Congenital anomalies of the kidney and urinary tract (CAKUT) are a major cause of pediatric kidney failure. We performed a genome-wide analysis of copy number variants (CNVs) in 2,824 cases and 21,498 controls. Affected individuals carried a significant burden of rare exonic (i.e. affecting coding regions) CNVs and were enriched for known genomic disorders (GD). Kidney anomaly (KA) cases were most enriched for exonic CNVs, encompassing GD-CNVs and novel deletions; obstructive uropathy (OU) had a lower CNV burden and an intermediate prevalence of GD-CNVs; vesicoureteral reflux (VUR) had the fewest GD-CNVs but was enriched for novel exonic CNVs, particularly duplications. Six loci (1q21, 4p16.1-p16.3, 16p11.2, 16p13.11, 17q12, and 22q11.2) accounted for 65% of patients with GD-CNVs. Deletions at 17q12, 4p16.1-p16.3, and 22q11.2 were specific for KA; the 16p11.2 locus showed extensive pleiotropy. Using a multidisciplinary approach, we identified TBX6 as a driver for the CAKUT subphenotypes in the 16p11.2 microdeletion syndrome.

Summary A number of mitochondrial diseases arise from Single Nucleotide Variant (SNV) accumulation in multiple mitochondria. Here we present a method for identification of variants present at the single mitochondrion level in individual mouse and human neuronal cells allowing for extremely high resolution study of mitochondrial mutation dynamics. We identified extensive heteroplasmy between individual mitochondrion, along with three high confidence variants in mouse and one in human that were present in multiple mitochondria across cells. The pattern of variation revealed by single mitochondrion data shows surprisingly pervasive levels of heteroplasmy in inbred mice. Distribution of SNV loci suggests inheritance of variants across generations resulting in Poisson jackpot lines with large SNV load. Comparison of human and mouse variants suggests that the two species might employ distinct modes of somatic segregation. Single mitochondrion resolution revealed mitochondria mutational dynamics that we hypothesize to affect risk probabilities for mutations reaching disease thresholds.

Investigation of human CNS disease and drug effects has been hampered by the lack of a system that enables single cell analysis on live adult patient brain cells. We developed a culturing system, based on a papain-aided procedure, for resected adult human brain tissue removed during neurosurgery. We performed single-cell transcriptomics on over 300 cells permitting identification of oligodendrocytes, microglia, neurons, endothelial cells, and astrocytes after 3 weeks in culture. Using deep sequencing, we detected over 12,000 expressed genes including hundreds of cell-type enriched mRNAs, lncRNAs and pri-miRNAs. We describe cell-type and patient specific transcriptional hierarchies. Single-cell transcriptomics on cultured live adult patient derived cells is a prime example of the promise of personalized precision medicine. As these cells derive from subjects ranging in age into their sixties, this system permits human aging studies previously possible only in rodent systems.

Improved diagnostics for pancreatic ductal adenocarcinoma (PDAC) to detect the disease at earlier, curative stages and to guide treatments is crucial to progress against this disease. The development of a liquid biopsy for PDAC has proven challenging due to the sparsity and variable phenotypic expression of circulating biomarkers. Here we report methods we developed for isolating specific subsets of extracellular vesicles (EV) from plasma using a novel magnetic nanopore capture technique. In addition, we present a workflow for identifying EV miRNA biomarkers using RNA sequencing and machine-learning algorithms, which we used in combination to classify distinct cancer states. Applying this approach to a mouse model of PDAC, we identified a biomarker panel of 11 EV miRNAs that could distinguish mice with PDAC from either healthy mice or those with precancerous lesions in a training set of = 27 mice and a user-blinded validation set of = 57 mice (88% accuracy in a three-way classification). These results provide strong proof-of-concept support for the feasibility of using EV miRNA profiling and machine learning for liquid biopsy. These findings present a panel of extracellular vesicle miRNA blood-based biomarkers that can detect pancreatic cancer at a precancerous stage in a transgenic mouse model. .

Abstract.We focused on the transcriptional responses induced by low and very low doses of ionizing radiation with time effect. Regardless of their importance only a few limited studies have been done. Here we applied a large-scale gene transcript profile to elucidate the genes and biological pathways. Immortalized human mesenchymal stem cells were irradiated with 0.01, 0.05, 0.2 and 1 Gy of gamma radiation and total RNA was extracted from each cell line at 1, 4, 12 and 48 h after exposure. The essential transcriptional responses were identified according to dose and time. A total of 6,016 genes showed altered expression patterns at more than one time point or dose level among the investigated 10,800 genes. Genes that showed dose-dependent expression responses were involved in signal transduction, regulation of transcription, proteolysis, peptidolysis and metabolism. Those that showed time-dependent responses were divided into two distinct groups: the up-and-down group was associated with 'cellular defense mechanisms' such as apoptosis, cell adhesion, stress response and immune response and the down-and-up group with 'fundamental cellular processes' such as DNA replication, mitosis, RNA splicing, DNA repair and translation initiation. Genes showing both dose-and time-dependent responses exhibited a mixture of both features. A highly non-linear relationship between the IR dose and the transcriptional relative response was obtained from the dose-dependent group. The timedependent group also exhibited a non-linear relationship as the complex effect group did. Some of the early-reactive-phase (1-4 h) genes showed a differential expression response to 0.01, 0.05 and 0.2 Gy but were unresponsive to 1 Gy. Some of the late-recovery-phase (12-48 h) genes showed a differential expression to 1 Gy but were relatively unresponsive to other doses. We further characterized the gene expression patterns that could be implicated in the molecular mechanism of the cellular responses to low and very low-dose irradiation.

