ReviewBriefings in functional genomics2016
Functional genomics to assess biological responses to marine pollution at physiological and evolutionary timescales: toward a vision of predictive ecotoxicology.
Review in Briefings in functional genomics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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
7 citing papers in PubMed.
- Microplastic exposure is associated with epigenomic effects in the model organism Pimephales promelas (fathead minnow).The Journal of heredity · 2025Article
- Intraspecific diversity is critical to population-level risk assessments.Scientific reports · 2024Article
- Gene Expression Profiles in Two Razor Clam Populations: Discerning Drivers of Population Status.Life (Basel, Switzerland) · 2021Article
- Ocean acidification promotes broad transcriptomic responses in marine metazoans: a literature survey.Frontiers in zoology · 2020Review
- Evolutionary Toxicogenomics of the Striped Killifish (International journal of molecular sciences · 2019Article
- Evolutionary toxicology in an omics world.Evolutionary applications · 2017Article
- 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
2 authors.
Funding
Abstract
Marine pollution is ubiquitous, and is one of the key factors influencing contemporary marine biodiversity worldwide. To protect marine biodiversity, how do we surveil, document and predict the short- and long-term impacts of pollutants on at-risk species? Modern genomics tools offer high-throughput, information-rich and increasingly cost-effective approaches for characterizing biological responses to environmental stress, and are important tools within an increasing sophisticated kit for surveiling and assessing impacts of pollutants on marine species. Through the lens of recent research in marine killifish, we illustrate how genomics tools may be useful for screening chemicals and pollutants for biological activity and to reveal specific mechanisms of action. The high dimensionality of transcriptomic responses enables their usage as highly specific fingerprints of exposure, and these fingerprints can be used to diagnose environmental problems. We also emphasize that molecular pathways recruited to respond at physiological timescales are the same pathways that may be targets for natural selection during chronic exposure to pollutants. Gene complement and sequence variation in those pathways can be related to variation in sensitivity to environmental pollutants within and among species. Furthermore, allelic variation associated with evolved tolerance in those pathways could be tracked to estimate the pace of environmental health decline and recovery. We finish by integrating these paradigms into a vision of how genomics approaches could anchor a modernized framework for advancing the predictive capacity of environmental and ecotoxicological science.
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.