Silke Roedder

Computational Repositioning of the Anticonvulsant Topiramate for Inflammatory Bowel Disease

Computationally predicted repositioning of an anticonvulsant for inflammatory bowel disease is confirmed experimentally. Greening Drug Discovery Recycling is good for the environment—and for drug development too. Repurposing existing, approved drugs can speed their adoption in the clinic because they can often take advantage of the existing rigorous safety testing required by the Food and Drug Administration and other regulatory agencies. In a pair of papers, Sirota et al. and Dudley et al. examined publicly available gene expression data and determined the genes affected in 100 diseases and 164 drugs. By pairing drugs that correct abnormal gene expression in diseases, they confirm known effective drug-disease pairs and predict new indications for already approved agents. Experimental validation that an antiulcer drug and an antiepileptic can be reused for lung cancer and inflammatory bowel disease reinforces the promise of this approach. The authors scrutinized the data in Gene Expression Omnibus and identified a disease signature for 100 diseases, which they defined as the set of mRNAs that reliably increase or decrease in patients with that disease compared to normal individuals. They compared each of these disease signatures to each of the gene expression signatures for 164 drugs from the Connectivity Map, a collection of mRNA expression data from cultured human cells treated with bioactive small molecules that is maintained at the Broad Institute at Massachusetts Institute of Technology. A similarity score calculated by the authors for every possible pair of drug and disease ranged from +1 (a perfect correlation of signatures) to −1 (exactly opposite signatures). The investigators suggested that a similarity score of −1 would predict that the drug would ameliorate the abnormalities in the disease and thus be an effective therapy. This proved to be true for a number of drugs already on the market. The corticosteroid prednisolone, a common treatment for Crohn’s disease and ulcerative colitis, showed a strong similarity score for these two diseases. The histone deacetylase inhibitors trichostatin A, valproic acid, and vorinostat were predicted to work against brain tumors and other cancers (esophagus, lung, and colon), and there is experimental evidence that this is indeed the case. But in the ultimate test of method, the authors confirmed two new predictions in animal experiments: Cimetidine, an antiulcer drug, predicted by the authors to be effective against lung cancer, inhibited tumor cells in vitro and in vivo in mice. In addition, the antiepileptic topiramate, predicted to improve inflammatory bowel disease by similarity score, improved damage in colon tissue of rats treated with trinitrobenzenesulfonic acid, a model of the disease. These two drugs are therefore good candidates for recycling to treat two diseases in need of better therapies—lung cancer and inflammatory bowel disease—and we now have a way to mine available data for fast routes to new disease therapies. Inflammatory bowel disease (IBD) is a chronic inflammatory disorder of the gastrointestinal tract for which there are few safe and effective therapeutic options for long-term treatment and disease maintenance. Here, we applied a computational approach to discover new drug therapies for IBD in silico, using publicly available molecular data reporting gene expression in IBD samples and 164 small-molecule drug compounds. Among the top compounds predicted to be therapeutic for IBD by our approach were prednisolone, a corticosteroid used to treat IBD, and topiramate, an anticonvulsant drug not previously described to have efficacy for IBD or any related disorders of inflammation or the gastrointestinal tract. Using a trinitrobenzenesulfonic acid (TNBS)–induced rodent model of IBD, we experimentally validated our topiramate prediction in vivo. Oral administration of topiramate significantly reduced gross pathological signs and microscopic damage in primary affected colon tissue in the TNBS-induced rodent model of IBD. These findings suggest that topiramate might serve as a therapeutic option for IBD in humans and support the use of public molecular data and computational approaches to discover new therapeutic options for disease.

A common rejection module (CRM) for acute rejection across multiple organs identifies novel therapeutics for organ transplantation

A set of 11 genes, termed the common rejection module, predicts acute graft rejection in solid organ transplant patients and may help to identify novel drug targets in transplantation.

