Colour Vision: Self-Centered Fly Photoreceptors Communicate over Distances

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Dispatches REFERENCES

5. Rowe, N. (2018). Lianas. Curr. Biol. 28, R249– R252.

1. Ragni, L., and Greb, T. (2018). Secondary growth as a determinant of plant shape and form. Semin. Cell Dev. Biol. 79, 58–67.

6. Masrahi, Y.S. (2014). Ecological significance of wood anatomy in two lianas from arid southwestern Saudi Arabia. Saudi J. Biol. Sci. 21, 334–341.

2. Groover, A.T. (2005). What genes make a tree a tree? Trends Plant Sci. 10, 210–214. 3. Chery, J.G., Pace, M.R., Acevedo-Rodriguez, P., Specht, C.D., and Rothfels, C.J. (2020). Modifications during early plant development promote the evolution of nature’s most complex woods. Curr. Biol. 30, 237–244. 4. Schnitzer, S.A., and Bongers, F. (2002). The ecology of lianas and their role in forests. Trends Ecol. Evol. 17, 223–230.

7. Carlquist, S. (1999). Wood anatomy, stem anatomy, and cambial activity of Barbeuia (Caryophyllales). IAWA J. 20, 431–440. 8. Spicer, R., and Groover, A. (2010). The evolution of development of the vascular cambium and secondary growth. New Phytol. 186, 577–592. 9. Carlquist, S. (2007). Successive cambia revisited: ontogeny, histology, diversity, and functional significance. J. Torrey Bot. Soc. 134, 301–332.

10. Verbeeck, H., and Kearsley, E. (2016). The importance of including lianas in global vegetation models. Proc. Nat. Acad. Sci. USA 113, E4–E4.

11. Phillips, O.L., Va´squez Martı´nez, R., Arroyo, L., Baker, T.R., Killeen, T., Lewis, S.L., Malhi, Y., Monteagudo Mendoza, A., Neill, D., Nu´n˜ez Vargas, P., et al. (2002). Increasing dominance of large lianas in Amazonian forests. Nature 418, 770–774.

12. Tomescu, A.M.F., and Groover, A.T. (2019). Mosaic modularity: an updated perspective and research agenda for the evolution of vascular cambial growth. New Phytol. 222, 1719–1735.

Colour Vision: Self-Centered Fly Photoreceptors Communicate over Distances Juliane Uhlhorn and Mathias F. Wernet*

€t Berlin, Fachbereich Biologie, Chemie & Pharmazie, Institut fu¨r Biologie, Division of Neurobiology. Ko¨nigin-Luise Strasse 1-3, Freie Universita 14195 Berlin, Germany *Correspondence: [email protected] https://doi.org/10.1016/j.cub.2019.11.050

A new study shows that the synaptically interconnected axon terminals of colour-sensitive fly photoreceptors that sample the same point in visual space receive additional inhibition from surrounding units; the resulting additional chromatic comparisons result in an optimal decorrelation of photoreceptor inputs. There are striking parallels between newly identified horizontal interactions and those mediated by mammalian horizontal cells. The ability to tell apart differently coloured objects solely based on their spectral content and irrespective of their relative intensity is implemented by many visual systems across animal species, be it nectar-collecting honeybees visiting colourful flowers or human art lovers strolling through galleries and museums [1]. In these phylogenetically diverse systems, colour vision emerges via the comparison of outputs from different photoreceptor classes, each containing one specific opsin molecule of defined spectral sensitivity, for instance, short (S), mid (M), and long (L) wavelength cones in humans. Psychophysical and physiological studies in trichromatic primates revealed how these signals are further processed via separated, color-opponent retinal circuits [2].

