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.

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).

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.

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.

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.

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.

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.

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.

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 clinical endpoints definition ("histologic remission") in clinical trials of UC. Several scores are established in the microscopical assessment of disease activity, but the Nancy index (NI) stands out being a histological index with the least amount of scoring items among these scores. To which extent histologic assessment using NI is affected by interobserver reliability in a real word setting, is poorly understood. We therefore performed a single-center 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, Cohort #2 consisted of 448 biopsies from 235 patients. Two blinded pathologists with different levels of expertise scored all biopsies of 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%-CI: 0.771-0.820]). An improvement of the interobserver-agreement was found with growing 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]). The interobserver discordance was the highest in NI grade 1 (observer 1: n=128; observer 2: n=236). Interobserver discordance was the 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 to assess 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 the observers, NI grade 1 had a poorer agreement-level. This highlights the clinical need to specify histological characteristics leading to NI grade 1.

Genetic loci and prioritization of genes for kidney function decline derived from a meta-analysis of 62 longitudinal genome-wide association studies

Estimated glomerular filtration rate (eGFR) reflects kidney function. Progressive eGFR-decline can lead to kidney failure, necessitating dialysis or transplantation. Hundreds of loci from genome-wide association studies (GWAS) for eGFR help explain population cross section variability. Since the contribution of these or other loci to eGFR-decline remains largely unknown, we derived GWAS for annual eGFR-decline and meta-analyzed 62 longitudinal studies with eGFR assessed twice over time in all 343,339 individuals and in high-risk groups. We also explored different covariate adjustment. Twelve genome-wide significant independent variants for eGFR-decline unadjusted or adjusted for eGFR-baseline (11 novel, one known for this phenotype), including nine variants robustly associated across models were identified. All loci for eGFR-decline were known for cross-sectional eGFR and thus distinguished a subgroup of eGFR loci. Seven of the nine variants showed variant-by-age interaction on eGFR cross section (further about 350,000 individuals), which linked genetic associations for eGFR-decline with age-dependency of genetic cross-section associations. Clinically important were two to four-fold greater genetic effects on eGFR-decline in high-risk subgroups. Five variants associated also with chronic kidney disease progression mapped to genes with functional in-silico evidence (UMOD, SPATA7, GALNTL5, TPPP). An unfavorable versus favorable nine-variant genetic profile showed increased risk odds ratios of 1.35 for kidney failure (95% confidence intervals 1.03-1.77) and 1.27 for acute kidney injury (95% confidence intervals 1.08-1.50) in over 2000 cases each, with matched controls). Thus, we provide a large data resource, genetic loci, and prioritized genes for kidney function decline, which help inform drug development pipelines revealing important insights into the age-dependency of kidney function genetics.

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.

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.

ActiPso: definition of activity types for psoriatic disease: A novel marker for an advanced disease classification

BACKGROUND

Assessment of psoriasis is exclusively done measuring severity using somatic scores such as the psoriasis area and severity index or patient-reported outcomes such as the dermatology life quality index. There is no established tool to measure a patient's individual psoriasis activity over time.

OBJECTIVES

Development of a new tool to classify psoriasis activity types.

METHODS

Open patient interviews were performed and adapted in several steps and by using different groups of patients. Wording of the tool's axis and description how to use it was optimized with the input of patients. The final ActiPso tool was used in a prospective study in psoriasis patients.

RESULTS

Four activity types could be identified describing psoriasis intensity (e.g. severity, itch, pain) over one typical year and an event/trigger type describing flares. In the study in 586 psoriasis patients of the 536 patients eligible for analysis 40.9% self-classified as type 1 ('stable'), 22.6% as type 2 ('unstable'), 30.6% as type 3 ('winter type') and 6.0% as type 4 ('summer type'), respectively. Flares of psoriasis as identified by the event/trigger type were reported in 36.1% of patients with activity type 1, 67.8% with type 2, 73.8% of type 3 and 59.4% of type 4, respectively.

