Mechanism of action of baricitinib and identification of biomarkers and key immune pathways in patients with active systemic lupus erythematosus.

OBJECTIVES: To elucidate the mechanism of action of baricitinib, a Janus kinase (JAK) 1/2 inhibitor, and describe immunological pathways related to disease activity in adults with systemic lupus erythematosus (SLE) receiving standard background therapy in a phase II trial. METHODS: Patients with SLE were treated with baricitinib 2 mg or 4 mg in a phase II randomised, placebo-controlled study. Sera from 239 patients (baricitinib 2 mg: n=88; baricitinib 4 mg: n=82; placebo: n=69) and 49 healthy controls (HCs) were collected at baseline and week 12 and analysed using a proximity extension assay (Target 96 Inflammation Panel (Olink)). Interferon (IFN) scores were determined using an mRNA panel. Analytes were compared in patients with SLE versus HCs and in changes from baseline at week 12 between baricitinib 2 mg, 4 mg and placebo groups using a restricted maximum likelihood-based mixed models for repeated measures. Spearman correlations were computed for analytes and clinical measurements. RESULTS: At baseline, SLE sera had strong cytokine dysregulation relative to HC sera. C-C motif chemokine ligand (CCL) 19, C-X-C motif chemokine ligand (CXCL) 10, tumour necrosis factor alpha (TNF-α), TNF receptor superfamily member (TNFRSF)9/CD137, PD-L1, IL-6 and IL-12ß were significantly reduced in patients treated with baricitinib 4 mg versus placebo at week 12. Inflammatory biomarkers indicated correlations/associations with type I IFN (CCL19, CXCL10, TNF-α and PD-L1), anti-double stranded DNA (dsDNA) (TNF-α, CXCL10) and Systemic Lupus Erythematosus Disease Activity Index-2000, tender and swollen joint count and worst joint pain (CCL19, IL-6 and TNFRSF9/CD137). CONCLUSION: These results suggest that baricitinib 4 mg downregulated key cytokines that are upregulated in patients with SLE and may play a role in a multitargeted mechanism beyond the IFN signature although clinical relevance remains to be further delineated. TRIAL REGISTRATION NUMBER: NCT02708095.

IL17A Blockade with Ixekizumab Suppresses MuvB Signaling in Clinical Psoriasis.

Unbiased informatics approaches have the potential to generate insights into uncharacterized signaling pathways in human disease. In this study, we generated longitudinal transcriptomic profiles of plaque psoriasis lesions from patients enrolled in a clinical trial of the anti-IL17A antibody ixekizumab (IXE). This dataset was then computed against a curated matrix of over 700 million data points derived from published psoriasis and signaling node perturbation transcriptomic and chromatin immunoprecipitation-sequencing datasets. We observed substantive enrichment within both psoriasis-induced and IXE-repressed gene sets of transcriptional targets of members of the MuvB complex, a master regulator of the mitotic cell cycle. These gene sets were similarly enriched for pathways involved in the regulation of the G2/M transition of the cell cycle. Moreover, transcriptional targets for MuvB nodes were strongly enriched within IXE-repressed genes whose expression levels correlated strongly with the extent and severity of the psoriatic disease. In models of human keratinocyte proliferation, genes encoding MuvB nodes were transcriptionally repressed by IXE, and depletion of MuvB nodes reduced cell proliferation. Finally, we made the expression and regulatory networks that supported this study available as a freely accessible, cloud-based hypothesis generation platform. Our study positions inhibition of MuvB signaling as an important determinant of the therapeutic impact of IXE in psoriasis.

Prediction and analysis of functional RNA structures within the integrative genomics viewer.

