Unified Signature Research Articles

Genome-wide whole-blood transcriptome profiling across inherited bone marrow failure subtypes

AbstractGene expression profiling has long been used in understanding the contribution of genes and related pathways in disease pathogenesis and susceptibility. We have performed whole-blood transcriptomic profiling in a subset of patients with inherited bone marrow failure (IBMF) whose diseases are clinically and genetically characterized as Fanconi anemia (FA), Shwachman-Diamond syndrome (SDS), and dyskeratosis congenita (DC). We hypothesized that annotating whole-blood transcripts genome wide will aid in understanding the complexity of gene regulation across these IBMF subtypes. Initial analysis of these blood-derived transcriptomes revealed significant skewing toward upregulated genes in patients with FA when compared with controls. Patients with SDS or DC also showed similar skewing profiles in their transcriptional status revealing a common pattern across these different IBMF subtypes. Gene set enrichment analysis revealed shared pathways involved in protein translation and elongation (ribosome constituents), RNA metabolism (nonsense-mediated decay), and mitochondrial function (electron transport chain). We further identified a discovery set of 26 upregulated genes at stringent cutoff (false discovery rate < 0.05) that appeared as a unified signature across the IBMF subtypes. Subsequent transcriptomic analysis on genetically uncharacterized patients with BMF revealed a striking overlap of genes, including 22 from the discovery set, which indicates a unified transcriptional drive across the classic (FA, SDS, and DC) and uncharacterized BMF subtypes. This study has relevance in disease pathogenesis, for example, in explaining the features (including the BMF) common to all patients with IBMF and suggests harnessing this transcriptional signature for patient benefit.

Integrated, multicohort analysis reveals unified signature of systemic lupus erythematosus.

Systemic lupus erythematosus (SLE) is a complex autoimmune disease that follows an unpredictable disease course and affects multiple organs and tissues. We performed an integrated, multicohort analysis of 7,471 transcriptomic profiles from 40 independent studies to identify robust gene expression changes associated with SLE. We identified a 93-gene signature (SLE MetaSignature) that is differentially expressed in the blood of patients with SLE compared with healthy volunteers; distinguishes SLE from other autoimmune, inflammatory, and infectious diseases; and persists across diverse tissues and cell types. The SLE MetaSignature correlated significantly with disease activity and other clinical measures of inflammation. We prospectively validated the SLE MetaSignature in an independent cohort of pediatric patients with SLE using a microfluidic quantitative PCR (qPCR) array. We found that 14 of the 93 genes in the SLE MetaSignature were independent of IFN-induced and neutrophil-related transcriptional profiles that have previously been associated with SLE. Pathway analysis revealed dysregulation associated with nucleic acid biosynthesis and immunometabolism in SLE. We further refined a neutropoiesis signature and identified underappreciated transcripts related to immune cells and oxidative stress. In our multicohort, transcriptomic analysis has uncovered underappreciated genes and pathways associated with SLE pathogenesis, with the potential to advance clinical diagnosis, biomarker development, and targeted therapeutics for SLE.

Airway Secretory microRNAome Changes during Rhinovirus Infection in Early Childhood.

BackgroundInnate immune responses are fine-tuned by small noncoding RNA molecules termed microRNAs (miRs) that modify gene expression in response to the environment. During acute infections, miRs can be secreted in extracellular vesicles (EV) to facilitate cell-to-cell genetic communication. The purpose of this study was to characterize the baseline population of miRs secreted in EVs in the airways of young children (airway secretory microRNAome) and examine the changes during rhinovirus (RV) infection, the most common cause of asthma exacerbations and the most important early risk factor for the development of asthma beyond childhood.MethodsNasal airway secretions were obtained from children (≤3 yrs. old) during PCR-confirmed RV infections (n = 10) and age-matched controls (n = 10). Nasal EVs were isolated with polymer-based precipitation and global miR profiles generated using NanoString microarrays. We validated our in vivo airway secretory miR data in an in vitro airway epithelium model using apical secretions from primary human bronchial epithelial cells (HBEC) differentiated at air-liquid interface (ALI). Bioinformatics tools were used to determine the unified (nasal and bronchial) signature airway secretory miRNAome and changes during RV infection in children.ResultsMultiscale analysis identified four signature miRs comprising the baseline airway secretory miRNAome: hsa-miR-630, hsa-miR-302d-3p, hsa- miR-320e, hsa-miR-612. We identified hsa-miR-155 as the main change in the baseline miRNAome during RV infection in young children. We investigated the potential biological relevance of the airway secretion of hsa-mir-155 using in silico models derived from gene datasets of experimental in vivo human RV infection. These analyses confirmed that hsa-miR-155 targetome is an overrepresented pathway in the upper airways of individuals infected with RV.ConclusionsComparative analysis of the airway secretory microRNAome in children indicates that RV infection is associated with airway secretion of EVs containing miR-155, which is predicted in silico to regulate antiviral immunity. Further characterization of the airway secretory microRNAome during health and disease may lead to completely new strategies to treat and monitor respiratory conditions in all ages.

Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota.

In recent years, several associations between common chronic human disorders and altered gut microbiome composition and function have been reported. In most of these reports, treatment regimens were not controlled for and conclusions could thus be confounded by the effects of various drugs on the microbiota, which may obscure microbial causes, protective factors or diagnostically relevant signals. Our study addresses disease and drug signatures in the human gut microbiome of type 2 diabetes mellitus (T2D). Two previous quantitative gut metagenomics studies of T2D patients that were unstratified for treatment yielded divergent conclusions regarding its associated gut microbial dysbiosis. Here we show, using 784 available human gut metagenomes, how antidiabetic medication confounds these results, and analyse in detail the effects of the most widely used antidiabetic drug metformin. We provide support for microbial mediation of the therapeutic effects of metformin through short-chain fatty acid production, as well as for potential microbiota-mediated mechanisms behind known intestinal adverse effects in the form of a relative increase in abundance of Escherichia species. Controlling for metformin treatment, we report a unified signature of gut microbiome shifts in T2D with a depletion of butyrate-producing taxa. These in turn cause functional microbiome shifts, in part alleviated by metformin-induced changes. Overall, the present study emphasizes the need to disentangle gut microbiota signatures of specific human diseases from those of medication.

Grouping of histone deacetylase inhibitors and other toxicants disturbing neural crest migration by transcriptional profiling

Functional assays, such as the “migration inhibition of neural crest cells” (MINC) developmental toxicity test, can identify toxicants without requiring knowledge on their mode of action (MoA). Here, we were interested, whether (i) inhibition of migration by structurally diverse toxicants resulted in a unified signature of transcriptional changes; (ii) whether statistically-identified transcript patterns would inform on compound grouping even though individual genes were little regulated, and (iii) whether analysis of a small group of biologically-relevant transcripts would allow the grouping of compounds according to their MoA. We analyzed transcripts of 35 ‘migration genes’ after treatment with 16 migration-inhibiting toxicants. Clustering, principal component analysis and correlation analyses of the data showed that mechanistically related compounds (e.g. histone deacetylase inhibitors (HDACi), PCBs) triggered similar transcriptional changes, but groups of structurally diverse toxicants largely differed in their transcriptional effects. Linear discriminant analysis (LDA) confirmed the specific clustering of HDACi across multiple separate experiments. Similarity of the signatures of the HDACi trichostatin A and suberoylanilide hydroxamic acid to the one of valproic acid (VPA), suggested that the latter compound acts as HDACi when impairing neural crest migration. In conclusion, the data suggest that (i) a given functional effect (e.g. inhibition of migration) can be associated with highly diverse signatures of transcript changes; (ii) statistically significant grouping of mechanistically-related compounds can be achieved on the basis of few genes with small regulations. Thus, incorporation of mechanistic markers in functional in vitro tests may support read-across procedures, also for structurally un-related compounds.

Classifying directional Gaussian entanglement, Einstein-Podolsky-Rosen steering, and discord.

Using Venn diagrams, we classify the different types of two-mode Gaussian continuous variable quantum correlation including directional entanglement and Einstein-Podolsky-Rosen (EPR) steering. We establish unified signatures for one- and two-way quantum steering, entanglement, and discord beyond entanglement in terms of an EPR-type variance. By focusing on Gaussian states, we link an optimized condition for entanglement based on an EPR variance to the Simon-Peres condition. This allows us to quantify the asymmetry of the Gaussian entanglement, and to relate the asymmetry to a directional quantum teleportation protocol where Alice and Bob possess asymmetrically noisy channels. Our analysis enables a determination of the type and direction of quantum correlation in a way that is easily measured in experiment. We also find that for symmetric states, when discord exceeds a certain threshold, the states are necessarily steerable.

Toward a unified biosignature for tuberculosis.

Accurate and rapid diagnosis of active tuberculosis (TB) disease is still hampered by inadequate tools. Although current assays relying on single-marker readouts mostly display inadequate sensitivity and/or specificity, host-related multimarker signatures are especially poorly developed. As a consequence, research programs have been initiated to search for combinations of markers-so-called biosignatures with superior performance. Many such investigations harness high-throughput platforms to analyze the host response during infection and disease. A major challenge for these activities is the analysis of vast amounts of data produced. Specialized bioinformatic tools are being applied to identify the most robust biosignatures for classification of exposed and diseased individuals and prognosis of risk of disease in endemic areas. Validation of the most promising biosignatures in ongoing multicohort studies will bring us a step closer to the identification of an accurate unified signature.

