A composite score associated with spontaneous operational tolerance in kidney transplant recipients.

New challenges in renal transplantation include using biological information to devise a useful clinical test for discerning high- and low-risk patients for individual therapy and ascertaining the best combination and appropriate dosages of drugs. Based on a 20-gene signature from a microarray meta-analysis performed on 46 operationally tolerant patients and 266 renal transplant recipients with stable function, we applied the sparse Bolasso methodology to identify a minimal and robust combination of six genes and two demographic parameters associated with operational tolerance. This composite score of operational tolerance discriminated operationally tolerant patients with an area under the curve of 0.97 (95% confidence interval 0.94-1.00). The score was not influenced by immunosuppressive treatment, center of origin, donor type, or post-transplant lymphoproliferative disorder history of the patients. This composite score of operational tolerance was significantly associated with both de novo anti-HLA antibodies and tolerance loss. It was validated by quantitative polymerase chain reaction using independent samples and demonstrated specificity toward a model of tolerance induction. Thus, our score would allow clinicians to improve follow-up of patients, paving the way for individual therapy.

A common gene signature across multiple studies relate biomarkers and functional regulation in tolerance to renal allograft.

Patients tolerant to a kidney graft display a specific blood cell transcriptional pattern but results from five different studies were inconsistent, raising the question of relevance for future clinical application. To resolve this, we sought to identify a common gene signature, specific functional and cellular components, and discriminating biomarkers for tolerance following kidney transplantation. A meta-analysis of studies identified a robust gene signature involving proliferation of B and CD4 T cells, and inhibition of CD14 monocyte related functions among 96 tolerant samples. This signature was further supported through a cross-validation approach, yielding 92.5% accuracy independent of the study of origin. Experimental validation, performed on new tolerant samples and using a selection of the top-20 biomarkers, returned 91.7% of good classification. Beyond the confirmation of B-cell involvement, our data also indicated participation of other cell subsets in tolerance. Thus, the use of the top 20 biomarkers, mostly centered on B cells, may provide a common and standardized tool towards personalized medicine for the monitoring of tolerant or low-risk patients among kidney allotransplant recipients. These data point to a global preservation of genes favoring the maintenance of a homeostatic and 'healthy' environment in tolerant patients and may contribute to a better understanding of tolerance maintenance mechanisms.

MADGene: retrieval and processing of gene identifier lists for the analysis of heterogeneous microarray datasets.

UNLABELLED: MADGene is a software environment comprising a web-based database and a java application. This platform aims at unifying gene identifiers (ids) and performing gene set analysis. MADGene allows the user to perform inter-conversion of clone and gene ids over a large range of nomenclatures relative to 17 species. We propose a set of 23 functions to facilitate the analysis of gene sets and we give two microarray applications to show how MADGene can be used to conduct meta-analyses. AVAILABILITY: The MADGene resources are freely available online from http://www.madtools.org, a website dedicated to the analysis and annotation of DNA microarray data.

Meta-analysis of muscle transcriptome data using the MADMuscle database reveals biologically relevant gene patterns.

BACKGROUND: DNA microarray technology has had a great impact on muscle research and microarray gene expression data has been widely used to identify gene signatures characteristic of the studied conditions. With the rapid accumulation of muscle microarray data, it is of great interest to understand how to compare and combine data across multiple studies. Meta-analysis of transcriptome data is a valuable method to achieve it. It enables to highlight conserved gene signatures between multiple independent studies. However, using it is made difficult by the diversity of the available data: different microarray platforms, different gene nomenclature, different species studied, etc. DESCRIPTION: We have developed a system tool dedicated to muscle transcriptome data. This system comprises a collection of microarray data as well as a query tool. This latter allows the user to extract similar clusters of co-expressed genes from the database, using an input gene list. Common and relevant gene signatures can thus be searched more easily. The dedicated database consists in a large compendium of public data (more than 500 data sets) related to muscle (skeletal and heart). These studies included seven different animal species from invertebrates (Drosophila melanogaster, Caenorhabditis elegans) and vertebrates (Homo sapiens, Mus musculus, Rattus norvegicus, Canis familiaris, Gallus gallus). After a renormalization step, clusters of co-expressed genes were identified in each dataset. The lists of co-expressed genes were annotated using a unified re-annotation procedure. These gene lists were compared to find significant overlaps between studies. CONCLUSIONS: Applied to this large compendium of data sets, meta-analyses demonstrated that conserved patterns between species could be identified. Focusing on a specific pathology (Duchenne Muscular Dystrophy) we validated results across independent studies and revealed robust biomarkers and new pathways of interest. The meta-analyses performed with MADMuscle show the usefulness of this approach. Our method can be applied to all public transcriptome data.