In recent years, interest in RNA secondary structure has exploded due to its implications in almost all biological functions and its newly appreciated capacity as a therapeutic agent/target. This surge of interest has driven the development and adaptation of many computational and biochemical methods to discover novel, functional structures across the genome/transcriptome. To further enhance efforts to study RNA secondary structure, we have integrated the functional secondary structure prediction tool ScanFold, into IGV. This allows users to directly perform structure predictions and visualize results-in conjunction with probing data and other annotations-in one program. We illustrate the utility of this new tool by mapping the secondary structural landscape of the human MYC precursor mRNA. We leverage the power of vast 'omics' resources by comparing individually predicted structures with published data including: biochemical structure probing, RNA binding proteins, microRNA binding sites, RNA modifications, single nucleotide polymorphisms, and others that allow functional inferences to be made and aid in the discovery of potential drug targets. This new tool offers the RNA community an easy to use tool to find, analyze, and characterize RNA secondary structures in the context of all available data, in order to find those worthy of further analyses.

Leveraging health social networking communities in translational research.

Health social networking communities are emerging resources for translational research. We have designed and implemented a framework called HyGen, which combines Semantic Web technologies, graph algorithms and user profiling to discover and prioritize novel associations across disciplines. This manuscript focuses on the key strategies developed to overcome the challenges in handling patient-generated content in Health social networking communities. Heuristic and quantitative evaluations were carried out in colorectal cancer. The results demonstrate the potential of our approach to bridge silos and to identify hidden links among clinical observations, drugs, genes and diseases. In Amyotrophic Lateral Sclerosis case studies, HyGen has identified 15 of the 20 published disease genes. Additionally, HyGen has highlighted new candidates for future investigations, as well as a scientifically meaningful connection between riluzole and alcohol abuse.

Evolutionary computation for discovery of composite transcription factor binding sites.

Previous research demonstrated the use of evolutionary computation for the discovery of transcription factor binding sites (TFBS) in promoter regions upstream of coexpressed genes. However, it remained unclear whether or not composite TFBS elements, commonly found in higher organisms where two or more TFBSs form functional complexes, could also be identified by using this approach. Here, we present an important refinement of our previous algorithm and test the identification of composite elements using NFAT/AP-1 as an example. We demonstrate that by using appropriate existing parameters such as window size, novel-scoring methods such as central bonusing and methods of self-adaptation to automatically adjust the variation operators during the evolutionary search, TFBSs of different sizes and complexity can be identified as top solutions. Some of these solutions have known experimental relationships with NFAT/AP-1. We also indicate that even after properly tuning the model parameters, the choice of the appropriate window size has a significant effect on algorithm performance. We believe that this improved algorithm will greatly augment TFBS discovery.

The Janus kinase 1/2 inhibitor baricitinib reduces biomarkers of joint destruction in moderate to severe rheumatoid arthritis.

BACKGROUND: Tissue released blood-based biomarkers can provide insight into drug mode of action and response. To understand the changes in extracellular matrix turnover, we analyzed biomarkers associated with joint tissue turnover from a phase 3, randomized, placebo-controlled study of baricitinib in patients with active rheumatoid arthritis (RA). METHODS: Serum biomarkers associated with synovial inflammation (C1M, C3M, and C4M), cartilage degradation (C2M), bone resorption (CTX-I), and bone formation (osteocalcin) were analyzed at baseline, and weeks 4 and 12, from a subgroup of patients (n = 240) randomized to placebo or 2-mg or 4-mg baricitinib (RA-BUILD, NCT01721057). Mixed-model repeated measure was used to identify biomarkers altered by baricitinib. The relationship between changes in biomarkers and clinical measures was evaluated using correlation analysis. RESULTS: Treatment arms were well balanced for baseline biomarkers, demographics, and disease activity. At week 4, baricitinib 4-mg significantly reduced C1M from baseline by 21% compared to placebo (p < 0.01); suppression was sustained at week 12 (27%, p < 0.001). Baricitinib 4-mg reduced C3M and C4M at week 4 by 14% and 12% compared to placebo, respectively (p < 0.001); they remained reduced by 16% and 11% at week 12 (p < 0.001). In a pooled analysis including all treatment arms, patients with the largest reduction (upper 25% quartile) in C1M, C3M, and C4M by week 12 had significantly greater clinical improvement in the Simplified Disease Activity Index at week 12 compared to patients with the smallest reduction (lowest 25% quartile). CONCLUSION: Baricitinib treatment resulted in reduced circulating biomarkers associated with joint tissue destruction as well as concomitant RA clinical improvement. TRIAL REGISTRATION: ClinicalTrials.gov NCT01721057 ; date of registration: November 1, 2012.