IntroductionAlthough it has been suggested that rare coding variants could explain the substantial missing heritability, very few sequencing studies have been performed in rheumatoid arthritis (RA). We aimed to identify novel functional variants with rare to low frequency using targeted exon sequencing of RA in Korea.MethodsWe analyzed targeted exon sequencing data of 398 genes selected from a multifaceted approach in Korean RA patients (n = 1,217) and controls (n = 717). We conducted a single-marker association test and a gene-based analysis of rare variants. For meta-analysis or enrichment tests, we also used ethnically matched independent samples of Korean genome-wide association studies (GWAS) (n = 4,799) or immunochip data (n = 4,722).ResultsAfter stringent quality control, we analyzed 10,588 variants of 398 genes from 1,934 Korean RA case controls. We identified 13 nonsynonymous variants with nominal association in single-variant association tests. In a meta-analysis, we did not find any novel variant with genome-wide significance for RA risk. Using a gene-based approach, we identified 17 genes with nominal burden signals. Among them, VSTM1 showed the greatest association with RA (P = 7.80 × 10−4). In the enrichment test using Korean GWAS, although the significant signal appeared to be driven by total genic variants, we found no evidence for enriched association of coding variants only with RA.ConclusionsWe were unable to identify rare coding variants with large effect to explain the missing heritability for RA in the current targeted resequencing study. Our study raises skepticism about exon sequencing of targeted genes for complex diseases like RA.Electronic supplementary materialThe online version of this article (doi:10.1186/s13075-014-0447-7) contains supplementary material, which is available to authorized users.

In the version of Fig. 4b initially published, there was a calculation error in the estimates of shared environmental variance (c 2 ) for MaTCH functional domains. For all MaTCH functional domains except the 'all traits' functional domain, the estimate of c 2 was calculated with monozygotic twin correlation (r MZ ) and dizygotic twin correlation (r DZ ) for each functional domain provided by the MaTCH website (http://match.ctglab.nl/). The c 2 value should have been estimated as c 2 = 2r DZ -r MZ but, owing to a coding error, was erroneously estimated as c 2 = 2r DZ -r DZ . The c 2 estimate for the 'all traits' functional domain was correct in the version of the article initially published, and therefore no conclusions are affected; however, the contribution of c 2 among MaTCH functional domains is decreased. The authors thank G. Gibson and M. Nordborg for pointing out the error.To correct this error, Fig. 4 has been revised to include corrected c 2 estimates in the data in panel b as well as to include the numbers of phenotypes in both the CaTCH and MaTCH functional domains in the y axes of panels a and b. The number of phenotypes for each MaTCH functional domain in Fig. 4 is based on the number of phenotypes for which h 2 and c 2 were estimated with twin correlation (r MZ and r DZ ) taken from the MaTCH website. The total numbers of phenotypes within each MaTCH functional domain where h 2 /c 2 were estimated with either twin correlation or variance component models (ACE) and can be found in Supplementary Table 1. The legend of Fig. 4 has been revised to include descriptions of the red and blue values and a description of the numbers of phenotypes in the y axes in panels a and b. In the Results section, the description of Fig. 4b reading "For c 2 , the 95% CI from CaTCH estimates overlapped with the 95% CI from the MaTCH estimates for only the infection domain (Fig. 4b)" has been changed to "For c 2 , the 95% CI from CaTCH estimates overlapped with the 95% CI from the MaTCH estimates for 11 out of 21 functional domains, namely cardiovascular, dermatological, endocrine, gastrointestinal, hematological, immunological, infection, metabolic, psychiatric, reproduction, and skeletal functional domains (Fig. 4b). "

<div>Abstract<p>Improved diagnostics for pancreatic ductal adenocarcinoma (PDAC) to detect the disease at earlier, curative stages and to guide treatments is crucial to progress against this disease. The development of a liquid biopsy for PDAC has proven challenging due to the sparsity and variable phenotypic expression of circulating biomarkers. Here we report methods we developed for isolating specific subsets of extracellular vesicles (EV) from plasma using a novel magnetic nanopore capture technique. In addition, we present a workflow for identifying EV miRNA biomarkers using RNA sequencing and machine-learning algorithms, which we used in combination to classify distinct cancer states. Applying this approach to a mouse model of PDAC, we identified a biomarker panel of 11 EV miRNAs that could distinguish mice with PDAC from either healthy mice or those with precancerous lesions in a training set of <i>n</i> = 27 mice and a user-blinded validation set of <i>n</i> = 57 mice (88% accuracy in a three-way classification). These results provide strong proof-of-concept support for the feasibility of using EV miRNA profiling and machine learning for liquid biopsy.</p><p><b>Significance:</b> These findings present a panel of extracellular vesicle miRNA blood-based biomarkers that can detect pancreatic cancer at a precancerous stage in a transgenic mouse model. <i>Cancer Res; 78(13); 3688–97. ©2018 AACR</i>.</p></div>