ArticlePLoS biology2012
Stitching together multiple data dimensions reveals interacting metabolomic and transcriptomic networks that modulate cell regulation.
Article in PLoS biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 119 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
119 citing papers in PubMed, 3 syntheses or guidelines pooled it, 210 citations in OpenAlex.
- The landscape of multiscale transcriptomic networks and key regulators in Parkinson's disease.Nature communications · 2019Pooled it
- A whole-blood transcriptome meta-analysis identifies gene expression signatures of cigarette smoking.Human molecular genetics · 2016Pooled it
- Genomic and network patterns of schizophrenia genetic variation in human evolutionary accelerated regions.Molecular biology and evolution · 2015Pooled it
- Meditation and vacation effects have an impact on disease-associated molecular phenotypes.Translational psychiatry · 2016Trial
- Genotype×age interaction in human transcriptional ageing.Mechanisms of ageing and developmentTrial
- Enhancing genomic prediction accuracy in Huaxi cattle through integration of transcriptomic data and a self-attention-based SNP selection strategy.BMC bioinformatics · 2026Article
- Distinct blood transcriptomic profiling underlying asthma establishment and exacerbation.The journal of allergy and clinical immunology. Global · 2026Article
- Unraveling mechanisms of allergen sensitization and allergic rhinitis via the nasal transcriptome.The Journal of allergy and clinical immunology · 2026Article
- Airway transcriptome networks for ozone and PMGenome medicine · 2025Article
- Integrating multi-omics and machine learning for disease resistance prediction in legumes.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Causal network inference of cis- and trans-gene regulation of expression quantitative trait loci across human tissues.Genetics · 2025Article
- Learning directed acyclic graphs for ligands and receptors based on spatially resolved transcriptomic data of ovarian cancer.Briefings in bioinformatics · 2025Article
- Comprehensive applications of the artificial intelligence technology in new drug research and development.Health information science and systems · 2024Review
- Regulation of cell distancing in peri-plaque glial nets by Plexin-B1 affects glial activation and amyloid compaction in Alzheimer's disease.Nature neuroscience · 2024Article
- Joint transcriptomic and cytometric study of children with peanut allergy reveals molecular and cellular cross talk in reaction thresholds.The Journal of allergy and clinical immunology · 2024Article
- Article
- Integrated study of systemic and local airway transcriptomes in asthma reveals causal mediation of systemic effects by airway key drivers.Genome medicine · 2023Article
- Network Preservation Analysis Reveals Dysregulated Metabolic Pathways in Human Vascular Smooth Muscle Cell Phenotypic Switching.Circulation. Genomic and precision medicine · 2023Article
- Predictive network analysis identifies JMJD6 and other potential key drivers in Alzheimer's disease.Communications biology · 2023Article
- A Machine Learning-Based Approach Using Multi-omics Data to Predict Metabolic Pathways.Methods in molecular biology (Clifton, N.J.) · 2023Article
59 more citing papers are in PubMed but not listed here.
Corrections and comments
- Commented on by
Authors and funding
10 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cells employ multiple levels of regulation, including transcriptional and translational regulation, that drive core biological processes and enable cells to respond to genetic and environmental changes. Small-molecule metabolites are one category of critical cellular intermediates that can influence as well as be a target of cellular regulations. Because metabolites represent the direct output of protein-mediated cellular processes, endogenous metabolite concentrations can closely reflect cellular physiological states, especially when integrated with other molecular-profiling data. Here we develop and apply a network reconstruction approach that simultaneously integrates six different types of data: endogenous metabolite concentration, RNA expression, DNA variation, DNA-protein binding, protein-metabolite interaction, and protein-protein interaction data, to construct probabilistic causal networks that elucidate the complexity of cell regulation in a segregating yeast population. Because many of the metabolites are found to be under strong genetic control, we were able to employ a causal regulator detection algorithm to identify causal regulators of the resulting network that elucidated the mechanisms by which variations in their sequence affect gene expression and metabolite concentrations. We examined all four expression quantitative trait loci (eQTL) hot spots with colocalized metabolite QTLs, two of which recapitulated known biological processes, while the other two elucidated novel putative biological mechanisms for the eQTL hot spots.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.