Modulation of Monocyte Response by MicroRNA-15b/106a/374a During Antibody-mediated Rejection in Kidney Transplantation.

BACKGROUND: Increasing evidence suggest that microRNAs are involved in the physiopathology of acute or chronic renal disease. In kidney transplantation, as key regulators of cellular homeostasis, microRNAs may be involved in the regulation of immune cell function and the allograft response. Here, we investigated the change in circulating microRNA expression profile and their involvement in the profound transcriptional changes associated with antibody-mediated rejection (AMR). METHODS: Blood samples were collected at the time of the 710 kidney allograft biopsies at 4 European transplant centers. Messenger RNA and microRNA profiling analyses were performed in a discovery-to-validation study within 3 independent cohorts encompassing N = 126, N = 135, and N = 416 patients, respectively. RESULTS: Compared with samples with no AMR, 14 microRNAs were significantly decreased in AMR samples. Among them, expression levels of microRNA-15b, microRNA-106a, and microRNA-374a gradually decreased with the severity of AMR lesions. From their in silico-predicted target genes, a high proportion proved to be significantly upregulated in the paired transcriptomic analysis. Gene ontology analyses of microRNA-15b/-106a/-374a suggested enrichment in myeloid-related pathways, which was further refined by in silico and ex vivo transcriptomic analyses, showing a specific origin from classical CD14 + monocytes. Finally, human CD14 + monocytes were subjected to transduction by antago-microRNAs to mimic AMR pathology. MicroRNA-15b/-106a/-374a impairment resulted in cellular activation with an increased expression of CD69, CRIM1, IPO7, and CAAP1, direct and common targets of the 3 microRNAs. CONCLUSIONS: Together, our data provide new insights into circulating microRNAs as markers and key players in AMR, and they suggest monocyte involvement in this process.

A universal predictive and mechanistic urinary peptide signature in acute kidney injury.

BACKGROUND: The delayed diagnosis of acute kidney injury (AKI) episodes and the lack of specificity of current single AKI biomarkers hamper its management. Urinary peptidome analysis may help to identify early molecular changes in AKI and grasp its complexity to identify potential targetable molecular pathways. METHODS: In derivation and validation cohorts totalizing 1170 major cardiac bypass surgery patients and in an external cohort of 1569 intensive care unit (ICU) patients, a peptide-based score predictive of AKI (7-day KDIGO classification) was developed, validated, and compared to the reference biomarker urinary NGAL and NephroCheck and clinical scores. RESULTS: A set of 204 urinary peptides derived from 48 proteins related to hemolysis, inflammation, immune cells trafficking, innate immunity, and cell growth and survival was identified and validated for the early discrimination (< 4 h) of patients according to their risk to develop AKI (OR 6.13 [3.96-9.59], p < 0.001) outperforming reference biomarkers (urinary NGAL and [IGFBP7].[TIMP2] product) and clinical scores. In an external cohort of 1569 ICU patients, performances of the signature were similar (OR 5.92 [4.73-7.45], p < 0.001), and it was also associated with the in-hospital mortality (OR 2.62 [2.05-3.38], p < 0.001). CONCLUSIONS: An overarching AKI physiopathology-driven urinary peptide signature shows significant promise for identifying, at an early stage, patients who will progress to AKI and thus to develop tailored treatments for this frequent and life-threatening condition. Performance of the urine peptide signature is as high as or higher than that of single biomarkers but adds mechanistic information that may help to discriminate sub-phenotypes of AKI offering new therapeutic avenues.

The tryptophan pathway and nicotinamide supplementation in ischaemic acute kidney injury.

BACKGROUND: Down-regulation of the enzymes involved in tryptophan-derived nicotinamide (NAM) adenine dinucleotide (NAD+) production was identified after acute kidney injury (AKI), leading to the hypothesis that supplementation with NAM may increase the kidney NAD+ content, rescuing tryptophan pathways and subsequently improving kidney outcomes. METHODS: Urinary measurement of tryptophan and kynurenin using liquid chromatography-mass spectrometry metabolomics was used in a cohort of 167 cardiac bypass surgery patients along with tests for correlation to the development of postoperative AKI. A mouse model of ischaemic AKI using ischaemia-reperfusion injury (bilateral clamping of renal arteries for 25 min) was also used. RESULTS: We identified a significant decrease in urinary tryptophan and kynurenin in patients developing AKI, irrespective of the Kidney Disease: Improving Global Outcomes (KDIGO) stage. Although a significant difference was observed, tryptophan and kynurenin moderately discriminated for the development of all AKI KDIGO stages {area under the curve [AUC] 0.82 [95% confidence interval (CI) 0.75-0.88] and 0.75 [0.68-0.83], respectively} and severe KDIGO Stages 2-3 AKI [AUC 0.71 (95% CI 0.6-0.81) and 0.66 (0.55-0.77), respectively]. Sparked by this confirmation in humans, we aimed to confirm the potential preventive effect of NAM supplementation in wild-type male and female C57BL/6 mice subjected to ischaemic AKI. NAM supplementation had no effect on renal function (blood urea nitrogen at Day 1, sinistrin-fluorescein isothiocyanate glomerular filtration rate), architecture (periodic acid-Schiff staining) and injury or inflammation (kidney injury molecule 1 and IL18 messenger RNA expression). In addition, NAM supplementation did not increase post-AKI NAD+ kidney content. CONCLUSION: Notwithstanding the potential role of NAM supplementation in the setting of basal NAD+ deficiency, our findings in mice and the reanalysis of published data do not confirm that NAM supplementation can actually improve renal outcomes after ischaemic AKI in unselected animals and probably patients.

