Search

Urine is one of the most widely used biofluids in metabolomic studies, because it can be collected non-invasively and is available in large quantities. However, it shows large heterogeneity in sample concentration and consequently requires normalization to reduce unwanted variation and extract meaningful biological information. Biological samples like urine are commonly measured with electrospray ionization (ESI) coupled to a mass spectrometer, producing datasets for positive and negative mode. Combining these gives a more complete picture of the total metabolites present in a sample. However, the effect of this data merging on subsequent data analysis, especially in combination with normalization, has not yet been analysed. To address this issue, we conducted a neutral comparison study to evaluate the performance of eight post-acquisition normalization methods under different data merging procedures using 1029 urine samples from the Food Chain plus (FoCus) cohort. Samples were measured by a Fourier transform ion cyclotron resonance mass spectrometer (FT-ICR-MS). Normalization methods were evaluated by five criteria capturing the ability to remove sample concentration variation and preserve relevant biological information. Merging data after normalization was generally favourable for quality control (QC) sample similarity, sample classification and feature selection for most of the tested normalization methods. Merging data after normalization and the usage of probabilistic quotient normalization (PQN) in a similar setting are generally recommended. Relying on a single analyte to capture sample concentration differences, like with post-acquisition creatinine normalization, seems to be a less preferable approach, especially when data merging is applied.

Background Histological disease severity assessment in ulcerative colitis [UC] has become a mainstay in the definition of clinical endpoints [‘histological remission’] in clinical trials of UC. Several scores have been established in the microscopic assessment of disease activity, but the Nancy index [NI] stands out as being the histological index with the fewest scoring items. To what extent histological assessment using the NI is affected by interobserver reliability in a real-word setting is poorly understood. We therefore performed a single-centre retrospective analysis of NI assessment in patients with UC. Methods We retrospectively evaluated the NI in two independent cohorts [total: 1085 biopsies, 547 UC patients] of clinically diagnosed UC patients, who underwent colonoscopy between 2007 and 2020. Cohort #1 consisted of 637 biopsies from 312 patients, while Cohort #2 consisted of 448 biopsies from 235 patients. Two blinded pathologists with different levels of expertise scored all biopsies from each cohort. A consensus conference was held for cases with discrepant scoring results. Finally, an overall consensus scoring was obtained from both cohorts. Results The interobserver agreement of the NI was substantial after the assessment of 1085 biopsy samples (κ = 0.796 [95% confidence interval, CI: 0.771–0.820]). An improvement of the interobserver agreement was found with increasing numbers of samples evaluated by both observers (Cohort #1: κ = 0.772 [95% CI: 0.739–0.805]; Cohort #2: κ = 0.829 [95% CI: 0.793–0.864]). Interobserver discordance was highest in NI grade 1 [observer 1: n = 128; observer 2: n = 236]. Interobserver discordance was lowest in NI grades 0 [observer 1: n = 504; observer 2: n = 479] and 3 [observer 1: n = 71; observer 2: n = 66]. Conclusion The NI is an easy-to-use index with high interobserver reliability for assessment of the histological disease activity of UC patients in a real-world setting. While NI grades 0 and 3 had a high level of agreement between observers, NI grade 1 had a poorer level of agreement. This highlights the clinical need to specify histological characteristics leading to NI grade 1.

Human longevity is a complex trait influenced by both genetic and environmental factors, whose interaction is mediated by epigenetic mechanisms like DNA methylation. Here, we generated genome-wide whole-blood methylome data from 267 individuals, of which 71 were long-lived (90–104 years), by applying reduced representation bisulfite sequencing. We followed a stringent two-stage analysis procedure using discovery and replication samples to detect differentially methylated sites (DMSs) between young and long-lived study participants. Additionally, we performed a DNA methylation quantitative trait loci analysis to identify DMSs that underlie the longevity phenotype. We combined the DMSs results with gene expression data as an indicator of functional relevance. This approach yielded 21 new candidate genes, the majority of which are involved in neurophysiological processes or cancer. Notably, two candidates (PVRL2, ERCC1) are located on chromosome 19q, in close proximity to the well-known longevity- and Alzheimer’s disease-associated loci APOE and TOMM40. We propose this region as a longevity hub, operating on both a genetic (APOE, TOMM40) and an epigenetic (PVRL2, ERCC1) level. We hypothesize that the heritable methylation and associated gene expression changes reported here are overall advantageous for the LLI and may prevent/postpone age-related diseases and facilitate survival into very old age.

