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Additional file 1: Supplementary Figure S1: S100A9 expression after epithelial injury. A. Urinary calprotectin (S100A8/A9) abundance 4 hours after cardiac bypass-surgery. B–F. Blood urea nitrogen (B), mRNA S100a9 (C), S100A9 immunostaining (D-E) and mRNA Kim1 (F) in sham mice and after bilateral renal ischemia/reperfusion (hours 6, 24 and 48). G–J. mRNA expression of S100a9 and Kim1 in MCT cells submitted to interleukin-1b (IL1β, 10 ng/mL) or tumor necrosis factor-1a (TNFa, 10 ng/mL) (G-H) or hypoxia (I-J). AKI, acute kidney injury; BUN, blood urea nitrogen; Norm, normoxia; Hyp, hypoxia. Supplementary Figure S2: Performances of the peptide-based signature to identify AKI that developed within the first 2 days following cardiac surgery. ROC curves with corresponding AUROC and 95% confidence intervals of the local clinical score (blue, pointed), the full 204 peptides-based score (red), the urinary NGAL level (yellow, pointed) and the nephrocheck ([IGFBP7].[TIMP2] product) in the validation cohort. Supplementary Figure S3: Reduction and combination of the peptide-based signature. A. ROC curves with corresponding AUROC and 95% confidence intervals of the local clinical score (blue, pointed), the full 204 peptides-based score (red), the reduced 17 peptides-based score (black, dashed) and the combination of local clinical and full peptide-based score in the validation cohort. B. List of peptides included in the reduced 17-peptides signature according to their parental protein. LMAN2, Lectin mannose binding 2 ; MGP, Matrix gla protein. Supplementary Figure S4: Performances of the 204 peptides-based signature and the reference urinary biomarker NGAL for AKI prediction in the external ICU validation cohort. A. ROC curves with corresponding AUROC and 95% confidence intervals of the 204 peptides-based score and the reference urinary biomarker NGAL to predict AKI after ICU admission. B. ROC curves with corresponding AUROC and 95% confidence intervals of the 204 peptides-based score and reference urinary biomarker NGAL to predict the development of AKI within seven days after admission. Supplementary Figure S5: Performances of the 204 peptides-based score for in-hospital mortality prediction. Odds-ratio (OR) of in-hospital mortality were calculated with unadjusted, Euroscore-II-adjusted or propensity score-adjusted logistic regression. Supplementary Table S1: Correlations between clinical characteristics and the 204-peptides-based score. BMI, body mass index; PAOD, peripheral artery obliterans disease; COPD, chronic obstructive pulmonary disease; LVEF, left ventricular ejection fraction; eGFR, estimated glomerular filtration rate; CBP, cardiac bypass; RBC, red blood cells. Supplementary Table S2: Performance of the peptide-based score to predict acute kidney injury in the external ICU validation cohort, according to the cause of admission to the intensive care unit. ICU, intensive care unit; AUROC, area under the receiver operating characteristics curve. Supplementary file S1: Methodology. Urinary peptidomics and statistical analyses.

International audience; Les récentes avancées en biologie moléculaire et l’avènement des méthodes de séquençage à haut débit ont rendu possible la lecture, l’analyse et la réutilisation d’une très grande partie de l’information présente dans le génome. Malheureusement, notre capacité à analyser l’immense masse de données générée par ces nouvelles technologies de séquençage est aujourd’hui limitéepar nos moyens de calcul. Dans ces travaux, nous nous intéressons plus particulierèrement aux problématiques de l’assemblage de novo, dont l’objectif est de reconstruire une séquence ADN à partir d’un ensemble de fragments issus de cette même séquence. Nous mettons en évidence les avantages d’utiliser les outils (notamment méthodologiques) de la communauté “big data” pour résoudre ce problème sur des instances réelles de très grande taille. Nous discutons des résultats numériques prometteurs que nous avons pu obtenir en implémentant cette approche à l’aide du framework Apache Spark.

