Leonhard Med, a trusted research environment for processing sensitive research data.

This paper provides an overview of the development and operation of the Leonhard Med Trusted Research Environment (TRE) at ETH Zurich. Leonhard Med gives scientific researchers the ability to securely work on sensitive research data. We give an overview of the user perspective, the legal framework for processing sensitive data, design history, current status, and operations. Leonhard Med is an efficient, highly secure Trusted Research Environment for data processing, hosted at ETH Zurich and operated by the Scientific IT Services (SIS) of ETH. It provides a full stack of security controls that allow researchers to store, access, manage, and process sensitive data according to Swiss legislation and ETH Zurich Data Protection policies. In addition, Leonhard Med fulfills the BioMedIT Information Security Policies and is compatible with international data protection laws and therefore can be utilized within the scope of national and international collaboration research projects. Initially designed as a "bare-metal" High-Performance Computing (HPC) platform to achieve maximum performance, Leonhard Med was later re-designed as a virtualized, private cloud platform to offer more flexibility to its customers. Sensitive data can be analyzed in secure, segregated spaces called tenants. Technical and Organizational Measures (TOMs) are in place to assure the confidentiality, integrity, and availability of sensitive data. At the same time, Leonhard Med ensures broad access to cutting-edge research software, especially for the analysis of human -omics data and other personalized health applications.

A cross-omics data analysis strategy for metabolite-microbe pair identification.

Given the pivotal roles of metabolomics and microbiomics, numerous data mining approaches aim to uncover their intricate connections. However, the complex many-to-many associations between metabolome-microbiome profiles yield numerous statistically significant but biologically unvalidated candidates. To address these challenges, we introduce BiOFI, a strategic framework for identifying metabolome-microbiome correlation pairs (Bi-Omics). BiOFI employs a comprehensive scoring system, incorporating intergroup differences, effects on feature correlation networks, and organism abundance. Meanwhile, it establishes a built-in database of metabolite-microbe-KEGG functional pathway linking relationships. Furthermore, BiOFI can rank related feature pairs by combining importance scores and correlation strength. Validation on a dataset of cesarean-section infants confirms the strategy's validity and interpretability. The BiOFI R package is freely accessible at https://github.com/chentianlu/BiOFI.

Decreased circulating IPA levels identify subjects with metabolic comorbidities: A multi-omics study.

In recent years several experimental observations demonstrated that the gut microbiome plays a role in regulating positively or negatively metabolic homeostasis. Indole-3-propionic acid (IPA), a Tryptophan catabolic product mainly produced by C. Sporogenes, has been recently shown to exert either favorable or unfavorable effects in the context of metabolic and cardiovascular diseases. We performed a study to delineate clinical and multiomics characteristics of human subjects characterized by low and high IPA levels. Subjects with low IPA blood levels showed insulin resistance, overweight, low-grade inflammation, and features of metabolic syndrome compared to those with high IPA. Metabolomics analysis revealed that IPA was negatively correlated with leucine, isoleucine, and valine metabolism. Transcriptomics analysis in colon tissue revealed the enrichment of several signaling, regulatory, and metabolic processes. Metagenomics revealed several OTU of ruminococcus, alistipes, blautia, butyrivibrio and akkermansia were significantly enriched in highIPA group while in lowIPA group Escherichia-Shigella, megasphera, and Desulfovibrio genus were more abundant. Next, we tested the hypothesis that treatment with IPA in a mouse model may recapitulate the observations of human subjects, at least in part. We found that a short treatment with IPA (4 days at 20/mg/kg) improved glucose tolerance and Akt phosphorylation in the skeletal muscle level, while regulating blood BCAA levels and gene expression in colon tissue, all consistent with results observed in human subjects stratified for IPA levels. Our results suggest that treatment with IPA may be considered a potential strategy to improve insulin resistance in subjects with dysbiosis.

A polyphenol-rich green Mediterranean diet enhances epigenetic regulatory potential: the DIRECT PLUS randomized controlled trial.

