Publications

Signaling pathways feature multiple interacting ligand and receptor variants, which are thought to act in a combinatorial manner to elicit different cellular responses. Transcriptome analyses now suggest that many signaling pathways use their components in combinations that are surprisingly often shared between otherwise dissimilar cell states.

Background

Nicotinamide phosphoribosyltransferase (Nampt) is required for recycling NAD⁺ in numerous cellular contexts. Morpholino-based knockdown of zebrafish nampt-a has been shown to cause abnormal development and defective hematopoiesis concomitant with decreased NAD⁺ levels. However, surprisingly, nampt-a mutant zebrafish were recently found to be viable, suggesting a discrepancy between the phenotypes in knockdown and knockout conditions. Here, we address this discrepancy by directly comparing loss-of-function approaches that result in identical defective transcripts in morphants and mutants.

Results

Using CRISPR/Cas9-mediated mutagenesis, we generated nampt-a mutant lines that carry the same mis-spliced mRNA as nampt-a morphants. Despite reduced NAD⁺ levels and perturbed expression of specific blood markers, nampt-a mutants did not display obvious developmental defects and were found to be viable. In contrast, injection of nampt-a morpholinos into wild-type or mutant nampt-a embryos caused aberrant phenotypes. Moreover, nampt-a morpholinos caused additional reduction of blood-related markers in nampt-a mutants, suggesting that the defects observed in nampt-a morphants can be partially attributed to off-target effects of the morpholinos.

Conclusions

Our findings show that zebrafish nampt-a mutants are viable despite reduced NAD⁺ levels and a perturbed hematopoietic gene expression program, indicating strong robustness of primitive hematopoiesis during early embryogenesis.

BMP signaling has a conserved function in patterning the dorsal-ventral body axis in Bilateria and the directive axis in anthozoan cnidarians. So far, cnidarian studies have focused on the role of different BMP signaling network components in regulating pSMAD1/5 gradient formation. Much less is known about the target genes downstream of BMP signaling. To address this, we generated a genome-wide list of direct pSMAD1/5 target genes in the anthozoan Nematostella vectensis, several of which were conserved in Drosophila and Xenopus. Our ChIP-seq analysis revealed that many of the regulatory molecules with documented bilaterally symmetric expression in Nematostella are directly controlled by BMP signaling. We identified several so far uncharacterized BMP-dependent transcription factors and signaling molecules, whose bilaterally symmetric expression may be indicative of their involvement in secondary axis patterning. One of these molecules is zswim4-6, which encodes a novel nuclear protein that can modulate the pSMAD1/5 gradient and potentially promote BMP-dependent gene repression.

Evolutionarily conserved signaling pathways are essential for early embryogenesis, and reducing or abolishing their activity leads to characteristic developmental defects. Classification of phenotypic defects can identify the underlying signaling mechanisms, but this requires expert knowledge and the classification schemes have not been standardized. Here we use a machine learning approach for automated phenotyping to train a deep convolutional neural network, EmbryoNet, to accurately identify zebrafish signaling mutants in an unbiased manner. Combined with a model of time-dependent developmental trajectories, this approach identifies and classifies with high precision phenotypic defects caused by loss of function of the seven major signaling pathways relevant for vertebrate development. Our classification algorithms have wide applications in developmental biology and robustly identify signaling defects in evolutionarily distant species. Furthermore, using automated phenotyping in high-throughput drug screens, we show that EmbryoNet can resolve the mechanism of action of pharmaceutical substances. As part of this work, we freely provide more than 2 million images that were used to train and test EmbryoNet.

During animal development, embryos undergo complex morphological changes over time. Differences in developmental tempo between species are emerging as principal drivers of evolutionary novelty, but accurate description of these processes is very challenging. To address this challenge, we present here an automated and unbiased deep learning approach to analyze the similarity between embryos of different timepoints. Calculation of similarities across stages resulted in complex phenotypic fingerprints, which carry characteristic information about developmental time and tempo. Using this approach, we were able to accurately stage embryos, quantitatively determine temperature-dependent developmental tempo, detect naturally occurring and induced changes in the developmental progression of individual embryos, and derive staging atlases for several species de novo in an unsupervised manner. Our approach allows us to quantify developmental time and tempo objectively and provides a standardized way to analyze early embryogenesis.

The hindered diffusion model postulates that the movement of a signaling molecule through an embryo is affected by tissue geometry and binding-mediated hindrance, but these effects have not been directly demonstrated in vivo. Here, we visualize extracellular movement and binding of individual molecules of the activator-inhibitor signaling pair Nodal and Lefty in live developing zebrafish embryos using reflected light-sheet microscopy. We observe that diffusion coefficients of molecules are high in extracellular cavities, whereas mobility is reduced and bound fractions are high within cell-cell interfaces. Counterintuitively, molecules nevertheless accumulate in cavities, which we attribute to the geometry of the extracellular space by agent-based simulations. We further find that Nodal has a larger bound fraction than Lefty and shows a binding time of tens of seconds. Together, our measurements and simulations provide direct support for the hindered diffusion model and yield insights into the nanometer-to-micrometer-scale mechanisms that lead to macroscopic signal dispersal.

