Systems biology of embryonic development: Prospects for a complete understanding of the Caenorhabditis elegans embryo

Automated profiling of gene function during embryonic development

Systematic functional profiling of the gene set that directs embryonic development is an important challenge. To tackle this challenge, we used 4D imaging of C. elegans embryogenesis to capture the effects of 500 gene knockdowns and developed an automated approach to compare developmental phenotypes. The automated approach quantifies features-including germ layer cell numbers, tissue position, and tissue shape-to generate temporal curves whose parameterization yields numerical phenotypic signatures. In conjunction with a new similarity metric that operates across phenotypic space, these signatures enabled the generation of ranked lists of genes predicted to have similar functions, accessible in the PhenoBank web portal, for ∼25% of essential development genes. The approach identified new gene and pathway relationships in cell fate specification and morphogenesis and highlighted the utilization of specialized energy generation pathways during embryogenesis. Collectively, the effort establishes the foundation for comprehensive analysis of the gene set that builds a multicellular organism.

Does phenotypic plasticity initiate developmental bias?

The generation of variation is paramount for the action of natural selection. Although biologists are now moving beyond the idea that random mutation provides the sole source of variation for adaptive evolution, we still assume that variation occurs randomly. In this review, we discuss an alternative view for how phenotypic plasticity, which has become well accepted as a source of phenotypic variation within evolutionary biology, can generate nonrandom variation. Although phenotypic plasticity is often defined as a property of a genotype, we argue that it needs to be considered more explicitly as a property of developmental systems involving more than the genotype. We provide examples of where plasticity could be initiating developmental bias, either through direct active responses to similar stimuli across populations or as the result of programmed variation within developmental systems. Such biased variation can echo past adaptations that reflect the evolutionary history of a lineage but can also serve to initiate evolution when environments change. Such adaptive programs can remain latent for millions of years and allow development to harbor an array of complex adaptations that can initiate new bouts of evolution. Specifically, we address how ideas such as the flexible stem hypothesis and cryptic genetic variation overlap, how modularity among traits can direct the outcomes of plasticity, and how the structure of developmental signaling pathways is limited to a few outcomes. We highlight key questions throughout and conclude by providing suggestions for future research that can address how plasticity initiates and harbors developmental bias.

Maps of variability in cell lineage trees

New approaches to lineage tracking have allowed the study of differentiation in multicellular organisms over many generations of cells. Understanding the phenotypic variability observed in these lineage trees requires new statistical methods. Whereas an invariant cell lineage, such as that for the nematode Caenorhabditis elegans, can be described by a lineage map, defined as the pattern of phenotypes overlaid onto the binary tree, a traditional lineage map is static and does not describe the variability inherent in the cell lineages of higher organisms. Here, we introduce lineage variability maps which describe the pattern of second-order variation in lineage trees. These maps can be undirected graphs of the partial correlations between every lineal position, or directed graphs showing the dynamics of bifurcated patterns in each subtree. We show how to infer these graphical models for lineages of any depth from sample sizes of only a few pedigrees. This required developing the generalized spectral analysis for a binary tree, the natural framework for describing tree-structured variation. When tested on pedigrees from C. elegans expressing a marker for pharyngeal differentiation potential, the variability maps recover essential features of the known lineage map. When applied to highly-variable pedigrees monitoring cell size in T lymphocytes, the maps show that most of the phenotype is set by the founder naive T cell. Lineage variability maps thus elevate the concept of the lineage map to the population level, addressing questions about the potency and dynamics of cell lineages and providing a way to quantify the progressive restriction of cell fate with increasing depth in the tree.

Perspectives and applications of machine learning for evolutionary developmental biology

Evolutionary Developmental Biology (Evo-Devo) is an ever-expanding field that aims to understand how development was modulated by the evolutionary process. In this sense, "omic" studies emerged as a powerful ally to unravel the molecular mechanisms underlying development. In this scenario, bioinformatics tools become necessary to analyze the growing amount of information. Among computational approaches, machine learning stands out as a promising field to generate knowledge and trace new research perspectives for bioinformatics. In this review, we aim to expose the current advances of machine learning applied to evolution and development. We draw clear perspectives and argue how evolution impacted machine learning techniques.

Analysis of novel caudal hindbrain genes reveals different regulatory logic for gene expression in rhombomere 4 versus 5/6 in embryonic zebrafish

BACKGROUND

Previous work aimed at understanding the gene regulatory networks (GRNs) governing caudal hindbrain formation identified morphogens such as Retinoic Acid (RA) and Fibroblast growth factors (FGFs), as well as transcription factors like hoxb1b, hoxb1a, hnf1ba, and valentino as being required for rhombomere (r) r4-r6 formation in zebrafish. Considering that the caudal hindbrain is relatively complex - for instance, unique sets of neurons are formed in each rhombomere segment - it is likely that additional essential genes remain to be identified and integrated into the caudal hindbrain GRN.

METHODS

By taking advantage of gene expression data available in the Zebrafish Information Network (ZFIN), we identified 84 uncharacterized genes that are expressed in r4-r6. We selected a representative set of 22 genes and assayed their expression patterns in hoxb1b, hoxb1a, hnf1b, and valentino mutants with the goal of positioning them in the caudal hindbrain GRN. We also investigated the effects of RA and FGF on the expression of this gene set. To examine whether these genes are necessary for r4-r6 development, we analyzed germline mutants for six of the genes (gas6, gbx1, sall4, eglf6, celf2, and greb1l) for defects in hindbrain development.

RESULTS

Our results reveal that r4 gene expression is unaffected by the individual loss of hoxb1b, hoxb1a or RA, but is under the combinatorial regulation of RA together with hoxb1b. In contrast, r5/r6 gene expression is dependent on RA, FGF, hnf1ba and valentino - as individual loss of these factors abolishes r5/r6 gene expression. Our analysis of six mutant lines did not reveal rhombomere or neuronal defects, but transcriptome analysis of one line (gas6 mutant) identified expression changes for genes involved in several developmental processes - suggesting that these genes may have subtle roles in hindbrain development.

CONCLUSION

We conclude that r4-r6 formation is relatively robust, such that very few genes are absolutely required for this process. However, there are mechanistic differences in r4 versus r5/r6, such that no single factor is required for r4 development while several genes are individually required for r5/r6 formation.