Metabolic coherence (MC) is a network-based approach to dimensionality reduction that can be used, for example, to interpret the joint expression of genes linked to human metabolism. Computationally, the derivation of ‘transcriptomic’ MC involves mapping of an individual gene expression profile onto a gene-centric network derived beforehand from a metabolic network (currently Recon2), followed by the determination of the connectivity of a particular, profile-specific subnetwork. The biological significance of MC has been exemplified previously in the context of human inflammatory bowel disease, among others, but the genetic architecture of this quantitative cellular trait is still unclear. Therefore, we performed a genome-wide association study (GWAS) of MC in the 1000 Genomes/ GEUVADIS data (n = 457) and identified a solitary genome-wide significant association with single nucleotide polymorphisms (SNPs) in the intronic region of the cadherin 18 (CDH18) gene on chromosome 5 (lead SNP: rs11744487, p = 1.2 × 10− 8). Cadherin 18 is a transmembrane protein involved in human neural development and cell-to-cell signaling. Notably, genetic variation at the CDH18 locus has been associated with metabolic syndrome-related traits before. Replication of our genome-wide significant GWAS result was successful in another population study from the Netherlands (BIOS, n = 2661; lead SNP), but failed in two additional studies (KORA, Germany, n = 711; GENOA, USA, n = 411). Besides sample size issues, we surmise that these discrepant findings may be attributable to technical differences. While 1000 Genomes/GEUVADIS and BIOS gene expression profiles were generated by RNA sequencing, the KORA and GENOA data were microarray-based. In addition to providing first evidence for a link between regional genetic variation and a metabolism-related characteristic of human transcriptomes, our findings highlight the benefit of adopting a systems biology-oriented approach to molecular data analysis. Electronic supplementary material The online version of this article (10.1007/s00439-019-01994-x) contains supplementary material, which is available to authorized users.

Motivation It has been shown that the machine learning approach random forest can be successfully applied to omics data, such as gene expression data, for classification or regression and to select variables that are important for prediction. However, the complex relationships between predictor variables, in particular between causal predictor variables, make the interpretation of currently applied variable selection techniques difficult. Results Here we propose a new variable selection approach called surrogate minimal depth (SMD) that incorporates surrogate variables into the concept of minimal depth (MD) variable importance. Applying SMD, we show that simulated correlation patterns can be reconstructed and that the increased consideration of variable relationships improves variable selection. When compared with existing state-of-the-art methods and MD, SMD has higher empirical power to identify causal variables while the resulting variable lists are equally stable. In conclusion, SMD is a promising approach to get more insight into the complex interplay of predictor variables and outcome in a high-dimensional data setting. Availability and implementation https://github.com/StephanSeifert/SurrogateMinimalDepth. Supplementary information Supplementary data are available at Bioinformatics online.

Case-only (CO) studies are a powerful means to uncover gene-environment (G × E) interactions for complex human diseases. Moreover, such studies may in principle also draw upon genotype imputation to increase statistical power even further. However, genotype imputation usually employs healthy controls such as the Haplotype Reference Consortium (HRC) data as an imputation base, which may systematically perturb CO studies in genomic regions with main effects upon disease risk. Using genotype data from 719 German Crohn Disease (CD) patients, we investigated the level of imputation accuracy achievable for single nucleotide polymorphisms (SNPs) with or without a genetic main effect, and with varying minor allele frequency (MAF). Genotypes were imputed from neighbouring SNPs at different levels of linkage disequilibrium (LD) to the target SNP using the HRC data as an imputation base. Comparison of the true and imputed genotypes revealed lower imputation accuracy for SNPs with strong main effects. We also simulated different levels of G × E interaction to evaluate the potential loss of statistical validity and power incurred by the use of imputed genotypes. Simulations under the null hypothesis revealed that genotype imputation does not inflate the type I error rate of CO studies of G × E. However, the statistical power was found to be reduced by imputation, particularly for SNPs with low MAF, and a gradual loss of statistical power resulted when the level of LD to the SNPs driving the imputation decreased. Our study thus highlights that genotype imputation should be employed with great care in CO studies of G × E interaction. Supplementary Information The online version contains supplementary material available at 10.1007/s00439-021-02294-z.

