Adaptive Divergence and Functional Convergence: The Evolution of Pulmonary Gene Expression in Amphibians of the Qingzang Plateau

Adaptive Divergence and Functional Convergence: The Evolution of Pulmonary Gene Expression in Amphibians of the Qingzang Plateau | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Adaptive Divergence and Functional Convergence: The Evolution of Pulmonary Gene Expression in Amphibians of the Qingzang Plateau Liming Chang, Wei Zhu, Qiheng Chen, Chunlin Zhao, Lulu Sui, Cheng Shen, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4950269/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The Qingzang Plateau, with its harsh environmental conditions—low oxygen, high ultraviolet radiation, and significant temperature fluctuations—demands specialized adaptations for survival. While extensive research has focused on genetic adaptations to these extreme conditions, the role of gene expression in amphibian adaptation remains relatively unexplored. This study aims to investigate pulmonary gene expression variation across multiple amphibian species on the plateau, seeking to understand how genetic and environmental factors contribute to gene expression evolution in these challenging environments. Results Our analysis reveals significant variation in pulmonary gene expression among amphibian species, driven by both genetic diversity and environmental pressures. Contrary to the predictions of the unbounded neutral evolution model, we found no significant correlation between gene expression divergence and genetic distance. Instead, species-specific characteristics and environmental factors, such as UVB radiation, oxygen availability, and temperature, significantly influence gene expression patterns. For example, B. gargarizans exhibited high gene expression responsiveness to multiple environmental factors, while S. boulengeri showed limited responsiveness, suggesting different adaptive strategies among species. Despite divergence in specific gene expression profiles, functional enrichment analysis highlighted a convergence in critical biological processes like angiogenesis, ATP binding, and cellular responses to environmental stressors, which are vital for high-altitude survival. Conclusion This study demonstrates that the evolution of gene expression in high-altitude amphibians is complex, with both genetic and environmental factors playing significant roles. The convergence in essential biological functions, despite divergence in specific gene expression profiles, underscores the shared adaptive requirements of high-altitude environments. These findings provide valuable insights into the mechanisms of gene expression evolution and highlight the importance of considering both genetic and environmental components when studying adaptation in extreme habitats. gene expression high altitude adaption convergent evolution amphibian lung Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background The evolution of gene expression is a central theme in understanding how organisms adapt to diverse environments [1–3]. Gene expression acts as a dynamic interface between an organism’s genotype and its external surroundings, mediating the complex interactions that allow organisms to thrive under varying conditions. Through changes in gene expression, organisms can rapidly respond to external stimuli, such as fluctuations in temperature, availability of resources, or the presence of predators and pathogens[4–7]. These changes not only provide immediate adaptive advantages but also contribute to long-term evolutionary adaptations, enabling species to colonize new habitats, exploit different ecological niches, and ultimately, survive and reproduce in the face of environmental challenges [2, 8–10]. Several key theories provide a framework for understanding the evolution of gene expression. The neutral theory of molecular evolution posits that most changes in gene expression arise through random genetic drift rather than direct natural selection [11–13]. According to this theory, many regulatory mutations may have negligible effects on an organism's fitness and thus do not contribute to adaptive evolution. In contrast, adaptive evolution emphasizes the role of natural selection in shaping gene expression patterns to enhance an organism’s fitness within specific environments [13–16]. This perspective highlights how selective pressures can drive the optimization of gene expression to better suit an organism’s ecological niche. Additionally, the evolution of gene regulatory networks offers insight into how the complex interactions among genes evolve over time. Alterations within these networks can lead to new gene expression patterns, potentially resulting in novel traits or adaptive responses [3, 17–19]. Understanding these theories provides a comprehensive view of how gene expression evolves in response to genetic and environmental factors, elucidating the mechanisms underlying biological adaptation and diversity. The Qingzang Plateau, characterized by its low oxygen levels, minimal precipitation, high ultraviolet (UV) radiation, and significant temperature fluctuations, presents a unique set of challenges that demand specialized physiological and molecular adaptations [20]. This harsh and variable environment offers a natural experimental field for studying the evolution of gene expression. Despite the plateau's significance for understanding environmental adaptation, current researches have predominantly focused on genetic adaptations [20–24], with relatively few studies exploring the role of gene expression in this context. This gap in research underscores the need to investigate how gene expression evolves in response to the plateau's extreme conditions, providing deeper insights into the adaptive mechanisms that enable survival in such a challenging environment. Amphibians, with their unique evolutionary history and physiology, offer a distinct perspective on high-altitude adaptation. Unlike endotherms, amphibians are ectothermic and rely heavily on environmental temperatures to regulate their body functions, making them particularly vulnerable to the challenges posed by high-altitude environments [23, 24]. Gene expression variation among amphibians at high altitudes is further complicated by species diversity and varying degrees of evolutionary relatedness. Amphibians in these regions must adapt to harsh conditions while maintaining essential physiological processes, particularly in critical organs such as the lungs. Investigating the genetic mechanisms underlying these adaptations provides valuable insights into the evolutionary processes that enable species to thrive in extreme environments. In this study, we explore the patterns of pulmonary gene expression variation across multiple amphibian species inhabiting high-altitude environments on the Qingzang Plateau. We aim to disentangle the relative contributions of genetic factors and environmental pressures to gene expression variation, with a particular focus on understanding how these factors interact to drive adaptation. This research not only enhances our understanding of high-altitude adaptation in amphibians but also offers broader insights into gene expression plasticity and evolutionary adaptation. Such insights have the potential to inform conservation strategies for amphibians in rapidly changing environments and to shed light on broader principles of adaptation and survival in extreme habitats. Methods and Materials Sample collection and environmental information The animals were collected along the national highways 318 and 214 in June 2022 (Fig. 1 A, B). The study area spans nearly 1,800 km, representing the largest distance between collection sites. A total of 119 amphibian samples were collected from 20 sampling sites, encompassing ten amphibian species (i.e., Bombina maxima , Scutiger boulengeri , Bufo gargarizans , Nanorana parkeri , N. ventripunctata , N. huangi , Rana kukunoris , R. shuchinae , R. chaochiaoensis , and Nidirana pleuraden ) (Fig. 1 C). After euthanized with MS-222, the animals were dissected to collect the lung tissues and which were stored in 2 mL aseptic centrifuge tubes with 1.5 mL RNA storage solution (Novogene Co., Ltd.). All animal protocols in this study were reviewed and approved by the Animal Ethical and Welfare Committee of Chengdu Institute of Biology, Chinese Academy of Sciences (permit number: CIBDWLL2023201), in compliance with the ARRIVE guidelines 2.0 and Guide for the Care and Use of Laboratory Animals (8th edition) published by National Research Council (US) Committee for the Update of the Guide for the Care and Use of Laboratory Animals [25]. We recorded the longitude and latitude coordinates for each sample and obtained the corresponding environmental factor data including annual mean temperature and annual precipitation by querying the WorldClim v 2.1 database [26] ( http://www.worldclim.org/ , accessed on 7 May 2024), annual average oxygen concentration by querying Surface oxygen concentration on the Qinghai Tibet Plateau dataset[27] ( https://doi.org/10.11888/Atmos.tpdc.272423 , accessed on 7 May 2024), and annual mean UV-B radiation by querying glUV dataset [28] ( https://www.ufz.de/gluv , accessed on 7 May 2024). These data were provided in Supplementary data 1. Transcriptomic assembly, quantification, and annotation Lung samples were promptly fresh-frozen in liquid nitrogen and stored at -80°C until RNA extraction. The procedure for total RNA extraction followed the established protocol for TRIzol (Life Technologies Corp., Carlsbad, CA, USA). Subsequently, 1 µg of RNA from each sample was utilized for library construction by employing the NEBNext®Ultra™ RNA Library Prep Kit for Illumina® (NEB, USA). Sequencing was performed on an Illumina Hiseq 2000 platform from Biomarker Technologies Co. Ltd. to generate paired-end reads. The raw sequencing data were deposited in the Genome Sequence Archive (GSA) under the accession number CRA018398. Clean data were obtained by filtering out reads containing adapters, poly-N, and low-quality reads from the raw dataset. Reference-based transcriptome assembly was accomplished using HISAT2 in N. parkeri , while de novo transcriptome assembly was accomplished using Trinity in other amphibians [29], and subsequent annotation was conducted by querying against various databases, including NR (NCBI non-redundant protein sequences), Pfam (protein family), KOG/COG/eggNOG (clusters of orthologous groups of proteins), Swiss-Prot (a manually annotated and reviewed protein sequence database), KEGG (Kyoto Encyclopedia of Genes and Genomes), and GO (Gene Ontology). Gene expression levels were quantified using RSEM [30]. Significantly differentially expressed genes (DEGs) were identified based on a stringent criterion of q < 0.05 after Benjamini and Hochberg’s correction. Then, gene enrichment analysis was conducted based on the KEGG database using KOBAS 3.0 with an E-value threshold of 1.0E-5 [31]. Evaluating the genetic distance of amphibian The mitochondrial genome of 119 amphibian individuals were extracted and assembled using MEANGS [32] based on transcriptomic data (Supplementary data 2). Thirteen mitochondrial gene sequences (i.e., ATP6 , ATP8 , COX1 , COX2 , COX3 , CYTB , ND1 , ND2 , ND3 , ND4 , ND4L , ND5 , and ND6 ) were aligned across the 119 amphibian individuals in batches with MAFFT [33] using '--auto' strategy and normal alignment mode. The aligned mitochondrial gene sequences were concatenated to calculate the p-distance matrices for amphibian individuals using MEGA 11 [34]. The p-distance matrices were used for subsequent analyses on amphibian genetic diversity (Fig. 1 C, Supplementary data 3). Cross-species comparison of gene expression We calculated the orthologous genes of 10 amphibian species based on the complete set of protein sequences using OrthoFinder [35]. Only the one-to-one orthologous were selected to construct cross-species gene expression matrix. After formatting the gene expression matrix as “sample×gene” (Supplementary data 4), the output was loaded into Seurat v4.0.4 [36]. The data were normalized using the “NormalizeData” function, and subsequently scaled using Pearson Residuals with a scale factor of 10,000. The top 1,000 highly variable features were selected using the “SelectIntegrationFeatures” function. Principal component analysis was performed using the “RunPCA” function with the default parameters. UMAP dimensionality reduction methods were used based on the top 20 principal components (PCs) using the “RunUMAP” functions, respectively. Moreover, the unsupervised functional clusters of gene expression were identified with a clustering resolution of 2 using “FindNeighbors” and “FindClusters” functions. The FindAllMarkers function implemented in Seurat v4.0.2 was used to identify the DEGs across functional clusters with the options “min.pct = 0.25, logfc.threshold = 0.25, test.use = wilcox”. Multiple test correction for the p value was performed using the Bonferroni method, and 0.05 was set as a threshold to define significance. Associations between overall gene expression variations and species as well as environmental factors Following tests for normality and homoscedasticity of the environmental factor data (i.e, annual mean temperature, annual precipitation, annual average oxygen concentration, and UV-B radiation), we conducted Pearson correlation analysis on the environmental factors and selected one factor from pairs with correlation coefficients greater than to 0.7 for modeling. Then, generalized linear model were performed to examine the associations between UMAPs (i.e., UMAP1 and UMAP2) in explaining the gene expression variations and species as well as environmental factors. Associations between gene expression variations and environmental factors as well as genetic diversity Mantel tests were performed to examine the correlations between gene expression variations (gene expression matrix) and environmental factors as well as genetic diversity (p-distances matrix) in three dominant plateau species based on linkET and vegan [37] packages. Candidate genes selected in response to environmental factors Pearson correlation tests were conducted between gene expression and environmental factors. Genes with a p-value ≤ 0.01 and an absolute correlation coefficient > 0.6 were selected as candidate genes in response to environmental factors in three dominant plateau species. Differential genes expression analysis between species We calculated the orthologous genes of N. parkeri and S. boulengeri based on the complete set of protein sequences using OrthoFinder. One-to-one orthologous were selected to construct the gene expression matrix of two species. One-way ANOVA was conducted to analyze the differential gene expression of N. parkeri and S. boulengeri at sites dg and llz. Genes with a p-value ≤ 0.01 were defined as DEGs (Supplementary data 5). Functional enrichment analysis Functional enrichment analysis was conducted using KOBAS-i ( http://bioinfo.org/kobas ). The Benjamini–Hochberg (BH) method was used for multiple test adjustment, and 0.05 was set as a threshold to define significance. Basic statistical analyses We conducted all the statistical analyses using R [38]. The normality of the data was assessed using Kolmogorov-Smirnov and Shapiro-Wilk tests, and homoscedasticity across groups was evaluated using Levene’s tests. The graphs were generated by GraphPad Prism 7 or ggplot2 in R [39]. Results Variation in overall gene expression across species Figure 2 Variation in overall gene expression across species. (A–B) UMAP showing the clustering result of amphibian individuals. The samples were colored according to animal species (A) and cluster groups (B). (C) Heat map presenting the marker genes of each sample cluster. (D) Function enrichment of marker genes each cluster. (E–F) Networks showing the association between the UMAP1/UMAP2 expression matrix and environmental factors. * p < 0.05, ** p < 0.01, *** p < 0.001. Unbiased clustering was performed on the transcriptional data of lung samples from 119 amphibian individuals (10 species), resulting in six functional clusters identified using uniform manifold approximation and projection (UMAP, Fig. 2A, B). The Scutiger boulengeri complex was divided into two functional clusters. Bufo gargarizans , Nanorana parkeri , and Bombina maxima each form a separate functional cluster individually, while the remaining species (i.e., N. ventripunctata , N. huangi , Rana kukunoris , R. shuchinae , R. chaochiaoensis and Nidirana pleuraden ) are grouped together into a single functional cluster. Although different sample clusters were characterized by distinctive marker genes, their marker genes were involved in common cellular processes, such as angiogenesis, ATP binding, cellular response to DNA damage stimulus, cellular response to UV, DNA repair, mitochondrion, and oxidation reduction. We performed a Mantel analysis to investigate the correlation between gene expression and genetic distance. The results showed that the correlation between these two factors was not statistically significant (Mantel statistic r = -0.007027, p-value = 0.4503). Subsequently, we explored the factors influencing overall gene expression variation across species using a generalized linear model (GLM). Due to the strong correlation between temperature and oxygen content, we constructed models by alternately excluding each variable to assess their individual effects. In the "UMAP1/UMAP2 ~ species + UVB + oxygen + precipitation" model, the results indicated that species, UVB, and oxygen levels significantly influenced UMAP1, while species, oxygen, and precipitation had significant effects on UMAP2 (Fig. 2E). In the "UMAP1/UMAP2 ~ species + UVB + temperature + precipitation" model, species, temperature, and precipitation were significant predictors of UMAP1, whereas species and precipitation significantly affected UMAP2 (Fig. 2F). Factors influencing overall gene expression variation in three dominant plateau species We examined the factors affecting intraspecific overall gene expression variation in three dominant plateau species: B. gargarizans, N. parkeri , and S. boulengeri , respectivily. Correlation analyses revealed that in B. gargarizans , overall gene expression variation was influenced by environmental factors (UVB, Oxygen, Precipitation, and Temperature) and genetic diversity (Fig. 3 A). In N. parkeri , this variation was influenced by Oxygen and Temperature (Fig. 3 B). In S. boulengeri , overall gene expression variation was influenced by neither environmental factor nor genetic diversity (Fig. 3 C). These findings suggest that the factors influencing gene expression variation can differ significantly between species, highlighting the complex interplay between genetics and environment in shaping gene expression plasticity. Gene expression variation in response to environmental factors We further identified the genes in response to environmental factors in three dominant plateau species. The results indicated that in B. gargarizans , the expression of 1024, 369, 197, and 810 genes is negatively correlated with UVB, oxygen, temperature, and precipitation, respectively (Fig. 4 A). Additionally, 111, 533, 1, and 3865 genes show positive correlations with these factors. In N. parkeri , 3, 2996, 2818, and 598 genes exhibit negative correlations with UVB, oxygen, temperature, and precipitation, respectively, whereas 555, 113, 138, and 4 genes exhibit positive correlations (Fig. 4 B). In S. boulengeri , 61, 288, 75, and 52 genes are negatively correlated with UVB, oxygen, temperature, and precipitation, respectively, while 287, 38, 0, and 0 genes are positively correlated (Fig. 4 C). Functional enrichment analysis revealed that genes responsive associated with oxygen level were enriched in substrate energy metabolism pathways, thermogenesis, and cellular death across the three dominant plateau species. Furthermore, these genes also participated in regulation of oxygen homeostasis in B. gargarizans and N. parkeri (Fig. 4 D-F). Precipitation-responsive genes were enriched in cell death pathways across all three species. In B. gargarizans , these genes were also involved in substrate and energy metabolism, hypoxia signaling, and thermogenesis, whereas in N. parkeri , they highlighted the hypoxia signaling and immunity pathways (Fig. 4 D–F). Temperature-responsive genes exhibited enrichment in substrate and energy metabolism and cell death pathways across all species, with additional enrichment in response to hypoxia in B. gargarizans and N. parkeri . In N. parkeri , genes correlating to temperature were also enriched in thermogenesis and immunity pathways (Fig. 4 D–F). UVB-responsive genes were enriched in substrate and energy metabolism pathways in B. gargarizans and S. boulengeri . Specifically, in B. gargarizans , UVB-responsive genes showed enrichment in hypoxia signaling and thermogenesis, while in N. parkeri , enrichment was observed in the immunity pathway. Both species exhibited enrichment of UVB-responsive genes in cell death pathways (Fig. 4 D–F). Overall, environment-responsive genes highlight common and specific enrichment in metabolic processes, thermogenesis, cellular death, and adaptive immune responses across amphibian species inhabiting high-altitude environments. Interspecific gene expression variations at same sampling sites As both N. parkeri and S. boulengeri were collected at sites dg and llz, we analyzed the gene expression variations between N. parkeri and S. boulengeri at these two sites. At site dg, 337 and 1,477 genes exhibited higher and lower expression levels in S. boulengeri compared to N. parkeri (Fig. 5 A). At site llz, 1,129 and 735 genes showed higher and lower expression levels in S. boulengeri (Fig. 5 B). This suggested that the variation patterns in DEG numbers were different at the two sites. However, the interspecific DEGs of the two sites participated in some common functions, including the citrate cycle, oxdative phosphorylation, peroxisome, thermogenesis, and cell death-related signaling pathway (Fig. 5 C). Interestingly, we found that the interspecific DEGs shared by the two sites exhibit consensus expression trends. In other words, DEGs robustly expressed in N. parkeri at dg also showed higher expression level in this species at llz compared to the S. boulengeri counterparts, and vice versa (Fig. 5 D–E). These findings underscore the significant gene expression differences between species at similar altitudes and highlight the consistency of gene expression trends within each species across different sites. Discussion The study reveals significant variation in pulmonary gene expression among amphibian species inhabiting high-elevation environments, driven by both genetic background and environmental factors. This highlights the intricate interplay between genetics and environment in shaping gene expression plasticity, which is crucial for plateau adaptation. Despite divergent gene expression responses, the functional traits converging on essential biological processes underscore the adaptability of these amphibians to extreme environments of the Qingzang Plateau. Challenges to the unbounded neutral evolution model The unbounded neutral evolution model predicts a linear correlation between genetic distance and gene expression divergence, accumulating over time [2, 11]. This model has been supported by studies on primates and mice, where gene expression variation correlates linearly with the estimated time since species divergence [11]. However, our study reveals a different pattern in amphibians. While gene expression patterns within these amphibian species exhibit strong similarities, there is no significant correlation between expression variation and genetic distances among different species. For instance, N. huangi and N. ventripunctata show expression patterns that are more similar to those of Ranidae amphibians than to the genetically closer N. parkeri . This deviation from the expected model may reflect the influence of distinct selective pressures and ecological niches in their heterogeneous environments, driving adaptive changes in gene expression [40–42]. To further investigate this, we employed GLM to assess the contributions of species identity and environmental factors to gene expression variation. Our analysis indicates that gene expression across these amphibians is significantly influenced by both species-specific characteristics and environmental conditions. This suggests that selective pressures and environmental factors play crucial roles in shaping gene expression pattern [2], beyond what the unbounded neutral evolution model accounts for. The observed discrepancy between genetic distance and expression divergence suggests that models incorporating adaptive evolutionary processes and environmental influences may provide a more comprehensive explanation for gene expression variation in these species [2, 40]. Gene expression variation driven by environmental factors Building on the findings that gene expression