of the top 500 most highly expressed genes in each patient were obtained. For each gene, if an edge existed between the gene and a given TF for both patients in the given pair, the logCells fixed in 1% formaldehyde were washed with PBS, scraped and pelleted. After cell lysis, chromatin was sheared using a Bioruptor (Diagenode). The lysates were immunoprecipitated (IP) overnight with 4ug anti-RARα ChIP-seq grade antibody  or control IgG . The immunoprecipitates were then washed, reverse-crosslinked, and subjected to proteinase K digestion. DNA from IP samples and input were eluted using Zymo-spin ChIP kit and quantified using SYBR green qPCR. Forward Primer 5′-TCCTCATTGCCAGCTTCAG-3′; Reverse Primer 5′ CGTGATCTTGCGGATCTTCT-3′. Data are shown as % input.An adequate number of samples is required in order to control the false positive (type I error) and false negative rates (type II error) when conducting hypothesis testing. Low-powered studies often produce more false negatives compared to high-powered studies. As part of our strategy to identify key cluster-specific TFs based on PPR, we developed a strategy based on hypothesis testing with control of the type I error, allowing for sufficient statistical power when identifying significant elements between two distributions.z-scores of its regulatees, thus obtaining a set of principal components that account for 90% of the data variance. The Hotelling T-Squared test, which has previously been used to compact the small sample size issues in microarray analysis, was applied to the regulatee dimension reduction matrix for each TF. Let p in group i (RA cluster), with k measurement variables . Here, let n1 be the number of patients in group 1, n2 be the number of patients in group 2, μk1 denote the mean value for the distribution of measurement variable k in group 1, and μk2 denote the mean value for the distribution of measurement variable k for group 2. The Hotelling’s T-squared test is based on the assumption that the covariance matrices for measurement variables in the two underlying groups are equal, so under the null hypothesis, we can calculate the assumed pooled common covariance matrix. Thus, the Hotelling’s T-squared test can be conducted as follows  using the PageRank lows Eq. :H0: all the μk1 = μk2H1: at least one μk1 ≠ μk2This strategy takes into account any potential relationships between regulatees and also gains increased statistical power because it uses a larger set of regulatees for each hypothesis test in identifying significant TFs.8, was re-run solely for the RA FLS lines in this study. 78,598 signal-enriched 5 kbp regions were clustered into 245 co-modified 5kbp clusters  were downloaded from ref. 41. Optimal RARα knockdown by siRNA transfection was performed as previously described42 and assessed in time course experiments. Cells were transfected with ON-TARGETplus SMARTpool siRNA targeting human RARα (Dharmacon) or ON-TARGET plus Nontargeting Pool siRNA (Dharmacon) using the Human Dermal Fibroblast Nucleofector Kit (Lonza) and cultured in 10%FCS for 3 days. These pools include multiple targeting or matched control siRNA sequences. Cells were serum starved for 18 h in 0.1%FCS/DMEM and subsequently treated with IL-1 (2 ng/ml) for 6 h. RNA was extracted using the RNeasy kit (Qiagen) and qPCR was performed using a cell-based standard approach43. Ct (threshold cycle) values were normalized to glyceraldehyde phosphate dehydrogenase (GAPDH).6 Primary RA FLS cell lines  and lowest ranked by RARα PPR: RA10, RA11, RA9 (CL2)) were cultured  in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with L-glutamine, gentamicin, penicillin/streptomycin, and 10% heat-inactivated fetal bovine serum (FBS) as previously describedFLS were stimulated with IL-1b for 6 h, after which RNA was extracted and processed for qPCR. Ct values were then normalized to glyceraldehyde 3-phosphate dehydrogenase (GAPDH).After transfection, RA FLS were cultured for 3 days. For CDKN2B analysis, cells were serum-starved for 18 h in 1%FCS/DMEM, and subsequently treated with IL-1 (2 ng/ml) for 24 h. FLS were lysed with RIPA buffer (Invitrogen) plus Protease Inhibitor Cocktail (Sigma). The protein concentrations in the lysate extracts were determined with the DC protein assay kit (Bio-Rad). Protein samples from FLS lysates (25–30 μg) were loaded into a NuPAGE 4–12% Bis Tris gel (Invitrogen) and transferred to a Polyvinylidene difluoride (PVDF) membrane (ThermoFisher). After blocking with Tris-buffered saline plus 0.1% Tween 20 (TBST) containing 5% non-fat milk for 1 h at room temperature, the membranes were incubated with mouse monoclonal RARα antibody  and rabbit monoclonal CDKN2B antibody  diluted 1:1000, and GAPDH antibody diluted 1:5000  at 4 °C overnight. The membranes were washed three times and incubated with horseradish peroxidase-conjugated anti-rabbit and anti-mouse IgG antibodies for 1 and 2 h, respectively. The protein visualization was performed with Clarity Western ECL substrate (Bio-Rad) and the quantitation was performed using Versadoc Imaging system and Quantity One software (Bio-Rad) version 4.6.6.44. Fluorescence was quantified using a plate reader with Tecan i-Control 2.0 software.11 RA FLS were cultured in triplicate in DMEM containing 10% FBS. Cell proliferation was assayed after 4 h incubation with MTT −2,5-diphenyl tetrazolium bromide) at 1 and 5 days after plating6 Primary RA FLS cell lines (highest ranked (CL1) and lowest ranked (CL2) by RARα PPR) were plated on a Matrigel-coated invasion chamber (Corning). After 4 h serum starvation, the medium was replaced, and TGFβ (20 ng/ml) was added to the top chamber. After 24 h incubation, chamber inserts were fixed in 100% methanol and stained with crystal violet solution. The migrated cells on the bottom of the membrane were quantified using ImageJ software. Blinded quantification was done either manually or in a semi-automatic manner via adjustment thresholds, binarising images and subsequently using the ‘Analyze particle’ tool. Fold changes for TGFβ-induced invasion were calculated by normalizing on respective controls with media alone.8 primary RAFLS were plated, and serum starved for 18 h in 0.1%FCS/DMEM and subsequently treated with TGFβ (20 ng/ml) for 30 min. Cell lysates were collected and phospho SMAD2/3 and total SMAD2/3  were measure by ELISA, according to the manufacturer’s protocol. Fluorescence was quantified using a plate reader with Tecan i-Control 2.0 software.After transfection, 8 primary RAFLSs were seeded in 24-well plates and cultured in DMEM with 10% FCS at 37 °C for 24 h. Cells were serum starved for 18 h in 1%FCS/DMEM and then treated with IL-1 (2 ng/ml) for 24 h. TGFβ in cell culture supernatants were measure by ELISA (R&D system) according to the protocols provided by the manufacturer. Fluorescence was quantified using a plate reader with Tecan i-Control 2.0 software.CL1 and CL2 FLS were plated in triplicate in a 96-well plate (2 × 103 cells/well) with DMEM containing 10% FBS. After 24 h, medium was replaced by a serum-deprived (1% FBS) medium containing 1 μM of ATRA  or with DMSO for 1 h. PDGF-BB (10 ng/ml) was added to the wells and cells were incubated for 5 and 7 days. Cell growth was determined after 4 h incubation with MTT −2,5-diphenyl tetrazolium bromide) and was read at 550 nm with a spectrophotometer. Fold change in cell growth was calculated by normalizing each cell line with its median value from day 1.Further information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Dataset 1Supplementary Dataset 2Supplementary Dataset 3Reporting Summary


Recently, FGFR inhibitors have been highlighted as promising targeted drugs due to the high prevalence of FGFR1 amplification in cancer patients. Although various potential biomarkers for FGFR inhibitors have been suggested, their functional effects have been shown to be limited due to the complexity of the cancer signaling network and the heterogenous genomic conditions of patients. To overcome such limitations, we have reconstructed a lung cancer network model by integrating a cell line genomic database and analyzing the model in order to understand the underlying mechanism of heterogeneous drug responses. Here, we identify novel genomic context-specific candidates that can increase the efficacy of FGFR inhibitors. Furthermore, we suggest optimal targets that can induce more effective therapeutic responses than that of FGFR inhibitors in each of the FGFR-resistant lung cancer cells through computational simulations at a system level. Our findings provide new insights into the regulatory mechanism of differential responses to FGFR inhibitors for optimal therapeutic strategies in lung cancer. With the rapid accumulation of the clinical data of patients, precision medicine has emerged as one of the most promising approaches to tackling various diseases, including cancer, by developing therapeutic strategies and improving treatment effectiveness . DespiteResearchers have speculated that this is due to complex interactions between genes and molecules within various signaling pathways that are responsible for drug sensitivity. One example would be the fibroblast growth factor receptor (FGFR) signaling pathway which determines major cellular phenotypes, including proliferation, apoptosis, and growth arrest in various cancers such as lung cancer ,4,5. RecSystems biological analysis of network dynamics using quantitative mathematical models can allow us to predict heterogeneous drug responses of cells according to their various genomic conditions. Molecular interactions within a cell are often represented as network models with their molecular components as nodes  and interactions as edges . A well-In this study, we aim to understand the underlying mechanisms of heterogeneous responses to FGFR inhibitors and identify potential targets that can induce optimal therapeutic responses. For this, we have reconstructed Boolean network models for FGFR/FGF aberrant lung cancer cell lines with different genomic conditions and investigated the fundamental mechanisms of differential responses to FGFR inhibitors from each model. Moreover, we performed extensive perturbation analysis using these network models to identify optimal targets that can induce therapeutic responses to FGFR inhibitors or other alternative drugs according to their genomic conditions and elucidate underlying mechanisms from each of our models. Our study provides insights into a mechanism-based therapeutic strategy by using a dynamical network model to increase the effectiveness of various targeted therapies for cancer.In a Boolean network model, the state of each node represents the activity of a signaling protein in a simple way, that is, 1 (ON) for an active state or 0 (OFF) for an inactive state without detailed kinetic information ,21. In