Similar Degenerative Patterns of Olfactory Receptor Genes in the Giant Panda and Marine Carnivores

Similar Degenerative Patterns of Olfactory Receptor Genes in the Giant Panda and Marine Carnivores | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 26 December 2025 V1 Latest version Share on Similar Degenerative Patterns of Olfactory Receptor Genes in the Giant Panda and Marine Carnivores Authors : LiWen Kang 0009-0006-7414-0489 , Junjie Chen , Mingsheng Hong 0000-0001-7830-5398 , Wei Wei 0009-0006-6092-4856 , Zuofu Xiang 0000-0001-6133-3261 , and zejun Zhang 0000-0003-3555-4647 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.176673629.99880144/v1 217 views 109 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The olfactory receptor (OR) gene family is the largest in mammals, with its repertoire size and functional status tightly linked to ecological adaptation. Here, we systematically identified and compared OR genes in 25 carnivoran species, including both marine and terrestrial carnivores, to investigate evolutionary patterns under different ecological backgrounds. Marine carnivores exhibit a significant reduction in OR gene numbers and an increased proportion of pseudogenes, consistent with diminished reliance on olfaction in aquatic environments. Strikingly, giant panda—despite being terrestrial—shows a similar genomic signature, with reduced OR gene numbers and high pseudogene proportions. Phylogenetic and selective stress analyses revealed that both giant pandas and Marine carnivores experienced the loss of OR genes and weakened functional constraints. Principal component and regression analyses further demonstrate that dietary specialization and restricted ecological niches are likely major drivers of OR gene evolution. Collectively, these findings suggest that the degeneration of giant panda’s OR genes is closely tied to its unique ecological niche and resembles patterns observed in marine carnivores, providing new insights into the ecological drivers of sensory genome evolution. Similar Degenerative Patterns of Olfactory Receptor Genes in the Giant Panda and Marine Carnivores Authors: Liwen Kang 1,2 , Junjie Chen 1 , Mingsheng Hong 2 , Wei Wei 2 , Zuofu Xiang 1, *, Zejun Zhang 2,3, * Affiliations 1 College of Forestry, Central South University of Forestry and Technology, Changsha, Hunan 410004, China 2 Key Laboratory of Southwest China Wildlife Resources Conservation (Ministry of Education), China West Normal University, Nanchong 637000, China 3 College of Pharmacy, Chengdu University of Traditional Chinese Medicine,Chengdu 611137, China *To whom correspondence should be addressed: Zuofu Xiang, College of Forestry, Central South University of Forestry and Technology, Changsha, Hunan, 410004, China. Email: [email protected] Zejun Zhang, College of Pharmacy, Chengdu University of Traditional Chinese Medicine,Chengdu 611137, China. Email: [email protected] Abstract: The olfactory receptor (OR) gene family is the largest in mammals, with its repertoire size and functional status tightly linked to ecological adaptation. Here, we systematically identified and compared OR genes in 25 carnivoran species, including both marine and terrestrial carnivores, to investigate evolutionary patterns under different ecological backgrounds. Marine carnivores exhibit a significant reduction in OR gene numbers and an increased proportion of pseudogenes, consistent with diminished reliance on olfaction in aquatic environments. Strikingly, giant panda—despite being terrestrial—shows a similar genomic signature, with reduced OR gene numbers and high pseudogene proportions. Phylogenetic and selective stress analyses revealed that both giant pandas and Marine carnivores experienced the loss of OR genes and weakened functional constraints. Principal component and regression analyses further demonstrate that dietary specialization and restricted ecological niches are likely major drivers of OR gene evolution. Collectively, these findings suggest that the degeneration of giant panda’s OR genes is closely tied to its unique ecological niche and resembles patterns observed in marine carnivores, providing new insights into the ecological drivers of sensory genome evolution. Keywords: olfactory receptor genes; Carnivora; gene loss; giant panda; ecological adaptation Introduction Olfaction is one of the most important sensory modalities in animals, playing a crucial role in perceiving the environment, locating food, selecting mates, and avoiding predators (Hildebrand and Shepherd 1997; Jacobs 2013; Graham et al. 2018; Motti et al. 2018). Odorant molecules in the environment are detected by olfactory receptors (OR) expressed in the olfactory epithelium of the nasal cavity (Krautwurst et al. 1998; Touhara and Vosshall 2009; Kaupp 2010). OR are G protein-coupled receptors (GPCRs) characterized by seven α-helical transmembrane domains, forming a region of approximately 310 amino acids (Lindemann and Hoener 2005). They are widely distributed among vertebrates and were first identified in rodents in 1991 (Gilad 2005). Each OR gene encodes a receptor protein, enabling animals to discriminate among a wide variety of chemical compounds (Saito et al. 2009). A single odorant molecule can be recognized by multiple OR, while a single OR can recognize multiple odorants. The combinatorial