FaDA: A web application for regular laboratory data analyses.

Web-based data analysis and visualization tools are mostly designed for specific purposes, such as the analysis of data from whole transcriptome RNA sequencing or single-cell RNA sequencing. However, generic tools designed for the analysis of common laboratory data for noncomputational scientists are also needed. The importance of such web-based tools is emphasized by the continuing increases in the sample capacity of conventional laboratory tools such as quantitative PCR, flow cytometry or ELISA instruments. We present a web-based application FaDA, developed with the R Shiny package that provides users with the ability to perform statistical group comparisons, including parametric and nonparametric tests, with multiple testing corrections suitable for most standard wet-laboratory analyses. FaDA provides data visualizations such as heatmaps, principal component analysis (PCA) plots, correlograms and receiver operating curves (ROCs). Calculations are performed through the R language. The FaDA application provides a free and intuitive interface that allows biologists without bioinformatic skill to easily and quickly perform common laboratory data analyses. The application is freely accessible at https://shiny-bird.univ-nantes.fr/app/Fada.

Molecular risk stratification in advanced heart failure patients.

Risk stratification in advanced heart failure (HF) is crucial for the individualization of therapeutic strategy, in particular for heart transplantation and ventricular assist device implantation. We tested the hypothesis that cardiac gene expression profiling can distinguish between HF patients with different disease severity. We obtained tissue samples from both left (LV) and right (RV) ventricle of explanted hearts of 44 patients undergoing cardiac transplantation or ventricular assist device placement. Gene expression profiles were obtained using an in-house microarray containing 4217 muscular organ-relevant genes. Based on their clinical status, patients were classified into three HF-severity groups: deteriorating (n= 12), intermediate (n= 19) and stable (n= 13). Two-class statistical analysis of gene expression profiles of deteriorating and stable patients identified a 170-gene and a 129-gene predictor for LV and RV samples, respectively. The LV molecular predictor identified patients with stable and deteriorating status with a sensitivity of 88% and 92%, and a specificity of 100% and 96%, respectively. The RV molecular predictor identified patients with stable and deteriorating status with a sensitivity of 100% and 96%, and a specificity of 100% and 100%, respectively. The molecular prediction was reproducible across biological replicates in LV and RV samples. Gene expression profiling has the potential to reproducibly detect HF patients with highest HF severity with high sensitivity and specificity. In addition, not only LV but also RV samples could be used for molecular risk stratification with similar predictive power.

VEGF121, is predictor for survival in activated B-cell-like diffuse large B-cell lymphoma and is related to an immune response gene signature conserved in cancers.

Tumor microenvironment including endothelial and immune cells plays a crucial role in tumor progression and has been shown to dramatically influence cancer survival. In this study, we investigated the clinical relevance of the gene expression of key mediators of angiogenesis, VEGF isoforms 121, 165, and 189, and their receptors (VEGFR-1 and R-2) in a cohort of patients (n = 37) with relapsed/refractory diffuse large B-cell lymphoma (DLBCL) from the Collaborative Trial in Relapsed Aggressive Lymphoma (CORAL). In patients with ABC-like DLBCL, but not in patients with GCB-like DLBCL, low VEGF121 expression was associated with a significantly better survival than in those with high VEGF121 level: 4-year overall survival at 100% vs 36% (p = .011), respectively. A specific gene signature including 57 genes was correlated to VEGF121 expression level and was analyzed using a discovery process in 1,842 GSE datasets of public microarray studies. This gene signature was significantly expressed in other cancer datasets and was associated with immune response. In conclusion, low VEGF121 expression level was significantly associated with a good prognosis in relapsed/refractory ABC-like DLBCL, and with a well-conserved gene-expression profiling signature related to immune response. These findings pave the way for rationalization of drugs targeting immune response in refractory/relapsed ABC-like DLBCL.

A dynamic, web-accessible resource to process raw microarray scan data into consolidated gene expression values: importance of replication.

We propose a freely accessible web-based pipeline, which processes raw microarray scan data to obtain experimentally consolidated gene expression values. The tool MADSCAN, which stands for MicroArray Data Suites of Computed ANalysis, makes a practical choice among the numerous methods available for filtering, normalizing and scaling of raw microarray expression data in a dynamic and automatic way. Different statistical methods have been adapted to extract reliable information from replicate gene spots as well as from replicate microarrays for each biological situation under study. A carefully constructed experimental design thus allows to detect outlying expression values and to identify statistically significant expression values, together with a list of quality controls with proposed threshold values. The integrated processing procedure described here, based on multiple measurements per gene, is decisive for reliably monitoring subtle gene expression changes typical for most biological events.

Relapsed diffuse large B-cell lymphoma present different genomic profiles between early and late relapses.