CONCLUSIONS

Interviewed patients were able to describe their course of psoriatic disease and to name potential triggering factors. By doing so, activity types of psoriasis were defined for the first time and the importance of events/triggers for flares described and integrated into ActiPso types as a basis for advanced patient-centric management. A limitation of ActiPso is that in regions with no seasonal variations types 3 and 4 may not apply.

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.

Host traits, lifestyle and environment are associated with human skin bacteria

BACKGROUND

The human skin offers diverse ecosystems for microbial symbionts. However, the factors shaping skin-microbiome interactions are still insufficiently characterized. This contrasts with the broader knowledge about factors influencing gut microbiota.

OBJECTIVES

We aimed to investigate major patterns of association 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), totalling 1794 skin samples. The V1-V2 regions of the 16S ribosomal RNA (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 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.

CONCLUSIONS

We expand the current understanding of factors associated with the skin bacterial community. We show the association of diet with skin bacteria. Finally, we hypothesize that the skin microenvironment and host physiology would shape the skin bacterial community to a greater extent compared with a single skin physiological feature, lifestyle and environmental exposure.

Meta-analysis uncovers genome-wide significant variants for rapid kidney function decline

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/1.73m2/year or more ("Rapid3"; encompassing 34,874 cases, 107,090 controls) and eGFRcrea decline 25% or more and eGFRcrea under 60 mL/min/1.73m2 at follow-up among those with eGFRcrea 60 mL/min/1.73m2 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.

Temporal offset between precipitation and water uptake of Mediterranean pine trees varies with elevation and season

For climate models that use paleo-environment data to predict future climate change, tree-ring isotope variations are one important archive for the reconstruction of paleo-hydrological conditions. Due to the rather complicated pathway of water, starting from precipitation until its uptake by trees and the final incorporation of its components into tree-ring cellulose, a closer inspection of seasonal variations of tree water uptake is important. In this study, branch and needle samples of two pine species (Pinus pinaster and Pinus nigra subsp. laricio) and several water compartments (precipitation, creek, soil) were sampled over a two-year period and analyzed for the temporal variations of their oxygen and hydrogen stable isotope ratios (δ¹⁸O and δ²H) at five sites over an elevation gradient from sea level to around 1600 m a.s.l. on the Mediterranean island of Corsica (France). A new model was established to disentangle temporal relationships of source water uptake of trees. It uses a calculation method that incorporates the two processes mostly expected to affect source water composition: mixing of waters and evaporation. The model results showed that the temporal offset from precipitation to water uptake is not constant and varies with elevation and season. Overall, seasonal source water origin was shown to be dominated by precipitation from autumn and spring. While autumn precipitation was a more important water source for trees growing at mid- (~800-1000 m a.s.l) and high-elevation (~1600 m a.s.l.) sites, trees at coastal sites mostly took up water from late winter and spring. These findings show that predicted decreases in precipitation amounts during the wet season in the Mediterranean can have strong impacts on water availability for pine trees, especially at higher elevations.

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.

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.

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.

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-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.

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.

Genome-wide association meta-analyses and fine-mapping elucidate pathways influencing albuminuria

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.

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.

A catalog of genetic loci associated with kidney function from analyses of a million individuals

Chronic kidney disease (CKD) is responsible for a public health burden with multi-systemic complications. Through trans-ancestry meta-analysis of genome-wide association studies of estimated glomerular filtration rate (eGFR) and independent replication (n = 1,046,070), we identified 264 associated loci (166 new). Of these, 147 were likely to be relevant for kidney function on the basis of associations with the alternative kidney function marker blood urea nitrogen (n = 416,178). Pathway and enrichment analyses, including mouse models with renal phenotypes, support the kidney as the main target organ. A genetic risk score for lower eGFR was associated with clinically diagnosed CKD in 452,264 independent individuals. Colocalization analyses of associations with eGFR among 783,978 European-ancestry individuals and gene expression across 46 human tissues, including tubulo-interstitial and glomerular kidney compartments, identified 17 genes differentially expressed in kidney. Fine-mapping highlighted missense driver variants in 11 genes and kidney-specific regulatory variants. These results provide a comprehensive priority list of molecular targets for translational research.

