Molecular Pathogenesis of Post-Transplant Acute Kidney Injury: Assessment of Whole-Genome mRNA and MiRNA Profiles.

Acute kidney injury (AKI) affects roughly 25% of all recipients of deceased donor organs. The prevention of post-transplant AKI is still an unmet clinical need. We prospectively collected zero-hour, indication as well as protocol kidney biopsies from 166 allografts between 2011 and 2013. In this cohort eight cases with AKI and ten matched allografts without pathology serving as control group were identified with a follow-up biopsy within the first twelve days after engraftment. For this set the zero-hour and follow-up biopsies were subjected to genome wide microRNA and mRNA profiling and analysis, followed by validation in independent expression profiles of 42 AKI and 21 protocol biopsies for strictly controlling the false discovery rate. Follow-up biopsies of AKI allografts compared to time-matched protocol biopsies, further baseline adjustment for zero-hour biopsy expression level and validation in independent datasets, revealed a molecular AKI signature holding 20 mRNAs and two miRNAs (miR-182-5p and miR-21-3p). Next to several established biomarkers such as lipocalin-2 also novel candidates of interest were identified in the signature. In further experimental evaluation the elevated transcript expression level of the secretory leukocyte peptidase inhibitor (SLPI) in AKI allografts was confirmed in plasma and urine on the protein level (p<0.001 and p = 0.003, respectively). miR-182-5p was identified as a molecular regulator of post-transplant AKI, strongly correlated with global gene expression changes during AKI. In summary, we identified an AKI-specific molecular signature providing the ground for novel biomarkers and target candidates such as SLPI and miR-182-5p in addressing AKI

MicroRNA expression in tumor cells from Waldenstrom's macroglobulinemia reflects both their normal and malignant cell counterparts

MicroRNAs (miRNAs) are involved in the regulation of many cellular processes including hematopoiesis, with the aberrant expression of differentiation-stage specific miRNA associated with lymphomagenesis. miRNA profiling has been essential for understanding the underlying biology of many hematological malignancies; however the miRNA signature of the diverse tumor clone associated with Waldenstrom's macroglobulinemia (WM), consisting of B lymphocytes, plasmacytes and lymphoplasmacytic cells, has not been characterized. We have investigated the expression of over 13 000 known and candidate miRNAs in both CD19+ and CD138+ WM tumor cells, as well as in their malignant and non-malignant counterparts. Although neither CD19+ nor CD138+ WM cells were defined by a distinct miRNA profile, the combination of all WM cells revealed a unique miRNA transcriptome characterized by the dysregulation of many miRNAs previously identified as crucial for normal B-cell lineage differentiation. Specifically, miRNA-9*/152/182 were underexpressed in WM, whereas the expression of miRNA-21/125b/181a/193b/223/363 were notably increased (analysis of variance; P<0.0001). Future studies focusing on the effects of these dysregulated miRNAs will provide further insight into the mechanisms responsible for the pathogenesis of WM

Mapping genetic variations to three- dimensional protein structures to enhance variant interpretation: a proposed framework

The translation of personal genomics to precision medicine depends on the accurate interpretation of the multitude of genetic variants observed for each individual. However, even when genetic variants are predicted to modify a protein, their functional implications may be unclear. Many diseases are caused by genetic variants affecting important protein features, such as enzyme active sites or interaction interfaces. The scientific community has catalogued millions of genetic variants in genomic databases and thousands of protein structures in the Protein Data Bank. Mapping mutations onto three-dimensional (3D) structures enables atomic-level analyses of protein positions that may be important for the stability or formation of interactions; these may explain the effect of mutations and in some cases even open a path for targeted drug development. To accelerate progress in the integration of these data types, we held a two-day Gene Variation to 3D (GVto3D) workshop to report on the latest advances and to discuss unmet needs. The overarching goal of the workshop was to address the question: what can be done together as a community to advance the integration of genetic variants and 3D protein structures that could not be done by a single investigator or laboratory? Here we describe the workshop outcomes, review the state of the field, and propose the development of a framework with which to promote progress in this arena. The framework will include a set of standard formats, common ontologies, a common application programming interface to enable interoperation of the resources, and a Tool Registry to make it easy to find and apply the tools to specific analysis problems. Interoperability will enable integration of diverse data sources and tools and collaborative development of variant effect prediction methods

