A model for crowdsourcing high-impact research questions for Castleman disease and other rare diseases

BACKGROUND

There are approximately 10,000 rare diseases that affect around 30,000,000 individuals in the U.S.A., most of which do not have an FDA-approved treatment. This fact highlights the failure of traditional research approaches to overcome the unique challenges of developing rare disease treatments. The Castleman Disease Collaborative Network was founded in 2012 to advance research and treatments for Castleman disease, a rare and deadly disease that involves the immune system attacking the body's vital organs for an unknown cause. It has spearheaded a novel strategy for advancing biomedical research, the Collaborative Network Approach. This approach consists of eight steps, one of which is to identify and prioritize high-impact research questions through crowdsourcing ideas from the entire community of stakeholders: patients, loved ones, physicians, and researchers. Rather than hoping that the right researcher will apply for the right research project at the right time, crowdsourcing high-priority research projects into a research strategy ensures that the most high-impact, patient-centered studies are prioritized. The Castleman Disease Collaborative Network launched an initiative in 2021 to systematically generate this list of community-directed studies to focus Castleman disease research efforts.

RESULTS

The Castleman Disease Collaborative Network was able to successfully create a patient-centered research agenda through engaging the entire community of stakeholders. The community contributed important questions about Castleman disease, which were prioritized and reviewed by our Scientific Advisory Board, and the result was a finalized list of studies that address these prioritized questions. We were also able to generate a best practices list which can serve as a model that can be utilized for other rare diseases.

CONCLUSION

Creating a patient-centered research agenda through crowdsourcing research ideas from the community is one of the most important ways that the Castleman Disease Collaborative Network operationalizes its commitment to keeping patients at the center of research and we hope that by sharing these insights we can assist other rare disease organizations to pursue a patient-centric approach.

Phase 3, multicentre, randomised, placebo-controlled study evaluating the efficacy and safety of ustekinumab in patients with systemic lupus erythematosus

OBJECTIVE

Evaluate the efficacy and safety of ustekinumab, an anti-interleukin-12/23 p40 antibody, in a phase 3, randomised, placebo-controlled study of patients with active systemic lupus erythematosus (SLE) despite receiving standard-of-care.

METHODS

Active SLE patients (SLE Disease Activity Index 2000 (SLEDAI-2K) ≥6 during screening and SLEDAI-2K ≥4 for clinical features at week 0) despite receiving oral glucocorticoids, antimalarials, or immunomodulatory drugs were randomised (3:2) to receive ustekinumab (intravenous infusion ~6 mg/kg at week 0, followed by subcutaneous injections of ustekinumab 90 mg at week 8 and every 8 weeks) or placebo through week 48. The primary endpoint was SLE Responder Index (SRI)-4 at week 52, and major secondary endpoints included time to flare through week 52 and SRI-4 at week 24.

RESULTS

At baseline, 516 patients were randomised to placebo (n=208) or ustekinumab (n=308). Following the planned interim analysis, the sponsor discontinued the study due to lack of efficacy but no safety concerns. Efficacy analyses included 289 patients (placebo, n=116; ustekinumab, n=173) who completed or would have had a week 52 visit at study discontinuation. At week 52, 44% of ustekinumab patients and 56% of placebo patients had an SRI-4 response; there were no appreciable differences between the treatment groups in the major secondary endpoints. Through week 52, 28% of ustekinumab patients and 32% of placebo patients had a British Isles Lupus Assessment Group flare, with a mean time to first flare of 204.7 and 200.4 days, respectively. Through week 52, 70% of ustekinumab patients and 74% of placebo patients had ≥1 adverse event.

CONCLUSIONS

Ustekinumab did not demonstrate superiority over placebo in this population of adults with active SLE; adverse events were consistent with the known safety profile of ustekinumab.

TRIAL REGISTRATION NUMBER

NCT03517722.

Molecular Taxonomy of Systemic Lupus Erythematosus Through Data-Driven Patient Stratification: Molecular Endotypes and Cluster-Tailored Drugs

Objectives

Treatment of Systemic Lupus Erythematosus (SLE) is characterized by a largely empirical approach and relative paucity of novel compound development. We sought to stratify SLE patients based on their molecular phenotype and identify putative therapeutic compounds for each molecular fingerprint.

Methods

By the use of whole blood RNA-seq data from 120 SLE patients, and in a data-driven, clinically unbiased manner, we established modules of commonly regulated genes (molecular endotypes) and re-stratified patients through hierarchical clustering. Disease activity and severity were assessed using SLEDAI-2K and Lupus Severity Index, respectively. Through an in silico drug prediction pipeline, we investigated drugs currently in use, tested in lupus clinical trials, and listed in the iLINCS prediction databases, for their ability to reverse the gene expression signatures in each molecular endotype. Drug repurposing analysis was also performed to identify perturbagens that counteract group-specific SLE signatures.

