A multi-omics data analysis workflow packaged as a FAIR Digital Object.

BACKGROUND: Applying good data management and FAIR (Findable, Accessible, Interoperable, and Reusable) data principles in research projects can help disentangle knowledge discovery, study result reproducibility, and data reuse in future studies. Based on the concepts of the original FAIR principles for research data, FAIR principles for research software were recently proposed. FAIR Digital Objects enable discovery and reuse of Research Objects, including computational workflows for both humans and machines. Practical examples can help promote the adoption of FAIR practices for computational workflows in the research community. We developed a multi-omics data analysis workflow implementing FAIR practices to share it as a FAIR Digital Object. FINDINGS: We conducted a case study investigating shared patterns between multi-omics data and childhood externalizing behavior. The analysis workflow was implemented as a modular pipeline in the workflow manager Nextflow, including containers with software dependencies. We adhered to software development practices like version control, documentation, and licensing. Finally, the workflow was described with rich semantic metadata, packaged as a Research Object Crate, and shared via WorkflowHub. CONCLUSIONS: Along with the packaged multi-omics data analysis workflow, we share our experiences adopting various FAIR practices and creating a FAIR Digital Object. We hope our experiences can help other researchers who develop omics data analysis workflows to turn FAIR principles into practice.

Validation of disease-specific biomarkers for the early detection of bronchopulmonary dysplasia.

OBJECTIVE: To demonstrate and validate the improvement of current risk stratification for bronchopulmonary dysplasia (BPD) early after birth by plasma protein markers (sialic acid-binding Ig-like lectin 14 (SIGLEC-14), basal cell adhesion molecule (BCAM), angiopoietin-like 3 protein (ANGPTL-3)) in extremely premature infants. METHODS AND RESULTS: Proteome screening in first-week-of-life plasma samples of n = 52 preterm infants <32 weeks gestational age (GA) on two proteomic platforms (SomaLogic®, Olink-Proteomics®) confirmed three biomarkers with significant predictive power: BCAM, SIGLEC-14, and ANGPTL-3. We demonstrate high sensitivity (0.92) and specificity (0.86) under consideration of GA, show the proteins' critical contribution to the predictive power of known clinical risk factors, e.g., birth weight and GA, and predicted the duration of mechanical ventilation, oxygen supplementation, as well as neonatal intensive care stay. We confirmed significant predictive power for BPD cases when switching to a clinically applicable method (enzyme-linked immunosorbent assay) in an independent sample set (n = 25, p < 0.001) and demonstrated disease specificity in different cohorts of neonatal and adult lung disease. CONCLUSION: While successfully addressing typical challenges of clinical biomarker studies, we demonstrated the potential of BCAM, SIGLEC-14, and ANGPTL-3 to inform future clinical decision making in the preterm infant at risk for BPD. TRIAL REGISTRATION: Deutsches Register Klinische Studien (DRKS) No. 00004600; https://www.drks.de . IMPACT: The urgent need for biomarkers that enable early decision making and personalized monitoring strategies in preterm infants with BPD is challenged by targeted marker analyses, cohort size, and disease heterogeneity. We demonstrate the potential of the plasma proteins BCAM, SIGLEC-14, and ANGPTL-3 to identify infants with BPD early after birth while improving the predictive power of clinical variables, confirming the robustness toward proteome assays and proving disease specificity. Our comprehensive analysis enables a phase-III clinical trial that allows full implementation of the biomarkers into clinical routine to enable early risk stratification in preterms with BPD.

The Colorectal Cancer Lipidome: Identification of a Robust Tumor-Specific Lipid Species Signature.

OBJECTIVE: Lipidomic changes were causally linked to metabolic diseases, but the scenario for colorectal cancer (CRC) is less clear. We investigated the CRC lipidome for putative tumor-specific alterations through analysis of 3 independent retrospective patient cohorts from 2 clinical centers, to derive a clinically useful signature. DESIGN: Quantitative comprehensive lipidomic analysis was performed using direct infusion electrospray ionization coupled with tandem mass spectrometry (ESI-MS/MS) and high-resolution mass spectrometry (HR-MS) on matched nondiseased mucosa and tumor tissue in a discovery cohort (n = 106). Results were validated in 2 independent cohorts (n = 28, and n = 20), associated with genomic and clinical data, and lipidomic data from a genetic mouse tumor model (Apc1638N). RESULTS: Significant differences were found between tumor and normal tissue for glycero-, glycerophospho-, and sphingolipids in the discovery cohort. Comparison to the validation collectives unveiled that glycerophospholipids showed high interpatient variation and were strongly affected by preanalytical conditions, whereas glycero- and sphingolipids appeared more robust. Signatures of sphingomyelin and triacylglycerol (TG) species significantly differentiated cancerous from nondiseased tissue in both validation studies. Moreover, lipogenic enzymes were significantly up-regulated in CRC, and FASN gene expression was prognostically detrimental. The TG profile was significantly associated with postoperative disease-free survival and lymphovascular invasion, and was essentially conserved in murine digestive cancer, but not associated with microsatellite status, KRAS or BRAF mutations, or T-cell infiltration. CONCLUSION: Analysis of the CRC lipidome revealed a robust TG-species signature with prognostic potential. A better understanding of the cancer-associated glycerolipid and sphingolipid metabolism may lead to novel therapeutic strategies.

