Inference of Developmental Gene Regulatory Networks Beyond Classical Model Systems: New Approaches in the Post-genomic Era

Abstract

The advent of high-throughput sequencing (HTS) technologies has revolutionized the way we understand the transformation of genetic information into morphological traits. Elucidating the network of interactions between genes that govern cell differentiation through development is one of the core challenges in genome research. These networks are known as developmental gene regulatory networks (dGRNs) and consist largely of the functional linkage between developmental control genes, cis-regulatory modules, and differentiation genes, which generate spatially and temporally refined patterns of gene expression. Over the last 20 years, great advances have been made in determining these gene interactions mainly in classical model systems, including human, mouse, sea urchin, fruit fly, and worm. This has brought about a radical transformation in the fields of developmental biology and evolutionary biology, allowing the generation of high-resolution gene regulatory maps to analyze cell differentiation during animal development. Such maps have enabled the identification of gene regulatory circuits and have led to the development of network inference methods that can recapitulate the differentiation of specific cell-types or developmental stages. In contrast, dGRN research in non-classical model systems has been limited to the identification of developmental control genes via the candidate gene approach and the characterization of their spatiotemporal expression patterns, as well as to the discovery of cis-regulatory modules via patterns of sequence conservation and/or predicted transcription-factor binding sites. However, thanks to the continuous advances in HTS technologies, this scenario is rapidly changing. Here, we give a historical overview on the architecture and elucidation of the dGRNs. Subsequently, we summarize the approaches available to unravel these regulatory networks, highlighting the vast range of possibilities of integrating multiple technical advances and theoretical approaches to expand our understanding on the global gene regulation during animal development in non-classical model systems. Such new knowledge will not only lead to greater insights into the evolution of molecular mechanisms underlying cell identity and animal body plans, but also into the evolution of morphological key innovations in animals.

Gene Regulatory Networks of Penicillium echinulatum 2HH and Penicillium oxalicum 114-2 Inferred by a Computational Biology Approach

“…In particular, GRNs can benefit from orthology-based knowledge from closely related species; where the key concept is that a TF-TG relationship proven in one organism can be conserved in another one (Mercatelli et al, 2020). However, this knowledge transfer requires reliable methods to define orthology between different genes for any TF-TG pair, as well as taking into account the phylogenetic positioning of the species analyzed (Fernandez-Valverde et al, 2018).…”

PEAK Predicts Gene Regulatory Network Linkages during Sea Urchin Development with High Sensitivity from Gene Expression Data Alone

“…For many emerging model systems crucial for understanding evolution, gene expression data from developmental time series is readily available but, due to limited resources, these systems lack extensive prior information regarding transcription factor binding sites, ChIPseq data, and proteomics. Therefore, these emerging model systems have a demonstrated need for GRN inference to guide future experiments on regulatory interactions during embryonic development (Tulin et al 2013;Fernandez-Valverde et al 2018).…”

Intracellular and intercellular gene regulatory networks inference from time-course individual RNA-Seq

“…Originally, GRN inference was applied for gene expression data from tissue and pooled cells (bulk samples), generated using DNA microarray and RNA-Seq [reviewed in (Ko and Brandizzi, 2020;Fernandez-Valverde et al, 2018)]. Compared to steady-state data, time-course data allows GRN inference by comparing the timing of gene upregulation and downregulation (Iglesias-Martinez et al, 2016;Zhang et al, 2019;Krouk et al, 2010;Ogami et al, 2012).…”

“…Originally, GRN inference was applied for gene expression data from tissue and pooled cells (bulk samples), generated using DNA microarray and RNA-Seq 10,11 . Compared to steady-state data, time-course data allows GRN inference by comparing the timing of gene upregulation and downregulation 12–15 .…”