Rapid decline of glomerular filtration rate estimated from creatinine (eGFRcrea) is associated with severe clinical endpoints. In contrast to cross-sectionally assessed eGFRcrea, the genetic basis for rapid eGFRcrea decline is largely unknown. To help define this, we meta-analyzed 42 genome-wide association studies from the Chronic Kidney Diseases Genetics Consortium and United Kingdom Biobank to identify genetic loci for rapid eGFRcrea decline. Two definitions of eGFRcrea decline were used: 3 mL/min/1.73m(2)/year or more ("Rapid3"; encompassing 34,874 cases, 107,090 controls) and eGFRcrea decline 25% or more and eGFRcrea under 60 mL/min/1.73m(2) at follow-up among those with eGFRcrea 60 mL/min/1.73m(2) or more at baseline ("CKDi25"; encompassing 19,901 cases, 175,244 controls). Seven independent variants were identified across six loci for Rapid3 and/or CKDi25: consisting of five variants at four loci with genome-wide significance (near UMOD-PDILT (2), PRKAG2, WDR72, OR2S2) and two variants among 265 known eGFRcrea variants (near GATM, LARP4B). All these loci were novel for Rapid3 and/or CKDi25 and our bioinformatic follow-up prioritized variants and genes underneath these loci. The OR2S2 locus is novel for any eGFRcrea trait including interesting candidates. For the five genome-wide significant lead variants, we found supporting effects for annual change in blood urea nitrogen or cystatin-based eGFR, but not for GATM or (LARP4B). Individuals at high compared to those at low genetic risk (8-14 vs. 0-5 adverse alleles) had a 1.20-fold increased risk of acute kidney injury (95% confidence interval 1.08-1.33). Thus, our identified loci for rapid kidney function decline may help prioritize therapeutic targets and identify mechanisms and individuals at risk for sustained deterioration of kidney function., Zweitveröffentlichungen der Universität Potsdam : Reihe der Digital Engineering Fakultät; 19

BACKGROUND: The human skin offers diverse ecosystems for microbial symbionts. However, the factors shaping skin-microbiome interactions are still insufficiently characterized. This contrasts to the broader knowledge about the factors influencing the gut microbiota. OBJECTIVES: We aimed to investigate major patterns of associations of host traits, lifestyle, and environmental factors with skin bacteria in two German populations. METHODS: This is a cross-sectional study with 647 participants from two population-based German cohorts, PopGen (n=294) and KORA FF4 (n=353), totaling 1794 skin samples. The V1-V2 regions of the 16S rRNA gene were sequenced. Associations were tested with two bacterial levels, community (beta-diversity) and 16S rRNA gene amplicon sequence variants (ASVs). RESULTS: We validated known associations of the skin microbiota with skin microenvironment, age, body mass index (BMI) and sex. These factors were associated with beta diversity and abundance of ASVs in PopGen, which was largely replicated in KORA FF4. Most intriguingly, dietary macronutrients and total dietary energy were associated with several ASVs. ASVs were also associated with smoking, alcohol consumption, skin pH, skin type, transepidermal water loss, education, and several environmental exposures, including hours spent outdoors. Associated ASVs included members of the genera Propionibacterium, Corynebacterium, and Staphylococcus. DISCUSSION: We expand the current understanding of factors associated with the skin bacterial community. We show the association of diet with the skin bacteria. Finally, we hypothesize that the skin microenvironment and the host physiology would shape the skin bacterial community at greater extent in comparison to a single skin physiological feature, lifestyle and environmental exposition.

