Bias in the reporting of sex and age in biomedical research on mouse models

Abstract

In animal-based biomedical research, both the sex and the age of the animals studied affect disease phenotypes by modifying their susceptibility, presentation and response to treatment. The accurate reporting of experimental methods and materials, including the sex and age of animals, is essential so that other researchers can build on the results of such studies. Here we use text mining to study 15,311 research papers in which mice were the focus of the study. We find that the percentage of papers reporting the sex and age of mice has increased over the past two decades: however, only about 50% of the papers published in 2014 reported these two variables. We also compared the quality of reporting in six preclinical research areas and found evidence for different levels of sex-bias in these areas: the strongest male-bias was observed in cardiovascular disease models and the strongest female-bias was found in infectious disease models. These results demonstrate the ability of text mining to contribute to the ongoing debate about the reproducibility of research, and confirm the need to continue efforts to improve the reporting of experimental methods and materials.

Article and author information

Author details

  1. Oscar Flórez-Vargas

    Bio-health Informatics Group, School of Computer Science, The University of Manchester, Manchester, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  2. Andy Brass

    Bio-health Informatics Group, School of Computer Science, The University of Manchester, Manchester, United Kingdom
    For correspondence
    andy.brass@manchester.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
  3. George Karystianis

    Text Mining Group, School of Computer Science, The University of Manchester, Manchester, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  4. Michael Bramhall

    Bio-health Informatics Group, School of Computer Science, The University of Manchester, Manchester, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  5. Robert Stevens

    Bio-health Informatics Group, School of Computer Science, The University of Manchester, Manchester, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  6. Sheena Cruickshank

    Manchester Immunology Group, Faculty of Life Science, The University of Manchester, Manchester, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  7. Goran Nenadic

    Text Mining Group, School of Computer Science, The University of Manchester, Manchester, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.

Copyright

© 2016, Flórez-Vargas et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 5,051
    views
  • 689
    downloads
  • 88
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Oscar Flórez-Vargas
  2. Andy Brass
  3. George Karystianis
  4. Michael Bramhall
  5. Robert Stevens
  6. Sheena Cruickshank
  7. Goran Nenadic
(2016)
Bias in the reporting of sex and age in biomedical research on mouse models
eLife 5:e13615.
https://doi.org/10.7554/eLife.13615
  1. Further reading

Further reading

    1. Computational and Systems Biology
    2. Medicine
    Hong Yang, Cheng Zhang ... Adil Mardinoglu
    Research Article

    Excessive consumption of sucrose, in the form of sugar-sweetened beverages, has been implicated in the pathogenesis of metabolic dysfunction‐associated fatty liver disease (MAFLD) and other related metabolic syndromes. The c-Jun N-terminal kinase (JNK) pathway plays a crucial role in response to dietary stressors, and it was demonstrated that the inhibition of the JNK pathway could potentially be used in the treatment of MAFLD. However, the intricate mechanisms underlying these interventions remain incompletely understood given their multifaceted effects across multiple tissues. In this study, we challenged rats with sucrose-sweetened water and investigated the potential effects of JNK inhibition by employing network analysis based on the transcriptome profiling obtained from hepatic and extrahepatic tissues, including visceral white adipose tissue, skeletal muscle, and brain. Our data demonstrate that JNK inhibition by JNK-IN-5A effectively reduces the circulating triglyceride accumulation and inflammation in rats subjected to sucrose consumption. Coexpression analysis and genome-scale metabolic modeling reveal that sucrose overconsumption primarily induces transcriptional dysfunction related to fatty acid and oxidative metabolism in the liver and adipose tissues, which are largely rectified after JNK inhibition at a clinically relevant dose. Skeletal muscle exhibited minimal transcriptional changes to sucrose overconsumption but underwent substantial metabolic adaptation following the JNK inhibition. Overall, our data provides novel insights into the molecular basis by which JNK inhibition exerts its metabolic effect in the metabolically active tissues. Furthermore, our findings underpin the critical role of extrahepatic metabolism in the development of diet-induced steatosis, offering valuable guidance for future studies focused on JNK-targeting for effective treatment of MAFLD.

    1. Computational and Systems Biology
    Jun Ren, Ying Zhou ... Qiyuan Li
    Research Article

    Manifold-learning is particularly useful to resolve the complex cellular state space from single-cell RNA sequences. While current manifold-learning methods provide insights into cell fate by inferring graph-based trajectory at cell level, challenges remain to retrieve interpretable biology underlying the diverse cellular states. Here, we described MGPfactXMBD, a model-based manifold-learning framework and capable to factorize complex development trajectories into independent bifurcation processes of gene sets, and thus enables trajectory inference based on relevant features. MGPfactXMBD offers a more nuanced understanding of the biological processes underlying cellular trajectories with potential determinants. When bench-tested across 239 datasets, MGPfactXMBD showed advantages in major quantity-control metrics, such as branch division accuracy and trajectory topology, outperforming most established methods. In real datasets, MGPfactXMBD recovered the critical pathways and cell types in microglia development with experimentally valid regulons and markers. Furthermore, MGPfactXMBD discovered evolutionary trajectories of tumor-associated CD8+ T cells and yielded new subtypes of CD8+ T cells with gene expression signatures significantly predictive of the responses to immune checkpoint inhibitor in independent cohorts. In summary, MGPfactXMBD offers a manifold-learning framework in scRNA-seq data which enables feature selection for specific biological processes and contributing to advance our understanding of biological determination of cell fate.