Biomarkers in solid organ transplantation: establishing personalized transplantation medicine

Technological advances in molecular and in silico research have enabled significant progress towards personalized transplantation medicine. It is now possible to conduct comprehensive biomarker development studies of transplant organ pathologies, correlating genomic, transcriptomic and proteomic information from donor and recipient with clinical and histological phenotypes. Translation of these advances to the clinical setting will allow assessment of an individual patients risk of allograft damage or accommodation. Transplantation biomarkers are needed for active monitoring of immunosuppression, to reduce patient morbidity, and to improve long-term allograft function and life expectancy. Here, we highlight recent pre- and post-transplantation biomarkers of acute and chronic allograft damage or adaptation, focusing on peripheral blood-based methodologies for non-invasive application. We then critically discuss current findings with respect to their future application in routine clinical transplantation medicine. Complement-system-associated SNPs present potential biomarkers that may be used to indicate the baseline risk for allograft damage prior to transplantation. The detection of antibodies against novel, non-HLA, MICA antigens, and the expression of cytokine genes and proteins and cytotoxicity-related genes have been correlated with allograft damage and are potential post-transplantation biomarkers indicating allograft damage at the molecular level, although these do not have clinical relevance yet. Several multi-gene expression-based biomarker panels have been identified that accurately predicted graft accommodation in liver transplant recipients and may be developed into a predictive biomarker assay.

Biologic markers of chronic GVHD

Biologic markers of chronic GVHD may provide insight into the pathogenesis of the syndrome, identify molecular targets for novel interventions, and facilitate advances in clinical management. Despite extensive work performed to date largely focused on prediction and diagnosis of the syndrome, little synthesis of findings and validation of promising candidate markers in independent populations has been performed. Studies suggest that risk for subsequent chronic GVHD development may be associated with donor–recipient genetic polymorphism, deficiency in regulatory immune cell populations (NK, Treg, DC2), and variation in inflammatory and immunoregulatory mediators post-HCT (increased TNFα, IL-10 and BAFF, and decreased TGFβ and IL-15). Established chronic GVHD is associated with alteration in immune cell populations (increased CD3+ T cells, Th17, CD4+ and CD8+ effector memory cells, monocytes, CD86 expression, BAFF/B cell ratio, and deficiency of Treg, NK cells, and naïve CD8+ T cells). Inflammatory and immunomodulatory factors (TNFα, IL-6, IL-1β, IL-8, sIL-2R, and IL-1Ra, BAFF, anti-dsDNA, sIL-2Rα, and sCD13) are also perturbed. Little is known about biologic markers of chronic GVHD phenotype and severity, response to therapy, and prognosis.

A Three-Gene Assay for Monitoring Immune Quiescence in Kidney Transplantation

Organ transplant recipients face life-long immunosuppression and consequently are at high risk of comorbidities. Occasionally, kidney transplant recipients develop a state of targeted immune quiescence (operational tolerance) against an HLA-mismatched graft, allowing them to withdraw all immunosuppression and retain stable graft function while resuming immune responses to third-party antigens. Methods to better understand and monitor this state of alloimmune quiescence by transcriptional profiling may reveal a gene signature that identifies patients for whom immunosuppression could be titrated to reduce patient and graft morbidities. Therefore, we investigated 571 unique peripheral blood samples from 348 HLA-mismatched renal transplant recipients and 101 nontransplant controls in a four-stage study including microarray, quantitative PCR, and flow cytometry analyses. We report a refined and highly validated (area under the curve, 0.95; 95% confidence interval, 0.92 to 0.97) peripheral blood three-gene assay (KLF6, BNC2, CYP1B1) to detect the state of operational tolerance by quantitative PCR. The frequency of predicted alloimmune quiescence in stable renal transplant patients receiving long-term immunosuppression (n=150) was 7.3% by the three-gene assay. Targeted cell sorting of peripheral blood from operationally tolerant patients showed a significant shift in the ratio of circulating monocyte-derived dendritic cells with significantly different expression of the genes constituting the three-gene assay. Our results suggest that incorporation of patient screening by specific cellular and gene expression assays may support the safety of drug minimization trials and protocols.

Identification of common blood gene signatures for the diagnosis of renal and cardiac acute allograft rejection.