Mutations in NOTCH1 Cause Adams-Oliver Syndrome

© 2014 The American Society of Human Genetics Notch signaling determines and reinforces cell fate in multicellular eukaryotes. Despite the involvement of Notch in many key developmental systems, human mutations in Notch signaling components have mainly been described in disorders with vascular and bone effects. Here, we report five heterozygous NOTCH1 variants in unrelated individuals with Adams-Oliver syndrome (AOS), a rare disease with major features of aplasia cutis of the scalp and terminal transverse limb defects. Using whole-genome sequencing in a cohort of 11 families lacking mutations in the four genes with known roles in AOS pathology (ARHGAP31, RBPJ, DOCK6, and EOGT), we found a heterozygous de novo 85 kb deletion spanning the NOTCH1 5′ region and three coding variants (c.1285T>C [p.Cys429Arg], c.4487G>A [p.Cys1496Tyr], and c.5965G>A [p.Asp1989Asn]), two of which are de novo, in four unrelated probands. In a fifth family, we identified a heterozygous canonical splice-site variant (c.743−1 G>T) in an affected father and daughter. These variants were not present in 5,077 in-house control genomes or in public databases. In keeping with the prominent developmental role described for Notch1 in mouse vasculature, we observed cardiac and multiple vascular defects in four of the five families. We propose that the limb and scalp defects might also be due to a vasculopathy in NOTCH1-related AOS. Our results suggest that mutations in NOTCH1 are the most common cause of AOS and add to a growing list of human diseases that have a vascular and/or bony component and are caused by alterations in the Notch signaling pathway

Identification of copy number variants in whole-genome data using Reference Coverage Profiles

The identification of DNA copy numbers from short-read sequencing data remains a challenge for both technical and algorithmic reasons. The raw data for these analyses are measured in tens to hundreds of gigabytes per genome; transmitting, storing and analyzing such large files is cumbersome, particularly for methods that analyze several samples simultaneously. We developed a very efficient representation of depth of coverage (150-1000x compression) that enables such analyses. Current methods for analyzing variants in whole-genome sequencing data frequently miss copy number variants (CNVs), particularly hemizygous deletions in the 1-100 kb range. To fill this gap, we developed a method to identify CNVs in individual genomes, based on comparison to joint profiles pre-computed from a large set of genomes.We analyzed depth of coverage in over 6000 high quality (>40x) genomes. The depth of coverage has strong sequence-specific fluctuations only partially explained by global parameters like %GC. To account for these fluctuations, we constructed multi-genome profiles representing the observed or inferred diploid depth of coverage at each position along the genome. These Reference Coverage Profiles (RCPs) take into account the diverse technologies and pipeline versions used. Normalization of the scaled coverage to the RCP followed by hidden Markov model (HMM) segmentation enables efficient detection of CNVs and large deletions in individual genomes.Use of pre-computed multi-genome coverage profiles improves our ability to analyze each individual genome. We make available RCPs and tools for performing these analyses on personal genomes. We expect the increased sensitivity and specificity for individual genome analysis to be critical for achieving clinical-grade genome interpretation

Author Correction: Parent-of-origin-specific signatures of de novo mutations.

In the version of this article published, the P values for the enrichment of single mutation categories were inadvertently not corrected for multiple testing. After multiple-testing correction, only two of the six mutation categories mentioned are still statistically significant. To reflect this, the text More specifically, paternally derived DNMs are enriched in transitions in A[.]G contexts, especially ACG\u3eATG and ATG\u3eACG (Bonferroni-corrected P = 1.3 × 1