recognition of odorants by different OR ultimately generates a unique odor-coding pattern, thereby allowing the identification of a vast array of environmental odors (Yau 1998; Shirasu et al. 2014). Based on amino acid sequence divergence, OR proteins are generally classified into Class I and Class II, with the former preferentially binding water-soluble odorants (Niimura and Nei 2005) and the latter binding hydrophobic odorants (Oka et al. 2006; Cichy et al. 2019). The number of OR genes varies among mammals, and the OR gene repertoires of different species differ greatly (Niimura and Nei 2005a), which is mainly due to the differences in ecological niches of each species (Graham et al. 2018). The most pronounced differences occur between terrestrial and aquatic mammals (Liu et al. 2019). For example, african elephant possesses the largest known functional OR gene repertoire, encoding nearly 2,000 genes (Niimura et al. 2014), whereas cetaceans harbor only a few dozen functional OR gene (Liu et al. 2019). During the transition from land to aquatic habitats, marine mammals underwent substantial functional OR genes loss, and eventually evolved an echolocation system adapted to the water environment (Kishida et al. 2015). Similarly, the platypus, in order to adapt to the aquatic environment, has suffered from the degeneration of olfaction and has only a relatively small functional OR gene of approximately 350 (Niimura and Nei 2005b). Primates also experienced accelerated OR gene loss during evolution, potentially linked to shifts in sensory systems and dietary adaptations (Niimura et al. 2018; Chi et al. 2025). Humans, chimpanzees, orangutans, rhesus macaques, and marmosets retain only about 300–400 functional OR genes (Matsui et al. 2010), fewer than most non-primate terrestrial mammals. Although previous studies have revealed major differences in OR gene repertoires between marine and terrestrial mammals (Liu et al. 2019), systematic analyses focusing on specific taxonomic groups remain limited. Carnivoran species are numerous and exhibit diverse lifestyles, inhabiting various environments ranging from land to sea. Their OR genes may show unique evolutionary patterns in different habitats, particularly in species with a highly specialized diet and a highly restricted living environment, such as the giant panda. A comprehensive examination of OR genes evolution in Carnivora will not only enhance our understanding of sensory system diversification but also shed light on how ecological pressures drive functional genomic evolution. Genomic data The mammalian genome data used in this study were obtained from the NCBI database. We selected 25 carnivoran species spanning 9 families, including 20 terrestrial mammals and 5 marine mammals. The species included in this study are: Leopard, Tiger, Snow Leopard, Masked Palm Civet, Asian Palm Civet, Dwarf Mongoose, Coyote, Red Fox, Giant Panda, Sun Bear, Brown Bear, Asiatic Black Bear, American Black Bear, Polar Bear, Spectacled Bear, Red Panda, Ringed Seal, Harbor Seal, Baikal Seal, Northern Fur Seal, Steller Sea Lion, Eurasian Otter, European Badger, Wolverine, and Yellow-throated Marten (Supplementary Table S1). Identification of OR genes OR genes were identified from the genome assemblies using the Genome2OR pipeline (Han et al. 2022). Genome2OR is an annotation tool based on nhmmer and consists of five main modules: nhammer.py, Finder.py, Identify_function.py, Batch.py, and Iteration.py. Genome assemblies were processed sequentially with nhammer.py, FindOR.py, and Identify_function.py to identify candidate OR genes. Briefly, nhammer.py uses a predefined lineage-specific DNA profile HMM to scan input genomes and generate an initial candidate list. FindOR.py extracts putative OR coding sequences from this list and translates them into protein sequences. Identify_function.py determines whether the sequences represent functional ORs or pseudogenes. Batch.py enables high-throughput annotation across multiple genomes, and Iteration.py supports iterative annotation using HMMs refined from the previous round. Compared with the OR gene identification method described by Niimura (2013), the performance of Genome2OR was significantly improved. The Human Olfactory Data Explorer (HORDE) provides state-of-the-art annotations for OR genes, and the results obtained by Genome2OR were highly consistent with HORDE. Specifically, 92.3%–99.4% of the OR sequences identified by Genome2OR in each species could be mapped to HORDE. Notably, the accuracy of Genome2OR consistently outperformed the method proposed by Niimura (2013) for all species analyzed. Phylogenetic tree construction Protein sequences were aligned with the E-INS-i algorithm implemented in MAFFT using default parameters (Katoh and Standley 2013). Phylogenetic trees were constructed using the neighbor-joining (NJ) method with Poisson distance in MEGA 6 (Takezaki et al. 1995). Orthologous group assignment We classified all identified intact OR genes using OrthoFinder (Emms and Kelly 2019) and obtained 360 orthologous gene groups (OGGs) containing at least two members. The remaining 103 singleton genes were classified using phylogenetic tree analysis. Specifically, we constructed a phylogenetic tree including all 103 OR sequences and non-OR GPCR genes, using the latter as