ahttps://www.cbioportal.org/study/summary?id=ccle_broad_2019, accessed on 26 March 2022). We selected seven cell lines  that have highly amplified expressions of FGFR1-3 and FGF with the available drug response data for AZD4547 or PD170374 from the GDSC2 database. To selectively choose the genes with functional genomic alteration, we analyzed the data as follows (refer to Genomic information of lung cancer cell lines from the Cancer Cell Lines Encyclopedia (CCLE) project, including somatic mutation, CNA, and messenger RNA (mRNA) expression, were obtained from cBioportal ), k = 3 in this study. We defined the value as input strength of the corresponding node. If an input node had a mutation, we set the input strength of it as 1 for GOF or 0 for LOF. The schematic representation of creating the functional genomic profile is described in We defined the cell line-specific initial state probability for each node as 1 according to its corresponding functional alteration profiles. For the nodes other than input, if their genomic profiles have either overexpression or AMP, the probability was set to 1; if they have DEL or underexpression, they were set to 0; and the remaining nodes were set to 0.5. For input nodes, the probabilities were set to their corresponding input strength. For example, if the input strength of an input node is 0.9, then it has a 90% chance of having ON states and a 10% chance of having OFF states as its initial condition for that input node. Based on the defined probability, we randomly generated 20,000 initial states for attractor landscape analysis.56 possible states that consist of the entire states of the network model with 56 nodes without three output nodes. We randomly sampled 20,000 initial states according to cell line-specific probability for each node , which was a sufficient number to cover the major attractors. Network dynamics of each cell were analyzed using attractor landscape, which consists of trajectories from the sampled initial states of the cell to its attractor states through synchronous updating schemes. Then, we calculated average node activity (A) for each node as follows:Our lung cancer network model has 2P) for each cell line in a specific simulation setting, including control and each perturbation, based on the average activities of phenotype nodes as follows:PA stands for the average node activity of Proliferation, AA for the average node activity of Apoptosis, and GA for the average node activity of Growth_arrest. Based on the phenotype score, we calculated the drug response score (D) for each cell line as follows:controlP represents the phenotype score before drug perturbation simulation and drugP represents the phenotype score after drug perturbation simulation.To quantify results from the network perturbation analysis, we derived a phenotype score . The AUC matrix was transformed to a gene-wise robust z-score and we took a subset of the seven cell lines that have genomic alterations in FGFR1-3 or their cognate ligands.Area under the curve (AUC) values of AZD4547 and PD173074 from the GDSC 2 for all cancer cell lines were downloaded from DepMap 22Q1  construction of a Boolean model by extending a generic Boolean cancer network model , which There are complex interactions within various signaling pathways of lung cancer cells underlying the regulatory mechanism that leads to resistance to FGFR inhibitors. To comprehensively understand this mechanism, we have constructed a prior-knowledge network model based on a generic cancer Boolean network model . Thus, oPrior to computational simulation, we reconstructed our network model to cell line-specific networks by integrating genetic alterations of lung cancer cell lines from the CCLE cohort. For this, we selected lung cancer cell lines with amplified or overexpressed FGFR1-3, or any of the FGF family that were overexpressed. Among the selected cell lines, we chose the cell lines with available drug response data of FGFR1-3 inhibitors such as AZD4547 and PD173074. We selected a total of seven cell lines and applied the functional genomic profile of each cell line onto our lung cancer network to create differentially wired network models that represent the genomic landscape observed in distinct cancer cells .To investigate cellular responses to FGFR inhibitors, we performed an attractor landscape analysis using our differentially wired network models of the corresponding cell lines. We compared the simulation results with FGFR inhibitor response datasets of the selected lung cancer cell lines from the GDSC. As a result, our predicted drug responses to FGFR inhibitors of the corresponding cell lines were concordant with the public databases. We then performed a clustering analysis, and the cell lines were categorized into sensitive and resistant groups B.In FGFR inhibitor-sensitive cell lines such as NCI-H1581 A, LK2 FB, and NCIn FGFR resistant cell lines, FGFR inhibition does not affect the activity of downstream molecules in the FGFR signaling cascade. Interestingly, these cell lines also have distinct genetic alterations of their own which confer resistance to FGFR inhibition. For instance, in the NCI-H1703 cell line, there are extremely high levels of PDGFB and PDGFC as well as copy-number amplification of their cognate receptor, PDGFRA . The PDGTo validate our hypothesis and identify effective combinatorial targets to induce FGFR drug responses