Atopic Dermatitis Is an IL-13-Dominant Disease with Greater Molecular Heterogeneity Compared to Psoriasis

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.

NGS-based methylation profiling differentiates TCF3-HLF and TCF3-PBX1 positive B-cell acute lymphoblastic leukemia

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.

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.

Topography of essential tremor

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.

Primary and secondary prevention of cardiovascular disease in patients with hyperlipoproteinemia (a)

General lipoprotein (Lp) (a) screening can help to identify patients at high risk for cardiovascular disease. Non-invasive methods allow early detection of clinically asymptomatic incipient atherosclerotic disease. Medical treatment options are still unsatisfactory. Lp(a) apheresis is an established treatment in Germany for secondary prevention of progressive cardiovascular disease. Statin-based lowering of LDL cholesterol and thrombocyte aggregation inhibitors still represent the basis of medical treatment. Target levels for LDL-cholesterol should be modified in patients with hyperlipoproteinemia (a).

Comparison of parametric and machine methods for variable selection in simulated Genetic Analysis Workshop 19 data

Current findings from genetic studies of complex human traits often do not explain a large proportion of the estimated variation of these traits due to genetic factors. This could be, in part, due to overly stringent significance thresholds in traditional statistical methods, such as linear and logistic regression. Machine learning methods, such as Random Forests (RF), are an alternative approach to identify potentially interesting variants. One major issue with these methods is that there is no clear way to distinguish between probable true hits and noise variables based on the importance metric calculated. To this end, we are developing a method called the Relative Recurrency Variable Importance Metric (r2VIM), a RF-based variable selection method. Here, we apply r2VIM to the unrelated Genetic Analysis Workshop 19 data with simulated systolic blood pressure as the phenotype. We compare the number of “true” functional variants identified by r2VIM with those identified by linear regression analyses that use a Bonferroni correction to calculate a significance threshold. Our results show that r2VIM performed comparably to linear regression. Our findings are proof-of-concept for r2VIM, as it identifies a similar number of functional and nonfunctional variants as a more commonly used technique when the optimal importance score threshold is used.

Paternal chronic colitis causes epigenetic inheritance of susceptibility to colitis

Inflammatory bowel disease (IBD) arises by unknown environmental triggers in genetically susceptible individuals. Epigenetic regulation of gene expression may integrate internal and external influences and may thereby modulate disease susceptibility. Epigenetic modification may also affect the germ-line and in certain contexts can be inherited to offspring. This study investigates epigenetic alterations consequent to experimental murine colitis induced by dextran sodium sulphate (DSS), and their paternal transmission to offspring. Genome-wide methylome- and transcriptome-profiling of intestinal epithelial cells (IECs) and sperm cells of males of the F₀ generation, which received either DSS and consequently developed colitis (F₀^DSS), or non-supplemented tap water (F₀^Ctrl) and hence remained healthy, and of their F₁ offspring was performed using reduced representation bisulfite sequencing (RRBS) and RNA-sequencing (RNA-Seq), respectively. Offspring of F₀^DSS males exhibited aberrant methylation and expression patterns of multiple genes, including Igf1r and Nr4a2, which are involved in energy metabolism. Importantly, DSS colitis in F₀^DSS mice was associated with decreased body weight at baseline of their F₁ offspring, and these F₁ mice exhibited increased susceptibility to DSS-induced colitis compared to offspring from F₀^Ctrl males. This study hence demonstrates epigenetic transmissibility of metabolic and inflammatory traits resulting from experimental colitis.

