A multifunctional wnt regulator underlies the evolution of rodent stripe patterns

ABSTRACT Animal pigment patterns are excellent models to elucidate mechanisms of biological organization. Although theoretical simulations, such as Turing reaction–diffusion systems,

recapitulate many animal patterns, they are insufficient to account for those showing a high degree of spatial organization and reproducibility. Here, we study the coat of the African

striped mouse (_Rhabdomys pumilio_) to uncover how periodic stripes form. Combining transcriptomics, mathematical modelling and mouse transgenics, we show that the Wnt modulator _Sfrp2_

regulates the distribution of hair follicles and establishes an embryonic prepattern that foreshadows pigment stripes. Moreover, by developing in vivo gene editing in striped mice, we find

that _Sfrp2_ knockout is sufficient to alter the stripe pattern. Strikingly, mutants exhibited changes in pigmentation, revealing that _Sfrp2_ also regulates hair colour. Lastly, through

evolutionary analyses, we find that striped mice have evolved lineage-specific changes in regulatory elements surrounding _Sfrp2_, many of which may be implicated in modulating the

expression of this gene. Altogether, our results show that a single factor controls coat pattern formation by acting both as an orienting signalling mechanism and a modulator of

pigmentation. More broadly, our work provides insights into how spatial patterns are established in developing embryos and the mechanisms by which phenotypic novelty originates. Access

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VIEWED BY OTHERS INFLUENCE OF SURVIVAL, PROMOTION, AND GROWTH ON PATTERN FORMATION IN ZEBRAFISH SKIN Article Open access 10 May 2021 PMEL IS INVOLVED IN SNAKE COLOUR PATTERN TRANSITION FROM

BLOTCHES TO STRIPES Article Open access 03 September 2024 REACTION-DIFFUSION IN A GROWING 3D DOMAIN OF SKIN SCALES GENERATES A DISCRETE CELLULAR AUTOMATON Article Open access 23 April 2021

DATA AVAILABILITY The bulk RNA-seq, scRNA-seq and ATAC-seq reads are submitted under an NCBI BioProject: PRJNA1004353.

https://figshare.com/projects/Data_repository_for_A_multifunctional_Wnt_regulator_underlies_the_evolution_of_rodent_stripe_patterns_/175200. Source data are provided with this paper. CODE

AVAILABILITY Code used for scRNA-seq analysis, bulk RNA-seq analysis and comparative genomics is deposited at

https://figshare.com/projects/Data_repository_for_A_multifunctional_Wnt_regulator_underlies_the_evolution_of_rodent_stripe_patterns_/175200. REFERENCES * Mills, M. G. & Patterson, L. B.

Not just black and white: pigment pattern development and evolution in vertebrates. _Semin. Cell Dev. Biol._ 20, 72–81 (2009). Article  CAS  PubMed  Google Scholar  * Caro, T. &

Mallarino, R. Coloration in mammals. _Trends Ecol. Evol._ 35, 357–366 (2020). Article  PubMed  Google Scholar  * Cuthill, I. C. et al. The biology of color. _Science_ 357, eaan0221 (2017).

Article  PubMed  Google Scholar  * Kratochwil, C. F. & Mallarino, R. Mechanisms underlying the formation and evolution of vertebrate color patterns. _Annu. Rev. Genet_.

https://doi.org/10.1146/annurev-genet-031423-120918 (2023). * Kondo, S. & Miura, T. Reaction–diffusion model as a framework for understanding biological pattern formation. _Science_ 329,

1616–1620 (2010). Article  CAS  PubMed  Google Scholar  * Kondo, S. An updated kernel-based Turing model for studying the mechanisms of biological pattern formation. _J. Theor. Biol._ 414,

120–127 (2017). Article  PubMed  Google Scholar  * Turing, A. M. The chemical basis of morphogenesis. 1953. _Bull. Math. Biol._ 52, 153–197 (1990). Article  CAS  PubMed  Google Scholar  *

Vittadello, S. T., Leyshon, T., Schnoerr, D. & Stumpf, M. P. H. Turing pattern design principles and their robustness. _Philos. Trans. A_ 379, 20200272 (2021). Article  Google Scholar  *

Patterson, L. B. & Parichy, D. M. Zebrafish pigment pattern formation: insights into the development and evolution of adult form. _Annu. Rev. Genet._ 53, 505–530 (2019). Article  CAS 

PubMed  Google Scholar  * Kaelin, C. B., McGowan, K. A. & Barsh, G. S. Developmental genetics of color pattern establishment in cats. _Nat. Commun._ 12, 5127 (2021). Article  CAS  PubMed

  PubMed Central  Google Scholar  * Mallarino, R. et al. Developmental mechanisms of stripe patterns in rodents. _Nature_ 539, 518–523 (2016). Article  CAS  PubMed  PubMed Central  Google

Scholar  * Haupaix, N. & Manceau, M. The embryonic origin of periodic color patterns. _Dev. Biol._ 460, 70–76 (2020). Article  CAS  PubMed  Google Scholar  * Kaelin, C. B. et al.

