From gene to biomolecular networks: a review of evidences for understanding complex biological function in plants

https://doi.org/10.1016/j.copbio.2021.10.023Get rights and content

Highlights

  • Biological function of genes is a complex interaction among genes and environment.

  • PPIN, co-expression and GRN are system-level tools to study beyond single gene.

  • These tools help functional annotation of many unknown genes.

  • Network approaches quickly decode genotypes and multi-environment interactions.

  • Hub genes regulate a series of genes of the same or different pathways.

Although at the infancy stage, biomolecular network biology is a comprehensive approach to understand complex biological function in plants. Recent advancements in the accumulation of multi-omics data coupled with computational approach have accelerated our current understanding of the complexities of gene function at the system level. Biomolecular networks such as protein–protein interaction, co-expression and gene regulatory networks have extensively been used to decipher the intricacies of transcriptional reprogramming of hundreds of genes and their regulatory interaction in response to various environmental perturbations mainly in the model plant Arabidopsis. This review describes recent applications of network-based approaches to understand the biological functions in plants and focuses on the challenges and opportunities to harness the full potential of the approach.

Introduction

Plant phenotype is determined by a complex interaction of plants’ internal composition (biomolecules, cells, tissues, organs, etc.) and the external environment (abiotic and biotic factors). To study such complex interaction, system biology is being considered as one of the most promising approaches. The system biology approaches have proven helpful in understanding how the chain of different physio-chemical reactions and interactions initiated by various biomolecules (gene, RNA, proteins and metabolites) through a vast diversity of complex pathways modulated by several external environments regulates plant phenotype [1]. In the pre-genomic era, research priority was primarily dependent on utilizing a single gene-based biotechnological approach because of the unavailability of genome-wide information on various biomolecules to alter the desired phenotypes with little emphasis on environmental variations. In the post-genomic era, with sufficient genome-wide information on genes, proteins and metabolites in many plants and advancements in computational biology, it is now easier to hunt for desirable genes and their interaction network with other biomolecules to decipher the complex biological function [2, 3, 4].

Recent advancement in computational science facilitates graphical visualization of a complex biological system in the form of the network, which helps understand complex interactions. A network has two major components, that is, nodes (represent biomolecule) and edges (represent the interaction between these biomolecules). Edges of the network are of prime importance since they signify the possible relation between the different biomolecules. Most of the current biomolecular networks deal with three networks, namely (i) protein–protein interaction network (PPIN) in which a group of proteins having specific biological functions are linked together [5,6]; (ii) gene co-expression network in which a set of genes show a similar co-expression pattern [7]; (iii) gene regulatory network (GRN) that shows the relationships between a protein, usually transcription factor (TF) and a gene transcript [8]. Among these, GRN ensures fast and accurate prediction of a plant’s regulatory response to numerous environmental and intrinsic signals by fine-tuning multiple TFs that regulate target genes [9]. As witnessed by the increasing number of research publications in the last ten years (Figure 1; Tables S1–S3), GRN (∼5205) has emerged as one of the most preferred tools, followed by PPIN (∼1442) and co-expression network (∼1012), to decode the intricacies of plant’s biological function. In light of the growing importance of network-based approaches, here, we discussed the evidence-based advancement by citing pioneering work to illustrate the biological mechanism in plants. More particularly, we have briefly described the recent developments associated with GRN, PPIN, and co-expression-based networks to understand the complex molecular function of genes associated with desired traits. Furthermore, challenges for the efficient exploration of network-based approaches are highlighted. Future directions of network-based understanding of plant phenotypes are also discussed. Overall, this review shall encourage molecular biologists with little or no relevant understanding of the approach, in general, and system biologists, in particular, to explore its potential application in plant improvement.