International audience; Background: FP7 Biomargin aimed at detecting and validating biomarkers of kidney graft lesions. In this study, we investigated the diagnostic potential of microRNAs (miRNAs) in allograft biopsy samples.Methods/Materials: Biopsies were collected from protocol or for-cause biopsies in 4 European clinical centers. Samples were retrospectively selected after centralized histological reading by expert pathologists, and classified into 4 groups (Normal, ABMR, TCMR or IF/TA), to build two independent case control studies (discovery- and selection sets). Global miRNA profiling was performed by microfluidic cards (TLDA, Life Technologies) on the discovery set. A statistical pipeline including 2 uni- and 5 multivariate analyses was applied to identify an extended list of biomarker candidates associated with one of the 4 groups. This extended list of miRNAs was quantified using custom TLDA plates on the selection set. Multivariate models were then built to define miRNA signatures of graft lesions.Results: A total of 754 miRNAs was quantified in the discovery set that included 32 Normal, 13 TCMR, 25 IF/TA and 18 ABMR samples. Our statistical pipeline identified 140 candidates that were assessed in the validation cohort of 32 Normal, 13 TCMR, 26 IF/TA and 28 ABMR samples. The table shows the association between histological phenotypes and miRNA-derived statisticalmodels in the validation cohort.Conclusion: We identified a small set of miRNAs within kidney allograft biopsies with a strong association with TCMR and ABMR. These miRNA signatures might provide useful molecular tools to improve allograft assessment. Their diagnostic performance is currently being investigated in our BIOMARGIN trans-sectional study of 312 consecutive allograft samples.

International audience; Background: Immune cells have been implicated in the graft failure after kidney transplantation. However, the best therapeutic target(s) among immune cells subtypes have not been identified yet. We aimed therefore to identify which immune cell subtypes possess the highest prognostic value for graft failure.Methods: We recently described the role of activated NK cell transcripts in ABMR diagnosis and activity in BIOMARGIN. Here, we applied an innovative deconvolution algorithm to estimate the relative fraction of immune cells subsets, using RNA transcripts expression of 547 genes in a dataset of 282 indication biopsy samples. These estimates were corrected for the globalinflammation in each biopsy.Results: Analysis of estimated immune cells subsets infiltration, based on deconvolution of transcriptomic data, showed that activated NK cells have the highest discriminative power in predicting graft failure at both 1 and 2 years post-biopsy in all biopsies; irrespective of rejection (AUC = 0.74; p < 0.0001 and AUC = 0.67; p = 0.0006, respectively), and within biopsies with rejection (AUC = 0.79 (p = 0.002) and AUC = 0.68 (p = 0.03), respectively). Cox proportional hazard analyses confirmed that activated NK cells were the cell type most robustly associated with graft failure (HR 3.60; p < 0.0001), especially in biopsies with acute rejection (HR 3.89; p = 0.002). We then evaluated individual NK cell transcripts expression in relation to graft failure. Using both the differentially expressed genes from the deconvolution algorithm and a literature-based NK-cell related transcript set, the prognostic performance of individual activated NK cell genes for post-biopsy kidney allograft failure was confirmed.Conclusion: These findings, in addition the emerging role of NK cells infiltration in ABMR diagnosis and activity, offer promising new avenues for development of NK-cell targeted prognostic biomarkers and therapeutic strategies to prevent or attenuate graft failure.

Contains two tables: the first table displays the effect on the coverage of the pathogenic genome of iteratively adding reads from the three most discriminative final (3 L) clusters; the second table lists the accession numbers of the 700 bacterial genomes used for generating the distinct simulated datasets. (DOC 576 kb)

Contains a figure illustrating the definition of reference abundance classes for evaluating the accuracy of the abundance-based clustering module. (DOC 88 kb)

https://patentscope.wipo.int/search/fr/detail.jsf?docId=WO20100128; The invention relates to an individual prediction device for the screening or diagnosis or therapeutic care or prognosis of prostate cancer, that comprises collecting individual input data (xi) and providing risk prediction information (y) related to a type of disease, characterised in that the input data includes at least one variable or variable combination of the genetic type such as the identification of genetic polymorphism markers which are considered to be related with the development of the disease. The invention also relates to an individual prediction device for the screening or diagnosis or medical care or prognosis of prostate cancer, that comprises first means for the input by a user of individual information data, at least a first software interface on which said first means runs, characterised in that it further comprises a software implementing the method of the invention and providing a risk prediction information related to a disease.; L'invention a pour objet un procédé de prédiction individuelle pour le dépistage ou le diagnostic ou la prise en charge thérapeutique ou le pronostic du cancer de la prostate comportant le recueil de données individuelles (xi) d'entrée et la fourniture d'une information prédictive de risque (y) liée à un type de maladie, caractérisé en ce que les données d'entrée comportent au moins une variable ou une combinaison de variables type génétique telles que l'identification de marqueurs de polymorphismes génétiques considérés comme étant liés au développement de la maladie. L'invention a aussi pour objet un dispositif de prédiction individuelle pour le dépistage ou le diagnostic ou la prise en charge thérapeutique ou le pronostic du cancer de la prostate comportant des premiers moyens de saisie de données d'information individuelles par un utilisateur, au moins une première interface logicielle sur laquelle opère lesdits premiers moyens caractérisé en ce qu'il comporte en outre un logiciel mettant en œuvre le procédé de l'invention et fournissant une information prédictive de risque lié à une maladie.