Specifying and sustaining pigmentation patterns in domestic and wild cats. _Science_ 337, 1536–1541 (2012). Article  CAS  PubMed  PubMed Central  Google Scholar  * Mallarino, R., Pillay, N.,

Hoekstra, H. E. & Schradin, C. African striped mice. _Curr. Biol._ 28, R299–R301 (2018). Article  CAS  PubMed  Google Scholar  * Hardy, M. H. The secret life of the hair follicle.

_Trends Genet._ 8, 55–61 (1992). Article  CAS  PubMed  Google Scholar  * Millar, S. E. Molecular mechanisms regulating hair follicle development. _J. Invest. Dermatol._ 118, 216–225 (2002).

Article  CAS  PubMed  Google Scholar  * Andl, T., Reddy, S. T., Gaddapara, T. & Millar, S. E. WNT signals are required for the initiation of hair follicle development. _Dev. Cell_ 2,

643–653 (2002). Article  CAS  PubMed  Google Scholar  * van Loon, K., Huijbers, E. J. M. & Griffioen, A. W. Secreted frizzled-related protein 2: a key player in noncanonical Wnt

signaling and tumor angiogenesis. _Cancer Metastasis Rev._ 40, 191–203 (2021). Article  PubMed  Google Scholar  * Kim, M., Han, J. H., Kim, J.-H., Park, T. J. & Kang, H. Y. Secreted

frizzled-related protein 2 (sFRP2) functions as a melanogenic stimulator; the role of sFRP2 in UV-induced hyperpigmentary disorders. _J. Invest. Dermatol._ 136, 236–244 (2016). Article  CAS

  PubMed  Google Scholar  * Liang, C.-J. et al. SFRPs are biphasic modulators of Wnt-signaling-elicited cancer stem cell properties beyond extracellular control. _Cell Rep._ 28, 1511–1525

(2019). Article  CAS  PubMed  Google Scholar  * Lin, H. et al. sFRP2 activates Wnt/β-catenin signaling in cardiac fibroblasts: differential roles in cell growth, energy metabolism

extracellular matrix remodeling. _Am. J. Physiol. Cell Physiol._ 311, C710–C719 (2016). Article  PubMed  PubMed Central  Google Scholar  * Gupta, K. et al. Single-cell analysis reveals a

hair follicle dermal niche molecular differentiation trajectory that begins prior to morphogenesis. _Dev. Cell_ 48, 17–31 (2019). Article  CAS  PubMed  Google Scholar  * Sennett, R. et al.

An integrated transcriptome atlas of embryonic hair follicle progenitors, their niche, and the developing skin. _Dev. Cell_ 34, 577–591 (2015). Article  CAS  PubMed  PubMed Central  Google

Scholar  * Rezza, A. et al. Signaling networks among stem cell precursors, transit-amplifying progenitors, and their niche in developing hair follicles. _Cell Rep._ 14, 3001–3018 (2016).

Article  CAS  PubMed  PubMed Central  Google Scholar  * Sulic, A.-M. et al. Transcriptomic landscape of early hair follicle and epidermal development. _Cell Rep._ 42, 112643 (2023). Article

  CAS  PubMed  Google Scholar  * Saxena, N., Mok, K.-W. & Rendl, M. An updated classification of hair follicle morphogenesis. _Exp. Dermatol._ 28, 332–344 (2019). Article  PubMed  PubMed

Central  Google Scholar  * Tsai, S.-Y. et al. Wnt/β-catenin signaling in dermal condensates is required for hair follicle formation. _Dev. Biol._ 385, 179–188 (2014). Article  CAS  PubMed 

Google Scholar  * Gat, U., DasGupta, R., Degenstein, L. & Fuchs, E. De Novo hair follicle morphogenesis and hair tumors in mice expressing a truncated beta-catenin in skin. _Cell_ 95,

605–614 (1998). Article  CAS  PubMed  Google Scholar  * Yu, K. et al. Conditional inactivation of FGF receptor 2 reveals an essential role for FGF signaling in the regulation of osteoblast

function and bone growth. _Development_ 130, 3063–3074 (2003). Article  CAS  PubMed  Google Scholar  * Šošić, D., Richardson, J. A., Yu, K., Ornitz, D. M. & Olson, E. N. Twist regulates

cytokine gene expression through a negative feedback loop that represses NF-κB activity. _Cell_ 112, 169–180 (2003). Article  PubMed  Google Scholar  * Hiscock, T. W. & Megason, S. G.

