Evaluation of network-guided random forest for disease gene discovery.

BACKGROUND: Gene network information is believed to be beneficial for disease module and pathway identification, but has not been explicitly utilized in the standard random forest (RF) algorithm for gene expression data analysis. We investigate the performance of a network-guided RF where the network information is summarized into a sampling probability of predictor variables which is further used in the construction of the RF. RESULTS: Our simulation results suggest that network-guided RF does not provide better disease prediction than the standard RF. In terms of disease gene discovery, if disease genes form module(s), network-guided RF identifies them more accurately. In addition, when disease status is independent from genes in the given network, spurious gene selection results can occur when using network information, especially on hub genes. Our empirical analysis on two balanced microarray and RNA-Seq breast cancer datasets from The Cancer Genome Atlas (TCGA) for classification of progesterone receptor (PR) status also demonstrates that network-guided RF can identify genes from PGR-related pathways, which leads to a better connected module of identified genes. CONCLUSIONS: Gene networks can provide additional information to aid the gene expression analysis for disease module and pathway identification. But they need to be used with caution and validation on the results need to be carried out to guard against spurious gene selection. More robust approaches to incorporate such information into RF construction also warrant further study.

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.

Tryptophan degradation as a systems phenomenon in inflammation - an analysis across 13 chronic inflammatory diseases.

BACKGROUND: Chronic inflammatory diseases (CIDs) are systems disorders that affect diverse organs including the intestine, joints and skin. The essential amino acid tryptophan (Trp) can be broken down to various bioactive derivatives important for immune regulation. Increased Trp catabolism has been observed in some CIDs, so we aimed to characterise the specificity and extent of Trp degradation as a systems phenomenon across CIDs. METHODS: We used high performance liquid chromatography and targeted mass spectrometry to assess the serum and stool levels of Trp and Trp derivatives. Our retrospective study incorporates both cross-sectional and longitudinal components, as we have included a healthy population as a reference and there are also multiple observations per patient over time. FINDINGS: We found reduced serum Trp levels across the majority of CIDs, and a prevailing negative relationship between Trp and systemic inflammatory marker C-reactive protein (CRP). Notably, serum Trp was low in several CIDs even in the absence of measurable systemic inflammation. Increases in the kynurenine-to-Trp ratio (Kyn:Trp) suggest that these changes result from increased degradation along the kynurenine pathway. INTERPRETATION: Increases in Kyn:Trp indicate the kynurenine pathway as a major route for CID-related Trp metabolism disruption and the specificity of the network changes indicates excessive Trp degradation relative to other proteogenic amino acids. Our results suggest that increased Trp catabolism is a common metabolic occurrence in CIDs that may directly affect systemic immunity. FUNDING: This work was supported by the DFG Cluster of Excellence 2167 "Precision medicine in chronic inflammation" (KA, SSchr, PR, BH, SWa), the BMBF (e:Med Juniorverbund "Try-IBD" 01ZX1915A and 01ZX2215, the e:Med Network iTREAT 01ZX2202A, and GUIDE-IBD 031L0188A), EKFS (2020_EKCS.11, KA), DFG RU5042 (PR, KA), and Innovative Medicines Initiative 2 Joint Undertakings ("Taxonomy, Treatments, Targets and Remission", 831434, "ImmUniverse", 853995, "BIOMAP", 821511).

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.

Network-based quantitative trait linkage analysis of microbiome composition in inflammatory bowel disease families.

Introduction: Inflammatory bowel disease (IBD) is characterized by a dysbiosis of the gut microbiome that results from the interaction of the constituting taxa with one another, and with the host. At the same time, host genetic variation is associated with both IBD risk and microbiome composition. Methods: In the present study, we defined quantitative traits (QTs) from modules identified in microbial co-occurrence networks to measure the inter-individual consistency of microbial abundance and subjected these QTs to a genome-wide quantitative trait locus (QTL) linkage analysis. Results: Four microbial network modules were consistently identified in two cohorts of healthy individuals, but three of the corresponding QTs differed significantly between IBD patients and unaffected individuals. The QTL linkage analysis was performed in a sub-sample of the Kiel IBD family cohort (IBD-KC), an ongoing study of 256 German families comprising 455 IBD patients and 575 first- and second-degree, non-affected relatives. The analysis revealed five chromosomal regions linked to one of three microbial module QTs, namely on chromosomes 3 (spanning 10.79 cM) and 11 (6.69 cM) for the first module, chr9 (0.13 cM) and chr16 (1.20 cM) for the second module, and chr13 (19.98 cM) for the third module. None of these loci have been implicated in a microbial phenotype before. Discussion: Our study illustrates the benefit of combining network and family-based linkage analysis to identify novel genetic drivers of microbiome composition in a specific disease context.