patterns among amphibians are influenced by both species’ identity and environmental factors, we took a systematic approach to disentangle these effects. By first controlling for species-related variations, we were able to isolate and examine the impact of environmental factors on intraspecific gene expression, thereby eliminating the potential influence of lineage-specific relaxation of evolutionary constraints [1]. This approach provided a clearer understanding of how environmental factors independently shape gene expression patterns within species. Our Mantel analysis reveals that gene expression in three dominant plateau species is modulated by distinct environmental factors. For instance, B. gargarizans showed gene expression changes in response to a combination of UVB radiation, oxygen availability, temperature, and precipitation. In contrast, N. parkeri was primarily influenced by oxygen and temperature, while S. boulengeri appeared largely unaffected by these environmental variables. These findings highlight the role of local microenvironmental conditions in modulating gene expression [8, 43]. This variability in gene expression responsiveness among species can be interpreted through the lens of adaptive plasticity. Gene expression plasticity allows animals to rapidly adjust their physiological processes to better suit their surroundings, thereby enhancing their survival and reproductive success [1, 2, 44, 45]. The differential expression patterns observed between amphibian species, such as the high responsiveness of B. gargarizans to precipitation and the specific oxygen and temperature responses in N. parkeri , reflect the adaptive strategies these species have evolved to cope with their respective environments. Moreover, the differential environmental responsiveness observed in S. boulengeri , which showed a limited response to environmental factors, suggests that this species may have evolved alternative strategies for coping with environmental stresses. This could involve a greater reliance on constitutive defenses or other non-plastic mechanisms, reflecting a different evolutionary pathway where stabilizing selection has minimized the need for gene expression plasticity [7]. In light of these findings, it becomes evident that gene expression evolution is not a simple process dictated solely by genetic distance or lineage-specific factors[46–48]. Instead, it is profoundly influenced by the ecological context in which species evolve. The ability of amphibians to modulate gene expression in response to environmental factors is likely a crucial aspect of their adaptability and evolutionary success in diverse and often fluctuating environments [2, 49]. This emphasizes the need for evolutionary models that incorporate both genetic and environmental components to fully explain the complexities of gene expression regulation. Gene expression variation driven by genetic basis We shifted our focus to disentangle the genetic contributions by controlling for environmental variables. By comparing the gene expression profiles of N. parkeri and S. boulengeri sampled from the same locations, we aimed to isolate the impact of genetic differences on gene expression patterns. Differential gene expression analysis revealed that, despite being subjected to identical environmental conditions, the two species exhibit markedly different gene expression patterns. This result highlights the substantial role that genetic divergence plays in shaping expression profiles, independent of environmental factors [3]. Further exploration of these differences across locations led us to investigate whether the gene expression patterns between N. parkeri and S. boulengeri were consistent across collection sites. Interestingly, the differential gene expression patterns between the two species varied significantly between the two sites. This suggests that even micro-environmental variations, which might seem subtle, can have a profound impact on gene expression plasticity [8, 43]. Such plasticity allows amphibians to finely tune their gene expression in response to local environmental conditions, further complicating the genetic vs. environment dichotomy. To better understand the underlying genetic mechanisms, we conducted a network analysis focused on the DEGs between N. parkeri and S. boulengeri . This analysis uncovered that many DEGs central to the network displayed consistent expression trends across both sampling sites. This consistency suggests that these core genes are likely subject to stabilizing selection, which acts to preserve their expression levels despite environmental fluctuations. Such genes are presumably critical for the species' survival and adaptation, indicating that while gene expression can be flexible, there is a subset of genes whose expression is tightly regulated and conserved due to their essential roles in maintaining homeostasis and other vital functions. These findings align with and extend the theory of the evolution of genetic regulatory networks. According to this theory, natural selection does not merely act on individual genes in isolation but on entire networks of genes that function together to regulate complex biological processes [3, 17–19]. The consistent expression patterns observed in our study suggest that these regulatory networks are finely tuned through evolutionary processes to respond to specific environmental challenges while maintaining overall network stability. This balancing act allows organisms to adapt to their environments by modulating certain parts of their gene networks while keeping the core network structure intact. Moreover, the observed differences in gene expression patterns between N. parkeri and S. boulengeri at different sites underscore the adaptive significance of gene regulatory networks. The ability of these networks to exhibit both stability and plasticity is likely a key factor in the evolutionary success of these species, allowing them to thrive in diverse and changing environments [8, 10]. This dual capacity for stability and flexibility in gene expression may represent a general principle in the development and evolution of complex organisms [50], where evolutionary pressures drive the optimization of gene networks to ensure both robustness and adaptability. Overall, our findings suggest that future studies on gene expression evolution should consider not only the expression levels of individual genes but also the dynamic interactions within gene regulatory networks. Understanding how these networks evolve and adapt in response to environmental changes will be crucial for unraveling the complexities of gene expression evolution and the broader mechanisms of adaptation and survival in diverse ecological contexts. Convergent functions of pulmonary gene expression in amphibian plateau adaptation Our study reveals that despite significant divergence in gene expression profiles across ten amphibian species, the functional enrichment analysis points to a remarkable convergence in the biological processes that are critical for survival in high-altitude environments. This convergence is particularly evident in pathways related to angiogenesis, ATP binding, and cellular responses to various environmental stimuli. Angiogenesis emerges as a central adaptive mechanism in response to hypoxia, a common challenge in high-altitude environments [51, 52]. The upregulation of genes involved in blood vessel formation suggests that these amphibians have evolved to enhance oxygen delivery throughout their bodies. By improving vascularization, these species ensure that even in low-oxygen conditions, sufficient oxygen reaches vital organs and tissues, thereby supporting their metabolic needs and overall survival. Similarly, ATP binding-related pathways are enriched in these high-altitude species, indicating an adaptation to optimize energy production and utilization. Oxygen is a critical factor for efficient ATP generation via aerobic respiration, and in oxygen-limited environments, these amphibians appear to have developed more efficient metabolic pathways [53–56]. This adaptation likely allows them to sustain cellular activities and maintain energy homeostasis even when oxygen availability is scarce. The enrichment of pathways associated with cellular responses to various stimuli further underscores the complex environmental challenges these amphibians face at high altitudes. In addition to hypoxia, these environments are characterized by cold temperatures and increased UV radiation. The upregulation of genes involved in cellular stress responses, such as DNA repair and protein folding, suggests that these amphibians have enhanced their ability to mitigate the effects of these stressors, thereby maintaining cellular integrity and homeostasis under extreme conditions [57, 58]. This convergence in function, despite divergence in specific gene expression profiles, reflects the shared adaptive requirements of high-altitude environments. While the individual genes and their expression levels differ across species, the overall functional demands of these environments have driven the evolution of similar biological processes, highlighting the critical role of these pathways in facilitating adaptation to high-altitude habitats. When focusing on the three dominant plateau species, where gene expression in response to high-altitude environmental factors shows distinct differences, the functional roles of these genes converge on similar pathways. These functions are primarily enriched in metabolism, cellular activity (including cell cycle, apoptosis, and autophagy), and thermogenesis. Metabolic processes, particularly those related to energy production, are crucial for sustaining cellular functions under hypoxic conditions [53–56, 59]. The enrichment of pathways related to cellular activity—such as cell cycle regulation, apoptosis, and autophagy—reflects the essential roles these processes play in maintaining and regulating cell populations. In high-altitude environments, where environmental stressors are prevalent, these pathways ensure that cells can adapt, repair, and survive under adverse conditions [60–62]. Thermogenesis also stands out as a key adaptive mechanism for high-altitude species. The genes involved in thermogenesis are critical for generating heat to maintain body temperature in cold environments, ensuring that these species can sustain metabolic processes and protect against cold-induced stress [5, 57, 59, 63, 64]. Interestingly, even though the gene expression patterns between N. parkeri and S. boulengeri differ at both site dg and llz, the functional enrichment of these genes still converges on pathways related to metabolism, cellular activity, and thermogenesis. This finding emphasizes that while gene expression can be highly variable and influenced by local environmental conditions, the fundamental biological processes required for survival in high-altitude environments remain similar across species. In summary, our study demonstrates that the evolution of gene expression in high-altitude amphibians is characterized by a convergence in critical biological functions, despite the divergence in specific gene expression profiles. The ability of these amphibians to fine-tune these pathways highlights the complex interplay between genetic and environmental factors in shaping adaptive responses and ensuring survival in these challenging habitats. Declarations Data availability Sequencing data and relevant files have been uploaded to Genome Sequence Archive (https://ngdc.cncb.ac.cn/gsub/) with the accession number CRA018398. Acknowledgements This work was supported by the Second Tibetan Plateau Scientific Expedition and Research Program (STEP, Grant No. 2019QZKK05010503 & 2019QZKK05010203), National Natural Science Foundation of China (32300350), Natural Science Foundation of Sichuan Province of China (Grant no. 2024NSFSC1182), and the Projects from the West Light Foundation of The Chinese Academy of Sciences (Grant No. 2021XBZG_XBQNXZ_A_006). Author contributions Liming Chang : Conceptualization, Investigation, Data analysis, Writing-original draft, Writing - review & editing, Funding acquisition, Resources. Wei Zhu : Conceptualization, Investigation, Data analysis, Writing-original draft, Writing - review & editing, Resources. Qiheng Chen : Data analysis. Chunlin Zhao : Investigation, Resources. Lulu Sui: Investigation, Resources. Cheng Shen : Investigation, Resources. Qunde Zhang : Investigation, Resources. Bin Wang : Conceptualization, Writing - review & editing, Project administration. Jianping Jiang : Conceptualization, Writing - review & editing, Funding acquisition, Project administration. Declaration of interests The authors declare no competing interests. References Romero IG, Ruvinsky I, Gilad Y: Comparative studies of gene expression and the evolution of gene regulation . Nature Reviews Genetics 2012, 13 (7):505–516. Hodgins-Davis A, Townsend JP: Evolving gene expression: from G to E to G×E . Trends in Ecology & Evolution 2009, 24 (12):649–658. Hill MS, Vande Zande P, Wittkopp PJ: Molecular and evolutionary processes generating variation in gene expression . 