A zero inflated log-normal model for inference of sparse microbial association networks.

The advent of high-throughput metagenomic sequencing has prompted the development of efficient taxonomic profiling methods allowing to measure the presence, abundance and phylogeny of organisms in a wide range of environmental samples. Multivariate sequence-derived abundance data further has the potential to enable inference of ecological associations between microbial populations, but several technical issues need to be accounted for, like the compositional nature of the data, its extreme sparsity and overdispersion, as well as the frequent need to operate in under-determined regimes. The ecological network reconstruction problem is frequently cast into the paradigm of Gaussian Graphical Models (GGMs) for which efficient structure inference algorithms are available, like the graphical lasso and neighborhood selection. Unfortunately, GGMs or variants thereof can not properly account for the extremely sparse patterns occurring in real-world metagenomic taxonomic profiles. In particular, structural zeros (as opposed to sampling zeros) corresponding to true absences of biological signals fail to be properly handled by most statistical methods. We present here a zero-inflated log-normal graphical model (available at https://github.com/vincentprost/Zi-LN) specifically aimed at handling such "biological" zeros, and demonstrate significant performance gains over state-of-the-art statistical methods for the inference of microbial association networks, with most notable gains obtained when analyzing taxonomic profiles displaying sparsity levels on par with real-world metagenomic datasets.

Genetic variability in 13q33 and 9q34 is linked to aggressiveness patterns and a higher risk of progression of non-muscle-invasive bladder cancer at the time of diagnosis.

OBJECTIVE: To identify single nucleotide polymorphisms (SNPs) associated with patterns of aggressiveness of non-muscle-invasive bladder cancer (NMIBC). PATIENTS AND METHODS: From January 2011 to December 2018, 476 patients with NMIBC were prospectively included. The first step aimed to identify SNPs associated with aggressiveness patterns (e.g. ≥pT1or high-grade/Grade 3 or presence of carcinoma in situ) by analysing the data of a genome-wide association study (GWAS) on 165 patients with BC. The second step aimed to validate the SNPs previously identified, by genotyping the germline DNA of 311 patients with NMIBC. RESULTS: Overall, the median (interquartile range) age was 66 (58-75) years and the rate of patients with aggressive NMIBC was comparable between both groups (46% vs 46%, P = 1). GWAS data analysis identified four SNPs associated with an aggressive NMIBC (rs12615669, rs4976845, rs2989734, and rs2802288). In the validation cohort, the genotype CC of rs12615669, as well as age >70 years at the time of diagnosis were associated with aggressive NMIBC (P = 0.008 and P < 0.001, respectively). Genotyping of the entire cohort showed an association between aggressive NMIBC and the T allele of rs12615669 (P = 0.0007), the A allele of rs4976845 (P = 0.012), and the A allele of rs2989734 (P = 0.007). A significant association was also found for the entire cohort between the risk of progression and the A allele of rs4976845 (P = 0.04). CONCLUSION: This two-phase study identified three SNPs (rs12615669, rs4976845, and rs2989734) associated with aggressive NMIBC and one SNP (rs4976845) associated with a higher risk of progression.

Binning unassembled short reads based on k-mer abundance covariance using sparse coding.

BACKGROUND: Sequence-binning techniques enable the recovery of an increasing number of genomes from complex microbial metagenomes and typically require prior metagenome assembly, incurring the computational cost and drawbacks of the latter, e.g., biases against low-abundance genomes and inability to conveniently assemble multi-terabyte datasets. RESULTS: We present here a scalable pre-assembly binning scheme (i.e., operating on unassembled short reads) enabling latent genome recovery by leveraging sparse dictionary learning and elastic-net regularization, and its use to recover hundreds of metagenome-assembled genomes, including very low-abundance genomes, from a joint analysis of microbiomes from the LifeLines DEEP population cohort (n = 1,135, >1010 reads). CONCLUSION: We showed that sparse coding techniques can be leveraged to carry out read-level binning at large scale and that, despite lower genome reconstruction yields compared to assembly-based approaches, bin-first strategies can complement the more widely used assembly-first protocols by targeting distinct genome segregation profiles. Read enrichment levels across 6 orders of magnitude in relative abundance were observed, indicating that the method has the power to recover genomes consistently segregating at low levels.