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, β = 0.063, CI_0.95 [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, β = -0.034, CI_0.95 [-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.

r2VIM: A new variable selection method for random forests in genome-wide association studies

BACKGROUND

Machine learning methods and in particular random forests (RFs) are a promising alternative to standard single SNP analyses in genome-wide association studies (GWAS). RFs provide variable importance measures (VIMs) to rank SNPs according to their predictive power. However, in contrast to the established genome-wide significance threshold, no clear criteria exist to determine how many SNPs should be selected for downstream analyses.

RESULTS

We propose a new variable selection approach, recurrent relative variable importance measure (r2VIM). Importance values are calculated relative to an observed minimal importance score for several runs of RF and only SNPs with large relative VIMs in all of the runs are selected as important. Evaluations on simulated GWAS data show that the new method controls the number of false-positives under the null hypothesis. Under a simple alternative hypothesis with several independent main effects it is only slightly less powerful than logistic regression. In an experimental GWAS data set, the same strong signal is identified while the approach selects none of the SNPs in an underpowered GWAS.

CONCLUSIONS

The novel variable selection method r2VIM is a promising extension to standard RF for objectively selecting relevant SNPs in GWAS while controlling the number of false-positive results.

Trans-ancestry genome-wide association study identifies 12 genetic loci influencing blood pressure and implicates a role for DNA methylation

We carried out a trans-ancestry genome-wide association and replication study of blood pressure phenotypes among up to 320,251 individuals of East Asian, European and South Asian ancestry. We find genetic variants at 12 new loci to be associated with blood pressure (P = 3.9 × 10(-11) to 5.0 × 10(-21)). The sentinel blood pressure SNPs are enriched for association with DNA methylation at multiple nearby CpG sites, suggesting that, at some of the loci identified, DNA methylation may lie on the regulatory pathway linking sequence variation to blood pressure. The sentinel SNPs at the 12 new loci point to genes involved in vascular smooth muscle (IGFBP3, KCNK3, PDE3A and PRDM6) and renal (ARHGAP24, OSR1, SLC22A7 and TBX2) function. The new and known genetic variants predict increased left ventricular mass, circulating levels of NT-proBNP, and cardiovascular and all-cause mortality (P = 0.04 to 8.6 × 10(-6)). Our results provide new evidence for the role of DNA methylation in blood pressure regulation.

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.

False-positive rates in two-point parametric linkage analysis

Two-point linkage analyses of whole genome sequence data are a promising approach to identify rare variants that segregate with complex diseases in large pedigrees because, in theory, the causal variants have been genotyped. We used whole genome sequence data and simulated traits provided by Genetic Analysis Workshop 18 to evaluate the proportion of false-positive findings in a binary trait using classic two-point parametric linkage analysis. False-positive genome-wide significant log of odds (LOD) scores were identified in more than 80% of 50 replicates for a binary phenotype generated by dichotomizing a quantitative trait that was simulated with a polygenic component (that was not based on any of the provided whole genome sequence genotypes). In contrast, when the trait was truly nongenetic (created by randomly assigning affected-unaffected status), the number of false-positive results was well controlled. These results suggest that when using two-point linkage analyses on whole genome sequence data, one should carefully examine regions yielding significant two-point LOD scores with multipoint analysis and that a more stringent significance threshold may be needed.

Risk estimation using probability machines

Background

Logistic regression has been the de facto, and often the only, model used in the description and analysis of relationships between a binary outcome and observed features. It is widely used to obtain the conditional probabilities of the outcome given predictors, as well as predictor effect size estimates using conditional odds ratios.

Results

We show how statistical learning machines for binary outcomes, provably consistent for the nonparametric regression problem, can be used to provide both consistent conditional probability estimation and conditional effect size estimates. Effect size estimates from learning machines leverage our understanding of counterfactual arguments central to the interpretation of such estimates. We show that, if the data generating model is logistic, we can recover accurate probability predictions and effect size estimates with nearly the same efficiency as a correct logistic model, both for main effects and interactions. We also propose a method using learning machines to scan for possible interaction effects quickly and efficiently. Simulations using random forest probability machines are presented.