Evaluation of water material density images (wMDIm) of dual-energy CT (DECT) for earlier prediction of final infarct volume (fiV) in follow-up single-energy CT (SECT) and correlation with clinical outcome. Fifty patients (69 years, ± 12.1, 40–90, 50% female) with middle cerebral artery (MCA) occlusions were included. Early infarct volumes were analyzed in monoenergetic images (MonoIm) and wMDIm at 60 keV and compared with the fiV in SECT 4.9 days (± 4) after thrombectomy. Association between infarct volume and functional outcome was tested by linear regression analysis. wMDIm shows a prior visible infarct demarcation (60.7 ml, ± 74.9 ml) compared with the MonoIm (37.57 ml, ± 76.7 ml). Linear regression analysis, Bland–Altman plots and Pearson correlation coefficients show a close correlation of infarct volume in wMDIm to the fiV in SECT (r = 0.86; 95% CI 0.76–0.92), compared with MonoIm and SECT (r = 0.81; 95% CI 0.69–0.89). The agreement with SECT is substantially higher in patients with infarct volumes < 70 ml (n = 33; 66%). Coefficients were smaller with r = 0.59 (95% CI 0.31; 0.78) for MonoIm and SECT compared with r = 0.77 (95% CI 0.57; 0.88) for wMDIm and SECT. At admission, the mean NIHSS score and mRS were 17.02 (± 4.7) and 4.9 (± 0.2). mRS ≤ 2 was achieved in 56% at 90 days with a mean mRS of 2.5 (± 0.8) at discharge. Material decomposition allows earlier visibility of the final infarct volume. This promises an earlier evaluation of the dimension and severity of infarction and may lead to faster initiation of secondary stroke prophylaxis.

Yield losses caused by fungal pathogens represent a major threat to global food production. One of the most devastating fungal wheat pathogens is Zymoseptoria tritici. Despite the importance of this fungus, the underlying mechanisms of plant–pathogen interactions are poorly understood. Here we present a conceptual framework based on coinfection assays, comparative metabolomics, and microbiome profiling to study the interaction of Z. tritici in susceptible and resistant wheat. We demonstrate that Z. tritici suppresses the production of immune-related metabolites in a susceptible cultivar. Remarkably, this fungus-induced immune suppression spreads within the leaf and even to other leaves, a phenomenon that we term “systemic induced susceptibility”. Using a comparative metabolomics approach, we identify defense-related biosynthetic pathways that are suppressed and induced in susceptible and resistant cultivars, respectively. We show that these fungus-induced changes correlate with changes in the wheat leaf microbiome. Our findings suggest that immune suppression by this hemibiotrophic pathogen impacts specialized plant metabolism, alters its associated microbial communities, and renders wheat vulnerable to further infections., The fungal plant pathogen Zymoseptoria tritici is a major threat to wheat yield. Here Seybold et al. show that Z. tritici can suppress immune responses not only in infected tissue but also on other leaves, a phenomenon termed “systemic induced susceptibility” that is correlated with systemic changes in metabolite accumulation.

ObjectiveImpaired lysosomal degradation of α‐synuclein and other cellular constituents may play an important role in Parkinson's disease (PD). Rare genetic variants in the glucocerebrosidase (GBA) gene were consistently associated with PD. Here we examine the association between rare variants in lysosomal candidate genes and PD.MethodsWe investigated the association between PD and rare genetic variants in 23 lysosomal candidate genes in 4096 patients with PD and an equal number of controls using pooled targeted next‐generation DNA sequencing. Genewise association of rare variants in cases or controls was analyzed using the optimized sequence kernel association test with Bonferroni correction for the 23 tested genes.ResultsWe confirm the association of rare variants in GBA with PD and report novel associations for rare variants in ATP13A2, LAMP1, TMEM175, and VPS13C.ConclusionRare variants in selected lysosomal genes, first and foremost GBA, are associated with PD. Rare variants in ATP13A2 and VPC13C previously linked to monogenic PD and more common variants in TMEM175 and VPS13C previously linked to sporadic PD in genome‐wide association studies are associated with PD. © 2020 International Parkinson and Movement Disorder Society.