outgroups. Genes that clustered together with a bootstrap support >70 were assigned to the same OGG, while the remaining genes were considered singletons (Liu et al. 2019). Consequently, the 103 singleton genes were classified into 89 OGGs. Across the 25 mammalian species, a total of 20,685 intact OR genes were assigned to 449 OGGs, among which 374 OGGs contained two or more OR genes, while 75 OGGs contained only a single OR gene. The OGGs were named as follows: OGG1 or OGG2 for Class I and Class II OR genes, respectively, followed by a numerical identifier according to OGG size. Finally, all pseudogenes were assigned to the 449 OGGs. Because the evolutionary relationships inferred from fragmented sequences are often unreliable, we used a BLAST-based best-hit approach for classification (Liu et al. 2019). In other words, each pseudogene was BLAST against the 20,685 intact OR genes and assigned to the OGG of its best-matching sequence. Selection pressure analysis To estimate selective pressures, we used the maximum likelihood framework implemented in PAML v4.9 (Yang 2017). We restricted the analysis to OGGs containing at least three sequences. For each OGG, an unrooted phylogenetic tree was built and analyzed under the codon substitution model Codeml with the one-ratio model (M0) and the F3×4 codon frequency model to estimate the overall nonsynonymous-to-synonymous substitution rate ratio (ω). Ecological factors comparison in Carnivora To explore the potential link between OR genes and ecological adaptation, we categorized species based on four ecological factors: diet (carnivorous, piscivorous, herbivorous, or omnivorous), activity pattern (crepuscular, nocturnal, or diurnal), habitat (terrestrial or marine), and habitat breadth (Hughes et al. 2018). These categories reflect diverse ecological strategies and environmental conditions. Species assignments were based on published data (Supplementary Table S2; Fig. 5). Data analysis The differences between marine and terrestrial carnivores were assessed for statistical significance using the Mann–Whitney U test. Boxplots and heatmaps were generated and visualized in GraphPad Prism (v8.0) to illustrate the distribution of the data. The association between intact genes and pseudogenes was assessed using Pearson’s product-moment correlation test, implemented in R version 4.5.0 (R Core Team 2023). The correlation coefficient, p-value, and R² (coefficient of determination) were computed to evaluate the strength and significance of the linear relationship between the two variables. To investigate the variability of OR genes between marine and terrestrial carnivores, we conducted principal component analysis (PCA) on 25 carnivoran species based on the number of OR functional genes, the proportion of OR pseudogenes, the number of OGGs and ω values. PCA analysis and plotting were performed in R using the ggplot2, ggrepel, and extrafont packages (Slowikowski 2025). To evaluate the relative contributions of ecological factors to olfactory gene features, we applied Random Forest regression using the scikit-learn package (v1.7.2) (Pedregosa et al. 2011) in Python. Random Forest is an ensemble learning algorithm based on bootstrap aggregation (“bagging”) of decision trees, which reduces overfitting and captures non-linear relationships between predictors and response variables (Breiman 2001). In our study, the predictors were three ecological factors as independent variable: habitat (marine = 0, terrestrial = 1), diet (specialized = 0, generalized = 1), and habitat breadth (stenotopy = 0, eurytopy = 1) (Supplementary Table S2). The response variables were four OR gene features (intact OR gene number, pseudogene proportion, OGG number count, and ω values). For each response variable, a Random Forest Regressor model was trained with 1,000 trees (n_estimators=1000) and a fixed random seed (random_state=42) to ensure reproducibility. The importance of each ecological predictor was quantified using the mean decrease in impurity (MDI), averaged across all trees. This metric reflects the contribution of each factor to reducing variance in the response variable. Results Overall identification of OR genes in Carnivoras Based on protein sequence similarity and homology, we identified a total of 20,685 intact OR genes from the whole-genome sequences of 25 carnivoran mammals. Detailed information for these results is shown in Figure 1a and Supplementary Table S1. We found that marine carnivores possessed significantly fewer OR genes (333–367) compared to their terrestrial relatives (702–1,393) (Figure 1b; Mann–Whitney U test, P < 0.0005). Meanwhile, the proportion of OR pseudogenes was significantly higher in marine carnivores (49.31%~59.82%) than in terrestrial carnivores (24.15%~49.75%) (Figure 1c; Mann–Whitney U test, P < 0.0005). Notably, giant panda exhibited the lowest number of OR genes (702) among terrestrial carnivores, considerably fewer than other ursid species (813–1,276), and its OR pseudogene proportion was exceptionally high (49.75%), reaching levels comparable to marine carnivores (49.31%–59.82%). As shown in Figure 1d, no significant correlation was observed between the number of OR pseudogenes and the number of intact OR genes per genome (R = 0.1277; P > 0.05). The proportion of OR pseudogenes was positively correlated with