Results

Molecular taxonomy identified five lupus endotypes, each characterized by a unique gene module enrichment pattern. Neutrophilic signature group consisted primarily of patients with active lupus nephritis, while the B-cell expression group included patients with constitutional features. Patients with moderate severity and serologic activity exhibited a signature enriched for metabolic processes. Mild disease was distributed in two groups, exhibiting enhanced basic cellular functions, myelopoiesis, and autophagy. Bortezomib was predicted to reverse disturbances in the "neutrophilic" cluster, azathioprine and ixazomib in the "B-cell" cluster, and fostamatinib in the "metabolic" patient subgroup.

Conclusion

The clinical spectrum of SLE encompasses distinct molecular endotypes, each defined by unique pathophysiologic aberrancies potentially reversible by distinct compounds.

Tailored treatment strategies and future directions in systemic lupus erythematosus

Systemic lupus erythematosus (SLE) represents a diagnostic and therapeutic challenge for physicians due to its protean manifestations and unpredictable course. The disease may manifest as multisystemic or organ-dominant and severity at presentation may vary according to age at onset (childhood-, adult- or late-onset SLE). Different manifestations may respond variably to different immunosuppressive medications and, even within the same organ-system, the severity of inflammation may vary from mild to organ-threatening. Current "state-of-the-art" in SLE treatment aims at remission or low disease activity in all organ systems. Apart from hydroxychloroquine and glucocorticoids (which should be used with caution), the choice of the appropriate immunosuppressive agent should be individualized and depend on the prevailing manifestation, severity stratification and patient childbearing potential. In this review, we provide an overview of therapeutic options for the various organ manifestations and severity patterns of the disease, different phenotypes (such as multisystem versus organ-dominant disease), as well as specific considerations, including lupus with antiphospholipid antibodies, childhood and late-onset disease, as well as treatment options during pregnancy and lactation.

Nucleic Acid-Sensing and Interferon-Inducible Pathways Show Differential Methylation in MZ Twins Discordant for Lupus and Overexpression in Independent Lupus Samples: Implications for Pathogenic Mechanism and Drug Targeting

Systemic lupus erythematosus (SLE) is a chronic, multisystem, autoimmune inflammatory disease with genomic and non-genomic contributions to risk. We hypothesize that epigenetic factors are a significant contributor to SLE risk and may be informative for identifying pathogenic mechanisms and therapeutic targets. To test this hypothesis while controlling for genetic background, we performed an epigenome-wide analysis of DNA methylation in genomic DNA from whole blood in three pairs of female monozygotic (MZ) twins of European ancestry, discordant for SLE. Results were replicated on the same array in four cell types from a set of four Danish female MZ twin pairs discordant for SLE. Genes implicated by the epigenetic analyses were then evaluated in 10 independent SLE gene expression datasets from the Gene Expression Omnibus (GEO). There were 59 differentially methylated loci between unaffected and affected MZ twins in whole blood, including 11 novel loci. All but two of these loci were hypomethylated in the SLE twins relative to the unaffected twins. The genes harboring these hypomethylated loci exhibited increased expression in multiple independent datasets of SLE patients. This pattern was largely consistent regardless of disease activity, cell type, or renal tissue type. The genes proximal to CpGs exhibiting differential methylation (DM) in the SLE-discordant MZ twins and exhibiting differential expression (DE) in independent SLE GEO cohorts (DM-DE genes) clustered into two pathways: the nucleic acid-sensing pathway and the type I interferon pathway. The DM-DE genes were also informatically queried for potential gene–drug interactions, yielding a list of 41 drugs including a known SLE therapy. The DM-DE genes delineate two important biologic pathways that are not only reflective of the heterogeneity of SLE but may also correlate with distinct IFN responses that depend on the source, type, and location of nucleic acid molecules and the activated receptors in individual patients. Cell- and tissue-specific analyses will be critical to the understanding of genetic factors dysregulating the nucleic acid-sensing and IFN pathways and whether these factors could be appropriate targets for therapeutic intervention.

Digital health, big data and smart technologies for the care of patients with systemic autoimmune diseases: Where do we stand?

The past decade has seen tremendous development in digital health, including in innovative new technologies such as Electronic Health Records, telemedicine, virtual visits, wearable technology and sophisticated analytical tools such as artificial intelligence (AI) and machine learning for the deep-integration of big data. In the field of rare connective tissue diseases (rCTDs), these opportunities include increased access to scarce and remote expertise, improved patient monitoring, increased participation and therapeutic adherence, better patient outcomes and patient empowerment. In this review, we discuss opportunities and key-barriers to improve application of digital health technologies in the field of autoimmune diseases. We also describe what could be the fully digital pathway of rCTD patients. Smart technologies can be used to provide real-world evidence about the natural history of rCTDs, to determine real-life drug utilization, advanced efficacy and safety data for rare diseases and highlight significant unmet needs. Yet, digitalization remains one of the most challenging issues faced by rCTD patients, their physicians and healthcare systems. Digital health technologies offer enormous potential to improve autoimmune rCTD care but this potential has so far been largely unrealized due to those significant obstacles. The need for robust assessments of the efficacy, affordability and scalability of AI in the context of digital health is crucial to improve the care of patients with rare autoimmune diseases.