Development and validation of a peripheral blood mRNA assay for the assessment of antibody-mediated kidney allograft rejection: A multicentre, prospective study.

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.

Natural killer cell infiltration is discriminative for antibody-mediated rejection and predicts outcome after kidney transplantation.

Despite partial elucidation of the pathophysiology of antibody-mediated rejection (ABMR) after kidney transplantation, it remains largely unclear which of the involved immune cell types determine disease activity and outcome. We used microarray transcriptomic data from a case-control study (n=95) to identify genes that are differentially expressed in ABMR. Given the co-occurrence of ABMR and T-cell-mediated rejection (TCMR), we built a bioinformatics pipeline to distinguish ABMR-specific mRNA markers. Differential expression of 503 unique genes was identified in ABMR, with significant enrichment of natural killer (NK) cell pathways. CIBERSORT (Cell type Identification By Estimating Relative Subsets Of known RNA Transcripts) deconvolution analysis was performed to elucidate the corresponding cell subtypes and showed increased NK cell infiltration in ABMR in comparison to TCMR and normal biopsies. Other leukocyte types (including monocytes/macrophages, CD4 and CD8 T cells, and dendritic cells) were increased in rejection, but could not discriminate ABMR from TCMR. Deconvolution-based estimation of NK cell infiltration was validated using computerized morphometry, and specifically associated with glomerulitis and peritubular capillaritis. In an external data set of kidney transplant biopsies, activated NK cell infiltration best predicted graft failure amongst all immune cell subtypes and even outperformed a histologic diagnosis of acute rejection. These data suggest that NK cells play a central role in the pathophysiology of ABMR and graft failure after kidney transplantation.

Shared Nearest Neighbor Clustering in a Locality Sensitive Hashing Framework.

We present a new algorithm to cluster high-dimensional sequence data and its application to the field of metagenomics, which aims at reconstructing individual genomes from a mixture of genomes sampled from an environmental site, without any prior knowledge of reference data (genomes) or the shape of clusters. Such problems typically cannot be solved directly with classical approaches seeking to estimate the density of clusters, for example, using the shared nearest neighbors (SNN) rule, due to the prohibitive size of contemporary sequence datasets. We explore here a new approach based on combining the SNN rule with the concept of locality sensitive hashing (LSH). The proposed method, called LSH-SNN, works by randomly splitting the input data into smaller-sized subsets (buckets) and employing the SNN rule on each of these buckets. Links can be created among neighbors sharing a sufficient number of elements, hence allowing clusters to be grown from linked elements. LSH-SNN can scale up to larger datasets consisting of millions of sequences, while achieving high accuracy across a variety of sample sizes and complexities.

A scalable assembly-free variable selection algorithm for biomarker discovery from metagenomes.

BACKGROUND: Metagenomics 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. RESULTS: We 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. CONCLUSIONS: We 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.

Germline genetic variations at 11q13 and 12p11 locus modulate age at onset for renal cell carcinoma.

PURPOSE: Few risk factors have been identified for renal cell carcinoma. We performed a validation study in a population with a European background to identify the most significant variants previously identified in association with renal cell carcinoma risk. MATERIALS AND METHODS: We performed a case-control validation study after recruiting 463 controls and 463 patients with a histologically confirmed diagnosis of clear cell renal cell carcinoma. For each patient and matched control we genotyped 8 single nucleotide polymorphisms selected from previous studies to evaluate the association between candidate single nucleotide polymorphisms and renal cell carcinoma susceptibility. RESULTS: After adjusting for patient age, gender, smoking status and body mass index the AG + AA genotypes from rs7105934 (11q13) were associated with a decreased risk of renal cell carcinoma (OR 0.50, 95% CI 0.33-0.75, p = 0.001) and the AC + CC genotypes from rs1049380 (ITPR2) were associated with an increased risk (OR 1.66, 95% CI 1.28-2.16, p <0.001). Kidney cancer developed at an older age in patients carrying the dominant risk allele A for rs7105934 (mean age at diagnosis 73.1 vs 68.9 years, p = 0.002) and at a younger age in those carrying the dominant allele C for rs1049380 (mean 68.1 vs 70.8 years, p = 0.005). CONCLUSIONS: In what is to our knowledge the first validation study of the main 8 single nucleotide polymorphism variants associated with renal cell carcinoma susceptibility we confirmed the association of 2 single nucleotide polymorphisms with the risk of renal cell carcinoma. Each variant influenced patient age at disease diagnosis.