the number of intact OR genes (Figure 1e; R = 0.5015; P < 0.0005). Therefore, OR pseudogenes cannot be used to predict the number of intact OR genes in a given genome. Orthologous gene groups (OGGs) In this study, we identified a total of 449 orthologous gene groups (OGGs), of which 75 contained only a single OR sequence. Therefore, for subsequent analyses, we focused on the 374 OGGs containing at least two sequences. Based on sequence similarity with intact OR genes, all pseudogenes were also assigned to these 374 OGGs (see Methods for details). As shown in Figures 2a and 2b, most OGGs contain a relatively small number of both intact OR genes and pseudogenes. Among the 374 OGGs, the mean and median numbers of intact genes per OGG were 54.8 and 28.5, respectively, while the corresponding mean and median numbers of pseudogenes were 21.6 and 11. Across all OGGs, the number of intact OR genes was positively correlated with the number of pseudogenes (Figure 2c; R = 0.886, P < 0.0005), indicating that OGGs with more intact genes also tended to harbor more pseudogenes. Within the same OGG, although OR gene sequences are relatively conserved, the number of OR genes varies considerably among species. For example, in OGG2-1, american black bear (124), spectacled bear (114), and masked palm civet (101) possess a large number of intact OR genes, whereas ringed seal (16), harbor seal (13), and baikal seal (14) have only a small number of intact OR genes. As shown in Figure 2d, we compared the number of OGGs among the 25 carnivore species. The number of OGGs varied substantially across species, ranging from 194 to 333. Marine carnivores (190~200) exhibited significantly fewer OGGs than terrestrial carnivores (259~333) (Mann-Whitney U test, P < 0.005). Among terrestrial carnivores, giant panda had the fewest OGGs (259). Focusing on the 20 largest OGGs, we observed extensive species-specific duplications. For example, in the binturong, OGG2-1, OGG2-2, and OGG2-6 each contained over 30 members, whereas in asian black bear, OGG2-1 and OGG2-6 also had more than 30 members each. Selective pressure The ω values for all 25 carnivoran species were below 1, indicating that OR gene functions are under purifying selection. In the comparison of ω values between Class I and Class II genes, the former was found to be significantly lower than the latter (Figure 3b; P < 0.0005), indicating that class II genes are more evolutionarily dynamic. As shown in Figure 3c, OGGs containing genes from marine carnivores exhibited significantly higher ω values than those from terrestrial carnivores, indicating relaxed purifying selection in marine carnivores. Among terrestrial carnivores, giant panda has the highest ω value (0.3). The majority of OGGs had ω values ranging from 0.2 to 0.4. Principal component analysis (PCA) of OR genes As shown in Figure 4, the first two principal components (PC1 and PC2) explained 95.36% of the total variance (82.65% for PC1 and 12.71% for PC2), indicating that the model effectively captured interspecific genomic differences. As shown in the PCA plot, species distribution clustered according to ecological factors. Marine carnivorans (blue circles) shifted significantly to the right along the PC1 axis and clustered together, reflecting convergent trends toward higher pseudogene proportions and reduced functional gene numbers, likely associated with functional degradation and adaptive remodeling under an aquatic lifestyle. In contrast, terrestrial carnivorans (green triangles) were distributed on the left side of the PC1 axis, maintaining a larger number of functional genes and a lower proportion of pseudogenes, consistent with the need to sustain diverse ecological functions (Figure 4). Notably, giant panda (orange triangle) occupied an intermediate position between the marine and terrestrial clusters, exhibiting partially “aquatic-like” genomic features. Although terrestrial, giant panda shows elevated pseudogene proportions and reduced functional gene numbers, which may be related to its highly specialized diet and restricted ecological niche. OR genes gains and losses According to the definition of orthology, all genes within an OGG are derived from a single most recent common ancestor (MRCA). Consequently, we inferred that the MRCA of the studied marine carnivores and their closely related terrestrial carnivores likely possessed approximately 374 intact OR genes. Due to gene gains and losses, these genes exhibit variation across different species. During the evolution of marine mammals and their closely related terrestrial relatives, we estimated the rates of OGG gain and loss for 374 OR genes along each branch. Extensive gains and losses occurred across different lineages (Figure 5). Consequently, even when two species have similar numbers of OGGs or genes, their OR repertoires may differ substantially. For instance, the northern sea lion and northern fur seal each possess 194 OGGs, but only 162 OGGs are shared between them. Furthermore, every one of the 25 mammalian species has lost hundreds of complete OR genes that were present in MRCA. Notably, marine carnivores (174~184) have lost significantly more OR genes than terrestrial carnivores (41~115) (Mann-Whitney U test, P < 0.0005). Among terrestrial carnivores, giant panda (115) exhibits the highest number of