Current Status of the Evaluation and Management of Lupus Patients and Future Prospects

The vastly diverse nature of systemic lupus erythematosus (SLE) poses great challenges to clinicians and patients, as well as to research and drug development efforts. Precise management of lupus patients would be advanced by the ability to identify specific abnormalities operative in individual patients at the time of encounter with the clinician. Advances in new technologies and bioinformatics have greatly improved the understanding of the pathophysiology of SLE. Recent research has focused on the discovery and classification of sensitive and specific markers that could aid early accurate diagnosis, better monitoring of disease and identification of appropriate therapy choices based on specific dysregulated molecular pathways. Here, we summarize some of the advances and discuss the challenges in moving toward precise patient-centric management modalities in SLE.

Comprehensive transcriptomic analysis of COVID-19 blood, lung, and airway

SARS-CoV2 is a previously uncharacterized coronavirus and causative agent of the COVID-19 pandemic. The host response to SARS-CoV2 has not yet been fully delineated, hampering a precise approach to therapy. To address this, we carried out a comprehensive analysis of gene expression data from the blood, lung, and airway of COVID-19 patients. Our results indicate that COVID-19 pathogenesis is driven by populations of myeloid-lineage cells with highly inflammatory but distinct transcriptional signatures in each compartment. The relative absence of cytotoxic cells in the lung suggests a model in which delayed clearance of the virus may permit exaggerated myeloid cell activation that contributes to disease pathogenesis by the production of inflammatory mediators. The gene expression profiles also identify potential therapeutic targets that could be modified with available drugs. The data suggest that transcriptomic profiling can provide an understanding of the pathogenesis of COVID-19 in individual patients.

Generation of Systemic Lupus Erythematosus Patient-Derived Induced Pluripotent Stem Cells from Blood

Systemic lupus erythematosus (SLE) is a chronic inflammatory autoimmune disease characterized by the production of multiple autoimmune antibodies and potentially involves any organ or tissue with a broad range of clinical manifestations. Conventional therapy still utilizes glucocorticoids and immunosuppressants. However, some patients show inadequate responses to glucocorticoids and immunosuppression, which may induce secondary immune dysfunction and severe infection as well as lead to an increased tumor risk. The lack of in vitro models has hampered progress in understanding and treating SLE. Patient-derived induced pluripotent stem cells (iPSCs) may provide a unique opportunity for modeling in vitro diseases as well as a platform for drug screening in individual patients. We isolated peripheral blood mononuclear cells from blood to explore the establishment of an in vitro model platform for SLE and directly purified CD34+ cells and seeded them for expansion. CD34+ cells were forced to express seven pluripotency factors, OCT4, SOX2, NANOG, LIN28, c-MYC, KLF4, and SV40LT, through transduction in lentiviral vectors. The morphological characteristics of induced pluripotent stem-like cells, such as prominent nucleoli and a high nucleus-to-cytoplasm ratio, were observed. The pluripotency of established SLE patient-derived iPSCs was confirmed by the expression of embryonic stem cell (ESC) markers and the ability of cells to differentiate into multiple cell lines. SLE patient-derived iPSCs exhibited human ESC properties, including morphology; growth characteristics; expression of pluripotency, genes, and surface markers; and teratoma formation. In conclusion, we generated SLE patient-derived iPSCs and validated their pluripotency. This study is a first but critical step that can provide a model platform for research aimed at understanding the SLE mechanism, which may lead to the discovery of new targets or compounds for the treatment of this disease.

Gordon R, Branigan P, Lipsky P, van Vollenhoven R, Hahn BH, Tsokos GC, Chevrier M, Rose S, Baribaud F, Jordan J. Suppression of Serum Interferon-γ Levels as a Potential Measure of Response to Ustekinumab Treatment in Patients With Systemic Lupus Erythematosus

OBJECTIVE

In a previously reported phase II randomized, placebo-controlled, interventional trial, we demonstrated that treatment with ustekinumab, an anti-interleukin-12 (IL-12)/IL-23 p40 neutralizing monoclonal antibody, improved global and organ-specific measures of disease activity in patients with active systemic lupus erythematosus (SLE). Utilizing the biomarker data from this phase II clinical study, we sought to determine whether modulation of the expression of IL-12, IL-23, or both cytokines by ustekinumab is associated with clinical efficacy in patients with SLE.