Orientation of Turing-like patterns by morphogen gradients and tissue anisotropies. _Cell Syst._ 1, 408–416 (2015). Article  CAS  PubMed  PubMed Central  Google Scholar  * Sick, S., Reinker,

S., Timmer, J. & Schlake, T. WNT and DKK determine hair follicle spacing through a reaction–diffusion mechanism. _Science_ 314, 1447–1450 (2006). Article  CAS  PubMed  Google Scholar  *

Van Gorder, R. A. Pattern formation from spatially heterogeneous reaction–diffusion systems. _Philos. Trans. A_ 379, 20210001 (2021). Article  Google Scholar  * Gierer, A. & Meinhardt,

H. A theory of biological pattern formation. _Kybernetik_ 12, 30–39 (1972). Article  CAS  PubMed  Google Scholar  * Yochelis, A., Tintut, Y., Demer, L. L. & Garfinkel, A. The formation

of labyrinths, spots and stripe patterns in a biochemical approach to cardiovascular calcification. _New J. Phys._ 10, 055002 (2008). Article  Google Scholar  * McKay, R. & Kolokolnikov,

T. Stability transitions and dynamics of mesa patterns near the shadow limit of reaction–diffusion systems in one space dimension. _Discret. Contin. Dyn. Syst. B_ 17, 191–220 (2012). Google

Scholar  * Yoon, Y. et al. Streamlined ex vivo and in vivo genome editing in mouse embryos using recombinant adeno-associated viruses. _Nat. Commun._ 9, 412 (2018). * Iozumi, K., Hoganson,

G. E., Pennella, R., Everett, M. A. & Fuller, B. B. Role of tyrosinase as the determinant of pigmentation in cultured human melanocytes. _J. Invest. Dermatol._ 100, 806–811 (1993).

Article  CAS  PubMed  Google Scholar  * Edraki, A. et al. A compact, high-accuracy Cas9 with a dinucleotide PAM for in vivo genome editing. _Mol. Cell_ 73, 714–726 (2019). Article  CAS 

PubMed  Google Scholar  * Enshell-Seijffers, D., Lindon, C., Wu, E., Taketo, M. M. & Morgan, B. A. β-Catenin activity in the dermal papilla of the hair follicle regulates pigment-type

switching. _Proc. Natl Acad. Sci. USA_ 107, 21564–21569 (2010). Article  CAS  PubMed  PubMed Central  Google Scholar  * Morgan, B. A. The dermal papilla: an instructive niche for epithelial

stem and progenitor cells in development and regeneration of the hair follicle. _Cold Spring Harb. Perspect. Med._ 4, a015180 (2014). Article  PubMed  PubMed Central  Google Scholar  *

Steingrímsson, E., Copeland, N. G. & Jenkins, N. A. Melanocytes and the microphthalmia transcription factor network. _Annu. Rev. Genet._ 38, 365–411 (2004). Article  PubMed  Google

Scholar  * Jho, E.-H. et al. Wnt/beta-catenin/Tcf signaling induces the transcription of Axin2, a negative regulator of the signaling pathway. _Mol. Cell. Biol._ 22, 1172–1183 (2002).

Article  CAS  PubMed  PubMed Central  Google Scholar  * Shtutman, M. et al. The cyclin D1 gene is a target of the beta-catenin/LEF-1 pathway. _Proc. Natl Acad. Sci. USA_ 96, 5522–5527

(1999). Article  CAS  PubMed  PubMed Central  Google Scholar  * He, T. C. et al. Identification of c-MYC as a target of the APC pathway. _Science_ 281, 1509–1512 (1998). Article  CAS  PubMed

  Google Scholar  * Richardson, R. et al. The genomic basis of temporal niche evolution in a diurnal rodent. _Curr. Biol_. https://doi.org/10.1016/j.cub.2023.06.068 (2023). * Gao, F. et al.

EasyCodeML: a visual tool for analysis of selection using CodeML. _Ecol. Evol._ 9, 3891–3898 (2019). Article  PubMed  PubMed Central  Google Scholar  * Kaelin, C. B. & Barsh, G. S.

Genetics of pigmentation in dogs and cats. _Annu Rev. Anim. Biosci._ 1, 125–156 (2013). Article  PubMed  Google Scholar  * Keller, S. H., Jena, S. G., Yamazaki, Y. & Lim, B. Regulation

of spatiotemporal limits of developmental gene expression via enhancer grammar. _Proc. Natl Acad. Sci. USA_ 117, 15096–15103 (2020). Article  CAS  PubMed  PubMed Central  Google Scholar  *

Kaufman, M. H. _The Atlas of Mouse Development_ (Academic Press, 1992). * Wu, J. & Wang, X. Whole-mount in situ hybridization of mouse embryos using DIG-labeled RNA probes. _Methods Mol.

Biol._ 1922, 151–159 (2019). Article  CAS  PubMed  Google Scholar  * Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.

_Genome Biol._ 15, 550 (2014). Article  PubMed  PubMed Central  Google Scholar  * Conway, J. R., Lex, A. & Gehlenborg, N. UpSetR: an R package for the visualization of intersecting sets

and their properties. _Bioinformatics_ 33, 2938–2940 (2017). Article  CAS  PubMed  PubMed Central  Google Scholar  * Burns, K. J., Vasil, G. M., Oishi, J. S., Lecoanet, D. & Brown, B.

P. Dedalus: a flexible framework for numerical simulations with spectral methods. _Phys. Rev. Res._ 2, 023068 (2020). Article  CAS  Google Scholar  * Tuckerman, L. S. & Barkley, D. in

_Numerical Methods for Bifurcation Problems and Large-Scale Dynamical Systems_ (eds Doedel, E. & Tuckerman, L. S.) 453–466 (Springer, 2000). * Li, H. A statistical framework for SNP

calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. _Bioinformatics_ 27, 2987–2993 (2011). Article  CAS  PubMed  PubMed

Central  Google Scholar  * Wolock, S. L., Lopez, R. & Klein, A. M. Scrublet: computational identification of cell doublets in single-cell transcriptomic data. _Cell Syst._ 8, 281–291

(2019). * Fan, J. et al. Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis. _Nat. Methods_ 13, 241–244 (2016). Article  CAS  PubMed  PubMed

Central  Google Scholar  * Stuart, T. et al. Comprehensive integration of single-cell data. _Cell_ 177, 1888–1902 (2019). Article  CAS  PubMed  PubMed Central  Google Scholar  * Joost, S. et

al. The molecular anatomy of mouse skin during hair growth and rest. _Cell Stem Cell_ 26, 441–457 (2020). Article  CAS  PubMed  Google Scholar  * Beronja, S., Livshits, G., Williams, S.

& Fuchs, E. Rapid functional dissection of genetic networks via tissue-specific transduction and RNAi in mouse embryos. _Nat. Med._ 16, 821–827 (2010). Article  CAS  PubMed  PubMed

Central  Google Scholar  * Aasen, T. & Izpisúa Belmonte, J. C. Isolation and cultivation of human keratinocytes from skin or plucked hair for the generation of induced pluripotent stem

cells. _Nat. Protoc._ 5, 371–382 (2010). Article  CAS  PubMed  Google Scholar  * Hahn, W. C. et al. Enumeration of the Simian virus 40 early region elements necessary for human cell

transformation. _Mol. Cell. Biol._ 22, 2111–2123 (2002). Article  CAS  PubMed  PubMed Central  Google Scholar  * Concordet, J.-P. & Haeussler, M. CRISPOR: intuitive guide selection for

CRISPR/Cas9 genome editing experiments and screens. _Nucleic Acids Res._ 46, W242–W245 (2018). Article  CAS  PubMed  PubMed Central  Google Scholar  * Stamatakis, A. RAxML version 8: a tool

for phylogenetic analysis and post-analysis of large phylogenies. _Bioinformatics_ 30, 1312–1313 (2014). Article  CAS  PubMed  PubMed Central  Google Scholar  * Kowalczyk, A. et al.

RERconverge: an R package for associating evolutionary rates with convergent traits. _Bioinformatics_ 35, 4815–4817 (2019). Article  CAS  PubMed  PubMed Central  Google Scholar  * Yang, Z.

PAML: a program package for phylogenetic analysis by maximum likelihood. _Comput. Appl. Biosci._ 13, 555–556 (1997). CAS  PubMed  Google Scholar  * Álvarez-Carretero, S., Kapli, P. &

Yang, Z. Beginner’s guide on the use of PAML to detect positive selection. _Mol. Biol. Evol_. 40, msad041 (2023). * Corces, M. R. et al. An improved ATAC-seq protocol reduces background and

enables interrogation of frozen tissues. _Nat. Methods_ 14, 959–962 (2017). Article  CAS  PubMed  PubMed Central  Google Scholar  * Zhang, Y. et al. Model-based analysis of ChIP-seq (MACS).

_Genome Biol._ 9, R137 (2008). Article  PubMed  PubMed Central  Google Scholar  * Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features.

_Bioinformatics_ 26, 841–842 (2010). Article  CAS  PubMed  PubMed Central  Google Scholar  * Shumate, A. & Salzberg, S. L. Liftoff: accurate mapping of gene annotations. _Bioinformatics_

37, 1639–1643 (2021). Article  CAS  PubMed  PubMed Central  Google Scholar  * McLeay, R. C. & Bailey, T. L. Motif enrichment analysis: a unified framework and an evaluation on ChIP

data. _BMC Bioinform._ 11, 165 (2010). Article  Google Scholar  * Grant, C. E., Bailey, T. L. & Noble, W. S. FIMO: scanning for occurrences of a given motif. _Bioinformatics_ 27,

1017–1018 (2011). Article  CAS  PubMed  PubMed Central  Google Scholar  * Gupta, S., Stamatoyannopoulos, J. A., Bailey, T. L. & Noble, W. S. Quantifying similarity between motifs.

_Genome Biol._ 8, R24 (2007). Article  PubMed  PubMed Central  Google Scholar  Download references ACKNOWLEDGEMENTS We thank members of the Mallarino laboratory; Princeton LAR (C. Dmytrow,

K. Gerhart, G. Barnett and J. McGuire) for help with striped mice husbandry; the LSI Genomics Core (W. Wang, J. M. Miller, J. Wiggins and J. Arley Volmar) for help with library preparation

and sequencing; the Nikon Center of Excellence Confocal Microscopy Core (S. Wang and G. Laevsky); and members of the Rivera-Perez laboratory (Y. Yoon and J. Gallant) for help with in vivo

genome editing experiments. We also thank E. F. Wieschaus, G. Deshpande and P. Holl for insights and discussion. This project was supported by an NIH grant to R.M. (R35GM133758). M.R.J. was

supported by an NIH fellowship (F32 GM139253). S.L. was supported by a Presidential Postdoctoral Research fellowship (Princeton University). B.J.B. was supported by an NIH training grant

(T32GM007388). C.Y.F. was supported by an NIH fellowship (F32 GM139240-01). C.F.G.-J. is partially supported by UC Irvine Chancellor’s ADVANCE Postdoctoral Fellowship Program. Q.N. was

partially supported by an NSF grant DMS1763272 and a Simons Foundation grant (594598). AUTHOR INFORMATION Author notes * These authors contributed equally: Matthew R. Johnson, Sha Li.

AUTHORS AND AFFILIATIONS * Department of Molecular Biology, Princeton University, Princeton, NJ, USA Matthew R. Johnson, Sha Li, Benjamin J. Brack, Sarah A. Mereby, Jorge A. Moreno, Charles

Y. Feigin, Jenna Gaska, Alexander Ploss, Stanislav Y. Shvartsman & Ricardo Mallarino * Carle Illinois College of Medicine, University of Illinois at Urbana-Champaign, Urbana, IL, USA

Christian F. Guerrero-Juarez * Department of Developmental and Cell Biology, University of California, Irvine, CA, USA Christian F. Guerrero-Juarez & Qing Nie * Department of

Mathematics, University of California, Irvine, CA, USA Christian F. Guerrero-Juarez & Qing Nie * Center for Complex Biological Systems, University of California, Irvine, CA, USA

Christian F. Guerrero-Juarez & Qing Nie * NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, CA, USA Christian F. Guerrero-Juarez & Qing Nie *

Center for Computational Biology, Flatiron Institute, New York, NY, USA Pearson Miller & Stanislav Y. Shvartsman * Frederick National Laboratory for Cancer Research, Frederick, MA, USA

Jaime A. Rivera-Perez * The Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA Stanislav Y. Shvartsman Authors * Matthew R. Johnson View author

publications You can also search for this author inPubMed Google Scholar * Sha Li View author publications You can also search for this author inPubMed Google Scholar * Christian F.

Guerrero-Juarez View author publications You can also search for this author inPubMed Google Scholar * Pearson Miller View author publications You can also search for this author inPubMed 

Google Scholar * Benjamin J. Brack View author publications You can also search for this author inPubMed Google Scholar * Sarah A. Mereby View author publications You can also search for

this author inPubMed Google Scholar * Jorge A. Moreno View author publications You can also search for this author inPubMed Google Scholar * Charles Y. Feigin View author publications You

can also search for this author inPubMed Google Scholar * Jenna Gaska View author publications You can also search for this author inPubMed Google Scholar * Jaime A. Rivera-Perez View author

publications You can also search for this author inPubMed Google Scholar * Qing Nie View author publications You can also search for this author inPubMed Google Scholar * Alexander Ploss

View author publications You can also search for this author inPubMed Google Scholar * Stanislav Y. Shvartsman View author publications You can also search for this author inPubMed Google

Scholar * Ricardo Mallarino View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS M.R.J. and R.M. conceived the project and designed experiments.

M.R.J. performed RNA-seq experiments and bulk RNA-seq analysis. S.L. performed the in vitro and in vivo genome editing in striped mice, with help from S.A.M. and J.A.R.-P. M.R.J. and S.L.

performed all downstream processing and analysis of genome edited animals. P.M. and S.Y.S. did the mathematical modelling. C.F.G.-J. led the scRNA-seq analysis, with support from M.R.J. and

Q.N. M.R.J., B.J.B. and R.M. performed in situ hybridizations. M.R.J., B.J.B., S.A.M. and R.M. performed the phenotypic characterization of striped mouse and laboratory mouse tissues,

including immunofluorescence and histology. M.R.J. and S.A.M. performed the melanocyte cell culture experiments. J.A.M. did the evolutionary analysis. C.Y.F. generated the rhabdomyzed _Mus_

genome and lift-over annotation. J.G. and A.P. generated the immortalized _Rhabdomys_ fibroblasts. M.R.J. and R.M. wrote the manuscript with input from all authors. CORRESPONDING AUTHOR

Correspondence to Ricardo Mallarino. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing interests. PEER REVIEW PEER REVIEW INFORMATION _Nature Ecology & Evolution_

thanks Julien Debbache and Denis Headon for their contribution to the peer review of this work. Peer reviewer reports are available. ADDITIONAL INFORMATION PUBLISHER’S NOTE Springer Nature

remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. EXTENDED DATA EXTENDED DATA FIG. 1 PATTERNS OF HAIR PLACODE FORMATION IN STRIPED MICE.

A, Side views of E13.5–E15.5 striped mouse embryos showing stages before the emergence of trunk hair placodes. Whole-mount _in situ_ hybridization for placode markers _Dkk4_ and _Ctnnb1_

shows the presence of whisker placodes (arrows), which develop before trunk placodes. No expression is detected in dorsal skin. B, Side views of E16.5 striped mouse embryos displaying

spatially restricted patterns of trunk hair placode formation, as visualized by whole-mount _in situ_ hybridization for placode markers _Wif1_, _Bmp4_, _Wnt10b_ _Dkk1_. C, Hematoxylin-Eosin

staining on cross-sections of striped mouse E18.5 embryos reveals both mature placodes (arrows) and nascent placodes (asterisks); the latter are evidenced by thickening of the epidermis. D,

Side views of E18.5 striped mouse embryos showing placode emergence in previously placode-barren regions, as visualized by whole-mount _in situ_ hybridization for placode markers _Dkk1_ and

_Ctnnb1_. E, Hematoxilin and Eosin (H&E) stains of longitudinal sections from different dorsal regions in striped mouse embryos. Placodes in Regions 1 (R1) and 3 (R3) emerge later than

those in Region 2 (R2). Scale bars: 5 mm in (A and B); 200 µm (zoomed out) and 50 µm (inset) in (C); 5 mm in (D); 100 µm in (E). For A-E, three individuals per stage per gene were analysed.