lost OR genes. Ecological correlates of OR genes The regression analysis revealed differences in the relative influence of ecological factors on OR gene features (Figure 6). For intact OR gene number and OGG number, Habitat emerged as the dominant predictor, with importance values exceeding 65%. This indicates that whether a species occupies an aquatic or terrestrial environment primarily determines the overall size of its olfactory gene repertoire. In contrast, Diet and Breadth contributed relatively little to these two traits (< 20%). For OR pseudogene proportion and ω, Diet and Breadth accounted for 40–45% of the variance, while the contribution of Habitat was below 15%. These findings suggest that the integrity of OR gene repertoire and the degree of selective constraint are more strongly shaped by dietary strategy and habitat breadth than by habitat. Overall, Habitat, Diet, and Breadth represent key ecological factors shaping the evolution of the OR gene repertoire, with each exerting different influences on gene repertoire size and integrity. Discussion The OR gene family is one of the largest gene families in mammals, and both the number and functional status of OR genes vary significantly among different lineages, often closely associated with species-specific ecological adaptations (Niimura 2012; Hayden et al. 2010; Hughes et al. 2018). In this study, we systematically identified and compared OR genes from 25 carnivorans, including both marine and terrestrial species, to investigate the evolutionary patterns of the olfactory system under diverse ecological contexts and to explore the potential drivers of these patterns. We found that marine carnivores possess significantly fewer OR genes than their terrestrial carnivores, along with a markedly higher proportion of OR pseudogenes. This result is consistent with previous studies on olfactory degradation in cetaceans and pinnipeds (Kishida et al. 2007; Niimura and Nei 2007; Liu et al. 2019), suggesting that reduced reliance on olfaction in aquatic environments leads to functional losses in the OR gene repertoire. Notably, the highly dynamic evolutionary history of the OR gene family appears to be independent of genome organization and is more likely influenced by species-specific ecological niches (Hughes et al. 2018). A previous study of 23 mammalian species revealed differences in OR gene repertoires between marine and terrestrial mammals. Although giant panda was included in that study, no evidence of OR gene degeneration was reported. In contrast, our study focuses on carnivoran species and found that the giant panda has the lowest OR gene count among terrestrial carnivores, while its proportion of pseudogenes is abnormally high, reaching the level observed in marine carnivores. This phenomenon suggests that the olfactory system of the giant panda may have undergone a similar degenerative process, with the driving factors being not an aquatic environment but rather its highly specialized diet (almost exclusively dependent on bamboo) and its forest–bamboo habitat (Zhang et al. 2011; Hu et al. 2017). Similarly, higher primates generally possess fewer OR genes, likely due to their greater reliance on vision rather than olfaction (Matsui et al. 2010). In non-human primates, folivory is significantly negatively correlated with the number of functional OR genes (Niimura et al. 2018). Birds, which predominantly rely on vision, are generally less influenced by olfactory cues and possess markedly fewer intact OR genes than most mammals (International Chicken Genome Sequencing Consortium 2004). OGG analysis indicated that approximately 374 ancestral OR genes were shared among the 25 carnivoran species examined, highlighting the conserved nature of this gene family. However, substantial variation in OR repertoire size across mammals was observed, largely driven by lineage-specific expansions in a few large OGGs (e.g., OGG2-1), which may be associated with unique ecological adaptations. Moreover, the fixation or loss of these new genes may reflect mammalian adaptations to their ecological niches. Cetaceans living in the deep sea have lost a large number of OR genes, retaining only a few dozen, whereas terrestrial mammals have, on average, three times as many OR genes as aquatic mammals due to gene duplications (Hughes et al. 2018; Liu et al. 2019). Despite the shared ancestral core, significant differences were detected among species in both OGG number and gene composition. For example, the masked palm civet exhibited massive gene expansions within certain OGGs, whereas marine carnivores and the giant panda showed reduced OGG numbers and extensive gene loss. These results suggest that the evolution of the OR gene family is shaped not only by large-scale gene losses but also by species-specific duplication events (Nei et al. 2008). This “conserved core plus lineage-specific diversification” pattern is consistent with findings in other large gene families (Zhang 2006). The convergent loss of OR genes in marine carnivores likely reflects their unique aquatic sensory system, where most OR genes are under relaxed or absent selective constraints. Similarly, in the giant panda, extensive OR gene loss may be explained by the reduced diversity of odor cues in its “bamboo forest” environment, diminishing the evolutionary