Baricitinib-associated changes in global gene expression during a 24-week phase II clinical systemic lupus erythematosus trial implicates a mechanism of action through multiple immune-related pathways.

OBJECTIVE: To characterise the molecular pathways impacted by the pharmacologic effects of the Janus kinase (JAK) 1 and JAK2 inhibitor baricitinib in SLE. METHODS: In a phase II, 24-week, randomised, placebo-controlled, double-blind study (JAHH), RNA was isolated from whole blood in 274 patients and analysed using Affymetrix HTA2.0 array. Serum cytokines were measured using ultrasensitive quantitative assays. RESULTS: Gene expression profiling demonstrated an elevation of STAT1, STAT2 and multiple interferon (IFN) responsive genes at baseline in patients with SLE. Statistical and gene network analyses demonstrated that baricitinib treatment reduced the mRNA expression of functionally interconnected genes involved in SLE including STAT1-target, STAT2-target and STAT4-target genes and multiple IFN responsive genes. At baseline, serum cytokines IFN-α, IFN-γ, interleukin (IL)-12p40 and IL-6 were measurable and elevated above healthy controls. Treatment with baricitinib significantly decreased serum IL-12p40 and IL-6 cytokine levels at week 12, which persisted through week 24. CONCLUSION: Baricitinib treatment induced significant reduction in the RNA expression of a network of genes associated with the JAK/STAT pathway, cytokine signalling and SLE pathogenesis. Baricitinib consistently reduced serum levels of two key cytokines implicated in SLE pathogenesis, IL-12p40 and IL-6.

A Bayesian gene network reveals insight into the JAK-STAT pathway in systemic lupus erythematosus.

Systemic lupus erythematosus (SLE) is a chronic, remitting, and relapsing, inflammatory disease involving multiple organs, which exhibits abnormalities of both the innate and adaptive immune responses. A limited number of transcriptomic studies have characterized the gene pathways involved in SLE in an attempt to identify the key pathogenic drivers of the disease. In order to further advance our understanding of the pathogenesis of SLE, we used a novel Bayesian network algorithm to hybridize knowledge- and data-driven methods, and then applied the algorithm to build an SLE gene network using transcriptomic data from 1,760 SLE patients' RNA from the two tabalumab Phase III trials (ILLUMINATE-I & -II), the largest SLE RNA dataset to date. Further, based on the gene network, we carried out hub- and key driver-gene analyses for gene prioritization. Our analyses identified that the JAK-STAT pathway genes, including JAK2, STAT1, and STAT2, played essential roles in SLE pathogenesis, and reaffirmed the recent discovery of pathogenic relevance of JAK-STAT signaling in SLE. Additionally, we showed that other genes, such as IRF1, IRF7, PDIA4, FAM72C, TNFSF10, DHX58, SIGLEC1, and PML, may be also important in SLE and serve as potential therapeutic targets for SLE. In summary, using a hybridized network construction approach, we systematically investigated gene-gene interactions based on their transcriptomic profiles, prioritized genes based on their importance in the network structure, and revealed new insights into SLE activity.

Gene Expression and Pharmacodynamic Changes in 1,760 Systemic Lupus Erythematosus Patients From Two Phase III Trials of BAFF Blockade With Tabalumab.