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4950269","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":355094275,"identity":"2cdbc00d-292d-42a1-98aa-bffd568193be","order_by":0,"name":"Liming Chang","email":"","orcid":"","institution":"Chengdu Institute of Biology","correspondingAuthor":false,"prefix":"","firstName":"Liming","middleName":"","lastName":"Chang","suffix":""},{"id":355094276,"identity":"3da68cea-acfb-495e-93e4-861c2751d34a","order_by":1,"name":"Wei Zhu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsklEQVRIiWNgGAWjYDACCRBhYMPAJkGiljSStTAchjGIAPKze8wkPhScl+eTbmD88IPBLo+gFsY5Z8wkZxjcNmyTOcAs2cOQXExQC7NEjtltHoPbCWwSCQzSDAwHEhsIaWEDafljcA6khfk3UVp4QFoYDA6AtLARZ4uERFr5zx6DZKBfDrZZAhmEtcjPSN5s8OOPnbz87ObDN35U2BHWggQYgYoNSFA/CkbBKBgFowA3AADKBTNYrwi7DwAAAABJRU5ErkJggg==","orcid":"","institution":"Chengdu Institute of Biology","correspondingAuthor":true,"prefix":"","firstName":"Wei","middleName":"","lastName":"Zhu","suffix":""},{"id":355094277,"identity":"4cdd9158-0cad-4953-9a37-cee6d0c147e9","order_by":2,"name":"Qiheng Chen","email":"","orcid":"","institution":"Chengdu Institute of Biology","correspondingAuthor":false,"prefix":"","firstName":"Qiheng","middleName":"","lastName":"Chen","suffix":""},{"id":355094278,"identity":"17589bde-dde1-4d32-b435-3d8813a0914c","order_by":3,"name":"Chunlin Zhao","email":"","orcid":"","institution":"Panzhihua University","correspondingAuthor":false,"prefix":"","firstName":"Chunlin","middleName":"","lastName":"Zhao","suffix":""},{"id":355094279,"identity":"4b8981ae-b4ab-4345-94ea-7dbc576840d4","order_by":4,"name":"Lulu Sui","email":"","orcid":"","institution":"Chengdu Institute of Biology","correspondingAuthor":false,"prefix":"","firstName":"Lulu","middleName":"","lastName":"Sui","suffix":""},{"id":355094280,"identity":"cb32b6a1-e7db-4715-ab4f-0fbeeb18b70c","order_by":5,"name":"Cheng Shen","email":"","orcid":"","institution":"Chengdu Institute of Biology","correspondingAuthor":false,"prefix":"","firstName":"Cheng","middleName":"","lastName":"Shen","suffix":""},{"id":355094281,"identity":"cc059026-6df6-489c-9533-d28c730aa76f","order_by":6,"name":"Qunde Zhang","email":"","orcid":"","institution":"Chengdu Institute of Biology","correspondingAuthor":false,"prefix":"","firstName":"Qunde","middleName":"","lastName":"Zhang","suffix":""},{"id":355094284,"identity":"6db2650b-a4a6-4805-ad2c-6f1ee160fbae","order_by":7,"name":"Bin Wang","email":"","orcid":"","institution":"Chengdu Institute of Biology","correspondingAuthor":false,"prefix":"","firstName":"Bin","middleName":"","lastName":"Wang","suffix":""},{"id":355094285,"identity":"4ad79aed-5c96-4c2a-9aa5-c509530211be","order_by":8,"name":"Jianping Jiang","email":"","orcid":"","institution":"Chengdu Institute of Biology","correspondingAuthor":false,"prefix":"","firstName":"Jianping","middleName":"","lastName":"Jiang","suffix":""}],"badges":[],"createdAt":"2024-08-21 09:11:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4950269/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4950269/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64942210,"identity":"ab97df1c-11e4-4d96-8037-0db46e7bbb90","added_by":"auto","created_at":"2024-09-20 15:55:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":50248674,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSample informatio. (A) Location of Sampling sites. (B）The relationship between elevtion and typical enviromental factors. (C) Phylogenetic relationship of the 119 samples.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4950269/v1/48497d1bf5216ef87748c1d9.png"},{"id":64941180,"identity":"e77e9b0a-ac36-49fa-9ddc-5f8ecc8a4e6c","added_by":"auto","created_at":"2024-09-20 15:39:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3215442,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVariation in overall gene expression across species. (A–B) UMAP showing the clustering result of amphibian individuals. The samples were colored according to animal species (A) and cluster groups (B). (C) Heat map presenting the marker genes of each sample cluster. (D) Function enrichment of marker genes each cluster. (E–F) Networks showing the association between the UMAP1/UMAP2 expression matrix and environmental factors. *\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e p\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e \u0026lt; 0.05, ** \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e \u0026lt; 0.01, ***\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e p\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e \u0026lt; 0.001.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4950269/v1/b8e46e6410df19f0f46530b6.png"},{"id":64941841,"identity":"94d04ef2-ff11-4584-90fc-6346611da172","added_by":"auto","created_at":"2024-09-20 15:47:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":61376,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation between gene expression variation, genetic diversity and enviromnetal factor in three dominant plateau species. Correlations between gene expression, genetic diversity and enviromnetal factor in \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eB. gargarizans \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e(A)\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e, N. parkeri \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e(B)\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e, \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eand\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e S. boulengeri \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e(C)\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e, \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003erespectively.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4950269/v1/3d57f7fca6407cd4399d4bf1.png"},{"id":64941182,"identity":"d478e4aa-749d-447e-bba1-5fb818a3e37e","added_by":"auto","created_at":"2024-09-20 15:39:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":276763,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGene expression in response to various enviromental factors in three dominant plateau species. (A–C) The gene number with positive and negative correlations between expression patterns and environmental factors in in \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eB. gargarizans \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e(A)\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e, N. parkeri \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e(B)\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e, \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eand\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e S. boulengeri \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e(C)\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e, \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003erespectively. Red and blue bar showing the gene number with positive and negative correlations. (D–F) Functional enrichment of genes response to various enviromental factors in \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eB. gargarizans \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e(D)\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e, N. parkeri \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e(E)\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e, \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eand\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e S. boulengeri \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e(F)\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e, \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003erespectively. Different text background colors indicate various functional categories. Red and blue fill color denote gene sets with positive and negative correlations, respectively, while different shapes signify distinct environmental factors.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4950269/v1/3c69fb8f1df32ee5b03bee9a.png"},{"id":64941183,"identity":"897cfdde-c1af-4943-9603-b40bdc36c95f","added_by":"auto","created_at":"2024-09-20 15:39:54","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1708344,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDEGs between \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eN. parkeri\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eand \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eS. boulengeri\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e at same sample sites. (A–B) Heatmaps showing the expression patterns of DEGs of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eN. parkeri\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eS. boulengeri\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e at dg (A) and llz (B). (C) Functional enrichment of DEGs of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eN. parkeri\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eS. boulengeri \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eat same sample sites. Circular and triangular shapes represent sampling sites dg and llz, respectively. Red and blue fill colors denote genes with up-regulated expression in \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eN. parkeri \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eand \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eS. boulengeri\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e, respective. (D–E) Networks displaying the correlations between DEGs of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eN. parkeri\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eS. boulengeri\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e and enrichment terms at dg (D) and llz (E). Red and blue circles denote upregulated genes in\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e N. parkeri\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eS. boulengeri\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e, respectively. Green circles represent enrichment terms. The red and blue texts represent genes upregulated in \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eN. parkeri \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eand \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eS. boulengeri\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e with similar expression patterns at dg and llz.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"OnlineFigure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4950269/v1/0e1246d85bb8294f99c6d4a7.png"},{"id":65426362,"identity":"792df321-3cea-4da9-8733-1054ddd63d05","added_by":"auto","created_at":"2024-09-27 09:17:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":12608462,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4950269/v1/f415f2df-66ae-4b26-8e75-42f953c85470.pdf"},{"id":64941179,"identity":"a6b5f1e6-d292-48e0-bcf6-1bc3120ccf46","added_by":"auto","created_at":"2024-09-20 15:39:54","extension":"csv","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":39534,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarydata1.csv","url":"https://assets-eu.researchsquare.com/files/rs-4950269/v1/686a82097a41e1f04a4ae2f5.csv"},{"id":64941840,"identity":"a7bab97c-7a7f-46ab-9e2d-967cf8f92f18","added_by":"auto","created_at":"2024-09-20 15:47:54","extension":"fas","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":5952499,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarydata2.fas","url":"https://assets-eu.researchsquare.com/files/rs-4950269/v1/c6dc354f574074ecb9d5c451.fas"},{"id":64941839,"identity":"86078328-6026-455a-b016-35e20e9a1c4a","added_by":"auto","created_at":"2024-09-20 15:47:54","extension":"csv","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":126661,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarydata3.csv","url":"https://assets-eu.researchsquare.com/files/rs-4950269/v1/3a9a368f566c072c1868b3d0.csv"},{"id":64941185,"identity":"9ad08305-e43d-4d50-9d95-7563fac00710","added_by":"auto","created_at":"2024-09-20 15:39:54","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":1818727,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarydata4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4950269/v1/81bab5f9d358c1cc91926d71.xlsx"},{"id":64941188,"identity":"2d688fca-2f4b-4720-af08-acfb05a61302","added_by":"auto","created_at":"2024-09-20 15:39:54","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":4262842,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarydata5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4950269/v1/14d1a75be7274e315b69d70c.