The evolutionary history and spectral tuning of vertebrate visual opsins

Hagen, Joanna F.D.; Roberts, Natalie S.; Johnston, Robert J.

doi:10.1016/j.ydbio.2022.10.014

Cited by 34 publications

(22 citation statements)

References 312 publications

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“…In another example, the full WT model highlighted p181, a phylogenetically conserved counterion in the retinal-opsin Schiff base interaction for all non-vertebrate opsins [54]. Additionally, the transition from E to H at p181 (E181H) is a characteristic of the red-shifted vertebrate LWS opsins [32], easily visualized in Figure 4C. When predicting λ max of bovine rhodopsin with mutation E181H, the WT model predicted a red-shift compared to wild type, as observed with the natural evolution of the LWS opsin lineage.…”

Section: Predicts Tuning Sites From Wild-type Sequences Alonementioning

confidence: 99%

“…We also assume the photopigment uses 11-cis-retinal, as all heterologously expressed opsins in VPOD were reconstituted using this chromophore. However, this assumption is violated in some organisms because they use 13-cis-retinal as the in-vivo chromophore [71][72][73], which is associated with a red-shift in λ max [32,71] . At the organ-level, filters such as oil droplets in bird eyes [74][75][76][77], pigments in butterfly eyes [78], or a combination of transmissive filter and narrow band reflector in mantis shrimp larval eyes [79], each may selectively influence light reaching photoreceptor cells and therefore animal color sensitivity.…”

Section: The Important Relationship Between Data Availability and Pre...mentioning

confidence: 99%

“…Common methods of characterizing spectral sensitivities and λ max include organ-level electroretinograms (ERG) [24][25][26], cell-level microspectrophotometry (MSP) [27][28][29][30][31], and purification of heterologously expressed opsins followed by spectrophotometry. Different opsins are tuned by changes in amino acid sequences to respond to different wavelengths of light, and many previous studies have expressed experimentally mutated opsins and measured spectral sensitivities to establish genotype-phenotype connections [32][33][34][35][36]. Although other factors sometimes affect spectral responsiveness, opsins provide a rare case where an intrinsic molecular function extends rather directly to organismal phenotypes, especially those involving color sensitivity.…”

Section: Introductionmentioning

confidence: 99%

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Discovering genotype-phenotype relationships with machine learning and the Visual Physiology Opsin Database (VPOD)

Frazer,

Baghbanzadeh,

Rahnavard

et al. 2024

Preprint

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BackgroundPredicting phenotypes from genetic variation is foundational for fields as diverse as bioengineering and global change biology, highlighting the importance of efficient methods to predict gene functions. Linking genetic changes to phenotypic changes has been a goal of decades of experimental work, especially for some model gene families including light-sensitive opsin proteins. Opsins can be expressed in vitro to measure light absorption parameters, including λmax - the wavelength of maximum absorbance - which strongly affects organismal phenotypes like color vision. Despite extensive research on opsins, the data remain dispersed, uncompiled, and often challenging to access, thereby precluding systematic and comprehensive analyses of the intricate relationships between genotype and phenotype.ResultsHere, we report a newly compiled database of all heterologously expressed opsin genes with λmaxphenotypes called the Visual Physiology Opsin Database (VPOD).VPOD_1.0contains 864 unique opsin genotypes and corresponding λmaxphenotypes collected across all animals from 73 separate publications. We useVPODdata anddeepBreaksto show regression-based machine learning (ML) models often reliably predict λmax, account for non-additive effects of mutations on function, and identify functionally critical amino acid sites.ConclusionThe ability to reliably predict functions from gene sequences alone using ML will allow robust exploration of molecular-evolutionary patterns governing phenotype, will inform functional and evolutionary connections to an organism’s ecological niche, and may be used more broadly forde-novoprotein design. Together, our database, phenotype predictions, and model comparisons lay the groundwork for future research applicable to families of genes with quantifiable and comparable phenotypes.Key PointsWe introduce the Visual Physiology Opsin Database (VPOD_1.0), which includes 864 unique animal opsin genotypes and corresponding λmaxphenotypes from 73 separate publications.We demonstrate that regression-based ML models can reliably predict λmax from gene sequence alone, predict non-additive effects of mutations on function, and identify functionally critical amino acid sites.We provide an approach that lays the groundwork for future robust exploration of molecular-evolutionary patterns governing phenotype, with potential broader applications to any family of genes with quantifiable and comparable phenotypes.