OBJECTIVE: To characterize baseline gene expression and pharmacodynamically induced changes in whole blood gene expression in 1,760 systemic lupus erythematosus (SLE) patients from 2 phase III, 52-week, randomized, placebo-controlled, double-blind studies in which patients were treated with the BAFF-blocking IgG4 monoclonal antibody tabalumab. METHODS: Patient samples were obtained from SLE patients from the ILLUMINATE-1 and ILLUMINATE-2 studies, and control samples were obtained from healthy donors. Blood was collected in Tempus tubes at baseline, week 16, and week 52. RNA was analyzed using Affymetrix Human Transcriptome Array 2.0 and NanoString. RESULTS: At baseline, expression of the interferon (IFN) response gene was elevated in patients compared with controls, with 75% of patients being positive for this IFN response gene signature. There was, however, substantial heterogeneity of IFN response gene expression and complex relationships among gene networks. The IFN response gene signature was a predictor of time to disease flare, independent of anti-double-stranded DNA (anti-dsDNA) antibody and C3 and C4 levels, and overall disease activity. Pharmacodynamically induced changes in gene expression following tabalumab treatment were extensive, occurring predominantly in B cell-related and immunoglobulin genes, and were consistent with other pharmacodynamic changes including anti-dsDNA antibody, C3, and immunoglobulin levels. CONCLUSION: SLE patients demonstrated increased expression of an IFN response gene signature (75% of patients had an elevated IFN response gene signature) at baseline in ILLUMINATE-1 and ILLUMINATE-2. Substantial heterogeneity of gene expression was detected among individual patients and in gene networks. The IFN response gene signature was an independent risk factor for future disease flares. Pharmacodynamic changes in gene expression were consistent with the mechanism of BAFF blockade by tabalumab.

Discovery of sequence motifs related to coexpression of genes using evolutionary computation.

Transcription factors are key regulatory elements that control gene expression. Recognition of transcription factor binding site (TFBS) motifs in the upstream region of coexpressed genes is therefore critical towards a true understanding of the regulations of gene expression. The task of discovering eukaryotic TFBSs remains a challenging problem. Here, we demonstrate that evolutionary computation can be used to search for TFBSs in upstream regions of genes known to be coexpressed. Evolutionary computation was used to search for TFBSs of genes regulated by octamer-binding factor and nuclear factor kappa B. The discovered binding sites included experimentally determined known binding motifs as well as lists of putative, previously unknown TFBSs. We believe that this method to search nucleotide sequence information efficiently for similar motifs will be useful for discovering TFBSs that affect gene regulation.

A statistical analysis of the TRANSFAC database.

Transcription factors are key regulatory elements that control gene expression. The TRANSFAC database represents the largest repository for experimentally derived transcription factor binding sites (TFBS). Understanding TFBS, which are typically conserved during evolution, helps us identify genomic regions related to human health and disease, and regions that might be predictive of patient outcomes. Here we present a statistical analysis of all TFBS in the TRANSFAC database. Our analysis suggests that current definition of TFBS core regions in TRANSFAC should be re-examined so as to capture a more precise notion of "cores." We offer insight into more appropriate definitions of TFBS consensus sequences and core regions. These revised definitions provide a better understanding of the nature of transcription factor-DNA binding and assist with developing algorithms for de novo TFBS discovery as well as finding novel variants of known TFBS.

Integrating scientific data for drug discovery and development using the Life Sciences Grid.

BACKGROUND: There are many daunting challenges for companies who wish to bring novel drugs to market. The information complexity around potential drug targets has increased greatly with the introduction of microarrays, high-throughput screening and other technological advances over the past decade, but has not yet fundamentally increased our understanding of how to modify a disease with pharmaceuticals. Further, the bar has been raised in getting a successful drug to market as just being new is no longer enough: the drug must demonstrate improved performance compared with the ever increasing generic pharmacopeia to gain support from payers and government authorities. In addition, partly as a consequence of a climate of concern regarding the safety of drugs, regulatory authorities have approved fewer new molecular entities compared to historical norms over the past few years. OBJECTIVE: To overcome these challenges, the pharmaceutical industry must fully embrace information technology to bring better understood compounds to market. An important first step in addressing an unmet medical need is in understanding the disease and identifying the physiological target(s) to be modulated by the drug. Deciding which targets to pursue for a given disease requires a multidisciplinary effort that integrates heterogeneous data from many sources, including genetic variations of populations, changes in gene expression and biochemical assays. METHOD: The Life Science Grid was developed to provide a flexible framework to integrate such diverse biological, chemical and disease information to help scientists make better-informed decisions. RESULTS/CONCLUSION: The Life Science Grid has been used to rapidly and effectively integrate scientific information in the pharmaceutical industry and has been placed in the open source community to foster collaboration in the life sciences community.