BACKGROUND: The capacity of a polyphenol-enriched diet to modulate the epigenome in vivo is partly unknown. Given the beneficial metabolic effects of a Mediterranean (MED) diet enriched in polyphenols and reduced in red/processed meat (green-MED), as previously been proven by the 18-month DIRECT PLUS randomized controlled trial, we analyzed the effects of the green-MED diet on methylome and transcriptome levels to highlight molecular mechanisms underlying the observed metabolic improvements. METHODS: Our study included 260 participants (baseline BMI = 31.2 kg/m2, age = 5 years) of the DIRECT PLUS trial, initially randomized to one of the intervention arms: A. healthy dietary guidelines (HDG), B. MED (440 mg polyphenols additionally provided by walnuts), C. green-MED (1240 mg polyphenols additionally provided by walnuts, green tea, and Mankai: green duckweed shake). Blood methylome and transcriptome of all study subjects were analyzed at baseline and after completing the 18-month intervention using Illumina EPIC and RNA sequencing technologies. RESULTS: A total of 1573 differentially methylated regions (DMRs; false discovery rate (FDR) < 5 %) were found in the green-MED compared to the MED (177) and HDG (377) diet participants. This corresponded to 1753 differentially expressed genes (DEGs; FDR < 5 %) in the green-MED intervention compared to MED (7) and HDG (738). Consistently, the highest number (6 %) of epigenetic modulating genes was transcriptionally changed in subjects participating in the green-MED intervention. Weighted cluster network analysis relating transcriptional and phenotype changes among participants subjected to the green-MED intervention identified candidate genes associated with serum-folic acid change (all P < 1 × 10-3) and highlighted one module including the KIR3DS1 locus, being negatively associated with the polyphenol changes (e.g. P < 1 × 10-4), but positively associated with the MRI-assessed superficial subcutaneous adipose area-, weight- and waist circumference- 18-month change (all P < 0.05). Among others, this module included the DMR gene Cystathionine Beta-Synthase, playing a major role in homocysteine reduction. CONCLUSIONS: The green-MED high polyphenol diet, rich in green tea and Mankai, renders a high capacity to regulate an individual's epigenome. Our findings suggest epigenetic key drivers such as folate and green diet marker to mediate this capacity and indicate a direct effect of dietary polyphenols on the one­carbon metabolism.

Epigenetic and integrative cross-omics analyses of cerebral white matter hyperintensities on MRI.

Cerebral white matter hyperintensities on MRI are markers of cerebral small vessel disease, a major risk factor for dementia and stroke. Despite the successful identification of multiple genetic variants associated with this highly heritable condition, its genetic architecture remains incompletely understood. More specifically, the role of DNA methylation has received little attention. We investigated the association between white matter hyperintensity burden and DNA methylation in blood at ∼450 000 cytosine-phosphate-guanine (CpG) sites in 9732 middle-aged to older adults from 14 community-based studies. Single CpG and region-based association analyses were carried out. Functional annotation and integrative cross-omics analyses were performed to identify novel genes underlying the relationship between DNA methylation and white matter hyperintensities. We identified 12 single CpG and 46 region-based DNA methylation associations with white matter hyperintensity burden. Our top discovery single CpG, cg24202936 (P = 7.6 × 10-8), was associated with F2 expression in blood (P = 6.4 × 10-5) and co-localized with FOLH1 expression in brain (posterior probability = 0.75). Our top differentially methylated regions were in PRMT1 and in CCDC144NL-AS1, which were also represented in single CpG associations (cg17417856 and cg06809326, respectively). Through Mendelian randomization analyses cg06809326 was putatively associated with white matter hyperintensity burden (P = 0.03) and expression of CCDC144NL-AS1 possibly mediated this association. Differentially methylated region analysis, joint epigenetic association analysis and multi-omics co-localization analysis consistently identified a role of DNA methylation near SH3PXD2A, a locus previously identified in genome-wide association studies of white matter hyperintensities. Gene set enrichment analyses revealed functions of the identified DNA methylation loci in the blood-brain barrier and in the immune response. Integrative cross-omics analysis identified 19 key regulatory genes in two networks related to extracellular matrix organization, and lipid and lipoprotein metabolism. A drug-repositioning analysis indicated antihyperlipidaemic agents, more specifically peroxisome proliferator-activated receptor-alpha, as possible target drugs for white matter hyperintensities. Our epigenome-wide association study and integrative cross-omics analyses implicate novel genes influencing white matter hyperintensity burden, which converged on pathways related to the immune response and to a compromised blood-brain barrier possibly due to disrupted cell-cell and cell-extracellular matrix interactions. The results also suggest that antihyperlipidaemic therapy may contribute to lowering risk for white matter hyperintensities possibly through protection against blood-brain barrier disruption.

