ArticleMolecular systems biology2015
Drugs that reverse disease transcriptomic signatures are more effective in a mouse model of dyslipidemia.
Article in Molecular systems biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis 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
35 citing papers in PubMed, 1 synthesis or guideline pooled it, 76 citations in OpenAlex.
- Meta- and cross-species analyses of insulin resistance based on gene expression datasets in human white adipose tissues.Scientific reports · 2018Pooled it
- Innovative horizons: harnessing drug repositioning for targeted therapeutics in colorectal cancer.Naunyn-Schmiedeberg's archives of pharmacology · 2025Review
- Integrating population-level and cell-based signatures for drug repositioning.Bioinformatics (Oxford, England) · 2025Article
- Integrated Computational and Functional Screening Identifies G9a Inhibitors for SETD2-mutant Leukemia.Genomics, proteomics & bioinformatics · 2025Article
- Drug Discovery and Development for Heart Failure Using Multi-Omics Approaches.International journal of molecular sciences · 2025Review
- The future of metronomic chemotherapy: experimental and computational approaches of drug repurposing.Pharmacological reports : PR · 2025Review
- Drug Repurposing: A Leading Strategy for New Threats and Targets.ACS pharmacology & translational science · 2024Review
- RAGE engagement by SARS-CoV-2 enables monocyte infection and underlies COVID-19 severity.Cell reports. Medicine · 2023Article
- Prioritized polycystic kidney disease drug targets and repurposing candidates from pre-cystic and cystic mouse Pkd2 model gene expression reversion.Molecular medicine (Cambridge, Mass.) · 2023Article
- On the correspondence between the transcriptomic response of a compound and its effects on its targets.BMC bioinformatics · 2023Article
- Article
- Whole blood transcriptomic profiling identifies molecular pathways related to cardiovascular mortality in heart failure.European journal of heart failure · 2022Article
- A survey of optimal strategy for signature-based drug repositioning and an application to liver cancer.eLife · 2022Article
- Overcoming Drug Resistance in Advanced Prostate Cancer by Drug Repurposing.Medical sciences (Basel, Switzerland) · 2022Review
- A human-based multi-gene signature enables quantitative drug repurposing for metabolic disease.eLife · 2022Article
- Transcription Factor Activation Profiles (TFAP) identify compounds promoting differentiation of Acute Myeloid Leukemia cell lines.Cell death discovery · 2022Article
- Prediction and identification of synergistic compound combinations against pancreatic cancer cells.iScience · 2021Article
- Host-targeted Interventions as an Exciting Opportunity to Combat Malaria.Chemical reviews · 2021Review
- Paradoxical Pro-angiogenic Effect of Low-Dose Ellipticine Identified by In Silico Drug Repurposing.International journal of molecular sciences · 2021Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
High-throughput omics have proven invaluable in studying human disease, and yet day-to-day clinical practice still relies on physiological, non-omic markers. The metabolic syndrome, for example, is diagnosed and monitored by blood and urine indices such as blood cholesterol levels. Nevertheless, the association between the molecular and the physiological manifestations of the disease, especially in response to treatment, has not been investigated in a systematic manner. To this end, we studied a mouse model of diet-induced dyslipidemia and atherosclerosis that was subject to various drug treatments relevant to the disease in question. Both physiological data and gene expression data (from the liver and white adipose) were analyzed and compared. We find that treatments that restore gene expression patterns to their norm are associated with the successful restoration of physiological markers to their baselines. This holds in a tissue-specific manner—treatments that reverse the transcriptomic signatures of the disease in a particular tissue are associated with positive physiological effects in that tissue. Further, treatments that introduce large non-restorative gene expression alterations are associated with unfavorable physiological outcomes. These results provide a sound basis to in silico methods that rely on omic metrics for drug repurposing and drug discovery by searching for compounds that reverse a disease’s omic signatures. Moreover, they highlight the need to develop drugs that restore the global cellular state to its healthy norm rather than rectify particular disease phenotypes.
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.