A review on longitudinal data analysis with random forest.

In longitudinal studies variables are measured repeatedly over time, leading to clustered and correlated observations. If the goal of the study is to develop prediction models, machine learning approaches such as the powerful random forest (RF) are often promising alternatives to standard statistical methods, especially in the context of high-dimensional data. In this paper, we review extensions of the standard RF method for the purpose of longitudinal data analysis. Extension methods are categorized according to the data structures for which they are designed. We consider both univariate and multivariate response longitudinal data and further categorize the repeated measurements according to whether the time effect is relevant. Even though most extensions are proposed for low-dimensional data, some can be applied to high-dimensional data. Information of available software implementations of the reviewed extensions is also given. We conclude with discussions on the limitations of our review and some future research directions.

Evaluating the Effect of Data Merging and Postacquisition Normalization on Statistical Analysis of Untargeted High-Resolution Mass Spectrometry Based Urinary Metabolomics Data.

Urine is one of the most widely used biofluids in metabolomic studies because it can be collected noninvasively 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 data sets for positive and negative modes. 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 analyzed. To address this issue, we conducted a neutral comparison study to evaluate the performance of eight postacquisition normalization methods under different data merging procedures using 1029 urine samples from the Food Chain plus (FoCus) cohort. Samples were measured with 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 favorable 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 postacquisition creatinine normalization, seems to be a less preferable approach, especially when data merging is applied.

Blockade of IL-13 signaling improves skin barrier function and biology in patients with moderate to severe atopic dermatitis.

BACKGROUND: Interleukin (IL)-13 is a key driver of inflammation and barrier dysfunction in atopic dermatitis (AD). While there is robust evidence that tralokinumab, a monoclonal antibody neutralizing IL-13, reduces inflammation and clinical disease activity, less is known about its effects on barrier function. OBJECTIVES: To characterize effects of tralokinumab treatment on skin barrier function. METHODS: Transepidermal water loss (TEWL), stratum corneum hydration (SCH), natural moisturizing factor (NMF) content, histopathological characteristics, biomarker expression and microbiome composition were evaluated in lesional, non-lesional, and sodium lauryl sulfate (SLS)-irritated skin of 16 AD patients over the course of 16 weeks of tralokinumab treatment. RESULTS: All clinical severity scores decreased significantly over time. At week 16, mean TEWL in target lesions decreased by 32.66% (p = 0.01), and SCH increased by 58.44% (p = 0.004), along with histological reduction in spongiosis (p = 0.003), keratin 16 expression and epidermal thickness (p = 0.001). In parallel, there was a significant decrease in several barrier dysfunction-associated and pro-inflammatory proteins such as fibronectin (p = 0.006), CCL17/TARC (p = 0.025) and IL-8 (p = 0.014), with significant changes already at week 8. Total bacterial load and Staphylococcus aureus abundance were significantly reduced from week 2. CONCLUSION: Tralokinumab treatment improves skin physiology, epidermal pathology, and dysbiosis, further highlighting the pleiotropic role of IL-13 in AD pathogenesis.

Blood transcriptome profiling identifies 2 candidate endotypes of atopic dermatitis.

BACKGROUND: Few studies have analyzed the blood transcriptome in atopic dermatitis (AD). OBJECTIVE: We explored blood transcriptomic features of moderate to severe AD. METHODS: Blood messenger RNA sequencing on 60 adults from the TREATgermany registry including 49 patients before and after dupilumab treatment, as well as from an independent cohort of 31 patients and 43 controls was performed. Patient clustering, differential expression, correlation and coexpression network analysis, and unsupervised learning were conducted. RESULTS: AD patients showed pronounced inflammatory expression signatures with increased myeloid and IL-5-related patterns, and clearly segregated into 2 distinct clusters, with striking differences in particular for transcripts involved in eosinophil signaling. The eosinophil-high endotype showed a more pronounced global dysregulation, a positive correlation between disease activity and signatures related to IL-5 signaling, and strong correlations with several target proteins of antibodies or small molecules under development for AD. In contrast, the eosinophil-low endotype showed little transcriptomic dysregulation and no association between disease activity and gene expression. Clinical improvement with receipt of dupilumab was accompanied by a decrease of innate immune responses and an increase of lymphocyte signatures including B-cell activation and natural killer cell composition and/or function. The proportion of super responders was higher in the eosinophil-low endotype (32% vs 11%). Continued downregulation of IL18RAP, IFNG, and granzyme A in the eosinophil-high endotype suggests a residual disturbance of natural killer cell function despite clinical improvement. CONCLUSION: AD can be stratified into eosinophilic and noneosinophilic endotypes; such stratification may be useful when assessing stratified trial designs and treatment strategies.