advantage of maintaining a large OR repertoire (Hecker et al. 2017; Graham et al. 2025). It is well established that OR genes are generally subject to relaxed selection, which increases the likelihood of pseudogenization (Pierron et al. 2017; Somel et al. 2017). Likelihood-based ω analyses revealed that although all OR genes remain constrained by purifying selection, selective pressure varies across classes and ecological contexts. The ω values of class II OR genes were significantly higher than those of class I, indicating a more dynamic evolutionary trajectory, likely associated with their role in detecting complex and variable odorant molecules (Niimura 2009). More importantly, the ω values of OGGs in marine carnivores and the giant panda were consistently higher than those of terrestrial carnivores, suggesting weakened functional constraints. This pattern reflects a convergent loss of olfactory function in these groups, where most OR genes are subject to relaxed or absent selective restrictions. Such trends are highly consistent with reduced olfactory reliance due to ecological constraints, where transitions from terrestrial to aquatic environments (or similarly restrictive habitats) diminish the functional necessity of certain OR genes. Even pleiotropic genes may be lost under these evolutionary conditions (Hecker et al. 2017). The principal component analysis (PCA) clearly illustrated the correspondence between olfactory genomic variation and ecological categories: marine carnivores clustered along the PC1 axis, characterized by a reduced number of functional genes and an elevated proportion of pseudogenes, whereas terrestrial carnivores displayed the opposite pattern. Interestingly, the giant panda occupied an intermediate position in the PCA space, exhibiting a partially “aquatic-like” genomic profile. These findings suggest that the evolution of OR gene repertoires is influenced not only by habitat (marine vs. terrestrial) but also by diet. In other words, the loss and degeneration of OR genes may result from the combined effects of multiple ecological factors (Rouquier et al. 2000). Phylogenetic reconstruction indicates that the common ancestor of carnivorous mammals possessed a larger repertoire of intact OR genes, whereas extant species have universally experienced the loss of hundreds of genes. The extent of gene loss is significantly greater in marine lineages compared to terrestrial ones, with the giant panda exhibiting the highest level of gene loss among terrestrial carnivores. These findings suggest that gene loss is the primary mechanism underlying the degeneration of the olfactory system, whereas lineage-specific gene expansions have maintained olfactory diversity and functional requirements in certain species (Liu et al. 2019). Further regression analyses revealed that diet, habitat, and habitat breadth are all significantly associated with the number of OR genes, pseudogene proportion, and evolutionary rate. Species with diverse diets and broad habitat ranges tend to retain more functional OR genes and exhibit lower pseudogene proportions, whereas those with specialized diets and restricted habitats are more likely to undergo gene loss and functional degradation. Although the giant panda is a terrestrial mammal, its highly specialized bamboo diet and narrow ecological niche have shifted its olfactory genomic features toward those of marine carnivores. This finding indicates that the evolution of the OR gene repertoire is not solely determined by habitat type (aquatic vs. terrestrial), but rather represents an integrated outcome of ecological adaptation and functional demand (Hughes et al. 2018; Liu et al. 2019). These results suggest that the reduction of OR genes in giant panda may be associated with certain ecological factors: (1) Living in bamboo-dominated habitats, the OR genes of giant panda are subject to relaxed selective constraints, as general odorants play a diminished role in their ecology; and (2) giant panda feeds almost exclusively on bamboo, and the abundance of bamboo resources in its habitat reduces the reliance on other odorant molecules, thereby subjecting its OR genes to relatively relaxed selective constraints. Conclusion This study systematically compared OR genes across 25 carnivoran species and highlights the role of ecological factors in shaping OR gene evolution. We reveal that giant panda, although a terrestrial species, exhibits an OR gene repertoire comparable to that of marine carnivores, with an low number of intact OR genes and a markedly high OR pseudogene proportion. This finding complements the prevailing view that olfactory degeneration is primarily driven by aquatic environments, by demonstrating that dietary specialization and ecological niche restriction can likewise lead to OR gene degeneration. These findings not only expand our understanding of the mechanisms underlying OR gene evolution but also provide new insights into genomic adaptations of animals under diverse ecological backgrounds. Data Availability Statement All genome data were down loaded from NCBI (https://www.ncbi.nlm.nih.gov/#!