For instance, a ‘yellow-blue’ opponency mechanism compares signals of S cones with signals of both L and M cones [1]. The synaptic mechanisms for creating either ON- or OFF-pathways, mediating responses to light increments and decrements, respectively, with receptive fields of a center-surround organization (S-center with L+M surround in the case of ‘yellowblue’ opponency) are being characterized in great detail [2]. Importantly, the L+M surround information is provided by GABAergic horizontal cells, thereby shaping the responses of bipolar cells (and ultimately retinal ganglion cells), equipping them with receptive fields where the S center and the L+M surround have opposite signs. It should be noted that horizontal cells even provide direct synaptic

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feedback inhibition onto cone photoreceptors, thereby directly shaping the response properties of mammalian cone cells [3]. Dissecting the circuitry underlying mammalian colour vision is challenging given the high diversity in cellular subtypes [4], the complex synaptic connectivity at an ultrastructural level [5], and the technical and ethical challenges of working with transgenic primate models [6]. In this issue of Current Biology, Heath et al. [7] report evidence that axon terminals of stochastically distributed, colour-sensitive photoreceptors of the so-called ‘pale’ and ‘yellow’ subtypes in the fruit fly Drosophila melanogaster are also inhibited by surrounding units. At first glance, this finding is exciting given the lack of evolutionary conservation between fly and mammalian visual

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Dispatches systems. Furthermore, one would have naively expected more complex properties to arise successively in the fly optic lobe (instead of so early in visual pathways), as information is channeled through cellular complex neuropils towards the central brain [8] — similar to what has been demonstrated for the processing of visual motion via ON- and OFF-channels via a multitude of cell types [9]. Heath et al. [7] used a recent iteration of the genetically encoded calcium indicator GCaMP to visualize the activity changes in axon terminals of colour-sensitive photoreceptors (called R7 and R8 in flies) in response to colour stimuli. As described in an earlier study [10], the authors report inhibition within one columnar element in the optic lobe, where two photoreceptors sample the same point in visual space, leading to colouropponent signals in their terminals (intracolumnar inhibition, via direct, inhibitory photoreceptor–photoreceptor synapses) [10]. But in a surprising twist, Heath et al. [7] now show that photoreceptor terminals also receive additional inhibitory input from photoreceptor terminals in neighboring columnar elements (hence, the ‘surround’). For instance, in the case of a blue-sensitive ‘pale’ R8 photoreceptor, this leads to a distinct hyperpolarization at longer wavelengths (which matches the spectral sensitivity of ‘yellow’ R8 cells from the surround). How is this inter-columnar inhibition mediated between photoreceptor terminals? Heath et al. [7] focus on a ‘distal medulla’ cell type named Dm9, which is rather peculiar in that it not only receives massive synaptic input from both R7 and R8 photoreceptors, but it also provides strong synaptic feedback onto both (Figure 1A). More importantly, one given Dm9 cell is synaptically connected to R7 and R8 cells from approximately seven neighboring columnar units [11]. This cell type therefore seems ideally suited for mediating the colour-opponent centersurround effects, in addition to direct intra-columnar inhibition between R7 and R8 (Figure 1B,C). Heath et al. [7] went on to dissect the exact role of Dm9 in creating these photoreceptor responses in a very systematic, stepwise way, by

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Figure 1. Dm9 and photoreceptor connectivity. (A) Multicolumnar connectivity of one Dm9 cell (black), spanning over several ‘pale’ and ‘yellow’ columns, where blue-sensitive R8 cells are paired with UV-sensitive R7 (pale) or green-sensitive R8 with UVsensitive R7 (yellow). Inter-ommatidial connections of Dm9 and photoreceptors are located in medulla layers M1 and M6, whereas intra-ommatidial connections of Dm9 and photoreceptors spread from M1 to M6. (B) Distribution of synaptic contacts of photoreceptors onto each other in the same column (layer M1–M3): ‘pale’ R8 (blue) makes synaptic contacts (cyan) onto ‘pale’ R7 of the same columns (pink), and vice versa (red). (C) Distribution of synapses of photoreceptors onto Dm9 shown for one column. Feedback synapses from Dm9 onto R7 and R8 (red and light blue, respectively) as well as R8 synapses onto Dm9 (yellow) can be found between medulla layer M1 to M3. Synaptic contacts of R7 onto Dm9 (green) are mainly found in layer M6. Dm9 scaffold structure (A,C) and synaptic distributions adapted from [18] and processed using https://neuronlp.fruitflybrain.org/. For more information, see [19]. Photoreceptor–photoreceptor synapses (B) were deduced from the same connectomic dataset as well as light microscopy [20].

