c l i n i c a l i n v e s t i g a t i o n S Yazdani et al.: NK cells in antibody-mediated kidney rejection

Background Antibody-mediated rejection, a leading cause of renal allograft graft failure, is diagnosed by histological assessment of invasive allograft biopsies. Accurate non-invasive biomarkers are not available. Methods In the multicentre, prospective BIOMARGIN study, blood samples were prospectively collected at time of renal allograft biopsies between June 2011 and August 2016 and analyzed in three phases. The discovery and derivation phases of the study ( N = 117 and N = 183 respectively) followed a case-control design and included whole genome transcriptomics and targeted mRNA expression analysis to construct and lock a multigene model. The primary end point was the diagnostic accuracy of the locked multigene assay for antibody-mediated rejection in a third validation cohort of serially collected blood samples ( N = 387). This trial is registered with ClinicalTrials.gov , number NCT02832661 . Findings We identified and locked an 8-gene assay ( CXCL10, FCGR1A, FCGR1B, GBP1, GBP4, IL15, KLRC1, TIMP1 ) in blood samples from the discovery and derivation phases for discrimination between cases with ( N = 49) and without ( N = 134) antibody-mediated rejection. In the validation cohort, this 8-gene assay discriminated between cases with ( N = 41) and without antibody-mediated rejection ( N = 346) with good diagnostic accuracy (ROC AUC 79·9%; 95% CI 72·6 to 87·2, p < 0·0001). The diagnostic accuracy of the 8-gene assay was retained both at time of stable graft function and of graft dysfunction, within the first year and also later after transplantation. The 8-gene assay is correlated with microvascular inflammation and transplant glomerulopathy, but not with the histological lesions of T-cell mediated rejection. Interpretation We identified and validated a novel 8-gene expression assay that can be used for non-invasive diagnosis of antibody-mediated rejection. Funding The Seventh Framework Programme (FP7) of the European Commission.

BackgroundMetagenomics holds great promises for deepening our knowledge of key bacterial driven processes, but metagenome assembly remains problematic, typically resulting in representation biases and discarding significant amounts of non-redundant sequence information. In order to alleviate constraints assembly can impose on downstream analyses, and/or to increase the fraction of raw reads assembled via targeted assemblies relying on pre-assembly binning steps, we developed a set of binning modules and evaluated their combination in a new “assembly-free” binning protocol.ResultsWe describe a scalable multi-tiered binning algorithm that combines frequency and compositional features to cluster unassembled reads, and demonstrate i) significant runtime performance gains of the developed modules against state of the art software, obtained through parallelization and the efficient use of large lock-free concurrent hash maps, ii) its relevance for clustering unassembled reads from high complexity (e.g., harboring 700 distinct genomes) samples, iii) its relevance to experimental setups involving multiple samples, through a use case consisting in the “de novo” identification of sequences from a target genome (e.g., a pathogenic strain) segregating at low levels in a cohort of 50 complex microbiomes (harboring 100 distinct genomes each), in the background of closely related strains and the absence of reference genomes, iv) its ability to correctly identify clusters of sequences from the E. coli O104:H4 genome as the most strongly correlated to the infection status in 53 microbiomes sampled from the 2011 STEC outbreak in Germany, and to accurately cluster contigs of this pathogenic strain from a cross-assembly of these 53 microbiomes.ConclusionsWe present a set of sequence clustering (“binning”) modules and their application to biomarker (e.g., genomes of pathogenic organisms) discovery from large synthetic and real metagenomics datasets. Initially designed for the “assembly-free” analysis of individual metagenomic samples, we demonstrate their extension to setups involving multiple samples via the usage of the “alignment-free” d2S statistic to relate clusters across samples, and illustrate how the clustering modules can otherwise be leveraged for de novo “pre-assembly” tasks by segregating sequences into biologically meaningful partitions.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1186-3) contains supplementary material, which is available to authorized users.