Chemical Diversity and Classification of Secondary Metabolites in Nine Bryophyte Species.

The central aim in ecometabolomics and chemical ecology is to pinpoint chemical features that explain molecular functioning. The greatest challenge is the identification of compounds due to the lack of constitutive reference spectra, the large number of completely unknown compounds, and bioinformatic methods to analyze the big data. In this study we present an interdisciplinary methodological framework that extends ultra-performance liquid chromatography coupled to electrospray ionization quadrupole time-of-flight mass spectrometry (UPLC/ESI-QTOF-MS) with data-dependent acquisition (DDA-MS) and the automated in silico classification of fragment peaks into compound classes. We synthesize findings from a prior study that explored the influence of seasonal variations on the chemodiversity of secondary metabolites in nine bryophyte species. Here we reuse and extend the representative dataset with DDA-MS data. Hierarchical clustering, heatmaps, dbRDA, and ANOVA with post-hoc Tukey HSD were used to determine relationships of the study factors species, seasons, and ecological characteristics. The tested bryophytes showed species-specific metabolic responses to seasonal variations (50% vs. 5% of explained variation). Marchantia polymorpha, Plagiomnium undulatum, and Polytrichum strictum were biochemically most diverse and unique. Flavonoids and sesquiterpenoids were upregulated in all bryophytes in the growing seasons. We identified ecological functioning of compound classes indicating light protection (flavonoids), biotic and pathogen interactions (sesquiterpenoids, flavonoids), low temperature and desiccation tolerance (glycosides, sesquiterpenoids, anthocyanins, lactones), and moss growth supporting anatomic structures (few methoxyphenols and cinnamic acids as part of proto-lignin constituents). The reusable bioinformatic framework of this study can differentiate species based on automated compound classification. Our study allows detailed insights into the ecological roles of biochemical constituents of bryophytes with regard to seasonal variations. We demonstrate that compound classification can be improved with adding constitutive reference spectra to existing spectral libraries. We also show that generalization on compound classes improves our understanding of molecular ecological functioning and can be used to generate new research hypotheses.

Allele-specific methylation of type 1 diabetes susceptibility genes.

The susceptibility to autoimmune diseases is influenced by genes encoding major histocompatibility complex (MHC) proteins. By examining the epigenetic methylation maps of cord blood samples, we found marked differences in the methylation status of CpG sites within the MHC genes (cis-metQTLs) between carriers of the type 1 diabetes risk haplotypes HLA-DRB1*03-DQA1*0501-DQB1*0201 (DR3-DQ2) and HLA-DRB1*04-DQA1*0301-DQB1*0302 (DR4-DQ8). These differences were found in children and adults, and were accompanied by reduced HLA-DR protein expression in immune cells with the HLA-DR3-DQ2 haplotype. Extensive cis-metQTLs were identified in all 45 immune and non-immune type 1 diabetes susceptibility genes analyzed in this study. We observed and validated a novel association between the methylation status of CpG sites within the LDHC gene and the development of insulin autoantibodies in early childhood in children who are carriers of the highest type 1 diabetes risk genotype. Functionally relevant epigenetic changes in susceptibility genes may represent therapeutic targets for type 1 diabetes.

Loss of adipose triglyceride lipase is associated with human cancer and induces mouse pulmonary neoplasia.

Metabolic reprogramming is a hallmark of cancer. Understanding cancer metabolism is instrumental to devise innovative therapeutic approaches. Anabolic metabolism, including the induction of lipogenic enzymes, is a key feature of proliferating cells. Here, we report a novel tumor suppressive function for adipose triglyceride lipase (ATGL), the rate limiting enzyme in the triglyceride hydrolysis cascade.In immunohistochemical analysis, non-small cell lung cancers, pancreatic adenocarcinoma as well as leiomyosarcoma showed significantly reduced levels of ATGL protein compared to corresponding normal tissues. The ATGL gene was frequently deleted in various forms of cancers. Low levels of ATGL mRNA correlated with significantly reduced survival in patients with ovarian, breast, gastric and non-small cell lung cancers. Remarkably, pulmonary neoplasia including invasive adenocarcinoma developed spontaneously in mice lacking ATGL pointing to an important role for this lipase in controlling tumor development.Loss of ATGL, as detected in several forms of human cancer, induces spontaneous development of pulmonary neoplasia in a mouse model. Our results, therefore, suggest a novel tumor suppressor function for ATGL and contribute to the understanding of cancer metabolism. We propose to evaluate loss of ATGL protein expression for the diagnosis of malignant tumors. Finally, modulation of the lipolytic pathway may represent a novel therapeutic approach in the treatment of human cancer.

Expression quantitative trait Loci acting across multiple tissues are enriched in inherited risk for coronary artery disease.