GAE-LGA: integration of multi-omics data with graph autoencoders to identify lncRNA-PCG associations.

Long non-coding RNAs (lncRNAs) can disrupt the biological functions of protein-coding genes (PCGs) to cause cancer. However, the relationship between lncRNAs and PCGs remains unclear and difficult to predict. Machine learning has achieved a satisfactory performance in association prediction, but to our knowledge, it is currently less used in lncRNA-PCG association prediction. Therefore, we introduce GAE-LGA, a powerful deep learning model with graph autoencoders as components, to recognize potential lncRNA-PCG associations. GAE-LGA jointly explored lncRNA-PCG learning and cross-omics correlation learning for effective lncRNA-PCG association identification. The functional similarity and multi-omics similarity of lncRNAs and PCGs were accumulated and encoded by graph autoencoders to extract feature representations of lncRNAs and PCGs, which were subsequently used for decoding to obtain candidate lncRNA-PCG pairs. Comprehensive evaluation demonstrated that GAE-LGA can successfully capture lncRNA-PCG associations with strong robustness and outperformed other machine learning-based identification methods. Furthermore, multi-omics features were shown to improve the performance of lncRNA-PCG association identification. In conclusion, GAE-LGA can act as an efficient application for lncRNA-PCG association prediction with the following advantages: It fuses multi-omics information into the similarity network, making the feature representation more accurate; it can predict lncRNA-PCG associations for new lncRNAs and identify potential lncRNA-PCG associations with high accuracy.

Alterations of the Gut Microbiome and Metabolome in Patients With Proliferative Diabetic Retinopathy.

Diabetic retinopathy (DR) has been reported to associate with gut microbiota alterations in murine models and thus "gut-retina-axis" has been proposed. However, the role of gut microbiome and the associated metabolism in DR patients still need to be elucidated. In this study, we collected fecal samples from 45 patients with proliferative diabetic retinopathy (PDR) and 90 matched diabetic patients (1:2 according to age, sex, and duration of diabetes) without DR (NDR) and performed 16S rRNA gene sequencing and untargeted metabolomics. We observed significantly lower bacterial diversity in the PDR group than that in the NDR group. Differential gut bacterial composition was also found, with significant depletion of 22 families (e.g., Coriobacteriaceae, Veillonellaceae, and Streptococcaceae) and enrichment of two families (Burkholderiaceae and Burkholderiales_unclassified) in the PDR group as compared with the NDR group. There were significantly different fecal metabolic features, which were enriched in metabolic pathways such as arachidonic acid and microbial metabolism, between the two groups. Among 36 coabundance metabolite clusters, 11 were positively/negatively contributed to PDR using logistic regression analysis. Fifteen gut microbial families were significantly correlated with the 11 metabolite clusters. Furthermore, a fecal metabolite-based classifier was constructed to distinguish PDR patients from NDR patients accurately. In conclusion, PDR is associated with reduced diversity and altered composition of gut microbiota and specific microbe-metabolite interplay. Our findings help to better understand the disease pathogenesis and provide novel diagnostic and therapeutic targets for PDR.

Variability of multi-omics profiles in a population-based child cohort.

BACKGROUND: Multiple omics technologies are increasingly applied to detect early, subtle molecular responses to environmental stressors for future disease risk prevention. However, there is an urgent need for further evaluation of stability and variability of omics profiles in healthy individuals, especially during childhood. METHODS: We aimed to estimate intra-, inter-individual and cohort variability of multi-omics profiles (blood DNA methylation, gene expression, miRNA, proteins and serum and urine metabolites) measured 6 months apart in 156 healthy children from five European countries. We further performed a multi-omics network analysis to establish clusters of co-varying omics features and assessed the contribution of key variables (including biological traits and sample collection parameters) to omics variability. RESULTS: All omics displayed a large range of intra- and inter-individual variability depending on each omics feature, although all presented a highest median intra-individual variability. DNA methylation was the most stable profile (median 37.6% inter-individual variability) while gene expression was the least stable (6.6%). Among the least stable features, we identified 1% cross-omics co-variation between CpGs and metabolites (e.g. glucose and CpGs related to obesity and type 2 diabetes). Explanatory variables, including age and body mass index (BMI), explained up to 9% of serum metabolite variability. CONCLUSIONS: Methylation and targeted serum metabolomics are the most reliable omics to implement in single time-point measurements in large cross-sectional studies. In the case of metabolomics, sample collection and individual traits (e.g. BMI) are important parameters to control for improved comparability, at the study design or analysis stage. This study will be valuable for the design and interpretation of epidemiological studies that aim to link omics signatures to disease, environmental exposures, or both.