To test, whether 10 genes, diagnostic of renal allograft rejection in blood, are able to diagnose and predict cardiac allograft rejection, we analyzed 250 blood samples from heart transplant recipients with and without acute rejection (AR) and with cytomegalovirus (CMV) infection by QPCR. A QPCR-based logistic regression model was built on 5 of these 10 genes (AR threshold composite score>37% = AR) and tested for AR prediction in an independent set of 109 samples, where it correctly diagnosed AR with 89% accuracy, with no misclassifications for AR ISHLT grade 1b. CMV infection did not confound the AR score. The genes correctly diagnosed AR in a blood sample within 6 months prior to biopsy diagnosis with 80% sensitivity and untreated grade 1b AR episodes had persistently elevated scores until 6 months after biopsy diagnosis. The gene score was also correlated with presence or absence of cardiac allograft vasculopathy (CAV) irrespective of rejection grade. In conclusion, there is a common transcriptional axis of immunological trafficking in peripheral blood in both renal and cardiac organ transplant rejection, across a diverse recipient age range. A common gene signature, initially identified in the setting of renal transplant rejection, can be utilized serially after cardiac transplantation, to diagnose and predict biopsy confirmed acute heart transplant rejection.

Molecular and Functional Noninvasive Immune Monitoring in the ESCAPE Study for Prediction of Subclinical Renal Allograft Rejection.

Background Subclinical acute rejection (sc-AR) is a main cause for functional decline and kidney graft loss and may only be assessed through surveillance biopsies. Methods The predictive capacity of 2 novel noninvasive blood biomarkers, the transcriptional kidney Solid Organ Response Test (kSORT), and the IFN-&ggr; enzyme-linked immunosorbent spot assay (ELISPOT) assay were assessed in the Evaluation of Sub-Clinical Acute rejection PrEdiction (ESCAPE) Study in 75 consecutive kidney transplants who received 6-month protocol biopsies. Both assays were run individually and in combination to optimize the use of these techniques to predict sc-AR risk. Results Subclinical acute rejection was observed in 22 (29.3%) patients (17 T cell–mediated subclinical rejection [sc-TCMR], 5 antibody-mediated subclinical rejection [sc-ABMR]), whereas 53 (70.7%) showed a noninjured, preserved (stable [STA]) parenchyma. High-risk (HR), low-risk, and indeterminate-risk kSORT scores were observed in 15 (20%), 50 (66.7%), and 10 (13.3%) patients, respectively. The ELISPOT assay was positive in 31 (41%) and negative in 44 (58.7%) patients. The kSORT assay showed high accuracy predicting sc-AR (specificity, 98%; positive predictive value 93%) (all sc-ABMR and 58% sc-TCMR showed HR-kSORT), whereas the ELISPOT showed high precision ruling out sc-TCMR (specificity = 70%, negative predictive value = 92.5%), but could not predict sc-ABMR, unlike kSORT. The predictive probabilities for sc-AR, sc-TCMR, and sc-ABMR were significantly higher when combining both biomarkers (area under the curve > 0.85, P < 0.001) and independently predicted the risk of 6-month sc-AR in a multivariate regression analysis. Conclusions Combining a molecular and immune cell functional assay may help to identify HR patients for sc-AR, distinguishing between different driving alloimmune effector mechanisms.

The pits and pearls in translating operational tolerance biomarkers into clinical practice.

Purpose of reviewThis review highlights the importance and the role of key biomarker studies in liver and kidney transplant tolerance, discusses the most recent findings with respect to organ-type and cell-type specificity in blood and tissue, and points out the novel research directions in the field of immunological tolerance that involve both adult and pediatric recipients. Recent findingsRecent studies indicate that biomarkers for solid organ transplant tolerance are distinct with respect to organ type and cell type, suggesting distinct tolerogenic mechanisms for different organs. In both liver and kidney transplant tolerant recipients, novel cellular mechanisms have been proposed for natural killer cells, B cells, and dendritic cells in the maintenance of stable operational tolerance. SummaryMajor advances have been made with respect to the understanding of mechanisms and the process of discovery and early validation of peripheral blood biomarkers for operational transplant tolerance both in kidney and liver transplantation. These studies have shed light on the findings that these tolerance mechanisms may be organ specific, as the peripheral blood transcriptional profiling attempts by microarrays and PCR reveal distinct differences and suggest roles for specific cell types. Although these studies are mostly in adults and limited in children, the first tolerance gene signature for pediatric liver transplant tolerance suggests that there are common mechanisms, yet distinct peripheral biomarkers across age. Prospective trials and organ integrative studies are now needed to further develop these biomarkers for future clinical application in addition to expanding novel approaches such as the investigation of miRNAs to better understand the tolerance mechanisms.

