Abstract Global climate change has become a primary driving factor behind the biodiversity crisis in amphibians, making it crucial to understand how climate change affects species and their potential responses. The plateau brown frog (Rana kukunoris) is often regarded as an ideal ecological indicator species, yet research on its environmental adaptation mechanisms based on transcriptomic and microbiomic studies remains limited. Therefore, this study investigates the adaptation strategies of the plateau brown frog to environmental changes, providing extensive transcriptomic and the first comprehensive metagenomic dataset from two distinctly different environmental regions (eastern and western slopes of the Helan Mountains). We gathered transcriptomic data from three tissues (blood, liver, and muscle), resulting in 294,962 unigenes and 570,192 transcripts. Metagenomic sequencing identified major bacterial groups, including Firmicutes, Proteobacteria, Bacteroidetes, Spirochetes, and Actinobacteria. In summary, the results of this study can be used to further explore the associations among microbiota, host, and environment, which are crucial for comprehending the mechanisms of environmental adaptation in this species and contributing to the conservation of amphibian biodiversity. Subject terms: Transcriptomics, Metagenomics Background & Summary The biodiversity crisis is most pronounced in amphibians^[43]1,[44]2, with 40.7% of amphibian species worldwide facing the risk of extinction^[45]3. It is noteworthy that the primary driving factors are shifting from diseases to a new threat of climate change^[46]3. Changes in temperature and precipitation can directly impact the survival^[47]4, growth^[48]5, development^[49]6, reproduction^[50]7, behavior^[51]8, and morphological characteristics^[52]9 of amphibians. Furthermore, climate change can alter the habitats of amphibians^[53]10. At the molecular level, it also affects the expression of relevant genes and the production of metabolites within organisms, which contribute to enhancing their adaptation to climate change^[54]11. Therefore, understanding the molecular mechanisms by which amphibians adapt to environmental change is crucial. Mountain ranges harbor extremely high biodiversity, hosting approximately 87% of the world’s amphibian, avian, and mammalian species. However, they are currently threatened by ongoing global climate change and land use changes^[55]12. Isolated mountain peaks surrounded by inhospitable lowlands are referred to as sky islands (SIs)^[56]13. Their prolonged isolation and climatic differences from adjacent areas render them natural laboratories for studying the direct impacts of climate change^[57]14. The Helan Mountains are one of the few north-south oriented mountain ranges, surrounded by the Yellow River, urban areas, and deserts^[58]15. It can be regarded as a sky island standing out in an arid context. The natural environmental differences between the eastern and western slopes are pronounced. The eastern slope is steep, with a dry climate, high temperatures, and sparse vegetation. In contrast, the western slope is gentle, with a moist climate, lower temperatures, and dense vegetation^[59]16. This polarization and the unique geological structure of the region make it an excellent model for studying how amphibians respond to environmental changes. The dominant amphibian species in the Helan Mountains are the plateau brown frog (Rana kukunoris) and the flower-back toad (Strauchbufo raddei). The plateau brown frog belongs to the family Ranidae of the order Anura^[60]17. It inhabits altitudes ranging from 1500–4400 m^[61]18, primarily found in marshy wetlands and seasonal ponds^[62]19. The current distribution is in and around the waters of streams in the Helan Mountains. Its dorsal surface is mostly gray-brown or reddish-brown, with black stripes on the limbs (Fig. [63]1). It is phylogenetic nearest to the Chinese Brown Frog (Rana chensinensis)^[64]20. The plateau brown frog plays an important role in maintaining ecological stability and is also a significant indicator species in the ecological succession process of plateau wetland-meadow landscapes^[65]18. However, research on this species has mainly focused on its activity patterns^[66]21–[67]23, phylogenetic relationships^[68]24–[69]27, and life history traits^[70]19,[71]28–[72]31. Studies on its environmental adaptation mechanisms are relatively limited and primarily concentrated at the morphological^[73]32 and genomic levels^[74]33. Leung et al. studied the morphological variations of the plateau brown frog along altitudinal gradients on the Qinghai-Tibet Plateau, elucidating its ecological and geographical adaptations and gender differences in selection pressures related to limbs and body size^[75]32. Chen et al. described the chromosome-level genome assembly of the plateau brown frog, offering insights into the genomic evolution and adaptation of amphibians in high-altitude environments^[76]33. Fig. 1. [77]Fig. 1 [78]Open in a new tab Plateau brown frogs and their habitats in the Helan Mountains. (a) and (c) were taken on the eastern slope. (b) and (d) taken for the western slope. The interaction between gut microbiota and the host is vital for host nutrition, fitness, and health^[79]34–[80]42. External environmental changes can stimulate and disrupt the current microbiota-host equilibrium^[81]43. For example, alterations in microbial community structure can lead to a cascade of changes in transcriptional regulation, enzymes, metabolism, and other processes. The host perceives these changes and responds accordingly, such as by regulating body temperature^[82]44,[83]45 and altering body color^[84]46. Currently, research on the gut microbiota of the