consecutively ‘deconstructing’ all aspects of the GCaMP signals using a combination of cell-type-specific, molecular genetic manipulations. For instance, photoreceptors can be uncoupled from all other inputs (both intra- and inter-columnar) via cell autonomous rescue of phototransduction (called a norpA rescue), thereby revealing the basic tuning curve of the photoreceptor (shown for a ‘pale’ R8 subtype in Figure 2A). Using the same technique, with different pairwise norpA rescues, first intra-columnar inhibition is visualized (between R7 and R8 sampling the same point in visual space leading to an inhibition of R8 in the ultraviolet range; Figure 2B). This is followed by visualization of inter-columnar inhibition, resulting in additional color-opponent contributions. More importantly, silencing of Dm9 cells results in a loss of intercolumnar inhibition of the exemplary ‘pale’ R8 cells at higher wavelengths, hence eliminating the ‘center surround’ component of the color-opponent signal (Figure 2C). Finally, Heath et al. [7] used recent structural data to develop an anatomically

constrained recurrent model of this early colour circuit. This allowed them to predict the spatio-chromatic receptive fields of R7s and R8s consisting of a color-opponent center and a broadband surround (Figure 2D), by analogy to the mammalian retina. What is the role/consequence of this circuit mechanism on wavelength encoding? Inspired by a classical study in trichromatic primates [12], Heath et al. [7] were able to show that the described computation, comparing UV to visible light and blue light to both UV and green light, efficiently decorrelates the highly correlated photoreceptor inputs, while still preserving enough information to fully reconstruct chromatic stimuli. Given these striking similarities between flies and humans, one cannot help and wonder what other motifs are shared across species regarding the cellular and synaptic structure of neural circuits guiding the perception of different colours, downstream of colour-opponent input elements [13]. For such studies, Drosophila can serve as an attractive model system. For instance, the raw data now exist for reconstructing the whole

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Figure 2. Changes of pR8 responses with different input. (A) Tuning curve of a ‘pale’ R8 photoreceptor (blue) uncoupled from all other photoreceptor inputs (via norpA rescue), to different wavelengths (300–650 nm). Black dashed line depicts the baseline activity of ‘pale’ pR8 while stimulated with background light. For reference, tuning curves of uncoupled ‘pale’ R7 (pink) and ‘yellow’ R8 (green) are shown as dashed lines. (B) Tuning curve of a ‘pale’ R8 photoreceptor with the ‘pale’ R7 of the same column being functional, revealing intra-ommatidial inhibition at short wavelengths. (C) Tuning curve of a ‘pale’ R8 photoreceptor in wild-type background reveals additional inter-ommatidial inhibition, i.e. inhibition at longer wavelengths. The grey line depicts the same tuning curve with Dm9 cells inactivated. (D) Modeling of spectral filtering for inner photoreceptors. Shown is the modeled output for the predicted photoreceptors ‘pale’ R8 (blue) in the center and their surround over different wavelengths. The modeled outputs for ‘pale’ R7 (pink), ‘yellow’ R7 (magenta) or ‘yellow’ R8 (green) are seen below. Different peaks in the center are coupled with broadband surround inhibition, respectively. Adapted from [7].

synaptic connectome of an entire adult fly brain [14], and therefore circuits downstream of ‘pale’ and ‘yellow’ photoreceptors. Interestingly, Heath et al. [7] found that in this hard-wired circuit, the electron microscopy (EM)-derived synaptic count can approximate synaptic strength, giving credence to structural measurement as quantitative predictors of circuit function in this context. Furthermore, a growing arsenal of cell-type-specific driver lines allows for the systematic imaging of neuronal activity, as well as for very specific manipulations (activation or silencing of a given cell type, probing of synaptic connectivity between cell types) in the behaving animal, in an almost unbiased fashion [15]. Many questions need to be addressed regarding colour vision. In which ways (and by which cell types) are the photoreceptor responses described here further processed? Where is colour information ultimately represented in the central fly brain? What kinds of information are photoreceptor responses integrated with? For all these experiments to be successful, the behavioral paradigms must be constantly refined, since comparably little literature exists on colour-specific responses of flies, be it from single fly assays like virtual flight arenas or spherical treadmills,

or more unrestrained population assays [16]. The hope is that the relative simplicity of the fly brain will allow for a rapid and efficient access to neural circuit motifs that are conserved between flies and humans, similar to what was described for the detection of motion [17].