Cross-Omics: Integrating Genomics with Metabolomics in Clinical Diagnostics.

Next-generation sequencing and next-generation metabolic screening are, independently, increasingly applied in clinical diagnostics of inborn errors of metabolism (IEM). Integrated into a single bioinformatic method, these two -omics technologies can potentially further improve the diagnostic yield for IEM. Here, we present cross-omics: a method that uses untargeted metabolomics results of patient's dried blood spots (DBSs), indicated by Z-scores and mapped onto human metabolic pathways, to prioritize potentially affected genes. We demonstrate the optimization of three parameters: (1) maximum distance to the primary reaction of the affected protein, (2) an extension stringency threshold reflecting in how many reactions a metabolite can participate, to be able to extend the metabolite set associated with a certain gene, and (3) a biochemical stringency threshold reflecting paired Z-score thresholds for untargeted metabolomics results. Patients with known IEMs were included. We performed untargeted metabolomics on 168 DBSs of 97 patients with 46 different disease-causing genes, and we simulated their whole-exome sequencing results in silico. We showed that for accurate prioritization of disease-causing genes in IEM, it is essential to take into account not only the primary reaction of the affected protein but a larger network of potentially affected metabolites, multiple steps away from the primary reaction.

Transcriptomic and proteomic profiling reveals the intestinal immunotoxicity induced by aflatoxin M1 and ochratoxin A.

Mycotoxins-contaminated milk could threaten human health; therefore, it is necessary to demonstrate the toxicological effect of mycotoxins in milk. Most recently, researchers have paid more attention to the immunotoxic effects of the individual cereal-contaminating mycotoxins, namely, zearalenone and deoxynivalenol. However, there is scant information about the intestinal immunotoxicity of aflatoxin M1 (AFM1), let alone that of a combination of AFM1 and ochratoxin A (OTA), which often co-occur in milk. To reveal the inflammatory response caused by these mycotoxins, expression of inflammation-related genes in differentiated Caco-2 cells was analyzed, demonstrating a synergistic effect of the mixture of AFM1 (4 µg/mL) and OTA (4 µg/mL). Integrative transcriptomic and proteomic analyses were also performed. A cross-omics analysis identified several mechanisms underlying this synergy: (i) compared with stimulation with either compound alone, combined use resulted in stronger induction of proteins involved in immunity-related pathways; (ii) combination of the two agents targeted different points in the same pathways; and (iii) combination of the two agents activated specific inflammation-related pathways. These results suggested that combined use of AFM1 and OTA might exacerbate intestinal inflammation, indicating that regulatory authorities should pay more attention to food contamination by multiple mycotoxins when performing risk assessments.

Cross-omics analysis revealed gut microbiome-related metabolic pathways underlying atherosclerosis development after antibiotics treatment.

OBJECTIVE: The metabolic influence of gut microbiota plays a pivotal role in the pathogenesis of cardiometabolic diseases. Antibiotics affect intestinal bacterial diversity, and long-term usage has been identified as an independent risk factor for atherosclerosis-driven events. The aim of this study was to explore the interaction between gut dysbiosis by antibiotics and metabolic pathways with the impact on atherosclerosis development. METHODS: We combined oral antibiotics with different diets in an Apolipoprotein E-knockout mouse model linking gut microbiota to atherosclerotic lesion development via an integrative cross-omics approach including serum metabolomics and cecal 16S rRNA targeted metagenomic sequencing. We further investigated patients with carotid atherosclerosis compared to control subjects with comparable cardiovascular risk. RESULTS: Here, we show that increased atherosclerosis by antibiotics was connected to a loss of intestinal diversity and alterations of microbial metabolic functional capacity with a major impact on the host serum metabolome. Pathways that were modulated by antibiotics and connected to atherosclerosis included diminished tryptophan and disturbed lipid metabolism. These pathways were related to the reduction of certain members of Bacteroidetes and Clostridia by antibiotics in the gut. Patients with atherosclerosis presented a similar metabolic signature as those induced by antibiotics in our mouse model. CONCLUSION: Taken together, this work provides insights into the complex interaction between intestinal microbiota and host metabolism. Our data highlight that detrimental effects of antibiotics on the gut flora are connected to a pro-atherogenic metabolic phenotype beyond classical risk factors.