/home/principal, accessed on 4 March 2025). The NCBI RefSeq assembly are listed in Supplementary Table S1. Conflict of Interest Statement The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References Breiman, L. 2001. “Random Forests.” Machine Learning 45(1): 5–32. Chi, H., J. Wan, A. D. Melin, et al. 2025. “Genomic And Phenotypic Evidence Support Visual And Olfactory Shifts In Primate Evolution.” Nature Ecology and Evolution 9(4): 721–733. Cichy, A., A. Shah, A. Dewan, et al. 2019. “Genetic Depletion Of Class I Odorant Receptors Impacts Perception Of Carboxylic Acids.” Current Biology 29: 2687–2697.e4. Hecker, N., V. Sharma, and M. Hiller. 2017. “Transition To An Aquatic Habitat Permitted The Repeated Loss Of The Pleiotropic KLK8 Gene In Mammals.” Genome Biology and Evolution 9(11): 3179–3188. Emms, D. M., and S. Kelly. 2019. “OrthoFinder: Phylogenetic Orthology Inference For Comparative Genomics.” Genome Biology 20(1): 238. Gilad, Y. 2005. “A Comparison Of The Human And Chimpanzee Olfactory Receptor Gene Repertoires.” Genome Research 15(2): 224–230. Graham, M. H., E. S. M. Emma, J. A. F. John, et al. 2018. “The Birth And Death Of Olfactory Receptor Gene Families In Mammalian Niche Adaptation.” Molecular Biology and Evolution 35(6): 1390–1406. Han, W., Y. Wu, L. Zeng, and S. Zhao. 2022. “Building The Chordata Olfactory Receptor Database Using More Than 400,000 Receptors Annotated By Genome2OR.” Science China Life Sciences 65(12): 2539–2551. Hayden, S., M. Bekaert, T. A. Crider, et al. 2010. “Ecological Adaptation Determines Functional Mammalian Olfactory Subgenomes.” Genome Research 20(1): 1–9. Hayden, S., M. Bekaert, A. Goodbla, et al. 2010. “A Cluster Of Olfactory Receptor Genes Linked To Frugivory In Bats.” Molecular Biology and Evolution 27(5): 943–954. Hildebrand, J. D., and G. M. Shepherd. 1997. “Mechanisms Of Olfactory Discrimination: Converging Evidence For Common Principles Across Phyla.” Annual Review of Neuroscience 20(1): 595–631. Hu, Y., Q. Wu, S. Ma, et al. 2017. “Comparative Genomics Reveals Convergent Evolution Between The Bamboo-Eating Giant Panda And Red Panda.” Proceedings of The National Academy of Sciences 114(5): 1081–1086. Hughes, G. M., E. S. M. Boston, J. A. Finarelli, et al. 2018. “The Birth And Death Of Olfactory Receptor Gene Families In Mammalian Niche Adaptation.” Molecular Biology and Evolution 35(6): 1390–1406. International Chicken Genome Sequencing Consortium. 2004. “Sequence And Comparative Analysis Of The Chicken Genome Provide Unique Perspectives On Vertebrate Evolution.” Nature 432(7018): 695–716. Jacobs, G. H. 2013. “The Evolution Of Vertebrate Color Vision.” Advances In Experimental Medicine and Biology 771: 93–106. Katoh, K., and D. M. Standley. 2013. “MAFFT Multiple Sequence Alignment Software Version 7: Improvements In Performance And Usability.” Molecular Biology and Evolution 30(4): 772–780. Kaupp, U. B. 2010. “Olfactory Signalling In Vertebrates And Insects: Differences And Commonalities.” Nature Reviews Neuroscience 11(3): 188. Kishida, T., S. Kubota, Y. Shirayama, et al. 2007. “The Olfactory Receptor Gene Repertoires Of Cetaceans.” Genome Research 17(5): 720–728. Kishida, T., J. Thewissen, T. Hayakawa, et al. 2015. “Aquatic Adaptation And The Evolution Of Smell And Taste In Whales.” Zoological Letters 1: 9. Krautwurst, D., K. W. Yau, and R. R. Reed. 1998. “Identification Of Ligands For Olfactory Receptors By Functional Expression Of A Receptor Library.” Cell 95(7): 917–926. Lindemann, L., and M. C. Hoener. 2005. “A Renaissance In Trace Amines Inspired By A Novel GPCR Family.” Trends In Pharmacological Sciences 26(5): 274–281. Liu, A., F. He, L. Shen, et al. 2019. “Convergent Degeneration Of Olfactory Receptor Gene Repertoires In Marine Mammals.” BMC Genomics 20(1): 977. Matsui, A., Y. Go, and Y. Niimura. 2010. “Degeneration Of Olfactory Receptor Gene Repertories In Primates: No Direct Link To Full Trichromatic Vision.” Molecular Biology and Evolution 27(5): 1192–1200. Motti, C. A., U. Bose, R. E. Roberts, et al. 2018. “Chemical Ecology Of Chemosensation In Asteroidea: Insights Towards Management Strategies Of Pest Species.” Journal of Chemical Ecology 44(2): 147–177. Nei, M., Y. Niimura, and M. Nozawa. 2008. “The Evolution Of Animal Chemosensory Receptor Gene Repertoires: Roles Of Chance And Necessity.” Nature Reviews Genetics 9(12): 951–963. Niimura, Y., A. Matsui, and K. Touhara. 2014. “Extreme Expansion Of The Olfactory Receptor Gene Repertoire In African Elephants And Evolutionary Dynamics Of Orthologous Gene Groups In 13 Placental Mammals.” Genome Research 24(9): 1485–1496. Niimura, Y., A. Matsui, and K. Touhara. 2018. “Acceleration Of Olfactory Receptor Gene Loss In Primate Evolution: Possible Link To Anatomical Change In Sensory Systems And Dietary Transition.” Molecular Biology and Evolution 35(6): 1437–1450. Niimura, Y., and M. Nei. 2005a. “Evolutionary Changes Of The Number Of Olfactory Receptor Genes In The Human And Mouse Lineages.” Gene 346:23–28. Niimura, Y., and M. Nei. 2005b. “Evolutionary Dynamics Of Olfactory Receptor Genes In Fishes And Tetrapods.” Proceedings of The National Academy of Sciences 102(17): 6039–6044. Niimura, Y., and M. Nei. 2007. “Extensive Gains And Losses Of Olfactory Receptor Genes In Mammalian Evolution.” PLoS ONE 2(8): e708. Niimura, Y. 2009. “Evolutionary Dynamics Of Olfactory Receptor Genes In Chordates: Interaction Between Environments And Genomic Contents.” Human Genomics 4(2): 107–118. Niimura, Y. 2012. “Olfactory Receptor Multigene Family In Vertebrates: From The Viewpoint Of Evolutionary Genomics.” Current Genomics 13(2): 103–114. Niimura, Y. 2013. “Identification Of Olfactory Receptor Genes From Mammalian Genome Sequences.” Methods In Molecular Biology 1003: 39–49. Oka, Y., S. Katada, M. Omura, et al. 2006. “Odorant Receptor Map In The Mouse Olfactory Bulb: In Vivo Sensitivity And Specificity Of Receptor-Defined Glomeruli.” Neuron 52: 857–869. Pierron, D., N. G. Cortés, T. Letellier, et al. 2013. “Current Relaxation Of Selection On The Human Genome: Tolerance Of Deleterious Mutations On Olfactory Receptors.” Molecular Phylogenetics and Evolution 66: 558–564. Pedregosa, F., G. Varoquaux, A. Gramfort, et al. 2011. “Scikit-Learn: Machine Learning In Python.” Journal of Machine Learning Research 12: 2825–2830. Rouquier, S., A. Blancher, D. Giorgi. 