Assessment of diet-induced obese rats as an obesity model by comparative functional genomics.

OBJECTIVE: We applied a comparative functional genomics approach to evaluate whether diet-induced obese (DIO) rats serve as an effective obesity model. METHODS AND PROCEDURES: Gene-expression profiles of epididymal fat from DIO and lean rats were generated using microarrays and compared with the published array data of obese and non-obese human subcutaneous adipocytes. RESULTS: Caloric intake and fuel efficiency were significantly higher in DIO rats, which resulted in increased body weight and adiposity. Circulating glucose, cholesterol, triglyceride, insulin, and leptin levels in DIO rats were significantly higher than those in the lean controls. DIO rats also exhibited impaired insulin sensitivity. A direct comparison of gene-expression profiles from DIO and lean rats and those from obese and non-obese humans revealed that global gene-expression patterns in DIO rat fat resemble those of obese human adipocytes. Differentially expressed genes between obese and non-obese subjects in both human and rat studies were identified and associated with biological pathways by mapping genes to Gene Ontology (GO) categories. Immune response-related genes and angiogenesis-related genes exhibited significant upregulation in both obese humans and DIO rats when compared with non-obese controls. However, genes in fatty acid metabolism and oxidation exhibited a broad downregulation only in obese human adipocytes but not in DIO rat epididymal fat. DISCUSSION: Our study based on gene-expression profiling suggested that DIO rats in general represent an appropriate obesity model. However, the discrepancies in gene-expression alterations between DIO rats and obese humans, particularly in the metabolic pathways, may explain the limitations of using DIO rodent models in obesity research and drug discovery.

Changes in osteoblast, chondrocyte, and adipocyte lineages mediate the bone anabolic actions of PTH and small molecule GSK-3 inhibitor.

Parathyroid hormone (PTH) and glycogen synthase kinase-3 (GSK-3) inhibitor 603281-31-8, administered once daily increased bone formation in vivo. We investigated the molecular mechanisms of the anabolic responses of PTH and 603281-31-8 in rat osteopenia model. Female 6-month-old rats were ovariectomized (Ovx) and permitted to lose bone for 1 month, followed by treatment with PTH (1-38) at 10 microg/kg/day s.c. or 603281-31-8 at 3 mg/kg/day p.o. for 60 days. Twenty-four hours after the last treatment, RNA from distal femur metaphysis was subjected to gene expression analysis. Differentially expressed genes (P<0.05) were subjected to pathway analysis to delineate relevant bio-processes involved in skeletal biology. Genes involved in morphogenesis, cell growth/differentiation, and apoptosis were significantly altered by Ovx and the treatments. Analysis of morphogenesis genes showed an overrepresentation of genes involved in osteogenesis, chondrogenesis, and adipogenesis. A striking finding was that Ovx decreased several markers of osteogenesis/chondrogenesis and increased markers of adipogenesis/lipid metabolism. Treatment with either PTH or the GSK-3 inhibitor reversed these effects, albeit at different levels. Histological analysis confirmed that osteopenia in Ovx animals was associated with three-fold increase in marrow adiposity. PTH and GSK-3 inhibitor restored bone volume, and reversed or normalized marrow adiposity. Ex vivo studies showed that PTH and GSK-3 inhibitor increased the ratio of colony forming marrow stromal progenitors (CFU-fs) that were alkaline phosphatase positive (putative osteoblasts). Our results suggest that the bone anabolic actions of PTH and GSK-3 inhibitor in vivo involve concerted effects on mesenchymal lineages; osteoblasts, chondrocytes, and adipocytes.