METHODS

This phase II randomized, placebo-controlled study enrolled 102 patients with autoantibody-positive SLE whose disease remained active despite standard-of-care therapy. Patients were randomized at a 3:2 ratio to receive ~6 mg/kg ustekinumab intravenously or placebo at week 0, followed by subcutaneous injections of 90 mg ustekinumab or placebo every 8 weeks, with placebo crossover to 90 mg ustekinumab every 8 weeks. The SLE Responder Index 4 (SRI-4) at week 24 was used to determine which patients could be classified as ustekinumab responders and which could be classified as nonresponders. In addition to measurements of p40 and IL-23, serum levels of interferon-γ (IFNγ), IL-17A, IL-17F, and IL-22, as a proxy for the IL-12 and IL-23 pathways, were quantified by immunoassay.

RESULTS

Changes in the serum levels of IL-17A, IL-17F, and IL-22 at different time points after treatment were not consistently significantly associated with an SRI-4 clinical response to ustekinumab in patients with SLE. In contrast, an SRI-4 response to ustekinumab was significantly associated (P < 0.01) with durable reductions in the serum IFNγ protein levels at several time points relative to baseline, which was not observed in ustekinumab nonresponders or patients who received placebo.

CONCLUSION

While not diminishing a potential role of IL-23, these serum biomarker assessments indicate that IL-12 blockade has an important role in the mechanism of action of ustekinumab treatment in patients with SLE.

Gene expression signatures of target tissues in type 1 diabetes, lupus erythematosus, multiple sclerosis, and rheumatoid arthritis

Autoimmune diseases are typically studied with a focus on the immune system, and less attention is paid to responses of target tissues exposed to the immune assault. We presently evaluated, based on available RNA sequencing data, whether inflammation induces similar molecular signatures at the target tissues in type 1 diabetes, systemic lupus erythematosus, multiple sclerosis, and rheumatoid arthritis. We identified confluent signatures, many related to interferon signaling, indicating pathways that may be targeted for therapy, and observed a high (>80%) expression of candidate genes for the different diseases at the target tissue level. These observations suggest that future research on autoimmune diseases should focus on both the immune system and the target tissues, and on their dialog. Discovering similar disease-specific signatures may allow the identification of key pathways that could be targeted for therapy, including the repurposing of drugs already in clinical use for other diseases.

A systematic review of text mining approaches applied to various application areas in the biomedical domain

Purpose

This work shows the results of a systematic literature review on biomedical text mining. The purpose of this study is to identify the different text mining approaches used in different application areas of the biomedical domain, the common tools used and the challenges of biomedical text mining as compared to generic text mining algorithms. This study will be of value to biomedical researchers by allowing them to correlate text mining approaches to specific biomedical application areas. Implications for future research are also discussed.

Design/methodology/approach

The review was conducted following the principles of the Kitchenham method. A number of research questions were first formulated, followed by the definition of the search strategy. The papers were then selected based on a list of assessment criteria. Each of the papers were analyzed and information relevant to the research questions were extracted.

Findings

It was found that researchers have mostly harnessed data sources such as electronic health records, biomedical literature, social media and health-related forums. The most common text mining technique was natural language processing using tools such as MetaMap and Unstructured Information Management Architecture, alongside the use of medical terminologies such as Unified Medical Language System. The main application area was the detection of adverse drug events. Challenges identified included the need to deal with huge amounts of text, the heterogeneity of the different data sources, the duality of meaning of words in biomedical text and the amount of noise introduced mainly from social media and health-related forums.

Originality/value

To the best of the authors’ knowledge, other reviews in this area have focused on either specific techniques, specific application areas or specific data sources. The results of this review will help researchers to correlate most relevant and recent advances in text mining approaches to specific biomedical application areas by providing an up-to-date and holistic view of work done in this research area. The use of emerging text mining techniques has great potential to spur the development of innovative applications, thus considerably impacting on the advancement of biomedical research.

Analysis of Trans-Ancestral SLE Risk Loci Identifies Unique Biologic Networks and Drug Targets in African and European Ancestries

Systemic lupus erythematosus (SLE) is a multi-organ autoimmune disorder with a prominent genetic component. Individuals of African ancestry (AA) experience the disease more severely and with an increased co-morbidity burden compared to European ancestry (EA) populations. We hypothesize that the disparities in disease prevalence, activity, and response to standard medications between AA and EA populations is partially conferred by genomic influences on biological pathways. To address this, we applied a comprehensive approach to identify all genes predicted from SNP-associated risk loci detected with the Immunochip. By combining genes predicted via eQTL analysis, as well as those predicted from base-pair changes in intergenic enhancer sites, coding-region variants, and SNP-gene proximity, we were able to identify 1,731 potential ancestry-specific and trans-ancestry genetic drivers of SLE. Gene associations were linked to upstream and downstream regulators using connectivity mapping, and predicted biological pathways were mined for candidate drug targets. Examination of trans-ancestral pathways reflect the well-defined role for interferons in SLE and revealed pathways associated with tissue repair and remodeling. EA-dominant genetic drivers were more often associated with innate immune and myeloid cell function pathways, whereas AA-dominant pathways mirror clinical findings in AA subjects, suggesting disease progression is driven by aberrant B cell activity accompanied by ER stress and metabolic dysfunction. Finally, potential ancestry-specific and non-specific drug candidates were identified. The integration of all SLE SNP-predicted genes into functional pathways revealed critical molecular pathways representative of each population, underscoring the influence of ancestry on disease mechanism and also providing key insight for therapeutic selection.