CUBIC: an atlas of genetic architecture promises directed maize improvement.

BACKGROUND: Identifying genotype-phenotype links and causative genes from quantitative trait loci (QTL) is challenging for complex agronomically important traits. To accelerate maize gene discovery and breeding, we present the Complete-diallel design plus Unbalanced Breeding-like Inter-Cross (CUBIC) population, consisting of 1404 individuals created by extensively inter-crossing 24 widely used Chinese maize founders. RESULTS: Hundreds of QTL for 23 agronomic traits are uncovered with 14 million high-quality SNPs and a high-resolution identity-by-descent map, which account for an average of 75% of the heritability for each trait. We find epistasis contributes to phenotypic variance widely. Integrative cross-population analysis and cross-omics mapping allow effective and rapid discovery of underlying genes, validated here with a case study on leaf width. CONCLUSIONS: Through the integration of experimental genetics and genomics, our study provides useful resources and gene mining strategies to explore complex quantitative traits.

Transcriptome-proteome integration of archival human renal cell carcinoma biopsies enables identification of molecular mechanisms.

Renal cell cancer is among the most common forms of cancer in humans, with around 35,000 deaths attributed to kidney carcinoma in the European Union in 2012 alone. Clear cell renal cell carcinoma (ccRCC) represents the most common form of kidney cancer and the most lethal of all genitourinary cancers. Here, we apply omics technologies to archival core biopsies to investigate the biology underlying ccRCC. Knowledge of these underlying processes should be useful for the discovery and/or confirmation of novel therapeutic approaches and ccRCC biomarker development. From partial or full nephrectomies of 11 patients, paired core biopsies of ccRCC-affected tissue and adjacent ("peritumorous") nontumor tissue were both sampled and subjected to proteomics analyses. We combined proteomics results with our published mRNA sequencing data from the same patients and with published miRNA sequencing data from an overlapping patient cohort from our institution. Statistical analysis and pathway analysis were performed with JMP Genomics and Ingenuity Pathway Analysis (IPA), respectively. Proteomics analysis confirmed the involvement of metabolism and oxidative stress-related pathways in ccRCC, whereas the most affected pathways in the mRNA sequencing data were related to the immune system. Unlike proteomics or mRNA sequencing alone, a combinatorial cross-omics pathway analysis approach captured a broad spectrum of biological processes underlying ccRCC, such as mitochondrial damage, repression of apoptosis, and immune system pathways. Sirtuins, immunoproteasome genes, and CD74 are proposed as potential targets for the treatment of ccRCC.

ProfileDB: a resource for proteomics and cross-omics biomarker discovery.

The increasing size and complexity of high-throughput datasets pose a growing challenge for researchers. Often very different (cross-omics) techniques with individual data analysis pipelines are employed making a unified biomarker discovery strategy and a direct comparison of different experiments difficult and time consuming. Here we present the comprehensive web-based application ProfileDB. The application is designed to integrate data from different high-throughput 'omics' data types (Transcriptomics, Proteomics, Metabolomics) with clinical parameters and prior knowledge on pathways and ontologies. Beyond data storage, ProfileDB provides a set of dedicated tools for study inspection and data visualization. The user can gain insights into a complex experiment with just a few mouse clicks. We will demonstrate the application by presenting typical use cases for the identification of proteomics biomarkers. All presented analyses can be reproduced using the public ProfileDB web server. The ProfileDB application is available by standard browser (Firefox 18+, Internet Explorer Version 9+) technology via http://profileDB.-microdiscovery.de/ (login and pass-word: profileDB). The installation contains several public datasets including different cross-'omics' experiments. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge.