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Evidence for Deep Regulatory Similarities in Early Developmental Programs across Highly Diverged Insects
Abstract
Many genes familiar from Drosophila development, such as the so-called gap, pair-rule, and segment polarity genes, play important roles in the development of other insects and in many cases appear to be deployed in a similar fashion, despite the fact that Drosophila-like "long germband" development is highly derived and confined to a subset of insect families. Whether or not these similarities extend to the regulatory level is unknown. Identification of regulatory regions beyond the well-studied Drosophila has been challenging as even within the Diptera (flies, including mosquitoes) regulatory sequences have diverged past the point of recognition by standard alignment methods. Here, we demonstrate that methods we previously developed for computational cis-regulatory module (CRM) discovery in Drosophila can be used effectively in highly diverged (250-350 Myr) insect species including Anopheles gambiae, Tribolium castaneum, Apis mellifera, and Nasonia vitripennis. In Drosophila, we have successfully used small sets of known CRMs as "training data" to guide the search for other CRMs with related function. We show here that although species-specific CRM training data do not exist, training sets from Drosophila can facilitate CRM discovery in diverged insects. We validate in vivo over a dozen new CRMs, roughly doubling the number of known CRMs in the four non-Drosophila species. Given the growing wealth of Drosophila CRM annotation, these results suggest that extensive regulatory sequence annotation will be possible in newly sequenced insects without recourse to costly and labor-intensive genome-scale experiments. We develop a new method, Regulus, which computes a probabilistic score of similarity based on binding site composition (despite the absence of nucleotide-level sequence alignment), and demonstrate similarity between functionally related CRMs from orthologous loci. Our work represents an important step toward being able to trace the evolutionary history of gene regulatory networks and defining the mechanisms underlying insect evolution.
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![Fig. 4.— (a) Pipeline for cross-species supervised CRM prediction. Top: A set of CRMs that regulate similar gene expression patterns is selected as a training set. Expression driven by CRMs from the blastoderm (left) and CNS (right) training sets are pictured. Note that there is a range of related but nonidentical patterns. (Blastoderm embryo pictures are adapted from Schroeder et al. [2004] under the terms of the CC-BY license.) A statistical model is then trained on k-mers in this training set of CRMs as well as non-CRM sequences from Drosophila. Separately, an “expression gene set” is defined as Drosophila genes with expression patterns matching the training set. Middle: The trained model is used to scan a non-Drosophila target genome, and score every 500-bp window in the genome for similarity to the training set in Drosophila. Highest scoring windows (marked with asterisks) are predicted to be CRMs. Bottom: The expression gene set in Drosophila is mapped through homology to a gene set (in the target genome) whose expression is expected to be similar to that of predicted CRMs. Genes near predicted CRMs are tested for enrichment in this gene set, providing a preliminary statistical assessment (evaluation P value) of the predictions. *Additional data sets amenable to supervised CRM prediction in Drosophila are shown in supplementary table S2, Supplementary Material online. (b) Data sets amenable to cross-species supervised CRM prediction. Shown are the data sets where an evaluation P value ≤ 1E-5 was observed for at least one statistical model, in at least one non-Drosophila species. Color intensity of a cell is proportional to the negative logarithm of the evaluation P value, with any P value ≥ 1E-5 being represented as a white cell. The last row in top panel reports the total number of amenable data sets in each species. The bottom three rows show the number of data sets amenable to cross-species supervised CRM prediction (at the 1E-5 threshold) for each statistical model and each species. More detailed results are shown in supplementary table S2 and figure S2, Supplementary Material online. (c) Evaluation of three statistical models on known CRMs in An. gam, A. mel, and T. cas. For each CRM, the local rank over a 100-kb region (430 windows on average) surrounding the relevant gene, under each statistical model, is provided. Cases where local rank is ≤2 are highlighted. The given evaluation P value is the best from among the three statistical models. Global ranks for all three models are provided in supplementary table S5, Supplementary Material online.](/paper-figures/9cb01ea8233543349625143d25d0ce36/evidence-for-deep-regulatory-similarities-in-early-developmental-programs-across-figure-4.jpg)