Analysis of gene expression from systemic lupus erythematosus synovium reveals myeloid cell-driven pathogenesis of lupus arthritis

Arthritis is a common manifestation of systemic lupus erythematosus (SLE) yet understanding of the underlying pathogenic mechanisms remains incomplete. We, therefore, interrogated gene expression profiles of SLE synovium to gain insight into the nature of lupus arthritis (LA), using osteoarthritis (OA) and rheumatoid arthritis (RA) as comparators. Knee synovia from SLE, OA, and RA patients were analyzed for differentially expressed genes (DEGs) and also by Weighted Gene Co-expression Network Analysis (WGCNA) to identify modules of highly co-expressed genes. Genes upregulated and/or co-expressed in LA revealed numerous immune/inflammatory cells dominated by a myeloid phenotype, in which pathogenic macrophages, myeloid-lineage cells, and their secreted products perpetuate inflammation, whereas OA was characterized by fibroblasts and RA of lymphocytes. Genes governing trafficking of immune cells into the synovium by chemokines were identified, but not in situ generation of germinal centers (GCs). Gene Set Variation Analysis (GSVA) confirmed activation of specific immune cell types in LA. Numerous therapies were predicted to target LA, including TNF, NFκB, MAPK, and CDK inhibitors. Detailed gene expression analysis identified a unique pattern of cellular components and physiologic pathways operative in LA, as well as drugs potentially able to target this common manifestation of SLE.

Repurposing old drugs to fight multidrug resistant cancers

Overcoming multidrug resistance represents a major challenge for cancer treatment. In the search for new chemotherapeutics to treat malignant diseases, drug repurposing gained a tremendous interest during the past years. Repositioning candidates have often emerged through several stages of clinical drug development, and may even be marketed, thus attracting the attention and interest of pharmaceutical companies as well as regulatory agencies. Typically, drug repositioning has been serendipitous, using undesired side effects of small molecule drugs to exploit new disease indications. As bioinformatics gain increasing popularity as an integral component of drug discovery, more rational approaches are needed. Herein, we show some practical examples of in silico approaches such as pharmacophore modelling, as well as pharmacophore- and docking-based virtual screening for a fast and cost-effective repurposing of small molecule drugs against multidrug resistant cancers. We provide a timely and comprehensive overview of compounds with considerable potential to be repositioned for cancer therapeutics. These drugs are from diverse chemotherapeutic classes. We emphasize the scope and limitations of anthelmintics, antibiotics, antifungals, antivirals, antimalarials, antihypertensives, psychopharmaceuticals and antidiabetics that have shown extensive immunomodulatory, antiproliferative, pro-apoptotic, and antimetastatic potential. These drugs, either used alone or in combination with existing anticancer chemotherapeutics, represent strong candidates to prevent or overcome drug resistance. We particularly focus on outcomes and future perspectives of drug repositioning for the treatment of multidrug resistant tumors and discuss current possibilities and limitations of preclinical and clinical investigations.

Repositioning Quinacrine Toward Treatment of Ovarian Cancer by Rational Combination With TRAIL

Quinacrine has been identified as a potent DR5-inducing agent that sensitizes cancer cells to TRAIL-induced apoptosis. In the current study, we found that quinacrine increased DR5 mRNA levels significantly in ovarian cancer cell lines regardless of p53 status. Further study showed the half-life of DR5 in quinacrine-treated cells was significantly prolonged, indicating that DR5 protein degradation was inhibited by quinacrine. We tested if the combination of TRAIL and quinacrine could be effective in ovarian cancer treatment in vitro and in ovarian cancer xenograft mouse models. We found that quinacrine enhanced TRAIL sensitivity or reversed TRAIL resistance in all the ovarian cancer cell lines tested. Mice treated with quinacrine and TRAIL remained disease-free for up to 20 weeks, however, mice treated with TRAIL or quinacrine alone and in control group died within ~8 weeks after treatment. Intraperitoneal delivery of quinacrine and TRAIL is rational and practical with extraordinary synergistic anti-cancer effects in preclinical models of ovarian cancer. Clinical investigation of combining quinacrine with TRAIL for ovarian cancer treatment is warranted.