from the resistant cell lines, we calculated a synergy score between 51 possible pairs with FGFR inhibitor in our network model which is defined as follows :Synergy As a result, none of the resistant cell lines, except NCI-H1703, showed synergistic drug response when GAB1 was co-inhibited with FGFR. This is mainly due to the positive feedback regulation between PI3K and GAB1 within the FGFR-AKT pathway that ultimately induces proliferation even with the blockade of the FGFR cascade. Thus, inhibition of GAB1 is necessary to lead the cells to become dependent on the FGFR-AKT pathway for FGFR drug sensitivity .To identify effective target(s) to reduce proliferation and induce growth arrest, we performed systematic single or double node perturbation analysis by using the selected 22 nodes with the available targeted drugs. In total, there were 253 perturbation conditions for each cell line-specific network model. Among the simulation results, we identified optimal target(s) with the highest value after the summation of highly ranked drug response and synergy score. For detailed information, please refer to Conventional precision medicine in cancer has been focused on inhibiting genetically altered molecules within various signaling pathways as promising targets for anticancer drugs. However, the functional effects of these drugs have been reported to vary due to patient-specific genomic conditions which result in differential dynamics for each patient ,32,33. IOur study clearly highlights the influence of intercellular heterogeneity within signaling networks on predicting drug responses. In our simulation, the sensitive cell lines to FGFR inhibitors, including NCI-H1581, LK2 and NCI-H520, were highly dependent on the FGFR-ERK signaling due to their amplified expressions of FGFR and its cognate ligands. In contrast, inhibition of the PI3K/AKT signaling led the resistant cell lines to have partial responses to cell viability according to our simulation result . This isMoreover, most FGFR inhibitor-resistant cell lines did not have any optimal targets inducing FGFR drug sensitivity, except for the NCI-H1703 cell line. For this cell line, due to PDGFR amplification, the activity of Grb2/Sos (GS) decreases slightly when only FGFR is inhibited. However, such inhibitory signaling cannot be further transmitted to their downstream molecules due to strong positive feedback between PI3K and GAB1. Thus, no phenotypic change for growth arrest of proliferation can be expected. Although the positive feedback between PI3K and GAB1 is turned off and partial inhibitory signal is transmitted to their downstream molecules through PI3K when GAB1 is inhibited, this inhibitory effect is very limited due to the amplified expression of EGFR and PDGFR. Thus, inhibitory effect can be transmitted to the downstream when both FGFR and GAB1 are inhibited in order to synergistically induce growth arrest and reduce proliferation in this cell line-specific model .To identify optimal targets that can induce therapeutic responses from FGFR-resistant cell lines, we further analyzed the network models of FGFR-resistant cell lines, including NCI-H1703, HCC15, and NCI-H1781. According to our simulation results, there are several alternative targets that can induce therapeutic responses in these cell lines. We have listed the targets in Cancer is a heterogeneous disease with various genetic alterations that could be from different subgroups even within cancer patients from the same tissue. This can cause various limitations in precision medicine since cellular dynamics in each patient are different due to their distinct genomic alterations. Thus, it is imperative to study the effectiveness of each drug and its interactions with molecular components in a network model to predict drug responses and precisely identify optimal targets for each patient. Even with our effort to overcome these limitations, it is still hard to rationalize that our network can mimic the entire set of patients with diverse genomic backgrounds, including FGFR alterations, in the scope of this study. Therefore, it is essential to further extend our network model in order to represent the cellular dynamics of all lung cancer patients.In summary, we reconstructed lung cancer specific network models that can mimic cellular responses to FGFR inhibitors using the corresponding experimental data from previous studies. Here, we showed that differential cellular responses to FGFR inhibitors can be determined by various genomic alterations in lung cancer cell lines. We also showed that there are differential mechanisms of FGFR drug responses from both sensitive and resistant cell lines. Moreover, we identified optimal targets for specific lung cancer cell lines that are resistant to FGFR inhibitors and elucidated the underlying mechanisms that induce therapeutic responses to alternative potential drug targets from each of the cell line-specific network models at a system level. It is noteworthy that, even though those cell lines have FGFR alterations, other drug candidates may induce better therapeutic responses depending on their genomic context. Therefore, our study demonstrates that the identification of drug targets for sensitivity using patient-specific network models could be a useful tool in precision medicine when used to study and analyze the dynamics of individualized interactions within the network model.