DNA methylation QTL analysis identifies new regulators of human longevity.

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.

Familial Cerebellar Ataxia and Amyotrophic Lateral Sclerosis/Frontotemporal Dementia with DAB1 and C9ORF72 Repeat Expansions: An 18-Year Study.

BACKGROUND: Coding and noncoding repeat expansions are an important cause of neurodegenerative diseases. OBJECTIVE: This study determined the clinical and genetic features of a large German family that has been followed for almost 2 decades with an autosomal dominantly inherited spinocerebellar ataxia (SCA) and independent co-occurrence of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). METHODS: We carried out clinical examinations and telephone interviews, reviewed medical records, and performed magnetic resonance imaging and positron emission tomography scans of all available family members. Comprehensive genetic investigations included linkage analysis, short-read genome sequencing, long-read sequencing, repeat-primed polymerase chain reaction, and Southern blotting. RESULTS: The family comprises 118 members across seven generations, 30 of whom were definitely and five possibly affected. In this family, two different pathogenic mutations were found, a heterozygous repeat expansion in C9ORF72 in four patients with ALS/FTD and a heterozygous repeat expansion in DAB1 in at least nine patients with SCA, leading to a diagnosis of DAB1-related ataxia (ATX-DAB1; SCA37). One patient was affected by ALS and SCA and carried both repeat expansions. The repeat in DAB1 had the same configuration but was larger than those previously described ([ATTTT]≈75 [ATTTC]≈40-100 [ATTTT]≈415 ). Clinical features in patients with SCA included spinocerebellar symptoms, sometimes accompanied by additional ophthalmoplegia, vertical nystagmus, tremor, sensory deficits, and dystonia. After several decades, some of these patients suffered from cognitive decline and one from additional nonprogressive lower motor neuron affection. CONCLUSION: We demonstrate genetic and clinical findings during an 18-year period in a unique family carrying two different pathogenic repeat expansions, providing novel insights into their genotypic and phenotypic spectrums. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Genotype imputation in case-only studies of gene-environment interaction: validity and power.

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.

Evaluation of variable selection methods for random forests and omics data sets.

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.

Integrating biological knowledge and gene expression data using pathway-guided random forests: a benchmarking study.

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.

Early prediction of final infarct volume with material decomposition images of dual-energy CT after mechanical thrombectomy.

PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.

Metabolic Functions of Gut Microbes Associate With Efficacy of Tumor Necrosis Factor Antagonists in Patients With Inflammatory Bowel Diseases.

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-α4ß7 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.

Surrogate minimal depth as an importance measure for variables in random forests.

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.

Rare Variants in Specific Lysosomal Genes Are Associated With Parkinson's Disease.

OBJECTIVE: Impaired 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. METHODS: We 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. RESULTS: We confirm the association of rare variants in GBA with PD and report novel associations for rare variants in ATP13A2, LAMP1, TMEM175, and VPS13C. CONCLUSION: Rare 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.

The metabolic network coherence of human transcriptomes is associated with genetic variation at the cadherin 18 locus.

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.

Genome-wide association study of serum coenzyme Q10 levels identifies susceptibility loci linked to neuronal diseases.

Coenzyme Q10 (CoQ10) is a lipophilic redox molecule that is present in membranes of almost all cells in human tissues. CoQ10 is, amongst other functions, essential for the respiratory transport chain and is a modulator of inflammatory processes and gene expression. Rare monogenetic CoQ10 deficiencies show noticeable symptoms in tissues (e.g. kidney) and cell types (e.g. neurons) with a high energy demand. To identify common genetic variants influencing serum CoQ10 levels, we performed a fixed effects meta-analysis in two independent cross-sectional Northern German cohorts comprising 1300 individuals in total. We identified two genome-wide significant susceptibility loci. The best associated single nucleotide polymorphism (SNP) was rs9952641 (P value = 1.31 × 10 -8, ß = 0.063, CI0.95 [0.041, 0.085]) within the COLEC12 gene on chromosome 18. The SNP rs933585 within the NRXN-1 gene on chromosome 2 also showed genome wide significance (P value = 3.64 × 10 -8, ß = -0.034, CI0.95 [-0.046, -0.022]). Both genes have been previously linked to neuronal diseases like Alzheimer's disease, autism and schizophrenia. Among our 'top-10' associated variants, four additional loci with known neuronal connections showed suggestive associations with CoQ10 levels. In summary, this study demonstrates that serum CoQ10 levels are associated with common genetic loci that are linked to neuronal diseases.