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Adaptive Divergence and Functional Convergence: The Evolution of Pulmonary Gene Expression in Amphibians of the Qingzang Plateau","fulltext":[{"header":"Background","content":"\u003cp\u003eThe evolution of gene expression is a central theme in understanding how organisms adapt to diverse environments [1\u0026ndash;3]. Gene expression acts as a dynamic interface between an organism\u0026rsquo;s genotype and its external surroundings, mediating the complex interactions that allow organisms to thrive under varying conditions. Through changes in gene expression, organisms can rapidly respond to external stimuli, such as fluctuations in temperature, availability of resources, or the presence of predators and pathogens[4\u0026ndash;7]. These changes not only provide immediate adaptive advantages but also contribute to long-term evolutionary adaptations, enabling species to colonize new habitats, exploit different ecological niches, and ultimately, survive and reproduce in the face of environmental challenges [2, 8\u0026ndash;10].\u003c/p\u003e \u003cp\u003eSeveral key theories provide a framework for understanding the evolution of gene expression. The neutral theory of molecular evolution posits that most changes in gene expression arise through random genetic drift rather than direct natural selection [11\u0026ndash;13]. According to this theory, many regulatory mutations may have negligible effects on an organism's fitness and thus do not contribute to adaptive evolution. In contrast, adaptive evolution emphasizes the role of natural selection in shaping gene expression patterns to enhance an organism\u0026rsquo;s fitness within specific environments [13\u0026ndash;16]. This perspective highlights how selective pressures can drive the optimization of gene expression to better suit an organism\u0026rsquo;s ecological niche. Additionally, the evolution of gene regulatory networks offers insight into how the complex interactions among genes evolve over time. Alterations within these networks can lead to new gene expression patterns, potentially resulting in novel traits or adaptive responses [3, 17\u0026ndash;19]. Understanding these theories provides a comprehensive view of how gene expression evolves in response to genetic and environmental factors, elucidating the mechanisms underlying biological adaptation and diversity.\u003c/p\u003e \u003cp\u003eThe Qingzang Plateau, characterized by its low oxygen levels, minimal precipitation, high ultraviolet (UV) radiation, and significant temperature fluctuations, presents a unique set of challenges that demand specialized physiological and molecular adaptations [20]. This harsh and variable environment offers a natural experimental field for studying the evolution of gene expression. Despite the plateau's significance for understanding environmental adaptation, current researches have predominantly focused on genetic adaptations [20\u0026ndash;24], with relatively few studies exploring the role of gene expression in this context. This gap in research underscores the need to investigate how gene expression evolves in response to the plateau's extreme conditions, providing deeper insights into the adaptive mechanisms that enable survival in such a challenging environment.\u003c/p\u003e \u003cp\u003eAmphibians, with their unique evolutionary history and physiology, offer a distinct perspective on high-altitude adaptation. Unlike endotherms, amphibians are ectothermic and rely heavily on environmental temperatures to regulate their body functions, making them particularly vulnerable to the challenges posed by high-altitude environments [23, 24]. Gene expression variation among amphibians at high altitudes is further complicated by species diversity and varying degrees of evolutionary relatedness. Amphibians in these regions must adapt to harsh conditions while maintaining essential physiological processes, particularly in critical organs such as the lungs. Investigating the genetic mechanisms underlying these adaptations provides valuable insights into the evolutionary processes that enable species to thrive in extreme environments.\u003c/p\u003e \u003cp\u003eIn this study, we explore the patterns of pulmonary gene expression variation across multiple amphibian species inhabiting high-altitude environments on the Qingzang Plateau. We aim to disentangle the relative contributions of genetic factors and environmental pressures to gene expression variation, with a particular focus on understanding how these factors interact to drive adaptation. This research not only enhances our understanding of high-altitude adaptation in amphibians but also offers broader insights into gene expression plasticity and evolutionary adaptation. Such insights have the potential to inform conservation strategies for amphibians in rapidly changing environments and to shed light on broader principles of adaptation and survival in extreme habitats.\u003c/p\u003e"},{"header":"Methods and Materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSample collection and environmental information\u003c/h2\u003e \u003cp\u003eThe animals were collected along the national highways 318 and 214 in June 2022 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, B). The study area spans nearly 1,800 km, representing the largest distance between collection sites. A total of 119 amphibian samples were collected from 20 sampling sites, encompassing ten amphibian species (i.e., \u003cem\u003eBombina maxima\u003c/em\u003e, \u003cem\u003eScutiger boulengeri\u003c/em\u003e, \u003cem\u003eBufo gargarizans\u003c/em\u003e, \u003cem\u003eNanorana parkeri\u003c/em\u003e, \u003cem\u003eN. ventripunctata\u003c/em\u003e, \u003cem\u003eN. huangi\u003c/em\u003e, \u003cem\u003eRana kukunoris\u003c/em\u003e, \u003cem\u003eR. shuchinae\u003c/em\u003e, \u003cem\u003eR. chaochiaoensis\u003c/em\u003e, and \u003cem\u003eNidirana pleuraden\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). After euthanized with MS-222, the animals were dissected to collect the lung tissues and which were stored in 2 mL aseptic centrifuge tubes with 1.5 mL RNA storage solution (Novogene Co., Ltd.). All animal protocols in this study were reviewed and approved by the Animal Ethical and Welfare Committee of Chengdu Institute of Biology, Chinese Academy of Sciences (permit number: CIBDWLL2023201), in compliance with the ARRIVE guidelines 2.0 and Guide for the Care and Use of Laboratory Animals (8th edition) published by National Research Council (US) Committee for the Update of the Guide for the Care and Use of Laboratory Animals [25].\u003c/p\u003e \u003cp\u003eWe recorded the longitude and latitude coordinates for each sample and obtained the corresponding environmental factor data including annual mean temperature and annual precipitation by querying the WorldClim v 2.1 database [26] (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.worldclim.org/\u003c/span\u003e\u003cspan address=\"http://www.worldclim.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed on 7 May 2024), annual average oxygen concentration by querying Surface oxygen concentration on the Qinghai Tibet Plateau dataset[27] (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.11888/Atmos.tpdc.272423\u003c/span\u003e\u003cspan address=\"10.11888/Atmos.tpdc.272423\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed on 7 May 2024), and annual mean UV-B radiation by querying glUV dataset [28] (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ufz.de/gluv\u003c/span\u003e\u003cspan address=\"https://www.ufz.de/gluv\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed on 7 May 2024). These data were provided in Supplementary data 1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eTranscriptomic assembly, quantification, and annotation\u003c/h2\u003e \u003cp\u003eLung samples were promptly fresh-frozen in liquid nitrogen and stored at -80\u0026deg;C until RNA extraction. The procedure for total RNA extraction followed the established protocol for TRIzol (Life Technologies Corp., Carlsbad, CA, USA). Subsequently, 1 \u0026micro;g of RNA from each sample was utilized for library construction by employing the NEBNext\u0026reg;Ultra\u0026trade; RNA Library Prep Kit for Illumina\u0026reg; (NEB, USA). Sequencing was performed on an Illumina Hiseq 2000 platform from Biomarker Technologies Co. Ltd. to generate paired-end reads. The raw sequencing data were deposited in the Genome Sequence Archive (GSA) under the accession number CRA018398. Clean data were obtained by filtering out reads containing adapters, poly-N, and low-quality reads from the raw dataset. Reference-based transcriptome assembly was accomplished using HISAT2 in \u003cem\u003eN. parkeri\u003c/em\u003e, while de novo transcriptome assembly was accomplished using Trinity in other amphibians [29], and subsequent annotation was conducted by querying against various databases, including NR (NCBI non-redundant protein sequences), Pfam (protein family), KOG/COG/eggNOG (clusters of orthologous groups of proteins), Swiss-Prot (a manually annotated and reviewed protein sequence database), KEGG (Kyoto Encyclopedia of Genes and Genomes), and GO (Gene Ontology). Gene expression levels were quantified using RSEM [30].\u003c/p\u003e \u003cp\u003eSignificantly differentially expressed genes (DEGs) were identified based on a stringent criterion of q\u0026thinsp;\u0026lt;\u0026thinsp;0.05 after Benjamini and Hochberg\u0026rsquo;s correction. Then, gene enrichment analysis was conducted based on the KEGG database using KOBAS 3.0 with an E-value threshold of 1.0E-5 [31].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eEvaluating the genetic distance of amphibian\u003c/h2\u003e \u003cp\u003eThe mitochondrial genome of 119 amphibian individuals were extracted and assembled using MEANGS [32] based on transcriptomic data (Supplementary data 2). Thirteen mitochondrial gene sequences (i.e., \u003cem\u003eATP6\u003c/em\u003e, \u003cem\u003eATP8\u003c/em\u003e, \u003cem\u003eCOX1\u003c/em\u003e, \u003cem\u003eCOX2\u003c/em\u003e, \u003cem\u003eCOX3\u003c/em\u003e, \u003cem\u003eCYTB\u003c/em\u003e, \u003cem\u003eND1\u003c/em\u003e, \u003cem\u003eND2\u003c/em\u003e, \u003cem\u003eND3\u003c/em\u003e, \u003cem\u003eND4\u003c/em\u003e, \u003cem\u003eND4L\u003c/em\u003e, \u003cem\u003eND5\u003c/em\u003e, and \u003cem\u003eND6\u003c/em\u003e) were aligned across the 119 amphibian individuals in batches with MAFFT [33] using '--auto' strategy and normal alignment mode. The aligned mitochondrial gene sequences were concatenated to calculate the p-distance matrices for amphibian individuals using MEGA 11 [34]. The p-distance matrices were used for subsequent analyses on amphibian genetic diversity (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC, Supplementary data 3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eCross-species comparison of gene expression\u003c/h2\u003e \u003cp\u003eWe calculated the orthologous genes of 10 amphibian species based on the complete set of protein sequences using OrthoFinder [35]. Only the one-to-one orthologous were selected to construct cross-species gene expression matrix. After formatting the gene expression matrix as \u0026ldquo;sample\u0026times;gene\u0026rdquo; (Supplementary data 4), the output was loaded into Seurat v4.0.4 [36]. The data were normalized using the \u0026ldquo;NormalizeData\u0026rdquo; function, and subsequently scaled using Pearson Residuals with a scale factor of 10,000. The top 1,000 highly variable features were selected using the \u0026ldquo;SelectIntegrationFeatures\u0026rdquo; function. Principal component analysis was performed using the \u0026ldquo;RunPCA\u0026rdquo; function with the default parameters. UMAP dimensionality reduction methods were used based on the top 20 principal components (PCs) using the \u0026ldquo;RunUMAP\u0026rdquo; functions, respectively. Moreover, the unsupervised functional clusters of gene expression were identified with a clustering resolution of 2 using \u0026ldquo;FindNeighbors\u0026rdquo; and \u0026ldquo;FindClusters\u0026rdquo; functions. The FindAllMarkers function implemented in Seurat v4.0.2 was used to identify the DEGs across functional clusters with the options \u0026ldquo;min.pct\u0026thinsp;=\u0026thinsp;0.25, logfc.threshold\u0026thinsp;=\u0026thinsp;0.25, test.use\u0026thinsp;=\u0026thinsp;wilcox\u0026rdquo;. Multiple test correction for the p value was performed using the Bonferroni method, and 0.05 was set as a threshold to define significance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eAssociations between overall gene expression variations and species as well as environmental factors\u003c/h2\u003e \u003cp\u003eFollowing tests for normality and homoscedasticity of the environmental factor data (i.e, annual mean temperature, annual precipitation, annual average oxygen concentration, and UV-B radiation), we conducted Pearson correlation analysis on the environmental factors and selected one factor from pairs with correlation coefficients greater than to 0.7 for modeling. Then, generalized linear model were performed to examine the associations between UMAPs (i.e., UMAP1 and UMAP2) in explaining the gene expression variations and species as well as environmental factors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAssociations between gene expression variations and environmental factors as well as genetic diversity\u003c/h2\u003e \u003cp\u003eMantel tests were performed to examine the correlations between gene expression variations (gene expression matrix) and environmental factors as well as genetic diversity (p-distances matrix) in three dominant plateau species based on linkET and vegan [37] packages.