Clustering of known low and moderate risk alleles rather than a novel recessive high-risk gene in non-BRCA1/2 sib trios affected with breast cancer.

Breast cancer risk is approximately twice as high in first-degree relatives of female breast cancer cases than in women in the general population. Less than half of this risk can be attributed to the currently known genetic risk factors. Recessive risk alleles represent a relatively underexplored explanation for the remainder of familial risk. To address this, we selected 19 non-BRCA1/2 breast cancer families in which at least three siblings were affected, while no first-degree relatives of the previous or following generation had breast cancer. Germline DNA from one of the siblings was subjected to exome sequencing, while all affected siblings were genotyped using SNP arrays to assess haplotype sharing and to calculate a polygenic risk score (PRS) based on 160 low-risk variants. We found no convincing candidate recessive alleles among exome sequencing variants in genomic regions for which all three siblings shared two haplotypes. However, we found two families in which all affected siblings carried the CHEK2*1100delC. In addition, the average normalized PRS of the "recessive" family probands (0.81) was significantly higher than that in both general population cases (0.35, P = .026) and controls (P = .0004). These findings suggest that the familial aggregation is, at least in part, explained by a polygenic effect of common low-risk variants and rarer intermediate-risk variants, while we did not find evidence of a role for novel recessive risk alleles.

Retrospective Validation of a Metagenomic Sequencing Protocol for Combined Detection of RNA and DNA Viruses Using Respiratory Samples from Pediatric Patients.

Viruses are the main cause of respiratory tract infections. Metagenomic next-generation sequencing (mNGS) enables unbiased detection of all potential pathogens. To apply mNGS in viral diagnostics, sensitive and simultaneous detection of RNA and DNA viruses is needed. Herein, were studied the performance of an in-house mNGS protocol for routine diagnostics of viral respiratory infections with potential for automated pan-pathogen detection. The sequencing protocol and bioinformatics analysis were designed and optimized, including exogenous internal controls. Subsequently, the protocol was retrospectively validated using 25 clinical respiratory samples. The developed protocol using Illumina NextSeq 500 sequencing showed high repeatability. Use of the National Center for Biotechnology Information's RefSeq database as opposed to the National Center for Biotechnology Information's nucleotide database led to enhanced specificity of classification of viral pathogens. A correlation was established between read counts and PCR cycle threshold value. Sensitivity of mNGS, compared with PCR, varied up to 83%, with specificity of 94%, dependent on the cutoff for defining positive mNGS results. Viral pathogens only detected by mNGS, not present in the routine diagnostic workflow, were influenza C, KI polyomavirus, cytomegalovirus, and enterovirus. Sensitivity and analytical specificity of this mNGS protocol were comparable to PCR and higher when considering off-PCR target viral pathogens. One single test detected all potential viral pathogens and simultaneously obtained detailed information on detected viruses.

Dynamics of the Gut Microbiota in Children Receiving Selective or Total Gut Decontamination Treatment during Hematopoietic Stem Cell Transplantation.

Bloodstream infections and graft-versus-host disease are common complications after hematopoietic stem cell transplantation (HSCT) procedures, associated with the gut microbiota that acts as a reservoir for opportunistic pathogens. Selective gut decontamination (SGD) and total gut decontamination (TGD) during HSCT have been associated with a decreased risk of developing these complications after transplantation. However, because studies have shown conflicting results, the use of these treatments remains subject of debate. In addition, their impact on the gut microbiota is not well studied. The aim of this study was to elucidate the dynamics of the microbiota during and after TGD and to compare these with the dynamics of SGD. In this prospective, observational, single-center study fecal samples were longitudinally collected from 19 children eligible for allogenic HSCT (TGD, n=12; SGD, n=7), weekly during hospital admission and monthly after discharge. In addition, fecal samples were collected from 3 family stem cell donors. Fecal microbiota structure of patients and donors was determined by 16S rRNA gene amplicon sequencing. Microbiota richness and diversity markedly decreased during SGD and TGD and gradually increased after cessation of decontamination treatment. During SGD, gut microbiota composition was relatively stable and dominated by Bacteroides, whereas it showed high inter- and intraindividual variation and low Bacteroides abundance during TGD. In some children TGD allowed the genera Enterococcus and Streptococcus to thrive during treatment. A gut microbiota dominated by Bacteroides was associated with increased predicted activity of several metabolic processes. Comparing the microbiota of recipients and their donors indicated that receiving an SCT did not alter the patient's microbiota to become more similar to that of its donor. Overall, our findings indicate that SGD and TGD affect gut microbiota structure in a treatment-specific manner. Whether these treatments affect clinical outcomes via interference with the gut microbiota needs to be further elucidated.

A SNP panel for identification of DNA and RNA specimens.