BACKGROUND & AIMS: Altered interactions between the mucosal immune system and intestinal microbiota contribute to pathogenesis of inflammatory bowel diseases (IBD). It is not clear how inhibitors of cytokines, such as antagonists of tumor necrosis factor (anti-TNF), affect the intestinal microbiome. We investigated the effects of anti-TNF agents on gut microbe community structure and function in a longitudinal 2-step study of patients with IBD. We correlated our findings with outcomes of treatment and investigated patterns of metabolites in fecal samples before and after anti-TNF therapy. METHODS: We performed a prospective study of 2 cohorts of patients in Germany; the discovery cohort comprised 12 patients with IBD, 17 patients with rheumatic disease, and 19 healthy individuals (controls); fecal samples were collected at baseline and 2, 6, and 30 weeks after induction of anti-TNF therapy. The validation cohort comprised 23 patients with IBD treated with anti-TNF or vedolizumab (anti-α4β7 integrin) and 99 healthy controls; fecal samples were collected at baseline and at weeks 2, 6, and 14. Fecal microbiota were analyzed by V3-V4 16S ribosomal RNA gene amplicon sequencing. Clinical response and remission were determined by clinical disease activity scores. Metabolic network reconstruction and associated fecal metabolite level inference was performed in silico using the AGORA (Assembly of Gut Organisms through Reconstruction and Analysis) resource. Metabolomic analyses of fecal samples from a subset of patients were performed to validate metabolites associated with treatment outcomes. RESULTS: Anti-TNF therapy shifted the diversity of fecal microbiota in patients with IBD, but not with rheumatic disease, toward that of controls. Across timepoints, diversity indices did not vary significantly between patients with IBD who did or did not achieve clinical remission after therapy. In contrast, in silico modeling of metabolic interactions between gut microbes found metabolite exchange to be significantly reduced at baseline in fecal samples from patients with IBD and to be associated with later clinical remission. Predicted levels of butyrate and substrates involved in butyrate synthesis (ethanol or acetaldehyde) were significantly associated with clinical remission following anti-TNF therapy, verified by fecal metabolomic analyses. CONCLUSIONS: Metabolic network reconstruction and assessment of metabolic profiles of fecal samples might be used to identify patients with IBD likely to achieve clinical remission following anti-TNF therapy and increase our understanding of the heterogeneity of IBD. ispartof: GASTROENTEROLOGY vol:157 issue:5 pages:1279-+ ispartof: location:United States status: published

Motivation High-throughput technologies allow comprehensive characterization of individuals on many molecular levels. However, training computational models to predict disease status based on omics data is challenging. A promising solution is the integration of external knowledge about structural and functional relationships into the modeling process. We compared four published random forest-based approaches using two simulation studies and nine experimental datasets. Results The self-sufficient prediction error approach should be applied when large numbers of relevant pathways are expected. The competing methods hunting and learner of functional enrichment should be used when low numbers of relevant pathways are expected or the most strongly associated pathways are of interest. The hybrid approach synthetic features is not recommended because of its high false discovery rate. Availability and implementation An R package providing functions for data analysis and simulation is available at GitHub (https://github.com/szymczak-lab/PathwayGuidedRF). An accompanying R data package (https://github.com/szymczak-lab/DataPathwayGuidedRF) stores the processed and quality controlled experimental datasets downloaded from Gene Expression Omnibus (GEO). Supplementary information Supplementary data are available at Bioinformatics online.

Purpose Interleukin-6 (IL-6) production and signalling are increased in the inflamed mucosa in inflammatory bowel diseases (IBD). As published serum levels of IL-6 and its soluble receptors sIL-6R and sgp130 in IBD are from small cohorts and partly contradictory, we systematically evaluated IL-6, sIL-6R and sgp130 levels as markers of disease activity in Crohn’s disease (CD) and ulcerative colitis (UC). Methods Consecutive adult outpatients with confirmed CD or UC were included, and their disease activity and medication were monitored. Serum from 212 CD patients (815 measurements) and 166 UC patients (514 measurements) was analysed, and 100 age-matched healthy blood donors were used as controls. Results IL-6 serum levels were significantly elevated in active versus inactive CD and UC, also compared with healthy controls. However, only a fraction of IBD patients showed increased serum IL-6. IL-6 levels ranged up to 32.7 ng/mL in active CD (> 5000-fold higher than in controls), but also up to 6.9 ng/mL in inactive CD. Increases in active UC (up to 195 pg/mL) and inactive UC (up to 27 pg/mL) were less pronounced. Associations between IL-6 serum levels and C-reactive protein concentrations as well as leukocyte and thrombocyte counts were observed. Median sIL-6R and sgp130 levels were only increased by up to 15%, which was considered of no diagnostic significance. Conclusions Only a minority of IBD patients shows elevated IL-6 serum levels. However, in these patients, IL-6 is strongly associated with disease activity. Its soluble receptors sIL-6R and sgp130 do not appear useful as biomarkers in IBD. Electronic supplementary material The online version of this article (10.1007/s00384-018-3069-8) contains supplementary material, which is available to authorized users.