EXTENDED DATA FIG. 2 EXPRESSION OF SELECTED WNT MODULATORS IN E16.5 STRIPED MOUSE EMBRYOS. Fold expression changes of Wnt modulators in skin regions (R1, R2, R3, R4) dissected for bulk

RNA-seq analysis. Shown are selected modulators that are expressed in a dorsoventral gradient. Fold expression changes were calculated from average FPKM values (n = 3 biologically

independent samples. EXTENDED DATA FIG. 3 ANALYSIS OF HAIR PLACODE AND DERMAL CONDENSATE MARKERS. A-B, Plots showing the subset of cells that express established hair placode (a) and dermal

condensate (b) markers in the dorsal skin of E16.5 striped mice. C-D, Dot plots of hair placode25 (c) and dermal condensate26 (d) markers showing expression changes among the three different

dorsal regions sampled. The size of the dot encodes the percentage of cells within a dorsal region, while the colour encodes the average expression level across all cells within a dorsal

region (blue is high, red is low). Asterisks depict markers with high expression levels in Region 2 (R2), compared to Region 1 (R1) and Region 3 (R3). As described in the main text, R2 has

visible hair follicles at this developmental stage, whereas R1 and R3 do not. EXTENDED DATA FIG. 4 EXPRESSION OF _SFRP2_ IN DERMAL FIBROBLASTS. A, _Sfrp2_ expressing fibroblasts are

expressed primarily in the reticular (lower) dermis. Papillary (upper) and reticular (lower) dermis fibroblasts were defined based on previously established markers3; Papillary dermis:

_Ntn1, Pdpn, Ackr4, Lrig1, Apcdd1;_ Reticular dermis: _Tgm2, Cnn1, Cdh2, Mgp, Dlk1_. B, At E16.5, expression levels of _Sfrp2_ and the percentage of fibroblasts expressing _Sfrp2_ are

highest in Region 1 (R1) and lowest in Region 3 (R3), in agreement with the dorsoventral gradient revealed by the bulk RNA-seq data. In B, n = 3 biologically independent samples. Left panel:

bars represent average expression levels. Right panel: mean values (+/- SEM). EXTENDED DATA FIG. 5 HIGH EXPRESSION OF _SFRP2_ IN THE RETICULAR (LOWER) DERMIS COINCIDES WITH LOW EXPRESSION

OF LEF1. A, _In situ_ hybridization in striped mouse E16.5 embryos shows that _Sfrp2_ is primarily expressed in the reticular dermis. Right side image shows expression of _Sfrp2_ at

subcellular resolution. B-C, LEF1 immunostaining in staged matched striped (B) and laboratory (C) mouse embryos. Red boxes denote zoomed-in regions. Scale bars: 200 µm (zoomed out) and 100 

µm (zoomed in) in A; 200 µm (zoomed out) and 50 µm (zoomed in) in B and C. NT = neural tube. For A-C, three different individuals were analysed. EXTENDED DATA FIG. 6 _DERMO1_ AND _SFRP2_

EXPRESSING FIBROBLASTS. A _Dermo-Cre_ mouse was used to drive Cre expression in dermal fibroblasts. As illustrated above, a subset of _Dermo1_ expressing fibroblasts express _Sfrp2_. Thus,

this mouse strain is adequate for driving expression of Cre in cells expressing _Sfrp2_. EXTENDED DATA FIG. 7 MATHEMATICAL SIMULATIONS. A, Schematic showing the role of _Sfrp2_ as an

inhibitor of Wnt signalling. B, Gradient steepness increases central stripe width independent of model. Each row depicts a schematic and equations governing a particular variant of our

modulator-activator-inhibitor system (left) and the resulting simulations of stripe spacing for different gradient steepness values using these models (right). In all cases, gradient

steepness affects stripe spacing. C, Predictions from an alternative model of positional information. Patterning based on positional information is inconsistent with our experimental

results. We illustrate this by considering two standard paradigms for stripe patterning by positional information. Under a classic ‘French Flag’ model (left, top), each stripe (marked in

grey) is assigned to a region of space in which a single morphogen gradient exists between two pathway-specific threshold concentrations (horizontal red lines). (top, left) Under such a

paradigm, a substantial reduction in morphogen expression, in this case by 80 percent, makes it impossible for the gradient to reach certain thresholds entirely, leading to stripe loss.