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eCandidate genes selected in response to environmental factors\u003c/h2\u003e \u003cp\u003ePearson correlation tests were conducted between gene expression and environmental factors. Genes with a p-value\u0026thinsp;\u0026le;\u0026thinsp;0.01 and an absolute correlation coefficient\u0026thinsp;\u0026gt;\u0026thinsp;0.6 were selected as candidate genes in response to environmental factors in three dominant plateau species.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eDifferential genes expression analysis between species\u003c/h2\u003e \u003cp\u003eWe calculated the orthologous genes of \u003cem\u003eN. parkeri\u003c/em\u003e and \u003cem\u003eS. boulengeri\u003c/em\u003e based on the complete set of protein sequences using OrthoFinder. One-to-one orthologous were selected to construct the gene expression matrix of two species. One-way ANOVA was conducted to analyze the differential gene expression of \u003cem\u003eN. parkeri\u003c/em\u003e and \u003cem\u003eS. boulengeri\u003c/em\u003e at sites dg and llz. Genes with a p-value\u0026thinsp;\u0026le;\u0026thinsp;0.01 were defined as DEGs (Supplementary data 5).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eFunctional enrichment analysis\u003c/h2\u003e \u003cp\u003eFunctional enrichment analysis was conducted using KOBAS-i (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioinfo.org/kobas\u003c/span\u003e\u003cspan address=\"http://bioinfo.org/kobas\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The Benjamini\u0026ndash;Hochberg (BH) method was used for multiple test adjustment, and 0.05 was set as a threshold to define significance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eBasic statistical analyses\u003c/h2\u003e \u003cp\u003eWe conducted all the statistical analyses using R [38]. The normality of the data was assessed using Kolmogorov-Smirnov and Shapiro-Wilk tests, and homoscedasticity across groups was evaluated using Levene\u0026rsquo;s tests. The graphs were generated by GraphPad Prism 7 or ggplot2 in R [39].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eVariation in overall gene expression across species\u003c/b\u003e \u003c/h2\u003e \u003cp\u003e \u003cb\u003eFigure 2 Variation in overall gene expression across species. (A\u0026ndash;B) UMAP showing the clustering result of amphibian individuals. The samples were colored according to animal species (A) and cluster groups (B). (C) Heat map presenting the marker genes of each sample cluster. (D) Function enrichment of marker genes each cluster. (E\u0026ndash;F) Networks showing the association between the UMAP1/UMAP2 expression matrix and environmental factors. *\u003c/b\u003e \u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.05, **\u003c/b\u003e \u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.01, ***\u003c/b\u003e \u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.001.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eUnbiased clustering was performed on the transcriptional data of lung samples from 119 amphibian individuals (10 species), resulting in six functional clusters identified using uniform manifold approximation and projection (UMAP, Fig.\u0026nbsp;2A, B). The \u003cem\u003eScutiger boulengeri\u003c/em\u003e complex was divided into two functional clusters. \u003cem\u003eBufo gargarizans\u003c/em\u003e, \u003cem\u003eNanorana parkeri\u003c/em\u003e, and \u003cem\u003eBombina maxima\u003c/em\u003e each form a separate functional cluster individually, while the remaining species (i.e., \u003cem\u003eN. ventripunctata\u003c/em\u003e, \u003cem\u003eN. huangi\u003c/em\u003e, \u003cem\u003eRana kukunoris\u003c/em\u003e, \u003cem\u003eR. shuchinae\u003c/em\u003e, \u003cem\u003eR. chaochiaoensis\u003c/em\u003e and \u003cem\u003eNidirana pleuraden\u003c/em\u003e) are grouped together into a single functional cluster. Although different sample clusters were characterized by distinctive marker genes, their marker genes were involved in common cellular processes, such as angiogenesis, ATP binding, cellular response to DNA damage stimulus, cellular response to UV, DNA repair, mitochondrion, and oxidation reduction.\u003c/p\u003e \u003cp\u003eWe performed a Mantel analysis to investigate the correlation between gene expression and genetic distance. The results showed that the correlation between these two factors was not statistically significant (Mantel statistic r = -0.007027, \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.4503). Subsequently, we explored the factors influencing overall gene expression variation across species using a generalized linear model (GLM). Due to the strong correlation between temperature and oxygen content, we constructed models by alternately excluding each variable to assess their individual effects. In the \"UMAP1/UMAP2\u0026thinsp;~\u0026thinsp;species\u0026thinsp;+\u0026thinsp;UVB\u0026thinsp;+\u0026thinsp;oxygen\u0026thinsp;+\u0026thinsp;precipitation\" model, the results indicated that species, UVB, and oxygen levels significantly influenced UMAP1, while species, oxygen, and precipitation had significant effects on UMAP2 (Fig.\u0026nbsp;2E). In the \"UMAP1/UMAP2\u0026thinsp;~\u0026thinsp;species\u0026thinsp;+\u0026thinsp;UVB\u0026thinsp;+\u0026thinsp;temperature\u0026thinsp;+\u0026thinsp;precipitation\" model, species, temperature, and precipitation were significant predictors of UMAP1, whereas species and precipitation significantly affected UMAP2 (Fig.\u0026nbsp;2F).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eFactors influencing overall gene expression variation in three dominant plateau species\u003c/h2\u003e \u003cp\u003eWe examined the factors affecting intraspecific overall gene expression variation in three dominant plateau species: \u003cem\u003eB. gargarizans, N. parkeri\u003c/em\u003e, and \u003cem\u003eS. boulengeri\u003c/em\u003e, respectivily. Correlation analyses revealed that in \u003cem\u003eB. gargarizans\u003c/em\u003e, overall gene expression variation was influenced by environmental factors (UVB, Oxygen, Precipitation, and Temperature) and genetic diversity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). In \u003cem\u003eN. parkeri\u003c/em\u003e, this variation was influenced by Oxygen and Temperature (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). In \u003cem\u003eS. boulengeri\u003c/em\u003e, overall gene expression variation was influenced by neither environmental factor nor genetic diversity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). These findings suggest that the factors influencing gene expression variation can differ significantly between species, highlighting the complex interplay between genetics and environment in shaping gene expression plasticity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eGene expression variation in response to environmental factors\u003c/h2\u003e \u003cp\u003eWe further identified the genes in response to environmental factors in three dominant plateau species. The results indicated that in \u003cem\u003eB. gargarizans\u003c/em\u003e, the expression of 1024, 369, 197, and 810 genes is negatively correlated with UVB, oxygen, temperature, and precipitation, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Additionally, 111, 533, 1, and 3865 genes show positive correlations with these factors. In \u003cem\u003eN. parkeri\u003c/em\u003e, 3, 2996, 2818, and 598 genes exhibit negative correlations with UVB, oxygen, temperature, and precipitation, respectively, whereas 555, 113, 138, and 4 genes exhibit positive correlations (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). In \u003cem\u003eS. boulengeri\u003c/em\u003e, 61, 288, 75, and 52 genes are negatively correlated with UVB, oxygen, temperature, and precipitation, respectively, while 287, 38, 0, and 0 genes are positively correlated (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eFunctional enrichment analysis revealed that genes responsive associated with oxygen level were enriched in substrate energy metabolism pathways, thermogenesis, and cellular death across the three dominant plateau species. Furthermore, these genes also participated in regulation of oxygen homeostasis in \u003cem\u003eB. gargarizans\u003c/em\u003e and \u003cem\u003eN. parkeri\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eD-F). Precipitation-responsive genes were enriched in cell death pathways across all three species. In \u003cem\u003eB. gargarizans\u003c/em\u003e, these genes were also involved in substrate and energy metabolism, hypoxia signaling, and thermogenesis, whereas in \u003cem\u003eN. parkeri\u003c/em\u003e, they highlighted the hypoxia signaling and immunity pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eD\u0026ndash;F). Temperature-responsive genes exhibited enrichment in substrate and energy metabolism and cell death pathways across all species, with additional enrichment in response to hypoxia in \u003cem\u003eB. gargarizans\u003c/em\u003e and \u003cem\u003eN. parkeri\u003c/em\u003e. In \u003cem\u003eN. parkeri\u003c/em\u003e, genes correlating to temperature were also enriched in thermogenesis and immunity pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eD\u0026ndash;F). UVB-responsive genes were enriched in substrate and energy metabolism pathways in \u003cem\u003eB. gargarizans\u003c/em\u003e and \u003cem\u003eS. boulengeri\u003c/em\u003e. Specifically, in \u003cem\u003eB. gargarizans\u003c/em\u003e, UVB-responsive genes showed enrichment in hypoxia signaling and thermogenesis, while in \u003cem\u003eN. parkeri\u003c/em\u003e, enrichment was observed in the immunity pathway. Both species exhibited enrichment of UVB-responsive genes in cell death pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eD\u0026ndash;F).\u003c/p\u003e \u003cp\u003eOverall, environment-responsive genes highlight common and specific enrichment in metabolic processes, thermogenesis, cellular death, and adaptive immune responses across amphibian species inhabiting high-altitude environments.