Molecular profile of catabolic versus anabolic treatment regimens of parathyroid hormone (PTH) in rat bone: an analysis by DNA microarray.

Teriparatide, human PTH (1-34), a new therapy for osteoporosis, elicits markedly different skeletal responses depending on the treatment regimen. In order to understand potential mechanisms for this dichotomy, the present investigation utilized microarrays to delineate the genes and pathways that are regulated by intermittent (subcutaneous injection of 80 microg/kg/day) and continuous (subcutaneous infusion of 40 microg/kg/day by osmotic mini pump) PTH (1-34) for 1 week in 6-month-old female rats. The effect of each PTH regimen was confirmed by histomorphometric analysis of the proximal tibial metaphysis, and mRNA from the distal femoral metaphysis was analyzed using an Affymetrix microarray. Both PTH paradigms co-regulated 22 genes including known bone formation genes (i.e., collagens, osteocalcin, decorin, and osteonectin) and also uniquely modulated additional genes. Intermittent PTH regulated 19 additional genes while continuous treatment regulated 173 additional genes. This investigation details for the first time the broad profiling of the gene and pathway changes that occur in vivo following treatment of intermittent versus continuous PTH (1-34). These results extend previous observations of gene expression changes and reveal the in vivo regulation of BMP3 and multiple neuronal genes by PTH treatment.

Expression profiling of rat femur revealed suppression of bone formation genes by treatment with alendronate and estrogen but not raloxifene.

The pharmacological preservation of bone in the ovariectomized rat by estrogen, selective estrogen receptor modulators (SERMs), and bisphosphonates has been well described. However, comprehensive molecular analysis of the effects of these pharmacologically diverse antiresorptive agents on gene expression in bone has not been performed. This study used DNA microarrays to analyze RNA from the proximal femur metaphysis of sham and ovariectomized vehicle-treated rats, and ovariectomized rats treated for 35 days with maximally efficacious doses of 17-alpha ethinyl estradiol, the benzothiophene SERM, raloxifene, the benzopyran SERM, (S)-3-(4-hydroxyphenyl)-4-methyl-2-[4-[2-(1-piperidinyl)ethoxy]phenyl]-2H-1-benzopyran-7-ol (EM652), and the aminobisphosphonate, alendronate. Ovariectomy resulted in 644 significant probe set changes relative to sham control rats (p < 0.05), whereas E2, raloxifene, EM652, and alendronate regulated 613, 765, 652, and 737 probe sets, respectively, relative to ovariectomized control rats. An intersection of these data sets yielded 334 unique genes that were altered after ovariectomy and additionally changed by one or more antiresorptive treatment. Clustering analysis showed that the transcript profile was distinctly different for each pharmaceutical agent and that raloxifene maintained more genes at sham levels than any other treatment. In addition, E2 and alendronate suppressed a cluster of genes associated with bone formation activity below that of sham, whereas raloxifene had little effect on these genes. These data indicate stronger suppressive effects of E2 and alendronate on bone formation activity and that ovariectomy plus raloxifene resembles sham more closely than ovariectomized animals treated with E2, EM652, or alendronate.

Automated analysis of proton NMR spectra from combinatorial rapid parallel synthesis using self-organizing maps.

It is now quite routine to acquire proton NMR spectra of compounds in 96-well plates prepared in a rapid parallel synthesis fashion using a flow-NMR automation setup. However, the analysis of 96 NMR spectra obtained in this manner is often laborious and painstakingly slow. We have developed a new, automated method for rapidly analyzing 96 NMR spectra of compounds synthesized in an 8 x 12 matrix using self-organizing maps (SOM). This unsupervised neural network is capable of clustering together NMR spectra containing a common pattern of -R groups and identifying outliers from within such clusters. Analysis of these outlier spectra can quickly help indicate the presence of undesired products, impurities, starting materials, and other unexpected errors in a 96-well plate synthesis by focusing the chemists' attention on the aberrant NMR spectra. Thus, SOM can be a valuable tool in performing efficient quality control on combinatorial libraries.