(bottom, left) Alternatively, stripes are frequently determined via an ‘opposing gradients’ motif via the interaction of multiple gradients. We depict one example, in which each stripe is

determined by two opposite facing gradients, such that a stripe forms in the region where each gradient exceeds a morphogen-specific threshold. (right, bottom) Major reduction of a single

morphogen eliminates one stripe while leaving the other unperturbed. EXTENDED DATA FIG. 8 GENERATION OF _IN VIVO_ GENOME EDITING IN STRIPED MOUSE. A, Schematic of the _Sfrp2_ locus (exons in

red) showing the transcriptional start site (TSS), protospacer adjacent motif (PAM) short guide RNA (sgRNA) target/sequence. Four types of deletions were achieved: 2 bp, 13 bp, 466 bp 527 

bp (white boxes). All mutations are predicted to cause frameshift mutations. B, Representative western blot of individuals carrying different combinations of wild-type and a 13 bp deleted

allele (wild type: _Sfrp2__+/+_; heterozygous: _Sfrp2__+/-_; homozygous: (_Sfrp__-/-_). _Sfrp__-/-_ have no detectable SFRP2 Protein (green). Bands ~30 kDa correspond to SFRP2 protein.

b-TUBULIN (~50 kDa, red) was used as a loading control. In B, two different individuals from each genotype were analysed. EXTENDED DATA FIG. 9 PHENOTYPIC CHARACTERIZATION OF _SFRP2_ MUTANTS.

A and B, Whole-mount _in situ_ hybridization for _Dkk4_ in wild-type and _Sfrp2_ knockout E16.5 embryos (A) and corresponding width measurements of dorsal regions 1 and 3 (that is, R1 and

R3) (B). Note that _Dkk4_ expression diminishes in response to _Sfrp2_ knockout. C, Hair length measurements in postnatal day 3 wild-type and _Sfrp2_ knockout individuals. In B and C, n = 3

biologically independent samples for each _Sfrp2_ knockout and _Sfrp2_ wild-type individuals. Source data EXTENDED DATA FIG. 10 _SFRP2_ PROMOTES MELANOGENESIS BY ACTIVATING WNT SIGNALLING.

_In situ_ hybridization showing specific _Sfrp2_ expression in the dermal papilla of P4 striped mouse hair follicles. B, Melanocytes were stably transduced with either a control (LV-GFP) or

an experimental (LV-Sfrp2GFP) lentivirus and expression of Wnt targets and melanogenesis genes in stably transduced control and experimental cells, as determined was determined via qPCR (_P_

 = 0.12026 (_Axin_); _P_ = 0.001816 (_C-myc_); _P_ = 0.006739 (_CyclinD_); _P_ = 0.001040 (_Mitf_); _P_ = 0.010712 (_Tyr_); ANOVA test; N = 4). C, Quantitative PCR (qPCR) showing _Sfrp2_

mRNA fold change levels along different dorsal skin regions in embryonic and postnatal stages (E16.5: _P_ = 0.0283 (R1vsR2); _P_ = 0.0062 (R1vsR3); _P_ = 0.3959 (R2vsR3); E19.5: _P_ = 0.8685

(R1vsR2); _P_ = 0.6319 (R1vsR3); _P_ = 0.9015 (R2vsR3); P0: _P_ = 0.9724 (R1vsR2); _P_ = 0.8207 (R1vsR3); _P_ = 0.6971 (R2vsR3); P4: _P_ = 0.0003 (R1vsR2); _P_ = 0.0022 (R1vsR3); _P_ = 

0.0001 (R2vsR3); ANOVA test; N = 3 for E16.5, E19.5 P0, N = 4 for P4). Scale bars in A: 100 µm (left) and 25 µm (right). In A, three different individuals were analysed. In B and C, data are

presented as mean values +/− SEM. Source data SUPPLEMENTARY INFORMATION SUPPLEMENTARY INFORMATION Supplementary Tables 1–5. REPORTING SUMMARY PEER REVIEW FILE SUPPLEMENTARY DATA 1

Differentially expressed genes between regions 1 and 4 of E16.5 striped mice skin. Differentially expressed genes were determined using DESeq2. _P_ value corrected for multiple testing

(_P_adj < 0.05). SUPPLEMENTARY DATA 2 Differentially expressed genes between Sfrp2high and Sfrp2l°w fibroblast populations. Differentially expressed genes were determined using DESeq2.

_P_ value corrected for multiple testing (_P_adj < 0.05). SOURCE DATA SOURCE DATA FIGS. 2E,M AND 4D,F AND EXTENDED DATA FIGS. 9A,B AND 10B,C. RIGHTS AND PERMISSIONS Springer Nature or its

licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the

accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE

Johnson, M.R., Li, S., Guerrero-Juarez, C.F. _et al._ A multifunctional Wnt regulator underlies the evolution of rodent stripe patterns. _Nat Ecol Evol_ 7, 2143–2159 (2023).

https://doi.org/10.1038/s41559-023-02213-7 Download citation * Received: 29 March 2023 * Accepted: 27 August 2023 * Published: 09 October 2023 * Issue Date: December 2023 * DOI:

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