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eInterspecific gene expression variations at same sampling sites\u003c/h2\u003e \u003cp\u003eAs both \u003cem\u003eN. parkeri\u003c/em\u003e and \u003cem\u003eS. boulengeri\u003c/em\u003e were collected at sites dg and llz, we analyzed the gene expression variations between \u003cem\u003eN. parkeri\u003c/em\u003e and \u003cem\u003eS. boulengeri\u003c/em\u003e at these two sites. At site dg, 337 and 1,477 genes exhibited higher and lower expression levels in \u003cem\u003eS. boulengeri\u003c/em\u003e compared to \u003cem\u003eN. parkeri\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). At site llz, 1,129 and 735 genes showed higher and lower expression levels in \u003cem\u003eS. boulengeri\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). This suggested that the variation patterns in DEG numbers were different at the two sites. However, the interspecific DEGs of the two sites participated in some common functions, including the citrate cycle, oxdative phosphorylation, peroxisome, thermogenesis, and cell death-related signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Interestingly, we found that the interspecific DEGs shared by the two sites exhibit consensus expression trends. In other words, DEGs robustly expressed in \u003cem\u003eN. parkeri\u003c/em\u003e at dg also showed higher expression level in this species at llz compared to the \u003cem\u003eS. boulengeri\u003c/em\u003e counterparts, and vice versa (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eD\u0026ndash;E). These findings underscore the significant gene expression differences between species at similar altitudes and highlight the consistency of gene expression trends within each species across different sites.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe study reveals significant variation in pulmonary gene expression among amphibian species inhabiting high-elevation environments, driven by both genetic background and environmental factors. This highlights the intricate interplay between genetics and environment in shaping gene expression plasticity, which is crucial for plateau adaptation. Despite divergent gene expression responses, the functional traits converging on essential biological processes underscore the adaptability of these amphibians to extreme environments of the Qingzang Plateau.\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eChallenges to the unbounded neutral evolution model\u003c/h2\u003e \u003cp\u003eThe unbounded neutral evolution model predicts a linear correlation between genetic distance and gene expression divergence, accumulating over time [2, 11]. This model has been supported by studies on primates and mice, where gene expression variation correlates linearly with the estimated time since species divergence [11]. However, our study reveals a different pattern in amphibians. While gene expression patterns within these amphibian species exhibit strong similarities, there is no significant correlation between expression variation and genetic distances among different species. For instance, \u003cem\u003eN. huangi\u003c/em\u003e and \u003cem\u003eN. ventripunctata\u003c/em\u003e show expression patterns that are more similar to those of Ranidae amphibians than to the genetically closer \u003cem\u003eN. parkeri\u003c/em\u003e. This deviation from the expected model may reflect the influence of distinct selective pressures and ecological niches in their heterogeneous environments, driving adaptive changes in gene expression [40\u0026ndash;42].\u003c/p\u003e \u003cp\u003eTo further investigate this, we employed GLM to assess the contributions of species identity and environmental factors to gene expression variation. Our analysis indicates that gene expression across these amphibians is significantly influenced by both species-specific characteristics and environmental conditions. This suggests that selective pressures and environmental factors play crucial roles in shaping gene expression pattern [2], beyond what the unbounded neutral evolution model accounts for. The observed discrepancy between genetic distance and expression divergence suggests that models incorporating adaptive evolutionary processes and environmental influences may provide a more comprehensive explanation for gene expression variation in these species [2, 40].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eGene expression variation driven by environmental factors\u003c/h2\u003e \u003cp\u003eBuilding on the findings that gene expression patterns among amphibians are influenced by both species\u0026rsquo; identity and environmental factors, we took a systematic approach to disentangle these effects. By first controlling for species-related variations, we were able to isolate and examine the impact of environmental factors on intraspecific gene expression, thereby eliminating the potential influence of lineage-specific relaxation of evolutionary constraints [1]. This approach provided a clearer understanding of how environmental factors independently shape gene expression patterns within species.\u003c/p\u003e \u003cp\u003eOur Mantel analysis reveals that gene expression in three dominant plateau species is modulated by distinct environmental factors. For instance, \u003cem\u003eB. gargarizans\u003c/em\u003e showed gene expression changes in response to a combination of UVB radiation, oxygen availability, temperature, and precipitation. In contrast, \u003cem\u003eN. parkeri\u003c/em\u003e was primarily influenced by oxygen and temperature, while \u003cem\u003eS. boulengeri\u003c/em\u003e appeared largely unaffected by these environmental variables. These findings highlight the role of local microenvironmental conditions in modulating gene expression [8, 43].\u003c/p\u003e \u003cp\u003eThis variability in gene expression responsiveness among species can be interpreted through the lens of adaptive plasticity. Gene expression plasticity allows animals to rapidly adjust their physiological processes to better suit their surroundings, thereby enhancing their survival and reproductive success [1, 2, 44, 45]. The differential expression patterns observed between amphibian species, such as the high responsiveness of \u003cem\u003eB. gargarizans\u003c/em\u003e to precipitation and the specific oxygen and temperature responses in \u003cem\u003eN. parkeri\u003c/em\u003e, reflect the adaptive strategies these species have evolved to cope with their respective environments.\u003c/p\u003e \u003cp\u003eMoreover, the differential environmental responsiveness observed in \u003cem\u003eS. boulengeri\u003c/em\u003e, which showed a limited response to environmental factors, suggests that this species may have evolved alternative strategies for coping with environmental stresses. This could involve a greater reliance on constitutive defenses or other non-plastic mechanisms, reflecting a different evolutionary pathway where stabilizing selection has minimized the need for gene expression plasticity [7].\u003c/p\u003e \u003cp\u003eIn light of these findings, it becomes evident that gene expression evolution is not a simple process dictated solely by genetic distance or lineage-specific factors[46\u0026ndash;48]. Instead, it is profoundly influenced by the ecological context in which species evolve. The ability of amphibians to modulate gene expression in response to environmental factors is likely a crucial aspect of their adaptability and evolutionary success in diverse and often fluctuating environments [2, 49]. This emphasizes the need for evolutionary models that incorporate both genetic and environmental components to fully explain the complexities of gene expression regulation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eGene expression variation driven by genetic basis\u003c/h2\u003e \u003cp\u003eWe shifted our focus to disentangle the genetic contributions by controlling for environmental variables. By comparing the gene expression profiles of \u003cem\u003eN. parkeri\u003c/em\u003e and \u003cem\u003eS. boulengeri\u003c/em\u003e sampled from the same locations, we aimed to isolate the impact of genetic differences on gene expression patterns. Differential gene expression analysis revealed that, despite being subjected to identical environmental conditions, the two species exhibit markedly different gene expression patterns. This result highlights the substantial role that genetic divergence plays in shaping expression profiles, independent of environmental factors [3].\u003c/p\u003e \u003cp\u003eFurther exploration of these differences across locations led us to investigate whether the gene expression patterns between \u003cem\u003eN. parkeri\u003c/em\u003e and \u003cem\u003eS. boulengeri\u003c/em\u003e were consistent across collection sites. Interestingly, the differential gene expression patterns between the two species varied significantly between the two sites. This suggests that even micro-environmental variations, which might seem subtle, can have a profound impact on gene expression plasticity [8, 43]. Such plasticity allows amphibians to finely tune their gene expression in response to local environmental conditions, further complicating the genetic vs. environment dichotomy.\u003c/p\u003e \u003cp\u003eTo better understand the underlying genetic mechanisms, we conducted a network analysis focused on the DEGs between \u003cem\u003eN. parkeri\u003c/em\u003e and \u003cem\u003eS. boulengeri\u003c/em\u003e. This analysis uncovered that many DEGs central to the network displayed consistent expression trends across both sampling sites. This consistency suggests that these core genes are likely subject to stabilizing selection, which acts to preserve their expression levels despite environmental fluctuations. Such genes are presumably critical for the species' survival and adaptation, indicating that while gene expression can be flexible, there is a subset of genes whose expression is tightly regulated and conserved due to their essential roles in maintaining homeostasis and other vital functions.\u003c/p\u003e \u003cp\u003eThese findings align with and extend the theory of the evolution of genetic regulatory networks. According to this theory, natural selection does not merely act on individual genes in isolation but on entire networks of genes that function together to regulate complex biological processes [3, 17\u0026ndash;19]. The consistent expression patterns observed in our study suggest that these regulatory networks are finely tuned through evolutionary processes to respond to specific environmental challenges while maintaining overall network stability. This balancing act allows organisms to adapt to their environments by modulating certain parts of their gene networks while keeping the core network structure intact.\u003c/p\u003e \u003cp\u003eMoreover, the observed differences in gene expression patterns between \u003cem\u003eN. parkeri\u003c/em\u003e and \u003cem\u003eS. boulengeri\u003c/em\u003e at different sites underscore the adaptive significance of gene regulatory networks. The ability of these networks to exhibit both stability and plasticity is likely a key factor in the evolutionary success of these species, allowing them to thrive in diverse and changing environments [8, 10]. This dual capacity for stability and flexibility in gene expression may represent a general principle in the development and evolution of complex organisms [50], where evolutionary pressures drive the optimization of gene networks to ensure both robustness and adaptability.\u003c/p\u003e \u003cp\u003eOverall, our findings suggest that future studies on gene expression evolution should consider not only the expression levels of individual genes but also the dynamic interactions within gene regulatory networks. Understanding how these networks evolve and adapt in response to environmental changes will be crucial for unraveling the complexities of gene expression evolution and the broader mechanisms of adaptation and survival in diverse ecological contexts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eConvergent functions of pulmonary gene expression in amphibian plateau adaptation\u003c/h2\u003e \u003cp\u003eOur study reveals that despite significant divergence in gene expression profiles across ten amphibian species, the functional enrichment analysis points to a remarkable convergence in the biological processes that are critical for survival in high-altitude environments. This convergence is particularly evident in pathways related to angiogenesis, ATP binding, and cellular responses to various environmental stimuli. Angiogenesis emerges as a central adaptive mechanism in response to hypoxia, a common challenge in high-altitude environments [51, 52]. The upregulation of genes involved in blood vessel formation suggests that these amphibians have evolved to enhance oxygen delivery throughout their bodies. By improving vascularization, these species ensure that even in low-oxygen conditions, sufficient oxygen reaches vital organs and tissues, thereby supporting their metabolic needs and overall survival. Similarly, ATP binding-related pathways are enriched in these high-altitude species, indicating an adaptation to optimize energy production and utilization. Oxygen is a critical factor for efficient ATP generation via aerobic respiration, and in oxygen-limited environments, these amphibians appear to have developed more efficient metabolic pathways [53\u0026ndash;56]. This adaptation likely allows them to sustain cellular activities and maintain energy homeostasis even when oxygen availability is scarce. The enrichment of pathways associated with cellular responses to various stimuli further underscores the complex environmental challenges these amphibians face at high altitudes. In addition to hypoxia, these environments are characterized by cold temperatures and increased UV radiation. The upregulation of genes involved in cellular stress responses, such as DNA repair and protein folding, suggests that these amphibians have enhanced their ability to mitigate the effects of these stressors, thereby maintaining cellular integrity and homeostasis under extreme conditions [57, 58]. This convergence in function, despite divergence in specific gene expression profiles, reflects the shared adaptive requirements of high-altitude environments. While the individual genes and their expression levels differ across species, the overall functional demands of these environments have driven the evolution of similar biological processes, highlighting the critical role of these pathways in facilitating adaptation to high-altitude habitats.