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.

Interobserver Reliability of the Nancy Index for Ulcerative Colitis: An Assessment of the Practicability and Ease of Use in a Single-Centre Real-World Setting.

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.

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.

Linkage analysis identifies novel genetic modifiers of microbiome traits in families with inflammatory bowel disease.

Dysbiosis of the gut microbiome is a hallmark of inflammatory bowel disease (IBD) and both, IBD risk and microbiome composition, have been found to be associated with genetic variation. Using data from families of IBD patients, we examined the association between genetic and microbiome similarity in a specific IBD context, followed by a genome-wide quantitative trait locus (QTL) linkage analysis of various microbiome traits using the same data. SNP genotypes as well as gut microbiome and phenotype data were obtained from the Kiel IBD family cohort (IBD-KC). The IBD-KC is an ongoing prospective study in Germany currently comprising 256 families with 455 IBD patients and 575 first- and second-degree relatives. Initially focusing upon known IBD risk loci, we noted a statistically significant (FDR<0.05) association between genetic similarity at SNP rs11741861 and overall microbiome dissimilarity among pairs of relatives discordant for IBD. In a genome-wide QTL analysis, 12 chromosomal regions were found to be linked to the abundance of one of seven microbial genera, namely Barnesiella (chromosome 4, region spanning 10.34 cM), Clostridium_XIVa (chr4, 3.86 cM; chr14, two regions spanning 7.05 and 13.02 cM respectively), Pseudoflavonifractor (chr7, 12.80 cM) Parasutterella (chr14, 8.26 cM), Ruminococcus (chr16, two overlapping regions spanning 8.01 and 16.87 cM, respectively), Roseburia (chr19, 7.99 cM), and Odoribacter (chr22, three regions spanning 0.89, 5.57 and 1.71 cM, respectively), as well as the Shannon index of α diversity (chr3, 1.47 cM). Our study thus shows that, in families of IBD patients, pairwise genetic similarity for at least one IBD risk locus is associated with overall microbiome dissimilarity among discordant pairs of relatives, and that hitherto unknown genetic modifiers of microbiome traits are located in at least 12 human genomic regions.

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.

Cofactors of drug hypersensitivity-A monocenter retrospective analysis.

Background: Drug hypersensitivity reactions (DHRs) are major medical problems that influence the treatment of patients by both under- and overdiagnosis. Still, little is known about the role of predisposing or protecting cofactors of DHR. Objective: This study aims to determine drug-specific cofactors in patients with DHR. Methods: Retrospective file chart analysis of inpatients with suspected DHR in our department between 2015 and 2020 was performed. Descriptive statistics and multiple logistic regression were conducted for the estimation and statistical interference. Results: DHRs were suspected in 393 patients with 678 culprit drugs. In 183 cases, drug hypersensitivities were confirmed, mostly against nonopioid analgesic drugs and antibiotics. Multiple logistic regression analysis identified a positive association of antibiotic hypersensitivity with obesity [odds ratio (OR) 5.75, average marginal effect (AME) +24.4%] and age and a negative association with arterial hypertension, female sex, elevated immunoglobulin E (IgE), and allergic rhinitis. Hypersensitivity to nonopioid analgesics was associated with atopic dermatitis (OR 10.28, AME +28.5%), elevated IgE, and arterial hypertension. Conclusions: Drug-specific cofactors of DHR include obesity for antibiotics and atopic dermatitis for nonopioid analgesics, the knowledge of which may improve the risk calculation for drug provocation tests.

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.

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.

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.

A fungal pathogen induces systemic susceptibility and systemic shifts in wheat metabolome and microbiome composition.

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.

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.

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.

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.

Heterogeneous intracellular TRAIL-receptor distribution predicts poor outcome in breast cancer patients.

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.