Increased Tryptophan Metabolism Is Associated With Activity of Inflammatory Bowel Diseases.

BACKGROUND & AIMS: Administration of tryptophan and some of its metabolites reduces the severity of colitis in mice, whereas removing tryptophan from the diet increases susceptibility to colitis. Transfer of the intestinal microbiome transfers the colitogenic phenotype from tryptophan starved animals to normally nourished mice. We aimed to systematically evaluate serum levels of tryptophan and its metabolites in patients with inflammatory bowel diseases (IBD), and study their association with clinical and serologic features. METHODS: We studied 535 consecutive patients with IBD (211 with ulcerative colitis [UC], 234 with Crohn's disease [CD]; 236 male), enrolled in Germany from August 2013 through April 2014 and followed until July 2016. Serum samples were collected from patients and 291 matched individuals without IBD (controls); levels of tryptophan were measured using high-performance liquid chromatography. Metabolites of tryptophan were measured in serum from 148 patients and 100 controls by mass spectrometry. We measured levels of interleukin 22 in serum from 28 patients by enzyme-linked immunosorbent assay. Paired stool and serum samples were collected from a subset of patients with active UC (n = 10) or CD (n = 8) to investigate associations between serum levels of tryptophan and composition of the fecal microbiota, analyzed by 16S ribosomal DNA amplicon sequencing. We used real-time polymerase chain reaction to measure levels of messenger RNAs in colonic biopsies from 60 patients with UC, 50 with CD, and 30 controls. We collected information on patients' disease activity scores, medications, laboratory assessments, and clinical examinations during recruitment and follow-up visits. RESULTS: Serum levels of tryptophan were significantly lower in patients with IBD than in controls (P = 5.3 × 10-6) with a stronger reduction in patients with CD (vs control; P = 1.1 × 10-10) than UC (vs control; P = 2.8 × 10-3). We found a negative correlation between serum levels of tryptophan and disease activity or levels of C-reactive protein. Levels of messenger RNAs encoding tryptophan 2,3-dioxygenase-2 and solute carrier family 6 member 19 (also called B0AT1) were significantly decreased in colonic biopsies from patients with IBD compared with controls, whereas level of messenger RNA encoding indoleamine 2,3-dioxygenase-1 was significantly increased. The composition of the fecal microbiota associated with serum levels of tryptophan. Analysis of tryptophan metabolites revealed activation of the kynurenine pathway, based on high levels of quinolinic acid, in patients with IBD compared with controls. Serum concentration of interleukin 22 associated with disease activity in patients with IBD; there was an inverse association between levels of interleukin 22 and serum levels of tryptophan. CONCLUSIONS: In an analysis of serum samples from more than 500 patients with IBD, we observed a negative correlation between serum levels of tryptophan and disease activity. Increased levels of tryptophan metabolites-especially of quinolinic acid-indicated a high activity of tryptophan degradation in patients with active IBD. Tryptophan deficiency could contribute to development of IBD or aggravate disease activity. Interventional clinical studies are needed to determine whether modification of intestinal tryptophan pathways affects the severity of IBD.

Evaluation of interleukin-6 and its soluble receptor components sIL-6R and sgp130 as markers of inflammation in inflammatory bowel diseases.

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.

A Hereditary Form of Small Intestinal Carcinoid Associated With a Germline Mutation in Inositol Polyphosphate Multikinase.

BACKGROUND & AIMS: Small intestinal carcinoids are rare and difficult to diagnose and patients often present with advanced incurable disease. Although the disease occurs sporadically, there have been reports of family clusters. Hereditary small intestinal carcinoid has not been recognized and genetic factors have not been identified. We performed a genetic analysis of families with small intestinal carcinoids to establish a hereditary basis and find genes that might cause this cancer. METHODS: We performed a prospective study of 33 families with at least 2 cases of small intestinal carcinoids. Affected members were characterized clinically and asymptomatic relatives were screened and underwent exploratory laparotomy for suspected tumors. Disease-associated mutations were sought using linkage analysis, whole-exome sequencing, and copy number analyses of germline and tumor DNA collected from members of a single large family. We assessed expression of mutant protein, protein activity, and regulation of apoptosis and senescence in lymphoblasts derived from the cases. RESULTS: Familial and sporadic carcinoids are clinically indistinguishable except for the multiple synchronous primary tumors observed in most familial cases. Nearly 34% of asymptomatic relatives older than age 50 were found to have occult tumors; the tumors were cleared surgically from 87% of these individuals (20 of 23). Linkage analysis and whole-exome sequencing identified a germline 4-bp deletion in the gene inositol polyphosphate multikinase (IPMK), which truncates the protein. This mutation was detected in all 11 individuals with small intestinal carcinoids and in 17 of 35 family members whose carcinoid status was unknown. Mutant IPMK had reduced kinase activity and nuclear localization, compared with the full-length protein. This reduced activation of p53 and increased cell survival. CONCLUSIONS: We found that small intestinal carcinoids can occur as an inherited autosomal-dominant disease. The familial form is characterized by multiple synchronous primary tumors, which might account for 22%-35% of cases previously considered sporadic. Relatives of patients with familial carcinoids should be screened to detect curable early stage disease. IPMK haploinsufficiency promotes carcinoid tumorigenesis.

A trans-acting locus regulates an anti-viral expression network and type 1 diabetes risk.

Combined analyses of gene networks and DNA sequence variation can provide new insights into the aetiology of common diseases that may not be apparent from genome-wide association studies alone. Recent advances in rat genomics are facilitating systems-genetics approaches. Here we report the use of integrated genome-wide approaches across seven rat tissues to identify gene networks and the loci underlying their regulation. We defined an interferon regulatory factor 7 (IRF7)-driven inflammatory network (IDIN) enriched for viral response genes, which represents a molecular biomarker for macrophages and which was regulated in multiple tissues by a locus on rat chromosome 15q25. We show that Epstein-Barr virus induced gene 2 (Ebi2, also known as Gpr183), which lies at this locus and controls B lymphocyte migration, is expressed in macrophages and regulates the IDIN. The human orthologous locus on chromosome 13q32 controlled the human equivalent of the IDIN, which was conserved in monocytes. IDIN genes were more likely to associate with susceptibility to type 1 diabetes (T1D)-a macrophage-associated autoimmune disease-than randomly selected immune response genes (P = 8.85 × 10(-6)). The human locus controlling the IDIN was associated with the risk of T1D at single nucleotide polymorphism rs9585056 (P = 7.0 × 10(-10); odds ratio, 1.15), which was one of five single nucleotide polymorphisms in this region associated with EBI2 (GPR183) expression. These data implicate IRF7 network genes and their regulatory locus in the pathogenesis of T1D.

Association between SNPs in defined functional pathways and risk of early or late toxicity as well as individual radiosensitivity.

BACKGROUND AND PURPOSE: The aim of this study was to determine the impact of functional single nucleotide polymorphism (SNP) pathways involved in the ROS pathway, DNA repair, or TGFB1 signaling on acute or late normal toxicity as well as individual radiosensitivity. MATERIALS AND METHODS: Patients receiving breast-conserving surgery and radiotherapy were examined either for erythema (n = 83), fibrosis (n = 123), or individual radiosensitivity (n = 123). The 17 SNPs analyzed are involved in the ROS pathway (GSTP1, SOD2, NQO1, NOS3, XDH), DNA repair (XRCC1, XRCC3, XRCC6, ERCC2, LIG4, ATM) or TGFB signaling (SKIL, EP300, APC, AXIN1, TGFB1). Associations with biological and clinical endpoints were studied for single SNPs but especially for combinations of SNPs assuming that a SNP is either beneficial or deleterious and needs to be weighted. RESULTS: With one exception, no significant association was seen between a single SNP and the three endpoints studied. No significant associations were also observed when applying a multi-SNP model assuming that each SNP was deleterious. In contrast, significant associations were obtained when SNPs were suggested to be either beneficial or deleterious. These associations increased, when each SNP was weighted individually. Detailed analysis revealed that both erythema and individual radiosensitivity especially depend on SNPs affecting DNA repair and TGFB1 signaling, while SNPs in ROS pathway were of minor importance. CONCLUSION: Functional pathways of SNPs may be used to form a risk score allowing to predict acute and late radiation-induced toxicity but also to unravel the underlying biological mechanisms.