1. Computational and Systems Biology
  2. Developmental Biology
Download icon

Topology-driven protein-protein interaction network analysis detects genetic sub-networks regulating reproductive capacity

  1. Tarun Kumar
  2. Leo Blondel
  3. Cassandra G Extavour  Is a corresponding author
  1. Harvard University, United States
Research Article
  • Cited 0
  • Views 1,121
  • Annotations
Cite this article as: eLife 2020;9:e54082 doi: 10.7554/eLife.54082


Understanding the genetic regulation of organ structure is a fundamental problem in developmental biology. Here, we use egg-producing structures of insect ovaries, called ovarioles, to deduce systems-level gene regulatory relationships from quantitative functional genetic analysis. We previously showed that Hippo signalling, a conserved regulator of animal organ size, regulates ovariole number in Drosophila melanogaster. To comprehensively determine how Hippo signalling interacts with other pathways in this regulation, we screened all known signalling pathway genes, and identified Hpo-dependent and Hpo-independent signalling requirements. Network analysis of known protein-protein interactions among screen results identified independent gene regulatory sub-networks regulating one or both of ovariole number and egg laying. These sub-networks predict involvement of previously uncharacterised genes with higher accuracy than the original candidate screen. This shows that network analysis combining functional genetic and large-scale interaction data can predict function of novel genes regulating development.

Data availability

This study did not generate new unique reagents. This study generated new python3 code available on GitHub: https://github.com/extavourlab/hpo_ova_eggL_screen.

Article and author information

Author details

  1. Tarun Kumar

    Organismic and Evolutionary Biology, Harvard University, Cambridge, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4071-4342
  2. Leo Blondel

    Department of Molecular and Cellular Biology, Harvard University, Cambridge, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2276-4821
  3. Cassandra G Extavour

    Department of Organismic and Evolutionary Biology/Molecular and Cellular Biology, Harvard University, Cambridge, United States
    For correspondence
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2922-5855


National Institutes of Health (1R01-HD073499)

  • Tarun Kumar
  • Cassandra G Extavour

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Michael B Eisen, University of California, Berkeley, United States

Publication history

  1. Received: December 1, 2019
  2. Accepted: September 1, 2020
  3. Accepted Manuscript published: September 9, 2020 (version 1)
  4. Version of Record published: October 12, 2020 (version 2)


© 2020, Kumar 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.


  • 1,121
    Page views
  • 135
  • 0

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

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)

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

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

Further reading

    1. Computational and Systems Biology
    2. Immunology and Inflammation
    James W Opzoomer et al.
    Tools and Resources Updated

    High-dimensional cytometry is an innovative tool for immune monitoring in health and disease, and it has provided novel insight into the underlying biology as well as biomarkers for a variety of diseases. However, the analysis of large multiparametric datasets usually requires specialist computational knowledge. Here, we describe ImmunoCluster (https://github.com/kordastilab/ImmunoCluster), an R package for immune profiling cellular heterogeneity in high-dimensional liquid and imaging mass cytometry, and flow cytometry data, designed to facilitate computational analysis by a nonspecialist. The analysis framework implemented within ImmunoCluster is readily scalable to millions of cells and provides a variety of visualization and analytical approaches, as well as a rich array of plotting tools that can be tailored to users’ needs. The protocol consists of three core computational stages: (1) data import and quality control; (2) dimensionality reduction and unsupervised clustering; and (3) annotation and differential testing, all contained within an R-based open-source framework.

    1. Computational and Systems Biology
    2. Medicine
    Muhammad Arif et al.
    Research Article

    Myocardial infarction (MI) promotes a range of systemic effects, many of which are unknown. Here, we investigated the alterations associated with MI progression in heart and other metabolically active tissues (liver, skeletal muscle, and adipose) in a mouse model of MI (induced by ligating the left ascending coronary artery) and sham-operated mice. We performed a genome-wide transcriptomic analysis on tissue samples obtained 6- and 24-hours post MI or sham operation. By generating tissue-specific biological networks, we observed: (1) dysregulation in multiple biological processes (including immune system, mitochondrial dysfunction, fatty-acid beta-oxidation, and RNA and protein processing) across multiple tissues post MI; and (2) tissue-specific dysregulation in biological processes in liver and heart post MI. Finally, we validated our findings in two independent MI cohorts. Overall, our integrative analysis highlighted both common and specific biological responses to MI across a range of metabolically active tissues.