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Section: Predicts Tuning Sites From Wild-type Sequences Alonementioning

confidence: 99%

Section: The Important Relationship Between Data Availability and Pre...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Discovering genotype-phenotype relationships with machine learning and the Visual Physiology Opsin Database (VPOD)

Frazer,

Baghbanzadeh,

Rahnavard

et al. 2024

Preprint

View full text Add to dashboard Cite

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“…This results in a lower energy requirement for isomerization, leading to opsins that predominantly absorb long-wavelength/low-energy light, commonly found in the red spectrum. On the other hand, the presence of S/T near the Schiff-base site stabilizes the ground state of the retinal, requiring the activation of short-wavelength/highenergy light, typically found in the blue spectrum (Hagen et al, 2023). Our structure reveals that residues S87 2.57 , S289 7.39 and S292 7.42 are located in proximity to the Schiff-base region, while T201 5.39 is positioned near the β-ionone ring of the retinal (Figures 3H and 3I).…”

Section: Retinal Binding Pocketmentioning

confidence: 99%

Cryo-EM structures of human cone visual pigments

Peng,

Li,

Jiang

et al. 2024

Preprint

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Trichromatic color vision in humans constitutes a pivotal evolutionary adaptation, endowing individuals with the capacity to discern and discriminate a diverse spectrum of colors. This unique visual capability confers a selective advantage crucial for successful adaptation, survival, and reproductive success in the natural environment. Color vision in humans is facilitated by the red, green, and blue cone visual pigments within cone photoreceptor cells. These pigments consist of a G-protein-coupled receptor opsin apoprotein and a chromophore covalently linked to opsins. Despite the elucidated structure of rhodopsin, the structures of cone visual pigments have yet to be determined. Here, we present the cryo-EM structures of three human cone visual pigments in complex with G proteins. Our structural analysis reveals detailed interactions between cone opsins, all-trans-retinal, and G proteins, indicating their active state. We also provide a concise summary and analysis of mutations in human cone opsins, elucidating potential relationships between residue substitutions and spectral tuning. Notably, S1162.67Y, A2335.52S, Y2776.44F were found to induce a blue shift in the absorption spectrum of the red-pigment, while the substitutions W2816.48Y and K3127.43A resulted in the absence of the absorption spectrum. The structural elucidation of human cone visual pigments significantly contributes to our understanding of how distinct types of cone cells perceive light across varying wavelengths. Furthermore, it provides a deeper insight into the functioning of the human trichromatic vision system, probing the mechanisms enabling humans to perceive a broad spectrum of colors.

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“…The ability to perceive and respond to colours plays a significant role in the evolution of different species, shaping their visual systems, colour patterns, and behaviours in response to their environment and selective pressures (Osorio & Vorobyev, 1997; van der Kooi et al ., 2021; Hagen et al ., 2023). Many animals use colours for a range of competing functions, such as thermoregulation (Stuart-Fox et al ., 2017), photoprotection (Lahti & Ardia, 2016), conspecific recognition (Siebeck et al ., 2010), attracting mates (Endler & Houde, 1995), capturing preys (Heiling et al ., 2003), and avoiding parasites (Caro et al ., 2019) and predators ( e.g., camouflage, Price et al ., 2019; warning signals , Lim et al ., 2019; mimicry , Davis Rabosky et al ., 2016; and private communication , Marshall & Stevens, 2014).…”

Section: Introductionmentioning

confidence: 99%

Biofluorescence reveals hidden patterns in chitons with implications to visual ecology

Grimaldi,

Guidi,

Jardim

et al. 2023

Preprint

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Biofluorescence is apparently widespread in the tree of life. Fluorescence has the potential to contribute to the totality of light leaving an organism’s surface and will therefore circumscribe how an individual could be detected and discriminated by their visual predators. Here, we: (i) documented the first record of biofluorescence on polyplacophorans, (ii) spectrally characterized the biofluorescence on shells of living chitons, (iii) measured the colour patch pattern variation, (iv) separated the colour of their shells into their reflectance and fluorescence components, and (v) combined these data with field measurements to calculate perceptual distance in chromatic and achromatic contrasts based on the visual system of their major visually guided predators. We found a red biofluorescence that enhances the brightness of chiton shells, along with a correlation showing that as individuals grow larger, the fluorescing surface area becomes smaller. Our visual models suggest that fluorescence decreases the achromatic contrast of chitons against their naturally fluorescent substrates for most visual predators, making them less noticeable to specific predators. Our results support the potential visual functionality of biofluorescence and open new hypotheses regarding its ecological roles to further investigations.

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The evolutionary history and spectral tuning of vertebrate visual opsins

Cited by 34 publications

References 312 publications

Discovering genotype-phenotype relationships with machine learning and the Visual Physiology Opsin Database (VPOD)

Discovering genotype-phenotype relationships with machine learning and the Visual Physiology Opsin Database (VPOD)

Cryo-EM structures of human cone visual pigments

Biofluorescence reveals hidden patterns in chitons with implications to visual ecology

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