Section snippets

Protein–protein interaction network to understand the biological function in plants: advances and future perspectives

The PPIN is an effective approach to biomolecular network biology that ostensibly provides knowledge of the target protein’s function in complex biological processes. In vivo, a single protein hardly performs its function in isolation, and about 80% of the proteins have been observed to be functional in complexes with either other proteins or nucleic acids [10]. Despite the significant increase in the number of fully sequenced and annotated plant genomes, the interaction and function of many

Co-expression network to understand the gene function in plants: advances and future perspectives

The exquisiteness of the biomolecular network-based approach enables the identification of functionally related unknown biomolecules such as protein and genes, which are associated with similar biological processes. The rapid accumulation of omics data sets, especially RNA-Seq data in different plant species, has steered plant biologists to reconstruct and interpret biological processes at the system level. The co-expression network approach is intensively utilized to simultaneously identify

Gene-regulatory network: a highly explored approach

For decades, classical experimental approaches such as the gene-knock-out experiment have been widely used to observe the silencing effect of one gene on the changes in the steady-state expressions of other genes and the interacting effect of TF on the set of genes in the same pathway (Figure 2). However, the knock-out approach is primarily limited to only a few genes because of demanding time and input costs. In contrast, increasing advances in computational methods have enabled us to use many

Conclusion and future perspective

Here, current advances in biomolecular networks such as PPIN, co-expression analysis network, and GRN to understand the plant’s traits at the system level were discussed. With the technical and dynamic advancement in NGS-based technologies coupled with the dynamic computational approach, significant progress has been made towards understanding the regulatory circuit of the cellular processes, mainly in Arabidopsis. Nevertheless, it is still unclear or at the infancy stage whether current

Conflict of interest statement

Nothing declared.

References and recommended reading

Papers of particular interest, published within the period of review, have been highlighted as:

  • • of special interest

  • •• of outstanding interest

CRediT authorship contribution statement

Om Prakash Gupta: Conceptualization, Data curation, Methodology, Writing – original draft, Writing – review & editing. Rupesh Deshmukh : Data curation, Writing – original draft, Writing – review & editing. Awadhesh Kumar : Writing – review & editing, Investigation. Sanjay Kumar Singh: Writing – review & editing, Investigation. Pradeep Sharma: Writing – review & editing, Investigation. Sewa Ram: Resources, Writing - review & editing. Gyanendra Pratap Singh: Resources, Writing – review & editing.

Acknowledgements

The authors are thankful to the Indian Council of Agricultural Research, Department of Agricultural Research and Education, Govt. of India to provide financial help under grant CRSCIIWBRSIL 201500900190.

References (48)

  • A.R. Sonawane et al.

    Network medicine in the age of biomedical big data

    Front Genet

    (2019)
  • E.K. Silverman et al.

    Molecular networks in network medicine: development and applications

    Wiley Interdiscip Rev Syst Biol Med

    (2020)
  • Z. Ding et al.

    Computational identification of protein-protein interactions in model plant proteomes

    Sci Rep

    (2019)
  • X. Rao et al.

    Co-expression networks for plant biology: why and how

    Acta Biochim Biophys Sin

    (2019)
  • L. Van den Broeck et al.

    Gene regulatory network inference: connecting plant biology and mathematical modeling

    Front Genet

    (2020)
  • J. Swift et al.

    A matter of time - how transient transcription factor interactions create dynamic gene regulatory networks

    Biochim Biophys Acta

    (2017)
  • T. Berggard et al.

    Methods for the detection and analysis of protein-protein interactions

    Proteomics

    (2007)
  • E.M. Phizicky et al.

    Protein-protein interactions: methods for detection and analysis

    Microbiol Rev

    (1995)
  • V.S. Rao et al.

    Protein-protein interaction detection: methods and analysis

    Int J Proteomics

    (2014)
  • S. Xing et al.

    Techniques for the analysis of protein-protein interactions in vivo

    Plant Physiol

    (2016)
  • A. Chatr-Aryamontri et al.

    The BioGRID interaction database: 2017 update

    Nucleic Acids Res

    (2017)
  • Z. Zhang et al.

    Resurrected protein interaction networks reveal the innovation potential of ancient whole-genome duplication

    Plant Cell

    (2018)
  • B. Hosseinpour et al.

    Protein interaction network of Arabidopsis thaliana female gametophyte development identifies novel proteins and relations

    PLoS One

    (2012)
  • M. Nietzsche et al.

    A protein–protein interaction network linking the energy-sensor kinase SnRK1 to multiple signaling pathways in Arabidopsis thaliana

    Curr Plant Biol

    (2016)
  • Cited by (0)

    View full text