BACKGROUND: Despite recent discoveries of new genetic risk factors, the majority of risk for coronary artery disease (CAD) remains elusive. As the most proximal sensor of DNA variation, RNA abundance can help identify subpopulations of genetic variants active in and across tissues mediating CAD risk through gene expression. METHODS AND RESULTS: By generating new genomic data on DNA and RNA samples from the Stockholm Atherosclerosis Gene Expression (STAGE) study, 8156 cis-acting expression quantitative trait loci (eQTLs) for 6450 genes across 7 CAD-relevant tissues were detected. The inherited risk enrichments of tissue-defined sets of these eQTLs were assessed using 2 independent genome-wide association data sets. eQTLs acting across increasing numbers of tissues were found increasingly enriched for CAD risk and resided at regulatory hot spots. The risk enrichment of 42 eQTLs acting across 5 to 6 tissues was particularly high (≤7.3-fold) and confirmed in the combined genome-wide association data from Coronary Artery Disease Genome Wide Replication And Meta-Analysis Consortium. Sixteen of the 42 eQTLs associated with 19 master regulatory genes and 29 downstream gene sets (n>30) were further risk enriched comparable to that of the 153 genome-wide association risk single-nucleotide polymorphisms established for CAD (8.4-fold versus 10-fold). Three gene sets, governed by the master regulators FLYWCH1, PSORSIC3, and G3BP1, segregated the STAGE patients according to extent of CAD, and small interfering RNA targeting of these master regulators affected cholesterol-ester accumulation in foam cells of the THP1 monocytic cell line. CONCLUSIONS: eQTLs acting across multiple tissues are significant carriers of inherited risk for CAD. FLYWCH1, PSORSIC3, and G3BP1 are novel master regulatory genes in CAD that may be suitable targets.

Abundant local interactions in the 4p16.1 region suggest functional mechanisms underlying SLC2A9 associations with human serum uric acid.

Human serum uric acid concentration (SUA) is a complex trait. A recent meta-analysis of multiple genome-wide association studies (GWAS) identified 28 loci associated with SUA jointly explaining only 7.7% of the SUA variance, with 3.4% explained by two major loci (SLC2A9 and ABCG2). Here we examined whether gene-gene interactions had any roles in regulating SUA using two large GWAS cohorts included in the meta-analysis [the Atherosclerosis Risk in Communities study cohort (ARIC) and the Framingham Heart Study cohort (FHS)]. We found abundant genome-wide significant local interactions in ARIC in the 4p16.1 region located mostly in an intergenic area near SLC2A9 that were not driven by linkage disequilibrium and were replicated in FHS. Taking the forward selection approach, we constructed a model of five SNPs with marginal effects and three epistatic SNP pairs in ARIC-three marginal SNPs were located within SLC2A9 and the remaining SNPs were all located in the nearby intergenic area. The full model explained 1.5% more SUA variance than that explained by the lead SNP alone, but only 0.3% was contributed by the marginal and epistatic effects of the SNPs in the intergenic area. Functional analysis revealed strong evidence that the epistatically interacting SNPs in the intergenic area were unusually enriched at enhancers active in ENCODE hepatic (HepG2, P = 4.7E-05) and precursor red blood (K562, P = 5.0E-06) cells, putatively regulating transcription of WDR1 and SLC2A9. These results suggest that exploring epistatic interactions is valuable in uncovering the complex functional mechanisms underlying the 4p16.1 region.

The genomic signature of trait-associated variants.

BACKGROUND: Genome-wide association studies have identified thousands of SNP variants associated with hundreds of phenotypes. For most associations the causal variants and the molecular mechanisms underlying pathogenesis remain unknown. Exploration of the underlying functional annotations of trait-associated loci has thrown some light on their potential roles in pathogenesis. However, there are some shortcomings of the methods used to date, which may undermine efforts to prioritize variants for further analyses. Here, we introduce and apply novel methods to rigorously identify annotation classes showing enrichment or depletion of trait-associated variants taking into account the underlying associations due to co-location of different functional annotations and linkage disequilibrium. RESULTS: We assessed enrichment and depletion of variants in publicly available annotation classes such as genic regions, regulatory features, measures of conservation, and patterns of histone modifications. We used logistic regression to build a multivariate model that identified the most influential functional annotations for trait-association status of genome-wide significant variants. SNPs associated with all of the enriched annotations were 8 times more likely to be trait-associated variants than SNPs annotated with none of them. Annotations associated with chromatin state together with prior knowledge of the existence of a local expression QTL (eQTL) were the most important factors in the final logistic regression model. Surprisingly, despite the widespread use of evolutionary conservation to prioritize variants for study we find only modest enrichment of trait-associated SNPs in conserved regions. CONCLUSION: We established odds ratios of functional annotations that are more likely to contain significantly trait-associated SNPs, for the purpose of prioritizing GWAS hits for further studies. Additionally, we estimated the relative and combined influence of the different genomic annotations, which may facilitate future prioritization methods by adding substantial information.