Conclusions

The models we propose make no assumptions about the data structure, and capture the patterns in the data by just specifying the predictors involved and not any particular model structure. So they do not run the same risks of model mis-specification and the resultant estimation biases as a logistic model. This methodology, which we call a “risk machine”, will share properties from the statistical machine that it is derived from.

Genomewide RNAi screen identifies protein kinase Cb and new members of mitogen-activated protein kinase pathway as regulators of melanoma cell growth and metastasis

A large-scale RNAi screen was performed for eight different melanoma cell lines using a pooled whole-genome lentiviral shRNA library. shRNAs affecting proliferation of transduced melanoma cells were negatively selected during 10 days of culture. Overall, 617 shRNAs were identified by microarray hybridization. Pathway analyses identified mitogen-activated protein kinase (MAPK) pathway members such as ERK1/2, JNK1/2 and MAP3K7 and protein kinase C β (PKCβ) as candidate genes. Knockdown of PKCβ most consistently reduced cellular proliferation, colony formation and migratory capacity of melanoma cells and was selected for further validation. PKCβ showed enhanced expression in human primary melanomas and distant metastases as compared with benign melanocytic nevi. Moreover, treatment of melanoma cells with PKCβ-specific inhibitor enzastaurin reduced melanoma cell growth but had only small effects on benign fibroblasts. Finally, PKCβ-shRNA significantly reduced lung colonization capacity of stably transduced melanoma cells in mice. Taken together, this study identified new candidate genes for melanoma cell growth and proliferation. PKCβ seems to play an important role in these processes and might serve as a new target for the treatment of metastatic melanoma.

Chromosomal aneuploidy affects the global proteome equilibrium of colorectal cancer cells

BACKGROUND

Chromosomal aneuploidy has been identified as a prognostic factor in the majority of sporadic carcinomas. However, it is not known how chromosomal aneuploidy affects chromosome-specific protein expression in particular, and the cellular proteome equilibrium in general.

OBJECTIVE

The aim was to detect chromosomal aneuploidy-associated expression changes in cell clones carrying trisomies found in colorectal cancer.

METHODS

We used microcell-mediated chromosomal transfer to generate three artificial trisomic cell clones of the karyotypically stable, diploid, yet mismatch-deficient, colorectal cancer cell line DLD1--each of them harboring one extra copy of either chromosome 3, 7 or 13. Protein expression differences were assessed by two-dimensional gel electrophoresis and mass spectrometry, compared to whole-genome gene expression data, and evaluated by PANTHER classification system and Ingenuity Pathway Analysis (IPA).

RESULTS

In total, 79 differentially expressed proteins were identified between the trisomic clones and the parental cell line. Up-regulation of PCNA and HMGB1 as well as down-regulation of IDH3A and PSMB3 were revealed as trisomy-associated alterations involved in regulating genome stability.

CONCLUSIONS

These results show that trisomies affect the expression of genes and proteins that are not necessarily located on the trisomic chromosome, but reflect a pathway-related alteration of the cellular equilibrium.

Adaptive linear rank tests for eQTL studies

Expression quantitative trait loci (eQTL) studies are performed to identify single-nucleotide polymorphisms that modify average expression values of genes, proteins, or metabolites, depending on the genotype. As expression values are often not normally distributed, statistical methods for eQTL studies should be valid and powerful in these situations. Adaptive tests are promising alternatives to standard approaches, such as the analysis of variance or the Kruskal-Wallis test. In a two-stage procedure, skewness and tail length of the distributions are estimated and used to select one of several linear rank tests. In this study, we compare two adaptive tests that were proposed in the literature using extensive Monte Carlo simulations of a wide range of different symmetric and skewed distributions. We derive a new adaptive test that combines the advantages of both literature-based approaches. The new test does not require the user to specify a distribution. It is slightly less powerful than the locally most powerful rank test for the correct distribution and at least as powerful as the maximin efficiency robust rank test. We illustrate the application of all tests using two examples from different eQTL studies.

Association of single nucleotide polymorphisms in the genes ATM, GSTP1, SOD2, TGFB1, XPD and XRCC1 with risk of severe erythema after breast conserving radiotherapy

PURPOSE

To examine the association of polymorphisms in ATM (codon 158), GSTP1 (codon 105), SOD2 (codon 16), TGFB1 (position -509), XPD (codon 751), and XRCC1 (codon 399) with the risk of severe erythema after breast conserving radiotherapy.

METHODS AND MATERIALS

Retrospective analysis of 83 breast cancer patients treated with breast conserving radiotherapy. A total dose of 50.4 Gy was administered, applying 1.8 Gy/fraction within 42 days. Erythema was evaluated according to the Radiation Therapy Oncology Group (RTOG) score. DNA was extracted from blood samples and polymorphisms were determined using either the Polymerase Chain Reaction based Restriction-Fragment-Length-Polymorphism (PCR-RFL) technique or Matrix-Assisted-Laser-Desorption/Ionization -Time-Of-Flight-Mass-Spectrometry (MALDI-TOF). Relative excess heterozygosity (REH) was investigated to check compatibility of genotype frequencies with Hardy-Weinberg equilibrium (HWE). In addition, p-values from the standard exact HWE lack of fit test were calculated using 100,000 permutations. HWE analyses were performed using R.

RESULTS

Fifty-six percent (46/83) of all patients developed erythema of grade 2 or 3, with this risk being higher for patients with large breast volume (odds ratio, OR = 2.55, 95% confidence interval, CI: 1.03-6.31, p = 0.041). No significant association between SNPs and risk of erythema was found when all patients were considered. However, in patients with small breast volume the TGFB1 SNP was associated with erythema (p = 0.028), whereas the SNP in XPD showed an association in patients with large breast volume (p = 0.046). A risk score based on all risk alleles was neither significant in all patients nor in patients with small or large breast volume. Risk alleles of most SNPs were different compared to a previously identified risk profile for fibrosis.

CONCLUSIONS

The genetic risk profile for erythema appears to be different for patients with small and larger breast volume. This risk profile seems to be specific for erythema as compared to a risk profile for fibrosis.

Integrating genome-wide genetic variations and monocyte expression data reveals trans-regulated gene modules in humans

One major expectation from the transcriptome in humans is to characterize the biological basis of associations identified by genome-wide association studies. So far, few cis expression quantitative trait loci (eQTLs) have been reliably related to disease susceptibility. Trans-regulating mechanisms may play a more prominent role in disease susceptibility. We analyzed 12,808 genes detected in at least 5% of circulating monocyte samples from a population-based sample of 1,490 European unrelated subjects. We applied a method of extraction of expression patterns-independent component analysis-to identify sets of co-regulated genes. These patterns were then related to 675,350 SNPs to identify major trans-acting regulators. We detected three genomic regions significantly associated with co-regulated gene modules. Association of these loci with multiple expression traits was replicated in Cardiogenics, an independent study in which expression profiles of monocytes were available in 758 subjects. The locus 12q13 (lead SNP rs11171739), previously identified as a type 1 diabetes locus, was associated with a pattern including two cis eQTLs, RPS26 and SUOX, and 5 trans eQTLs, one of which (MADCAM1) is a potential candidate for mediating T1D susceptibility. The locus 12q24 (lead SNP rs653178), which has demonstrated extensive disease pleiotropy, including type 1 diabetes, hypertension, and celiac disease, was associated to a pattern strongly correlating to blood pressure level. The strongest trans eQTL in this pattern was CRIP1, a known marker of cellular proliferation in cancer. The locus 12q15 (lead SNP rs11177644) was associated with a pattern driven by two cis eQTLs, LYZ and YEATS4, and including 34 trans eQTLs, several of them tumor-related genes. This study shows that a method exploiting the structure of co-expressions among genes can help identify genomic regions involved in trans regulation of sets of genes and can provide clues for understanding the mechanisms linking genome-wide association loci to disease.