During vertebrate embryogenesis, the germ layers are patterned by secreted Nodal signals. In the classical model, Nodals elicit signaling by binding to a complex comprising Type I/II Activin receptors (Acvr) and the co-receptor Tdgf1. However, it is currently unclear whether receptor binding can also affect the distribution of Nodals themselves through the embryo, and it is unknown which of the putative Acvr paralogs mediate Nodal signaling in zebrafish. Here, we characterize three Type I (Acvr1) and four Type II (Acvr2) homologs and show that - except for Acvr1c - all receptor-encoding transcripts are maternally deposited and present during zebrafish embryogenesis. We generated mutants and used them together with combinatorial morpholino knockdown and CRISPR F0 knockout (KO) approaches to assess compound loss-of-function phenotypes. We discovered that the Acvr2 homologs function partly redundantly and partially independently of Nodal to pattern the early zebrafish embryo, whereas the Type I receptors Acvr1b-a and Acvr1b-b redundantly act as major mediators of Nodal signaling. By combining quantitative analyses with expression manipulations, we found that feedback-regulated Type I receptors and co-receptors can directly influence the diffusion and distribution of Nodals, providing a mechanism for the spatial restriction of Nodal signaling during germ layer patterning.

Protein therapeutics frequently face major challenges, including complicated production, instability, poor solubility, and aggregation. De novo protein design can readily address these challenges. Here, we demonstrate the utility of a topological refactoring strategy to design novel granulopoietic proteins starting from the granulocyte-colony stimulating factor (G-CSF) structure. We change a protein fold by rearranging the sequence and optimising it towards the new fold. Testing four designs, we obtain two that possess nanomolar activity, the most active of which is highly thermostable and protease-resistant, and matches its designed structure to atomic accuracy. While the designs possess starkly different sequence and structure from the native G-CSF, they show specific activity in differentiating primary human haematopoietic stem cells into mature neutrophils. The designs also show significant and specific activity in vivo. Our topological refactoring approach is largely independent of sequence or structural context, and is therefore applicable to a wide range of protein targets.

Growth factors are signaling molecules coordinating the complex functionality of multicellular organisms during development and homeostasis. Since aberrant expression of growth factors can cause diverse disorders, growth factors and their receptors are central targets for therapeutic modulation. Here, we present two different strategies of computational protein design to obtain inhibitors against growth factors which contribute to tumor progression. Adopting a re-engineering approach, we designed inhibitors of epidermal growth factor (EGF) using a single domain of EGF receptor as a template. Experimental evaluation of two designed candidates revealed that both of them bind EGF with nanomolar affinities and inhibit EGF-induced proliferation of epidermoid carcinoma cell line. Using a de novo design strategy, we designed inhibitors of vascular endothelial growth factor (VEGF). The best designs showed the ability to inhibit proliferation of VEGF-dependent cells in vitro and in zebrafish assays. Thus, our results demonstrate the feasibility of computational protein design approaches to create therapeutic leads in a time- and cost-effective manner.

By combining classical embryological experiments, advanced imaging, and active-particle simulations, a new study reveals how the coupling of signaling and active mechanical forces triggers the collective ordered internalization of mesendoderm cells during gastrulation in zebrafish embryos.

The central question in developmental biology is how an embryo self-organizes from a ball of cells into a structured animal. We use experimental and theoretical approaches to study how the activity of a small number of signaling molecules is spatiotemporally controlled to allow for embryonic self-construction. Here, we propose how machine learning could be harnessed to gain a new understanding of the mechanisms by which interactions between signaling pathways control robust embryogenesis and morphology changes in evolution.

Classical embryological manipulations, such as removing cells and transplanting cells within or between embryos, are powerful techniques to study complex developmental processes. Zebrafish embryos are ideally suited for these manipulations since they are easily accessible, relatively large in size, and transparent. However, previously developed devices for cell removal and transplantation are cumbersome to use or expensive to purchase. In contrast, the transplantation device presented here is economical, easy to assemble, and simple to use. In this protocol, we first introduce the handling of the transplantation device as well as its assembly from commercially and widely available parts. We then present three applications for its use: generation of ectopic clones to study signal dispersal from localized sources, extirpation of cells to produce size-reduced embryos, and germline transplantation to generate maternal-zygotic mutants. Finally, we show that the tool can also be used for embryological manipulations in other species such as the Japanese rice fish medaka.

Color patterns influence how attractive flowers are to bees, butterflies, and birds. By combining experiments and theory, a new study shows how a pair of MYB transcription factors orchestrates the formation of pigmentation patterns on monkeyflowers.

Computational protein design is rapidly becoming more powerful, and improving the accuracy of computational methods would greatly streamline protein engineering by eliminating the need for empirical optimization in the laboratory. In this work, we set out to design novel granulopoietic agents using a rescaffolding strategy with the goal of achieving simpler and more stable proteins. All of the 4 experimentally tested designs were folded, monomeric, and stable, while the 2 determined structures agreed with the design models within less than 2.5 Å. Despite the lack of significant topological or sequence similarity to their natural granulopoietic counterpart, 2 designs bound to the granulocyte colony-stimulating factor (G-CSF) receptor and exhibited potent, but delayed, in vitro proliferative activity in a G-CSF-dependent cell line. Interestingly, the designs also induced proliferation and differentiation of primary human hematopoietic stem cells into mature granulocytes, highlighting the utility of our approach to develop highly active therapeutic leads purely based on computational design.

Signaling molecules activate distinct patterns of gene expression to coordinate embryogenesis, but how spatiotemporal expression diversity is generated is an open question. In zebrafish, a BMP signaling gradient patterns the dorsal-ventral axis. We systematically identified target genes responding to BMP and found that they have diverse spatiotemporal expression patterns. Transcriptional responses to optogenetically delivered high- and low-amplitude BMP signaling pulses indicate that spatiotemporal expression is not fully defined by different BMP signaling activation thresholds. Additionally, we observed negligible correlations between spatiotemporal expression and transcription kinetics for the majority of analyzed genes in response to BMP signaling pulses. In contrast, spatial differences between BMP target genes largely collapsed when FGF and Nodal signaling were inhibited. Our results suggest that, similar to other patterning systems, combinatorial signaling is likely to be a major driver of spatial diversity in BMP-dependent gene expression in zebrafish.