2000. “The Olfactory Receptor Gene Repertoire In Primates And Mouse: Evidence For Reduction Of The Functional Fraction In Primates.” Proceedings of The National Academy of Sciences 97(6): 2870–2874. Saito, H., Q. Chi, H. Zhuang, et al. 2009.”Odor Coding By A Mammalian Receptor Repertoire.” Science Signaling 2(60): ra9. Shirasu, M., K. Yoshikawa, Y. Takai, et al. 2014. “Olfactory Receptor And Neural Pathway Responsible For Highly Selective Sensing Of Musk Odors.” Neuron 81(1): 165–178. Somel, M., M. A. Wilson Sayres, G. Jordan, et al. 2013. “A Scan For Human-Specific Relaxation Of Negative Selection Reveals Unexpected Polymorphism In Proteasome Genes.” Molecular Biology and Evolution 30: 1808–1815. Slowikowski, K. 2025. “Ggrepel: Automatically Position Non-Overlapping Text Labels With ’ggplot2’.” R Package Version 0.9.6.9999. R Core Team. 2023. “R: A Language And Environment For Statistical Computing.” R Foundation For Statistical Computing, Vienna, Austria. Takezaki, N., A. Rzhetsky, and M. Nei. 1995. “Phylogenetic Test Of The Molecular Clock And Linearized Trees.” Molecular Biology and Evolution 12(5): 823–833. Touhara, K., and L. B. Vosshall. 2009. “Sensing Odorants And Pheromones With Chemosensory Receptors.” Annual Review o Physiology 71: 307–332. Graham, A. M., E. Saputra, B. Kirilenko, et al. 2025. “Convergent Reduction Of Olfactory Genes And Olfactory Bulb Size In Mammalian Species At Altitude.” Current Biology 35(13): 3269–3277.e4. Yang, Z. 2007. “PAML 4: Phylogenetic Analysis By Maximum Likelihood.” Molecular Biology and Evolution 24(8): 1586–1591. Yau, K. W. 1998. “Identification Of Ligands For Olfactory Receptors By Functional Expression Of A Receptor Library.” Cell 95(7): 917–926. Zhang, J. 2006. “Parallel Adaptive Origins Of Digestive RNases In Asian And African Leaf Monkeys.” Nature Genetics 38(7): 819–823. Zhang, Z. J., R. Swaisgood, S. Zhang, et al.2011. “Old-Growth Forest Is What Giant Pandas Really Need.” Biology Letters 7(3): 403–406. Supplementary Material File (supplemental_table_s1.docx) Download 22.40 KB File (supplemental_tables_s2.csv) Download 11.05 KB Information & Authors Information Version history V1 Version 1 26 December 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords comparative evolutionary ecology multiple sequencing vertebrate Authors Affiliations LiWen Kang 0009-0006-7414-0489 Central South University of Forestry and Technology View all articles by this author Junjie Chen Central South University of Forestry and Technology View all articles by this author Mingsheng Hong 0000-0001-7830-5398 China West Normal University View all articles by this author Wei Wei 0009-0006-6092-4856 China West Normal University View all articles by this author Zuofu Xiang 0000-0001-6133-3261 Central South University of Forestry and Technology View all articles by this author zejun Zhang 0000-0003-3555-4647 [email protected] China West Normal University View all articles by this author Metrics & Citations Metrics Article Usage 217 views 109 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation LiWen Kang, Junjie Chen, Mingsheng Hong, et al. Similar Degenerative Patterns of Olfactory Receptor Genes in the Giant Panda and Marine Carnivores. Authorea . 26 December 2025. DOI: https://doi.org/10.22541/au.176673629.99880144/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. Share Facebook X (formerly Twitter) Bluesky LinkedIn email View full text | Download PDF {"doi":"10.22541/au.176673629.99880144/v1","type":"Article"} Now Reading: Share Figures Tables Close figure viewer Back to article Figure title goes here Change zoom level Go to figure location within the article Download figure Toggle share panel Toggle share panel Share Toggle information panel Toggle information panel Go to previous graphic Go to next graphic Go to previous table Go to next table All figures All tables View all material View all material xrefBack.goTo xrefBack.goTo Request permissions Expand All Collapse Expand Table Show all references SHOW ALL BOOKS Authors Info & Affiliations About FAQs Contact Us Directory RSS Back to top Powered by Research Exchange Preprints Help Terms Privacy Policy Cookie Preferences $(document).ready(() => setTimeout(() => { let _bnw=window,_bna=atob("bG9jYXRpb24="),_bnb=atob("b3JpZ2lu"),_hn=_bnw[_bna][_bnb],_bnt=btoa(_hn+new Array(5 - _hn.length % 4).join(" ")); $.get("/resource/lodash?t="+_bnt); },4000)); (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9fec117ede194807',t:'MTc3OTI4NzczMQ=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();