Behnia, R. (2020). Circuit mechanisms underlying chromatic encoding in Drosophila photoreceptors. Curr. Biol. 30, 264–275. 8. Fischbach, K.F., and Dittrich, A.P.M. (1989). The optic lobe of Drosophila melanogaster. 1. A golgi analysis of wild-type structure. Cell Tissue Res. 258, 441–475. 9. Borst, A. (2014). Neural circuits for motion vision in the fly. Cold Spring Harb. Symp. Quant. Biol. 79, 131–139.

REFERENCES 1. Kelber, A., Vorobyev, M., and Osorio, D. (2003). Animal colour vision–behavioural tests and physiological concepts. Biol. Rev. Camb. Philos. Soc. 78, 81–118. 2. Dacey, D.M. (2000). Parallel pathways for spectral coding in primate retina. Annu. Rev. Neurosci. 23, 743–775. 3. Chapot, C.A., Euler, T., and Schubert, T. (2017). How do horizontal cells ’talk’ to cone photoreceptors? Different levels of complexity at the cone-horizontal cell synapse. J. Physiol. 595, 5495–5506. 4. Baden, T., Berens, P., Franke, K., Roman Roson, M., Bethge, M., and Euler, T. (2016). The functional diversity of retinal ganglion cells in the mouse. Nature 529, 345–350. 5. Helmstaedter, M., Briggman, K.L., Turaga, S.C., Jain, V., Seung, H.S., and Denk, W. (2013). Connectomic reconstruction of the inner plexiform layer in the mouse retina. Nature 500, 168–174. 6. Coors, M.E., Glover, J.J., Juengst, E.T., and Sikela, J.M. (2010). The ethics of using transgenic non-human primates to study what makes us human. Nat. Rev. Genet. 11, 658–662. 7. Heath, S.L., Christenson, M.P., Oriol, E., Saavedra-Weisenhaus, M., Kohn, J.R., and

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10. Schnaitmann, C., Haikala, V., Abraham, E., Oberhauser, V., Thestrup, T., Griesbeck, O., and Reiff, D.F. (2018). Color processing in the early visual system of Drosophila. Cell 172, 318–330 e318. 11. Nern, A., Pfeiffer, B.D., and Rubin, G.M. (2015). Optimized tools for multicolor stochastic labeling reveal diverse stereotyped cell arrangements in the fly visual system. Proc. Natl. Acad. Sci. USA 112, E2967–E2976. 12. Buchsbaum, G., and Gottschalk, A. (1983). Trichromacy, opponent colours coding and optimum colour information transmission in the retina. Proc. R. Soc. Lond. B 220, 89–113. 13. Longden, K.D. (2016). Central brain circuitry for color-vision-modulated behaviors. Curr. Biol. 26, R981–R988. 14. Zheng, Z., Lauritzen, J.S., Perlman, E., Robinson, C.G., Nichols, M., Milkie, D., Torrens, O., Price, J., Fisher, C.B., Sharifi, N., et al. (2018). A complete electron mcroscopy volume of the brain of adult Drosophila melanogaster. Cell 174, 730–743.e722. 15. Jenett, A., Rubin, G.M., Ngo, T.T., Shepherd, D., Murphy, C., Dionne, H., Pfeiffer, B.D., Cavallaro, A., Hall, D., Jeter, J., et al. (2012). A GAL4-driver line resource for Drosophila neurobiology. Cell Rep. 2, 991–1001.

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Dispatches 16. Song, B.M., and Lee, C.H. (2018). Toward a mechanistic understanding of color vision in insects. Front. Neural Circuits 12, 16. 17. Mauss, A.S., Vlasits, A., Borst, A., and Feller, M. (2017). Visual circuits for direction selectivity. Annu. Rev. Neurosci. 40, 211–230. 18. Takemura, S.Y., Xu, C.S., Lu, Z., Rivlin, P.K., Parag, T., Olbris, D.J., Plaza, S., Zhao, T., Katz,