![Fig. 5.— (a) Experimentally validated enhancers from diverged arthropods. Predicted CRM sequences were used to drive reporter gene expression in transgenic Drosophila. Expression was visualized using immunohistochemistry (A, B, C, D, F, G, H, J) or in situ hybridization (E, I). All embryos are shown with anterior to the left. Panels (A), (B), (E), (H), (I), and (J) are lateral views with dorsal to the top; (C), (D), (F), and (G) are dorsal views. (A) A predicted T. cas wg CRM regulates gene expression in a pattern similar to that of the (B) Drosophila wg_Δwg enhancer (Von Ohlen and Hooper 1997) (compare arrows in [A] and [B]; panel B courtesy of Scott Barolo). Inset in (A) shows colocalization of GFP (green) and Wg (magenta) protein expression. (C) A predicted CRM for the T. cas Dichaete ortholog regulates gene expression in late-stage embryos identical to what is seen with the Drosophila Dichaete D_D/fsh_O-E enhancer (panel D; Ochoa-Espinosa et al. 2005). GFP expression in the anal ring (partially out of focus in C) likely represents perdurance of GFP protein from earlier stages as it is not observed using in situ hybridization. (E) A predicted early embryonic stripe CRM for the N. vit hairy ortholog gives a two-stripe pattern similar to that of a Drosophila hairy CRM (Howard and Struhl 1990) (see also fig. 6a). (F) A CRM for the N. vit neuralized gene drives expression in the central and peripheral nervous systems; arrow denotes the brain (see also supplementary fig. S3, Supplementary Material online). (G) A CRM for the ttk gene from A. mel regulates expression in the salivary gland and (H) the developing midgut; additional expression is shown in supplementary figure S3, Supplementary Material online. (I) A CRM predicted for An. gam sog regulates expression in the mesoderm during germ band extension. (J) A “false negative” result is obtained when a sequence not predicted to be a CRM, here from the A. mel wg locus, drives patterned gene expression (GFP expression, green). Note that unlike what we see with our predicted T. cas wg CRM (panel C), reporter gene expression in this case is not confined to Wg-expressing cells (magenta). (b) Summary of results from in vivo testing of 24 segments. Each segment is characterized as being a predicted CRM or not, and whether reporter activity was confirmed or not. (c) Summary of lines with positive expression. Shown are the numbers of lines with expression pattern matching to the gene or covered by one or more high scoring training sets.](/paper-figures/a34156a8470140118e9e5b964a0a5734/evidence-for-deep-regulatory-similarities-in-early-developmental-programs-across-figure-5.jpg)
![Fig. 6.— (a) Modeled gene expression regulated by hairy CRMs in D. mel (“Dmel_h_stripe_2+6,” construct ET15 of Howard and Struhl [1990]) and N. vit (“Nvit_h_m8,” this study). Expression mediated by the N. vit hairy CRM (Nvit_h_m8-observed) has an anterior stripe that starts slightly anterior to the endogenous D. mel h stripe 1 (“Dmel_h-observed,” leftmost green bar) and ends just before the posterior margin of the stripe, whereas the posterior stripe begins immediately posterior to the endogenous stripe 5 and extends almost to the posterior margin of stripe 6 (“Dmel_h-observed,” rightmost green bar). The GEMSTAT model predicts similar expression profiles for both CRMs (Dmel_h_stripe_2+6-GEMSTAT, Nvit_h_m8-GEMSTAT), with a posterior stripe overlapping the endogenous hairy stripe 6 and a broad anterior domain that straddles endogenous D. mel stripes 1 and 2. (b) Modeled gene expression regulated by sog CRMs in D. mel (“Dmel_sog_shadow”; Hong et al. 2008) and An. gam (“An. gam_sog_1,” this study). The endogenous expression pattern of sog in D. mel is ectodermal (blue) and agrees with the GEMSTAT-predicted profile (red) for the Dmel_sog_shadow CRM. The same model predicts a mesodermal expression pattern as output of the Agam_sog_1 CRM as reported in this work.](/paper-figures/7a8840f7830148d1bff4d7966af8d922/evidence-for-deep-regulatory-similarities-in-early-developmental-programs-across-figure-6.jpg)


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“dGRNs have provided a systems-level construct at which similarities of developmental regulation emerge across great evolutionary spans despite extensive sequence-level divergence (39, 40).”
From Behavior-related gene regulatory networks: A new level of organization in the brain (2020) · cites this paper as [39]
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Arunachalam, M., Jayasurya, K., Tomančák, P., & Ohler, U. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq358 · Cited by 20
Parallel Evolution of Chordate Cis-Regulatory Code for Development
Doglio, L. E., Goode, D. K., Pelleri, M. C., Pauls, S., Frabetti, F., Shimeld, S. M., Vavouri, T., & Elgar, G. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003904 · Cited by 17
Papatsenko, D., Levine, M., & Goltsev, Y. (2011) · PLoS Biology · doi:10.1371/journal.pbio.1000584 · Cited by 15
Holometabolous insects (Holometabola)
Wiegmann, B. M., Kim, J., & Trautwein, M. (2009) · doi:10.1093/oso/9780199535033.003.0031 · Cited by 15
Plasticity in mRNA expression and localization of orthodenticle within higher Diptera
Schetelig, M. F., Schmid, B. G. M., Zimowska, G. J., & Wimmer, E. A. (2008) · Evolution & Development · doi:10.1111/j.1525-142x.2008.00283.x · Cited by 13
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Ren, J., Song, K., Sun, F., Deng, M., & Reinert, G. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt462 · Cited by 13
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Cited by
Behavior-related gene regulatory networks: A new level of organization in the brain
Sinha, S., Jones, B. M., Traniello, I. M., Bukhari, S. A., Halfon, M. S., Hofmann, H. A., Huang, S., Katz, P. S., Keagy, J., Lynch, V. J., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1921625117 · Cited by 85
Self-citationCross‐species systems analysis of evolutionary toolkits of neurogenomic response to social challenge
Saul, M. C., Blatti, C., Yang, W., Bukhari, S. A., Shpigler, H. Y., Troy, J. M., Seward, C. H., Sloofman, L., Chandrasekaran, S., Bell, A. M., et al. (2019) · Genes, Brain and Behavior · doi:10.1111/gbb.12502 · Cited by 38
Self-citation
Metadata
- Authors
- Kazemian, M., Suryamohan, K., Chen, J.-Y., Zhang, Y., Samee, M. A. H., Halfon, M. S., & Sinha, S.
- Venue
- Genome Biology and Evolution
- Publisher
- Oxford University Press (OUP)
- Published
- 2014-08-29
- Volume
- 6
- Issue
- 9
- Pages
- 2301-2320
- ISSN
- 1759-6653
- PMC
- PMC4217690
- Cited by
- 42
- References
- 86
- Language
- en
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.