Aim: To determine whether methylation differences between mostly fatal TCF3-HLF and curable TCF3-PBX1 pediatric acute lymphoblastic leukemia subtypes can be associated with differential gene expression and remission. Materials & methods: Five (extremely rare) TCF3-HLF versus five (very similar) TCF3-PBX1 patients were sampled before and after remission and analyzed using reduced representation bisulfite sequencing and RNA-sequencing. Results: We identified 7000 differentially methylated CpG sites between subtypes, of which 78% had lower methylation levels in TCF3-HLF. Gene expression was negatively correlated with CpG sites in 23 genes. KBTBD11 clearly differed in methylation and expression between subtypes and before and after remission in TCF3-HLF samples. Conclusion: KBTBD11 hypomethylation may be a promising potential target for further experimental validation especially for the TCF3-HLF subtype.

Machine learning methods and in particular random forests are promising approaches for prediction based on high dimensional omics data sets. They provide variable importance measures to rank predictors according to their predictive power. If building a prediction model is the main goal of a study, often a minimal set of variables with good prediction performance is selected. However, if the objective is the identification of involved variables to find active networks and pathways, approaches that aim to select all relevant variables should be preferred. We evaluated several variable selection procedures based on simulated data as well as publicly available experimental methylation and gene expression data. Our comparison included the Boruta algorithm, the Vita method, recurrent relative variable importance, a permutation approach and its parametric variant (Altmann) as well as recursive feature elimination (RFE). In our simulation studies, Boruta was the most powerful approach, followed closely by the Vita method. Both approaches demonstrated similar stability in variable selection, while Vita was the most robust approach under a pure null model without any predictor variables related to the outcome. In the analysis of the different experimental data sets, Vita demonstrated slightly better stability in variable selection and was less computationally intensive than Boruta. In conclusion, we recommend the Boruta and Vita approaches for the analysis of high-dimensional data sets. Vita is considerably faster than Boruta and thus more suitable for large data sets, but only Boruta can also be applied in low-dimensional settings.

Introduction Topography of tremor manifestations is poorly investigated in essential tremor. The present study explores the prevalence and clinical correlates of head and/or voice tremor in essential tremor. Methods Out of a prospectively designed registry of 972 patients, 884 patients with definite and probable essential tremor had complete information on tremor localization. Demographic and clinical characteristics were compared among four subgroups: group A (without head or voice tremor, n = 619), B (with head but without voice tremor, n = 155), C (with voice but without head tremor, n = 47), and D (with both head and voice tremor, n = 63). Results In our patients, total prevalence of tremor was 24.7% for head, 12.4% for voice and 7.1% for the combination of head and voice. Logistic regression analyses showed that female gender is strongly associated with head tremor, which was confirmed by an additional meta-analysis. Severe hand tremor was the only factor associated with voice tremor. Both female gender and severe hand tremor increase the odds for having the combination of head and voice tremor. For males, hand tremor severity is significantly increased among those with head and voice tremor alone and in combination, but for females only for the combination. Patients with both head and voice tremor have more frequent involvement of legs and other localizations and are less responsive to β-blockers. Conclusions Female gender and severe hand tremor may increase the odds of head and/or voice tremor in essential tremor. The association of hand tremor severity with midline tremor is stronger for males than females.

BACKGROUND The presentation of Crohn's disease (CD) is heterogeneous and often leads to serious complications and need for surgery. We tested serum anti-zymogen granule glycoprotein 2 (GP2) antibodies, including its novel isoform alpha, for association with genetic variants, diagnosis, disease stratification, and prediction of CD courses in a combined cross-sectional and cohort study. METHODS Serum samples of 303 CD, 108 ulcerative colitis, 72 other inflammatory gastrointestinal diseases, and 206 controls without predominant gastrointestinal diseases controls (HC) were tested for the presence of Anti-GP2 and Anti-Saccharomyces cervisiae (ASCA) by enzyme-linked immunosorbent assay. Genetic analysis was performed using the Illumina Immunochip. RESULTS GP2 IgA and IgG had the highest discriminatory capability for CD versus ulcerative colitis and CD versus inflammatory gastrointestinal diseases. We identified an association of GP2 IgA and IgG each with 5 distinct single-nucleotide polymorphisms. Levels of anti-GP2 IgG were moderately associated with ileal disease location. Interestingly, both, anti-GP2 IgA and IgG were exclusively associated with the occurrence of stenosis and need for surgery, independently of disease location, but not with fistulizing CD, early disease onset or disease activity. ASCA IgG and IgA were qualitatively and quantitatively linked to CD, CD complications, and need for surgery. Increased levels of ASCA IgG and IgA and positivity for ASCA IgG, but neither levels nor positivity for GP2 IgG or IgA were predictive of the earlier occurrence of complications or surgery. CONCLUSIONS Anti-GP2 antibodies may aid as a tool for diagnosis and differentiation of CD and could indicate a more complicated CD course.