Drug repurposing to improve treatment of rheumatic autoimmune inflammatory diseases

The past century has been characterized by intensive efforts, within both academia and the pharmaceutical industry, to introduce new treatments to individuals with rheumatic autoimmune inflammatory diseases (RAIDs), often by 'borrowing' treatments already employed in one RAID or previously used in an entirely different disease, a concept known as drug repurposing. However, despite sharing some clinical manifestations and immune dysregulation, disease pathogenesis and phenotype vary greatly among RAIDs, and limited understanding of their aetiology has made repurposing drugs for RAIDs challenging. Nevertheless, the past century has been characterized by different 'waves' of repurposing. Early drug repurposing occurred in academia and was based on serendipitous observations or perceived disease similarity, often driven by the availability and popularity of drug classes. Since the 1990s, most biologic therapies have been developed for one or several RAIDs and then tested among the others, with varying levels of success. The past two decades have seen data-driven repurposing characterized by signature-based approaches that rely on molecular biology and genomics. Additionally, many data-driven strategies employ computational modelling and machine learning to integrate multiple sources of data. Together, these repurposing periods have led to advances in the treatment for many RAIDs.

Selective Histone Deacetylase 6 Inhibition Normalizes B Cell Activation and Germinal Center Formation in a Model of Systemic Lupus Erythematosus

Autoantibody production by plasma cells (PCs) plays a pivotal role in the pathogenesis of systemic lupus erythematosus (SLE). The molecular pathways by which B cells become pathogenic PC secreting autoantibodies in SLE are incompletely characterized. Histone deactylase 6 (HDAC6) is a unique cytoplasmic HDAC that modifies the interaction of a number of tubulin- associated proteins; inhibition of HDAC6 has been shown to be beneficial in murine models of SLE, but the downstream pathways accounting for the therapeutic benefit have not been clearly delineated. In the current study, we sought to determine whether selective HDAC6 inhibition would abrogate abnormal B cell activation in SLE. We treated NZB/W lupus mice with the selective HDAC6 inhibitor, ACY-738, for 4 weeks beginning at 20 weeks-of age. After only 4 weeks of treatment, manifestation of lupus nephritis (LN) were greatly reduced in these animals. We then used RNAseq to determine the genomic signatures of splenocytes from treated and untreated mice and applied computational cellular and pathway analysis to reveal multiple signaling events associated with B cell activation and differentiation in SLE that were modulated by HDAC6 inhibition. PC development was abrogated and germinal center (GC) formation was greatly reduced. When the HDAC6 inhibitor-treated lupus mouse gene signatures were compared to human lupus patient gene signatures, the results showed numerous immune, and inflammatory pathways increased in active human lupus were significantly decreased in the HDAC6 inhibitor treated animals. Pathway analysis suggested alterations in cellular metabolism might contribute to the normalization of lupus mouse spleen genomic signatures, and this was confirmed by direct measurement of the impact of the HDAC6 inhibitor on metabolic activities of murine spleen cells. Taken together, these studies show HDAC6 inhibition decreases B cell activation signaling pathways and reduces PC differentiation in SLE and suggest that a critical event might be modulation of cellular metabolism.

Machine learning approaches to predict lupus disease activity from gene expression data

The integration of gene expression data to predict systemic lupus erythematosus (SLE) disease activity is a significant challenge because of the high degree of heterogeneity among patients and study cohorts, especially those collected on different microarray platforms. Here we deployed machine learning approaches to integrate gene expression data from three SLE data sets and used it to classify patients as having active or inactive disease as characterized by standard clinical composite outcome measures. Both raw whole blood gene expression data and informative gene modules generated by Weighted Gene Co-expression Network Analysis from purified leukocyte populations were employed with various classification algorithms. Classifiers were evaluated by 10-fold cross-validation across three combined data sets or by training and testing in independent data sets, the latter of which amplified the effects of technical variation. A random forest classifier achieved a peak classification accuracy of 83 percent under 10-fold cross-validation, but its performance could be severely affected by technical variation among data sets. The use of gene modules rather than raw gene expression was more robust, achieving classification accuracies of approximately 70 percent regardless of how the training and testing sets were formed. Fine-tuning the algorithms and parameter sets may generate sufficient accuracy to be informative as a standalone estimate of disease activity.

Gene expression analysis delineates the potential roles of multiple interferons in systemic lupus erythematosus

A role for interferon (IFN) in systemic lupus erythematosus (SLE) pathogenesis is inferred from the prominent IFN gene signature (IGS), but the major IFN species and its relationship to disease activity are unknown. A bioinformatic approach employing individual IFN species gene signatures to interrogate SLE microarray datasets demonstrates a putative role for numerous IFN species, with prominent expression of IFNB1 and IFNW signatures. In contrast with other SLE-affected organs, the IGS is less prominent in lupus nephritis. SLE patients with active and inactive disease have readily detectable IGS and the IGS changes synchronously with a monocyte signature but not disease activity, and is significantly related to monocyte transcripts. Monocyte over-expression of three times as many IGS transcripts as T and B cells and IGS retention in monocytes, but not T and B cells from inactive SLE patients contribute to the lack of correlation between the IGS and SLE disease activity.