Improving Utilization of Hardware Signatures in Transactional Memory

The transactional memory (TM) paradigm promises to increase programmer productivity by making it easier to write correct parallel programs. In fulfilling this goal, a TM system should maximize its performance with limited hardware resources. Conflict detection is an essential element for maintaining correctness among concurrent transactions in a TM system. Hardware signatures have been proposed as an area-efficient method for detecting conflicts. However, signatures can degrade TM performance by falsely declaring conflicts. Hence, improving the accuracy of signatures within a given hardware budget is a crucial issue for TM to be adopted as a mainstream programming model. In this paper, we propose a simple and effective signature design, the unified signature. Instead of using separate read- and write-signatures, we implement a single signature to track all read- and write-accesses. By merging read- and write-signatures, a unified signature can effectively enlarge the signature coverage without additional overhead. Within the constraints of a given hardware budget, a TM system with a unified signature outperforms a baseline system with the same-sized traditional signatures by reducing the number of falsely detected conflicts. Even though the unified signature scheme incurs read-read dependencies, we show that these false dependencies do not negate the benefit of unified signatures and can effectively be filtered out. A TM system with a 2-Kbit unified signature with a helper signature scheme achieves speedups of 15 percent over baseline TM with 33 percent less area and 49 percent less power.

Gene Expression Profile Alone Is Inadequate In Predicting Complete Responses In Multiple Myeloma

AbstractAbstract 306Current therapy for multiple myeloma (MM) remains empiric. With advancement in the understanding of its molecular basis, newer therapies are emerging faster than ever with increasing the difficulty in the selection of treatment regime to maximize response and minimize the rising cost of therapy. In recent years, treatment response prediction using gene expression profiling is being evaluated to identify expression signature that can classify patients likely to benefit from chemotherapy, e.g., there are several multi-gene expression assays available to predict treatment responses. However, expression signatures and predictive power vary significantly among these assays. Here, we have assessed the ability of gene expression profile to predict complete response (CR) in patients with MM. We evaluated 128 newly-diagnosed patients with MM enrolled on IFM protocol and treated uniformly with high-dose melphalan followed by autologous stem cell transplant. Seventy one of 128 patients (56%) had achieved CR while the rest 57 (44%) had partial response (PR) or less to this therapeutic intervention. CD138+ MM cells collected at the time of diagnosis were profiled for gene expression and processed using the dChip and aroma.affymetrix module in R software. We have used all common machine learning packages in R/Bioconductor and BRB-array Tools software to build response signature models; the packages used but not limited to were: Decision tree, Support Vector Machines (SVM), Prediction Analysis of Microarray (PAM), K-Nearest Neighbors, Bayesian Additive Regression Trees (BART), Lasso, Ridge regression, amongst others. For accurate assessment of model prediction ability, the dataset was split into training and test sets. Classifier gene models were built, trained and evaluated using K-fold cross-validation followed by model selection based on minimum prediction error. We built several models using different classification methods and experimented with gene inclusion criteria in our datasets according to those features most differentially expressed between CR and non-CR patients. Final model from each of these methods was applied to test dataset to predict CR vs non-CR, and prediction results were evaluated using area under the ROC curve (AUC) as a predictive measure. The maximum AUC among all the training-testing splits was 0.63. The true positive rate (Sensitivity) to correctly predict CR case reached maximum 70% or more at the cost of higher false negative, which is to misclassify a patient as non-CR who might have responded to the treatment. Among the number of methods employed, our best predictive capability provided 66% sensitivity, 60% specificity, 67% positive predictive value and 59% negative predictive value. Importantly, comparing real CR proportion (71/128 = 56%) with that of predicted by the best model (66%), no statistically significant difference was observed (Chi-square; p-value: 0.09). We observe similar results using two independent datasets available in public data repository. Based on our analysis, we recognize and in fact foresee that the expression profile alone has limited ability to predict treatment response especially when response rate is high. This lack of predictability using current approach of response prediction with gene expression alone may be related to several limitations, like alternate splicing, miRNA-based gene regulation, post-translational modifications, binary distribution of response status, inherent variability of new samples, and developing unified signature without consideration of myeloma subtypes. A comprehensive model needs to be developed using global genomic changes to have meaningful output for clinical application. Disclosures:Munshi:Millennium Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees; Onyx: Membership on an entity's Board of Directors or advisory committees.

Complementary Gene Signature Integration in Multiplatform Microarray Experiments

The concept of gene signature overlap has been addressed previously in a number of research papers. A common conclusion is the absence of significant overlap. In this paper, we verify the aforementioned fact, but we also assess the issue of similarities not on the gene level, but on the biology level hidden underneath a given signature. We proceed by taking into account the biological knowledge that exists among different signatures, and use it as a means of integrating them and refining their statistical significance on the datasets. In this form, by integrating biological knowledge with information stemming from data distributions, we derive a unified signature that is significantly improved over its predecessors in terms of performance and robustness. Our motive behind this approach is to assess the problem of evaluating different signatures not in a competitive but rather in a complementary manner, where one is treated as a pool of knowledge contributing to a global and unified solution.