Yield losses caused by fungal pathogens represent a major threat to global food production. One of the most devastating fungal wheat pathogens is Zymoseptoria tritici. Despite the importance of this fungus and wheat as main staple food crop the underlying mechanisms of plant-pathogen interactions are poorly understood. Here we present a conceptual framework based on coinfection assays, comparative metabolomics, and microbiome profiling to study the interaction of Z. tritici in susceptible and resistant wheat. We demonstrate that Z. tritici suppresses the production of immune-related metabolites in a susceptible cultivar. Remarkably, this fungus-induced immune suppression spreads within the leaf and even to other leaves, a previously undescribed phenomenon that we term “systemic induced susceptibility”. Using a comparative metabolomics approach, we identified defense-related biosynthetic pathways that are suppressed and induced in susceptible and resistant cultivars, respectively. We show that these fungus-induced changes also dramatically affect the wheat leaf microbiome. Our findings emphasize that immune suppression by this hemibiotrophic pathogen impacts specialized plant metabolism, alters its associated microbial communities, and renders wheat vulnerable to further infections.

Atopic dermatitis (AD) affects up to 20% of children and adults worldwide. To gain a deeper understanding of the pathophysiology of AD, we conducted a large-scale transcriptomic study of AD with deeply sequenced RNA-sequencing samples using long (126-bp) paired-end reads. In addition to the comparisons against previous transcriptomic studies, we conducted in-depth analysis to obtain a high-resolution view of the global architecture of the AD transcriptome and contrasted it with that of psoriasis from the same cohort. By using 147 RNA samples in total, we found striking correlation between dysregulated genes in lesional psoriasis and lesional AD skin with 81% of AD dysregulated genes being shared with psoriasis. However, we described disease-specific molecular and cellular features, with AD skin showing dominance of IL-13 pathways, but with near undetectable IL-4 expression. We also demonstrated greater disease heterogeneity and larger proportion of dysregulated long noncoding RNAs in AD, and illustrated the translational impact, including skin-type classification and drug-target prediction. This study is by far the largest study comparing the AD and psoriasis transcriptomes using RNA sequencing and demonstrating the shared inflammatory components, as well as specific discordant cytokine signatures of these two skin diseases.

Publisher's version (útgefin grein)., Increased levels of the urinary albumin-to-creatinine ratio (UACR) are associated with higher risk of kidney disease progression and cardiovascular events, but underlying mechanisms are incompletely understood. Here, we conduct trans-ethnic (n = 564,257) and European-ancestry specific meta-analyses of genome-wide association studies of UACR, including ancestry- and diabetes-specific analyses, and identify 68 UACR-associated loci. Genetic correlation analyses and risk score associations in an independent electronic medical records database (n = 192,868) reveal connections with proteinuria, hyperlipidemia, gout, and hypertension. Fine-mapping and trans-Omics analyses with gene expression in 47 tissues and plasma protein levels implicate genes potentially operating through differential expression in kidney (including TGFB1, MUC1, PRKCI, and OAF), and allow coupling of UACR associations to altered plasma OAF concentrations. Knockdown of OAF and PRKCI orthologs in Drosophila nephrocytes reduces albumin endocytosis. Silencing fly PRKCI further impairs slit diaphragm formation. These results generate a priority list of genes and pathways for translational research to reduce albuminuria., Competing interests: Karsten B. Sieber is full-time employee of GlaxoSmithKline. Gardar Sveinbjornsson, Daniel F. Gudbjartsson, Hilma Holm, Unnur Thorsteinsdottir and Kari Stefansson are full-time employees of deCODE genetics, Amgen Inc. John Danesh is member of the Novartis Cardiovascular and Metabolic Advisory Board, received grant support from Novartis. Oscar H. Franco works in ErasmusAGE, a center for aging research across the life course funded by Nestlé Nutrition (Nestec Ltd.), Metagenics Inc., and AXA. Wolfgang Koenig received modest consultation fees for advisory board meetings from Amgen, DalCor, Kowa, Novartis, Pfizer and Sanofi, and modest personal fees for lectures from Amgen, AstraZeneca, Novartis, Pfizer and Sanofi. Anna I. Podgornaia and Dermot F. Reilly are employees of Merck Sharp Dohme Corp., Whitehouse Station, NJ, USA. Kevin Ho disclosed a research and financial relationship with Sanofi-Genzyme. Bruce M. Psaty serves on the DSMB of a clinical trial funded by the manufacturer (Zoll LifeCor) and on the Steering Committee of the Yale Open Data Access Project funded by Johnson & Johnson. Markus Scholz: Consultancy of and grant support from Merck Serono not related to this project. Adam S. Butterworth received grants from MSD, Pfizer, Novartis, Biogen and Bioverativ and personal fees from Novartis. Anna Köttgen received grant support from Gruenenthal not related to this project. The other authors declare no competing interests.