Crowdsourcing in medical research: concepts and applications

Crowdsourcing shifts medical research from a closed environment to an open collaboration between the public and researchers. We define crowdsourcing as an approach to problem solving which involves an organization having a large group attempt to solve a problem or part of a problem, then sharing solutions. Crowdsourcing allows large groups of individuals to participate in medical research through innovation challenges, hackathons, and related activities. The purpose of this literature review is to examine the definition, concepts, and applications of crowdsourcing in medicine. This multi-disciplinary review defines crowdsourcing for medicine, identifies conceptual antecedents (collective intelligence and open source models), and explores implications of the approach. Several critiques of crowdsourcing are also examined. Although several crowdsourcing definitions exist, there are two essential elements: (1) having a large group of individuals, including those with skills and those without skills, propose potential solutions; (2) sharing solutions through implementation or open access materials. The public can be a central force in contributing to formative, pre-clinical, and clinical research. A growing evidence base suggests that crowdsourcing in medicine can result in high-quality outcomes, broad community engagement, and more open science.

Identification of alterations in macrophage activation associated with disease activity in systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is characterized by abnormalities in B cell and T cell function, but the role of disturbances in the activation status of macrophages (Mϕ) has not been well described in human patients. To address this, gene expression profiles from isolated lymphoid and myeloid populations were analyzed to identify differentially expressed (DE) genes between healthy controls and patients with either inactive or active SLE. While hundreds of DE genes were identified in B and T cells of active SLE patients, there were no DE genes found in B or T cells from patients with inactive SLE compared to healthy controls. In contrast, large numbers of DE genes were found in myeloid cells (MC) from both active and inactive SLE patients. Among the DE genes were several known to play roles in Mϕ activation and polarization, including the M1 genes STAT1 and SOCS3 and the M2 genes STAT3, STAT6, and CD163. M1-associated genes were far more frequent in data sets from active versus inactive SLE patients. To characterize the relationship between Mϕ activation and disease activity in greater detail, weighted gene co-expression network analysis (WGCNA) was used to identify modules of genes associated with clinical activity in SLE patients. Among these were disease activity-correlated modules containing activation signatures of predominantly M1-associated genes. No disease activity-correlated modules were enriched in M2-associated genes. Pathway and upstream regulator analysis of DE genes from both active and inactive SLE MC were cross-referenced with high-scoring hits from the drug discovery Library of Integrated Network-based Cellular Signatures (LINCS) to identify new strategies to treat both stages of SLE. A machine learning approach employing MC gene modules and a generalized linear model was able to predict the disease activity status in unrelated gene expression data sets. In summary, altered MC gene expression is characteristic of both active and inactive SLE. However, disease activity is associated with an alteration in the activation of MC, with a bias toward the M1 proinflammatory phenotype. These data suggest that while hyperactivity of B cells and T cells is associated with active SLE, MC potentially direct flare-ups and remission by altering their activation status toward the M1 state.

A Review of Drug Repositioning Based Chemical-induced Cell Line Expression Data

Drug repositioning is an important area of biomedical research. The drug repositioning studies have shifted to computational approaches. Large-scale perturbation databases, such as the Connectivity Map and the Library of Integrated Network-Based Cellular Signatures, contain a number of chemical-induced gene expression profiles and provide great opportunities for computational biology and drug repositioning. One reason is that the profiles provided by the Connectivity Map and the Library of Integrated Network-Based Cellular Signatures databases show an overall view of biological mechanism in drugs, diseases and genes. In this article, we provide a review of the two databases and their recent applications in drug repositioning.

Efficacy and safety of ustekinumab, an IL-12 and IL-23 inhibitor, in patients with active systemic lupus erythematosus: results of a multicentre, double-blind, phase 2, randomised, controlled study

BACKGROUND

Ustekinumab is a monoclonal antibody targeting interleukin (IL)-12 and IL-23 and is approved for the treatment of plaque psoriasis, psoriatic arthritis, and Crohn's disease. IL-12 and IL-23 have been implicated in systemic lupus erythematosus. We aimed to assess the efficacy and safety of ustekinumab for the treatment of systemic lupus erythematosus in patients with moderate-to-severe disease activity despite conventional treatment.