Significance and Suppression of Redundant IL17 Responses in Acute Allograft Rejection by Bioinformatics Based Drug Repositioning of Fenofibrate

Despite advanced immunosuppression, redundancy in the molecular diversity of acute rejection (AR) often results in incomplete resolution of the injury response. We present a bioinformatics based approach for identification of these redundant molecular pathways in AR and a drug repositioning approach to suppress these using FDA approved drugs currently available for non-transplant indications. Two independent microarray data-sets from human renal allograft biopsies (n = 101) from patients on majorly Th1/IFN-y immune response targeted immunosuppression, with and without AR, were profiled. Using gene-set analysis across 3305 biological pathways, significant enrichment was found for the IL17 pathway in AR in both data-sets. Recent evidence suggests IL17 pathway as an important escape mechanism when Th1/IFN-y mediated responses are suppressed. As current immunosuppressions do not specifically target the IL17 axis, 7200 molecular compounds were interrogated for FDA approved drugs with specific inhibition of this axis. A combined IL17/IFN-y suppressive role was predicted for the antilipidemic drug Fenofibrate. To assess the immunregulatory action of Fenofibrate, we conducted in-vitro treatment of anti-CD3/CD28 stimulated human peripheral blood cells (PBMC), and, as predicted, Fenofibrate reduced IL17 and IFN-γ gene expression in stimulated PMBC. In-vivo Fenofibrate treatment of an experimental rodent model of cardiac AR reduced infiltration of total leukocytes, reduced expression of IL17/IFN-y and their pathway related genes in allografts and recipients’ spleens, and extended graft survival by 21 days (p<0.007). In conclusion, this study provides important proof of concept that meta-analyses of genomic data and drug databases can provide new insights into the redundancy of the rejection response and presents an economic methodology to reposition FDA approved drugs in organ transplantation.

A Computational Gene Expression Score for Predicting Immune Injury in Renal Allografts.

Background Whole genome microarray meta-analyses of 1030 kidney, heart, lung and liver allograft biopsies identified a common immune response module (CRM) of 11 genes that define acute rejection (AR) across different engrafted tissues. We evaluated if the CRM genes can provide a molecular microscope to quantify graft injury in acute rejection (AR) and predict risk of progressive interstitial fibrosis and tubular atrophy (IFTA) in histologically normal kidney biopsies. Methods Computational modeling was done on tissue qPCR based gene expression measurements for the 11 CRM genes in 146 independent renal allografts from 122 unique patients with AR (n = 54) and no-AR (n = 92). 24 demographically matched patients with no-AR had 6 and 24 month paired protocol biopsies; all had histologically normal 6 month biopsies, and 12 had evidence of progressive IFTA (pIFTA) on their 24 month biopsies. Results were correlated with demographic, clinical and pathology variables. Results The 11 gene qPCR based tissue CRM score (tCRM) was significantly increased in AR (5.68 ± 0.91) when compared to STA (1.29 ± 0.28; p < 0.001) and pIFTA (7.94 ± 2.278 versus 2.28 ± 0.66; p = 0.04), with greatest significance for CXCL9 and CXCL10 in AR (p <0.001) and CD6 (p<0.01), CXCL9 (p<0.05), and LCK (p<0.01) in pIFTA. tCRM was a significant independent correlate of biopsy confirmed AR (p < 0.001; AUC of 0.900; 95% CI = 0.705–903). Gene expression modeling of 6 month biopsies across 7/11 genes (CD6, INPP5D, ISG20, NKG7, PSMB9, RUNX3, and TAP1) significantly (p = 0.037) predicted the development of pIFTA at 24 months. Conclusions Genome-wide tissue gene expression data mining has supported the development of a tCRM-qPCR based assay for evaluating graft immune inflammation. The tCRM score quantifies injury in AR and stratifies patients at increased risk of future pIFTA prior to any perturbation of graft function or histology.