BACKGROUND: SNP panels that uniquely identify an individual are useful for genetic and forensic research. Previously recommended SNP panels are based on DNA profiles and mostly contain intragenic SNPs. With the increasing interest in RNA expression profiles, we aimed for establishing a SNP panel for both DNA and RNA-based genotyping. RESULTS: To determine a small set of SNPs with maximally discriminative power, genotype calls were obtained from DNA and blood-derived RNA sequencing data belonging to healthy, geographically dispersed, Dutch individuals. SNPs were selected based on different criteria like genotype call rate, minor allele frequency, Hardy-Weinberg equilibrium and linkage disequilibrium. A panel of 50 SNPs was sufficient to identify an individual uniquely: the probability of identity was 6.9 × 10- 20 when assuming no family relations and 1.2 × 10- 10 when accounting for the presence of full sibs. The ability of the SNP panel to uniquely identify individuals on DNA and RNA level was validated in an independent population dataset. The panel is applicable to individuals from European descent, with slightly lower power in non-Europeans. Whereas most of the genes containing the 50 SNPs are expressed in various tissues, our SNP panel needs optimization for other tissues than blood. CONCLUSIONS: This first DNA/RNA SNP panel will be useful to identify sample mix-ups in biomedical research and for assigning DNA and RNA stains in crime scenes to unique individuals.

Antisense Long Non-Coding RNAs Are Deregulated in Skin Tissue of Patients with Systemic Sclerosis.

Systemic sclerosis is an autoimmune disease characterized by fibrosis of skin and multiple organs of which the pathogenesis is poorly understood. We studied differentially expressed coding and non-coding genes in relation to systemic sclerosis pathogenesis with a specific focus on antisense non-coding RNAs. Skin biopsy-derived RNAs from 14 early systemic sclerosis patients and six healthy individuals were sequenced with ion-torrent and analyzed using DEseq2. Overall, 4,901 genes with a fold change >1.5 and a false discovery rate <5% were detected in patients versus controls. Upregulated genes clustered in immunologic, cell adhesion, and keratin-related processes. Interestingly, 676 deregulated non-coding genes were detected, 257 of which were classified as antisense genes. Sense genes expressed opposite of these antisense genes were also deregulated in 42% of the observed sense-antisense gene pairs. The majority of the antisense genes had a similar effect sizes in an independent North American dataset with three genes (CTBP1-AS2, OTUD6B-AS1, and AGAP2-AS1) exceeding the study-wide Bonferroni-corrected P-value (PBonf < 0.0023, Pcombined = 1.1 × 10-9, 1.4 × 10-8, 1.7 × 10-6, respectively). In this study, we highlight that together with coding genes, (antisense) long non-coding RNAs are deregulated in skin tissue of systemic sclerosis patients suggesting a novel class of genes involved in pathogenesis of systemic sclerosis.

Fecal Microbiota Transfer for Multidrug-Resistant Gram-Negatives: A Clinical Success Combined With Microbiological Failure.

Combined fecal microbiota transfer and antibiotic treatment prevented recurrences of urinary tract infections with multidrug-resistant (MDR) Pseudomonas aeruginosa, but it failed to eradicate intestinal colonization with MDR Escherichia coli. Based on microbiota analysis, failure was not associated with distinct diminished microbiota diversity.

Identification of context-dependent expression quantitative trait loci in whole blood.

Genetic risk factors often localize to noncoding regions of the genome with unknown effects on disease etiology. Expression quantitative trait loci (eQTLs) help to explain the regulatory mechanisms underlying these genetic associations. Knowledge of the context that determines the nature and strength of eQTLs may help identify cell types relevant to pathophysiology and the regulatory networks underlying disease. Here we generated peripheral blood RNA-seq data from 2,116 unrelated individuals and systematically identified context-dependent eQTLs using a hypothesis-free strategy that does not require previous knowledge of the identity of the modifiers. Of the 23,060 significant cis-regulated genes (false discovery rate (FDR) ≤ 0.05), 2,743 (12%) showed context-dependent eQTL effects. The majority of these effects were influenced by cell type composition. A set of 145 cis-eQTLs depended on type I interferon signaling. Others were modulated by specific transcription factors binding to the eQTL SNPs.

Disease variants alter transcription factor levels and methylation of their binding sites.

Most disease-associated genetic variants are noncoding, making it challenging to design experiments to understand their functional consequences. Identification of expression quantitative trait loci (eQTLs) has been a powerful approach to infer the downstream effects of disease-associated variants, but most of these variants remain unexplained. The analysis of DNA methylation, a key component of the epigenome, offers highly complementary data on the regulatory potential of genomic regions. Here we show that disease-associated variants have widespread effects on DNA methylation in trans that likely reflect differential occupancy of trans binding sites by cis-regulated transcription factors. Using multiple omics data sets from 3,841 Dutch individuals, we identified 1,907 established trait-associated SNPs that affect the methylation levels of 10,141 different CpG sites in trans (false discovery rate (FDR) < 0.05). These included SNPs that affect both the expression of a nearby transcription factor (such as NFKB1, CTCF and NKX2-3) and methylation of its respective binding site across the genome. Trans methylation QTLs effectively expose the downstream effects of disease-associated variants.