W.T., Umayam, L., et al. (2015). Synaptic circuits and their variations within different columns in the visual system of Drosophila. Proc. Natl. Acad. Sci. USA 112, 13711–13716. 19. Ukani, N.H., Tomkins, A., Yeh, C.-H., Bruning, W., Fenichel, A.L., Zhou, Y., Huang, Y.-C., Florescu, D., Ortiz, C.L., Richmond, P., et al. (2016). NeuroNLP: a natural language portal

for aggregated fruit fly brain data. bioRxiv, 092429. 20. Sancer, G., Kind, E., Plazaola-Sasieta, H., Balke, J., Pham, T., Hasan, A., Munch, L.O., Courgeon, M., Mathejczyk, T.F., and Wernet, M.F. (2019). Modality-specific circuits for skylight orientation in the fly visual system. Curr. Biol. 29, 2812–2825.

Plant Evolution: Assembling Land Plants Philip Donoghue1,* and Jordi Paps2 1School

of Earth Sciences, University of Bristol, Life Sciences Building, Tyndall Avenue, Bristol BS8 1TQ, UK of Biological Sciences, University of Bristol, Life Sciences Building, Tyndall Avenue, Bristol BS8 1TQ, UK *Correspondence: [email protected] https://doi.org/10.1016/j.cub.2019.11.084 2School

Traditional evolutionary scenarios posit that land plants emerged from land plant-like relatives, the charophytes. New phylogenies suggest a closer affinity to simpler pond scum relatives, and evidence the gradual assembly of the land plant genome, revealing a phenotypic simplification from the complex ancestors envisaged by traditional scenarios. The emergence of land plants (Embryophyta) is unquestionably one of the most formative episodes in the evolution of the Earth System; increasing the energy budget of life on Earth through photosynthesis on land, altering the albedo of the continents, increasing weathering rates, and increasing the complexity of global biogeochemical cycles [1]. It also constitutes the earliest step in the evolution of this great kingdom of multicellular organisms and led to the establishment of terrestrial habitats ripe for exploitation by animal lineages. When and how this revolution was brought about is a central question in the evolution of terrestrial systems. Evolutionary scenarios for evolution of land plants from within the streptophytes (Figure 1; the clade including land plants) have been built around plant-like relatives of the land plants, especially the Charophyceae and Coleochaetophyceae lineages of charophyte algae which, like land plants, exhibit branching, tissue-grade organization, cell walls with plasmodesmata, apical meristems, asymmetric cell division and zygotes

that produce sporopollenin [2,3]. Embryophytes are envisaged to have emerged through the evolution of a multicellular sporophyte that featured a protective cuticular layer, stomata, seta (stalks) to support sporangia, and spore-forming tissues and spores. However, phylogenomic analyses now indicate convincingly that the Zygnematophyceae, hitherto perceived as an outgroup of Charophyceae, Coleochaetophyceae and land plants, are instead the closest relatives of the land plants (for example [4]; Figure 1). Although some lineages of Zygnematophyceae exhibit multicellular branching, many are unicellular, casting doubt on long-standing evolutionary scenarios for the origin of land plants and increasing the phenotypic and ecological gulf between land plants and their immediate charophyte relatives. In a new study, Cheng and colleagues [5] attempt to bridge that gap, describing genome assemblies for Spirogloea muscicola and Mesotaenium endlicherianum. These two species are among the earliest-branching lineages of Zygnematophyceae, strategically positioned in streptophyte phylogeny to

provide insight into the evolutionary assembly of land plant genomes. The study reveals that the emergence of zygnematophycean algae and land plants is associated with the origin of new transcription factors, phytohormone signalling genes, and factors involved in the synthesis of the plant cell wall. Ancestry of Zygnematophyceae and land plants is also associated with gene families that, in extant land-plant model systems, are associated with responses to biotic and abiotic stressors, such as desiccation, essential adaptations to a life on land and in ephemeral water bodies. It appears increasingly clear that many of the essential elements of a land plant genome have a much deeper evolutionary origin within Streptophyta and beyond. For example, many of the genes required for fungal symbiosis were acquired prior to the last common ancestor of Zygnematophyceae and land plants [6]; genes implicated in cell wall biosynthesis evolved deep within Charophyta [7], whereas key land-plant hormones, signalling-pathway components, as well as drought- and light-stress-response factors all

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