\u003c/p\u003e \u003cp\u003eWhen focusing on the three dominant plateau species, where gene expression in response to high-altitude environmental factors shows distinct differences, the functional roles of these genes converge on similar pathways. These functions are primarily enriched in metabolism, cellular activity (including cell cycle, apoptosis, and autophagy), and thermogenesis. Metabolic processes, particularly those related to energy production, are crucial for sustaining cellular functions under hypoxic conditions [53\u0026ndash;56, 59]. The enrichment of pathways related to cellular activity\u0026mdash;such as cell cycle regulation, apoptosis, and autophagy\u0026mdash;reflects the essential roles these processes play in maintaining and regulating cell populations. In high-altitude environments, where environmental stressors are prevalent, these pathways ensure that cells can adapt, repair, and survive under adverse conditions [60\u0026ndash;62]. Thermogenesis also stands out as a key adaptive mechanism for high-altitude species. The genes involved in thermogenesis are critical for generating heat to maintain body temperature in cold environments, ensuring that these species can sustain metabolic processes and protect against cold-induced stress [5, 57, 59, 63, 64].\u003c/p\u003e \u003cp\u003eInterestingly, even though the gene expression patterns between \u003cem\u003eN. parkeri\u003c/em\u003e and \u003cem\u003eS. boulengeri\u003c/em\u003e differ at both site dg and llz, the functional enrichment of these genes still converges on pathways related to metabolism, cellular activity, and thermogenesis. This finding emphasizes that while gene expression can be highly variable and influenced by local environmental conditions, the fundamental biological processes required for survival in high-altitude environments remain similar across species.\u003c/p\u003e \u003cp\u003eIn summary, our study demonstrates that the evolution of gene expression in high-altitude amphibians is characterized by a convergence in critical biological functions, despite the divergence in specific gene expression profiles. The ability of these amphibians to fine-tune these pathways highlights the complex interplay between genetic and environmental factors in shaping adaptive responses and ensuring survival in these challenging habitats.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSequencing data and relevant files have been uploaded to Genome Sequence Archive (https://ngdc.cncb.ac.cn/gsub/) with the accession number\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eCRA018398.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Second Tibetan Plateau Scientific Expedition and Research Program (STEP, Grant No. 2019QZKK05010503 \u0026amp; 2019QZKK05010203), National Natural Science Foundation of China (32300350), Natural Science Foundation of Sichuan Province of China (Grant no. 2024NSFSC1182), and\u0026nbsp;the Projects from the West Light Foundation of The Chinese Academy of Sciences (Grant No. 2021XBZG_XBQNXZ_A_006).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLiming Chang\u003c/strong\u003e:\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eConceptualization, Investigation, Data analysis, Writing-original draft, Writing - review \u0026amp; editing, Funding acquisition, Resources.\u003cstrong\u003e\u0026nbsp;Wei Zhu\u003c/strong\u003e: Conceptualization, Investigation, Data analysis, Writing-original draft, Writing - review \u0026amp; editing, Resources.\u003cstrong\u003e\u0026nbsp;Qiheng Chen\u003c/strong\u003e: Data analysis.\u003cstrong\u003e\u0026nbsp;Chunlin Zhao\u003c/strong\u003e:\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eInvestigation, Resources. \u003cstrong\u003eLulu Sui:\u0026nbsp;\u003c/strong\u003eInvestigation, Resources. \u003cstrong\u003eCheng Shen\u003c/strong\u003e:\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eInvestigation, Resources. \u003cstrong\u003eQunde Zhang\u003c/strong\u003e:\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eInvestigation, Resources.\u003cstrong\u003e\u0026nbsp;Bin Wang\u003c/strong\u003e:\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eConceptualization, Writing - review \u0026amp; editing, Project administration. \u003cstrong\u003eJianping Jiang\u003c/strong\u003e:\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eConceptualization, Writing - review \u0026amp; editing, Funding acquisition, Project administration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eRomero IG, Ruvinsky I, Gilad Y: \u003cstrong\u003eComparative studies of gene expression and the evolution of gene regulation\u003c/strong\u003e. \u003cem\u003eNature Reviews Genetics\u003c/em\u003e 2012, \u003cstrong\u003e13\u003c/strong\u003e(7):505\u0026ndash;516.\u003c/li\u003e\n \u003cli\u003eHodgins-Davis A, Townsend JP: \u003cstrong\u003eEvolving gene expression: from G to E to G\u0026times;E\u003c/strong\u003e. \u003cem\u003eTrends in Ecology \u0026amp; Evolution\u003c/em\u003e 2009, \u003cstrong\u003e24\u003c/strong\u003e(12):649\u0026ndash;658.\u003c/li\u003e\n \u003cli\u003eHill MS, Vande Zande P, Wittkopp PJ: \u003cstrong\u003eMolecular and evolutionary processes generating variation in gene expression\u003c/strong\u003e. \u003cem\u003eNature Reviews Genetics\u003c/em\u003e 2021, \u003cstrong\u003e22\u003c/strong\u003e(4):203\u0026ndash;215.\u003c/li\u003e\n \u003cli\u003ePodrabsky JE, Somero GN: \u003cstrong\u003eChanges in gene expression associated with acclimation to constant temperatures and fluctuating daily temperatures in an annual killifish Austrofundulus limnaeus\u003c/strong\u003e. \u003cem\u003eJournal of Experimental Biology\u003c/em\u003e 2004, \u003cstrong\u003e207\u003c/strong\u003e(13):2237\u0026ndash;2254.\u003c/li\u003e\n \u003cli\u003eCheviron ZA, Bachman GC, Connaty AD, McClelland GB, Storz JF: \u003cstrong\u003eRegulatory changes contribute to the adaptive enhancement of thermogenic capacity in high-altitude deer mice\u003c/strong\u003e. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e 2012, \u003cstrong\u003e109\u003c/strong\u003e(22):8635\u0026ndash;8640.\u003c/li\u003e\n \u003cli\u003eHossain H, Tchatalbachev S, Chakraborty T: \u003cstrong\u003eHost gene expression profiling in pathogen\u0026ndash;host interactions\u003c/strong\u003e. \u003cem\u003eCurrent Opinion in Immunology\u003c/em\u003e 2006, \u003cstrong\u003e18\u003c/strong\u003e(4):422\u0026ndash;429.\u003c/li\u003e\n \u003cli\u003eGhalambor CK, Hoke KL, Ruell EW, Fischer EK, Reznick DN, Hughes KA: \u003cstrong\u003eNon-adaptive plasticity potentiates rapid adaptive evolution of gene expression in nature\u003c/strong\u003e. \u003cem\u003eNature\u003c/em\u003e 2015, \u003cstrong\u003e525\u003c/strong\u003e(7569):372\u0026ndash;375.\u003c/li\u003e\n \u003cli\u003eL\u0026oacute;pez-Maury L, Marguerat S, B\u0026auml;hler J: \u003cstrong\u003eTuning gene expression to changing environments: from rapid responses to evolutionary adaptation\u003c/strong\u003e. \u003cem\u003eNature Reviews Genetics\u003c/em\u003e 2008, \u003cstrong\u003e9\u003c/strong\u003e(8):583\u0026ndash;593.\u003c/li\u003e\n \u003cli\u003eRiehle MM, Bennett AF, Lenski RE, Long AD: \u003cstrong\u003eEvolutionary changes in heat-inducible gene expression in lines of Escherichia coli adapted to high temperature\u003c/strong\u003e. \u003cem\u003ePhysiological Genomics\u003c/em\u003e 2003, \u003cstrong\u003e14\u003c/strong\u003e(1):47\u0026ndash;58.\u003c/li\u003e\n \u003cli\u003eKenkel CD, Matz MV: \u003cstrong\u003eGene expression plasticity as a mechanism of coral adaptation to a variable environment\u003c/strong\u003e. \u003cem\u003eNature Ecology \u0026amp; 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Biology and Evolution\u003c/em\u003e 2021, \u003cstrong\u003e38\u003c/strong\u003e(7):2677\u0026ndash;2691.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"gene expression, high altitude, adaption, convergent evolution, amphibian lung","lastPublishedDoi":"10.21203/rs.3.rs-4950269/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4950269/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe Qingzang Plateau, with its harsh environmental conditions\u0026mdash;low oxygen, high ultraviolet radiation, and significant temperature fluctuations\u0026mdash;demands specialized adaptations for survival. While extensive research has focused on genetic adaptations to these extreme conditions, the role of gene expression in amphibian adaptation remains relatively unexplored. This study aims to investigate pulmonary gene expression variation across multiple amphibian species on the plateau, seeking to understand how genetic and environmental factors contribute to gene expression evolution in these challenging environments.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOur analysis reveals significant variation in pulmonary gene expression among amphibian species, driven by both genetic diversity and environmental pressures. Contrary to the predictions of the unbounded neutral evolution model, we found no significant correlation between gene expression divergence and genetic distance. Instead, species-specific characteristics and environmental factors, such as UVB radiation, oxygen availability, and temperature, significantly influence gene expression patterns. For example, \u003cem\u003eB. gargarizans\u003c/em\u003e exhibited high gene expression responsiveness to multiple environmental factors, while \u003cem\u003eS. boulengeri\u003c/em\u003e showed limited responsiveness, suggesting different adaptive strategies among species. Despite divergence in specific gene expression profiles, functional enrichment analysis highlighted a convergence in critical biological processes like angiogenesis, ATP binding, and cellular responses to environmental stressors, which are vital for high-altitude survival.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study demonstrates that the evolution of gene expression in high-altitude amphibians is complex, with both genetic and environmental factors playing significant roles. The convergence in essential biological functions, despite divergence in specific gene expression profiles, underscores the shared adaptive requirements of high-altitude environments. These findings provide valuable insights into the mechanisms of gene expression evolution and highlight the importance of considering both genetic and environmental components when studying adaptation in extreme habitats.\u003c/p\u003e","manuscriptTitle":"Adaptive Divergence and Functional Convergence: The Evolution of Pulmonary Gene Expression in Amphibians of the Qingzang Plateau","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-20 15:39:49","doi":"10.21203/rs.3.rs-4950269/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fed8a0f8-8c97-40df-8801-13cda83b8539","owner":[],"postedDate":"September 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-09-27T09:08:46+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-20 15:39:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4950269","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4950269","identity":"rs-4950269","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}