Despite major advances in first-line treatment, a significant proportion of patients with diffuse large B-cell lymphoma (DLBCL) will experience treatment failure. Prognosis is particularly poor for relapses occurring less than one year after the end of first-line treatment (early relapses/ER) compared to those occurring more than one year after (late relapses/LR). To better understand genomic alterations underlying the delay of relapse, we identified copy number variations (CNVs) on 39 tumor samples from a homogeneous series of patients included in the Collaborative Trial in Relapsed Aggressive Lymphoma (CORAL) prospective study. To identify CNVs associated with ER or LR, we devised an original method based on Significance Analysis of Microarrays, a permutation-based method which allows control of false positives due to multiple testing. Deletions of CDKN2A/B (28%) and IBTK (23%) were frequent events in relapsed DLBCLs. We identified 56 protein-coding genes and 25 long non-coding RNAs with significantly differential CNVs distribution between ER and LR DLBCLs, with a false discovery rate < 0.05. In ER DLBCLs, CNVs were related to transcription regulation, cell cycle and apoptosis, with duplications of histone H1T (31%), deletions of DIABLO (26%), PTMS (21%) and CK2B (15%). In LR DLBCLs, CNVs were related to immune response, with deletions of B2M (20%) and CD58 (10%), cell proliferation regulation, with duplications of HES1 (25%) and DVL3 (20%), and transcription regulation, with MTERF4 deletions (20%). This study provides new insights into the genetic aberrations in relapsed DLBCLs and suggest pathway-targeted therapies in ER and LR DLBCLs.

Expanded CD8 T-cell sharing between periphery and CNS in multiple sclerosis.

OBJECTIVE: In multiple sclerosis (MS), central nervous system (CNS), cerebrospinal fluid (CSF), and blood display TCR clonal expansions of CD8(+) T cells. These clones have been assumed - but never demonstrated - to be similar in the three compartments. Addressing this key question is essential to infer the implication of peripheral clonally expanded CD8(+) T cells in the disease. METHODS: For the first time, TCR Vß repertoire from paired blood (purified CD8(+) and CD4(+) T cells), CSF and CNS (22 lesions, various inflammatory and demyelination statuses) samples from three MS patients was studied using complementary determining region 3 (CDR3) spectratyping and high-throughput sequencing. In parallel, blood and CNS clonally expanded CD8(+) T cells were characterized by fluorescent staining. RESULTS: TCR Vß repertoire analysis revealed strong sharing of predominant T-cell clones between CNS lesions, CSF, and blood CD8(+) T cells. In parallel, we showed that blood oligoclonal CD8(+) T cells exhibit characteristics of pathogenic cells, as they displayed a bias toward a memory phenotype in MS patients, with increased expression of CCR5, CD11a and Granzyme B (GZM-B) compared to non oligoclonal counterparts. CNS-infiltrating T cells were mainly CD8 expressing CD11a and GZM-B. INTERPRETATION: This study highlights the predominant implication of CD8(+) T cells in MS pathophysiology and demonstrates that potentially aggressive CD8(+) T cells can be easily identified and characterized from blood and CSF samples.

Immune response and mitochondrial metabolism are commonly deregulated in DMD and aging skeletal muscle.

Duchenne Muscular Dystrophy (DMD) is a complex process involving multiple pathways downstream of the primary genetic insult leading to fatal muscle degeneration. Aging muscle is a multifactorial neuromuscular process characterized by impaired muscle regeneration leading to progressive atrophy. We hypothesized that these chronic atrophying situations may share specific myogenic adaptative responses at transcriptional level according to tissue remodeling. Muscle biopsies from four young DMD and four AGED subjects were referred to a group of seven muscle biopsies from young subjects without any neuromuscular disorder and explored through a dedicated expression microarray. We identified 528 differentially expressed genes (out of 2,745 analyzed), of which 328 could be validated by an exhaustive meta-analysis of public microarray datasets referring to DMD and Aging in skeletal muscle. Among the 328 validated co-expressed genes, 50% had the same expression profile in both groups and corresponded to immune/fibrosis responses and mitochondrial metabolism. Generalizing these observed meta-signatures with large compendia of public datasets reinforced our results as they could be also identified in other pathological processes and in diverse physiological conditions. Focusing on the common gene signatures in these two atrophying conditions, we observed enrichment in motifs for candidate transcription factors that may coordinate either the immune/fibrosis responses (ETS1, IRF1, NF1) or the mitochondrial metabolism (ESRRA). Deregulation in their expression could be responsible, at least in part, for the same transcriptome changes initiating the chronic muscle atrophy. This study suggests that distinct pathophysiological processes may share common gene responses and pathways related to specific transcription factors.