Upon ligand binding, plasma membrane-located TNF-related apoptosis-inducing ligand (TRAIL)-receptors 1 and 2 induce apoptosis as well as cancer-promoting signaling in cancer cells. TRAIL-R3 and TRAIL-R4 are believed to negatively regulate TRAIL-mediated apoptosis. Intracellular localization of TRAIL-receptors, as observed in many tumor cells, has been associated with oncogenic features, which are distinct from membrane-associated TRAIL-R signaling. Here, analyzing a panel of 354 breast cancer specimens, we found that an unfavorable outcome correlating with cancer-promoting properties of TRAIL-R1, TRAIL-R2, and TRAIL-R4 was most significantly defined by their intracellular distribution and mutual co-expression. A nuclear or cytoplasmic heterogeneous expression pattern correlated with markedly decreased overall survival and discriminated high-risk breast cancer patients from low-risk patients with a homogeneous distribution of expression, i.e., nuclear and cytoplasmic expression. The homogeneous TRAIL-R expression was associated with favorable breast cancer surrogate markers corresponding with excellent survival prognoses at 5 years after diagnosis (hazard ratio, 0.043) and over the complete course of follow-up (hazard ratio, 0.098; both p 0.001). No associations with specific intrinsic breast cancer subtypes were found. Our data suggest that the determination of intracellular co-expression patterns of TRAIL-R1, TRAIL-R2, and TRAIL-R4 provides an innovative and robust method for risk stratification in breast cancer patients beyond conventional prognostic markers. KEY MESSAGES: A total of 70% of breast cancer specimens show comparably high levels of intracellular TRAIL-Rs. Nuclear or cytoplasmic TRAIL-R co-expression occurs in the majority of tumors. A total of 25% of tumors show a heterogeneous expression of cytoplasmic or nuclear TRAIL-Rs. Patients with a heterogeneous TRAIL-R expression present with poor prognoses. Additive TRAIL-R-based risk stratification comprises different breast cancer subtypes.

Cancer proteomics provide a powerful approach to identify biomarkers for personalized medicine. Particularly, biomarkers for early detection, prognosis and therapeutic intervention of bone cancers, especially osteosarcomas, are missing. Initially, we compared two-dimensional gel electrophoresis (2-DE)-based protein expression pattern between cell lines of fetal osteoblasts, osteosarcoma and pulmonary metastasis derived from osteosarcoma. Two independent statistical analyses by means of PDQuest® and SameSpot® software revealed a common set of 34 differentially expressed protein spots (p < 0.05). 17 Proteins were identified by mass spectrometry and subjected to Ingenuity Pathway Analysis resulting in one high-ranked network associated with Gene Expression, Cell Death and Cell-To-Cell Signaling and Interaction. Ran/TC4-binding protein (RANBP1) and Cathepsin D (CTSD) were further validated by Western Blot in cell lines while the latter one showed higher expression differences also in cytospins and in clinical samples using tissue microarrays comprising osteosarcomas, metastases, other bone malignancies, and control tissues. The results show that protein expression patterns distinguish fetal osteoblasts from osteosarcomas, pulmonary metastases, and other bone diseases with relevant sensitivities between 55.56% and 100% at ≥87.50% specificity. Particularly, CTSD was validated in clinical material and could thus serve as a new biomarker for bone malignancies and potentially guide individualized treatment regimes.