METHODS

This was a multicentre, double-blind, phase 2, randomised, controlled trial of adult patients with active, seropositive systemic lupus erythematosus, done at 44 private practices and academic centres in Argentina, Australia, Germany, Hungary, Mexico, Poland, Spain, Taiwan, and the USA. Eligible adults were aged 18-75 years, weighed at least 35 kg, and had a diagnosis of systemic lupus erythematosus at least 3 months before the first administration of study drug. Eligible patients were randomly assigned (3:2) to the ustekinumab or placebo group using an interactive web response system with stratification by skin biopsy, lupus nephritis presence, baseline systemic lupus erythematosus medications and systemic lupus erythematosus disease activity index 2000 (SLEDAI-2K) score combined factor, site, region, and race. Patients and investigators were masked to treatment allocation. Patients received an intravenous infusion of ustekinumab (260 mg for patients weighing 35-55 kg, 390 mg for patients weighing >55 kg and ≤85 kg, and 520 mg for patients weighing >85 kg) followed by subcutaneous injections of ustekinumab 90 mg every 8 weeks or intravenous infusion of placebo at week 0 followed by subcutaneous injections of placebo every 8 weeks, both in addition to standard-of-care therapy. The primary endpoint was the proportion of patients achieving a SLEDAI-2K responder index-4 (SRI-4) response at week 24. Efficacy analyses were done in a modified intention-to-treat population of patients who received at least one dose (partial or complete, intravenous or subcutaneous) of their randomly assigned study treatment. Safety analyses were done in all patients who received at least one dose of study treatment, regardless of group assignment. This study is registered at ClinicalTrials.gov, number NCT02349061.

FINDINGS

Between Oct 6, 2015, and Nov 30, 2016, 166 patients were screened, of whom 102 were randomly assigned to receive ustekinumab (n=60) or placebo (n=42). At week 24, 37 (62%) of 60 patients in the ustekinumab group and 14 (33%) of 42 patients in the placebo group achieved an SRI-4 response (percentage difference 28% [95% CI 10-47], p=0·006). Between week 0 and week 24, 47 (78%) of 60 patients in the ustekinumab group and 28 (67%) of 42 patients in the placebo group had at least one adverse event. Infections were the most common type of adverse event (27 [45%] in the ustekinumab group vs 21 [50%] in the placebo group). No deaths or treatment-emergent opportunistic infections, herpes zoster, tuberculosis, or malignancies occurred between weeks 0-24.

INTERPRETATION

The addition of ustekinumab to standard-of-care treatment resulted in better efficacy in clinical and laboratory parameters than placebo in the treatment of active systemic lupus erythematosus and had a safety profile consistent with ustekinumab therapy in other diseases. The results of this study support further development of ustekinumab as a novel treatment in systemic lupus erythematosus.

FUNDING

Janssen Research & Development, LLC.

The prescribable drugs with efficacy in experimental epilepsies (PDE3) database for drug repurposing research in epilepsy

OBJECTIVE

Current antiepileptic drugs (AEDs) have several shortcomings. For example, they fail to control seizures in 30% of patients. Hence, there is a need to identify new AEDs. Drug repurposing is the discovery of new indications for approved drugs. This drug "recycling" offers the potential of significant savings in the time and cost of drug development. Many drugs licensed for other indications exhibit antiepileptic efficacy in animal models. Our aim was to create a database of "prescribable" drugs, approved for other conditions, with published evidence of efficacy in animal models of epilepsy, and to collate data that would assist in choosing the most promising candidates for drug repurposing.

METHODS

The database was created by the following: (1) computational literature-mining using novel software that identifies Medline abstracts containing the name of a prescribable drug, a rodent model of epilepsy, and a phrase indicating seizure reduction; then (2) crowdsourced manual curation of the identified abstracts.

RESULTS

The final database includes 173 drugs and 500 abstracts. It is made freely available at www.liverpool.ac.uk/D3RE/PDE3. The database is reliable: 94% of the included drugs have corroborative evidence of efficacy in animal models (for example, evidence from multiple independent studies). The database includes many drugs that are appealing candidates for repurposing, as they are widely accepted by prescribers and patients-the database includes half of the 20 most commonly prescribed drugs in England-and they target many proteins involved in epilepsy but not targeted by current AEDs. It is important to note that the drugs are of potential relevance to human epilepsy-the database is highly enriched with drugs that target proteins of known causal human epilepsy genes (Fisher's exact test P-value < 3 × 10^-5 ). We present data to help prioritize the most promising candidates for repurposing from the database.

SIGNIFICANCE

The PDE3 database is an important new resource for drug repurposing research in epilepsy.

Novel molecular signatures in mononuclear cell populations from patients with systemic lupus erythematosus

To gain novel insights into the immunopathogenesis of systemic lupus erythematosus we have analyzed gene expression data from isolated CD4⁺ T cells, CD8⁺ T cells, CD19⁺ B cells, and CD56⁺ NK-cell enriched peripheral blood cell fractions from patients and healthy donors. As predicted, type I interferon-inducible gene transcripts are overexpressed in all populations. Transcripts preferentially expressed in SLE CD4⁺ and CD8⁺ T cells include those associated with Tregulatory and Th17 effector cell programs, respectively, but in each case additional transcripts predicted to limit differentiation of those effector cells are detected. Evidence for involvement of the Wnt/β-catenin pathway was observed in both B and T cell fractions, and novel transcripts were identified in each cell population. These data point to disrupted T effector cell differentiation and the Wnt/β-catenin pathway as contributors to immune dysfunction in SLE while further supporting a central role for the type I interferon pathway in lupus.