plateau brown frog is limited, which hampers our understanding of its biological mechanisms. Therefore, we conducted metagenomic sequencing to better understand the functional interactions between gut microbiota, host, and environment. As one of the most effective strategies for understanding gene function, transcriptomic sequencing can provide information on all transcripts in cells or tissues under specific conditions^[85]47,[86]48, thereby reflecting the overall molecular response of different tissues^[87]16. Although there have been some studies on the transcriptome of the Plateau Brown Frog^[88]49–[89]51, they have mainly focused on the Qinghai-Tibet Plateau region and have been single-omics studies without considering environmental changes. In this study, high-throughput sequencing was performed on Plateau Brown Frog samples from the eastern and western slopes of the Helan Mountains, yielding a large transcriptomic dataset and the first comprehensive metagenomic dataset. Specifically, transcriptomic data was collected from three different tissue types: blood, liver, and muscle, along with metagenomic data from gut contents. In summary, the dataset provided in this study can be used to further explore the molecular mechanisms through which this species adapts to environmental changes. This could contribute to the conservation of amphibian biodiversity and enhance our understanding of global climate change impacts on ecological environments. Methods Sample collection for this study was conducted in compliance with the ethical standards outlined in the Chinese Animal Welfare Act (20090606) and received approval from the Ethics Committee of Northeast Forestry University (No. 20230271). Additionally, all experimental procedures were authorized by the Animal Care and Use Committee of Northeast Forestry University, ensuring adherence to legal requirements and regulations. Sample collection In September 2023, we captured 20 adult plateau brown frogs in the aquatic habitats of six streams and their vicinity on the eastern and western slopes of the Helan Mountains, as illustrated in Fig. [90]2. Each frog was individually numbered, and morphological data such as body weight and length were measured and recorded (Table [91]1). Euthanasia was performed using MS-222 (Ovaprim, USA) at a concentration of 0.3–0.6 g/L, followed by dissection (Ethical proof No. IACUC(AHU)-2022-007). Within 5 minutes post-mortem, cardiac blood was collected into anticoagulant tubes containing EDTA and supplemented with TRIzol reagent (Solarbio, CN) at a ratio of 1:3. Fresh liver and muscle tissues were collected and immediately preserved in RNA extraction solution (Solarbio, CN). Using sterile scissors, we excised the gut and expressed its contents using sterile forceps, placing them into sterile 2.0 mL centrifuge tubes. All collected samples were stored in a laboratory freezer at −80 °C for subsequent DNA and RNA extraction. Fig. 2. [92]Fig. 2 [93]Open in a new tab Location of the study area and sampling route in our study. The left site inserts show the location of the Helan Mountains in Ningxia Hui Autonomous Region and Inner Mongolia Autonomous Region, China. The right site insert shows the study area with the capture areas for Plateau brown frogs. The six regions are ① Shuimogou, ② Qianjingou, ③ Halawu, ④ Xiaoshuigou, ⑤ Dashuigou, ⑥ Helankou. Table 1. Morphological data on the individuals of plateau brown frog. Sample ID Group Weight (g) Length (cm) Head width (cm) Head length (cm) Eye diamete r(cm) Forelimb length (cm) Hindlimb length (cm) Location NX1 Eastern 13.3 6.1 1.9 1.9 0.4 2.8 8.2 Ningxia,China NX2 Eastern 17.88 6.2 2.1 2.2 0.4 2.6 7.7 Ningxia,China NX3 Eastern 17.95 5.7 2.2 2 0.3 2.5 8.1 Ningxia,China NX6 Eastern 19.23 5.4 2.4 2.2 0.5 3.3 9.1 Ningxia,China NX7 Eastern 15.54 5.1 2.1 1.9 0.4 2.6 8 Ningxia,China NX8 Eastern 15.74 5.3 2.1 2 0.4 2.5 7.9 Ningxia,China NX9 Eastern 10.44 5 2 1.9 0.3 2.7 7.3 Ningxia,China NX13 Eastern 3.54 3.1 1.4 1.3 0.3 1.8 5.3 Ningxia,China NX15 Eastern 13.01 5.2 1.8 1.7 0.4 2.6 7.8 Ningxia,China NX16 Eastern 7.48 4.9 1.7 1.6 0.3 2 6.4 Ningxia,China NM1 Western 14.09 4.8 2.1 2 0.3 2.8 8.2 Inner Mongolia,China NM2 Western 17.99 5.8 2.2 2 0.4 3.1 8.7 Inner Mongolia,China NM3 Western 19.41 5.7 2.2 2.1 0.4 2.7 9.1 Inner Mongolia,China NM7 Western 12.68 5.1 2 1.9 0.4 3 9 Inner Mongolia,China NM8 Western 21.53 5.5 2.2 2.2 0.4 3.2 9.1 Inner Mongolia,China NM10 Western 8.89 4.4 1.6 1.6 0.3 2 7.3 Inner Mongolia,China NM12 Western 8.19 4.2 1.6 1.5 0.3 2.2 7.5 Inner Mongolia,China NM14 Western 7.98 4.2 1.7 1.6 0.3 2.2 7.5 Inner Mongolia,China NM15 Western 14.19 5.2 2.3 2.0 0.4 2.3 8.2 Inner Mongolia,China NM18 Western 6.74 3.8 1.7 1.6 0.3 2.3 7.2 Inner Mongolia,China [94]Open in a new tab RNA extraction Each sample of approximately 50 mg was placed in a 2 mL pre-chilled grinding tube with liquid nitrogen and thoroughly ground using a grinder set to 60 Hz for 60 seconds. After adding 1 mL of TRIzol reagent (Ambion, USA), the mixture was immediately vortexed. To facilitate the complete separation of nuclear protein bodies in the homogenate, the grinder continued to run at 55 Hz for 30 seconds followed by a 5-minute incubation at room temperature. After this, centrifugation at 12,000 rpm for 5 minutes was performed to collect the supernatant, which was then carefully transferred to a new tube. To extract RNA, we mixed the supernatant with 0.2 mL of chloroform (China National Medicines, CN) and vigorously shook the mixture for 30 seconds. After resting at room temperature for 2-3 minutes, the mixture was centrifuged at 12,000 rpm for 15 minutes at 4 °C, resulting in a triphasic separation. The clear upper layer containing RNA was transferred (400 μL) to a 1.5 mL tube. We added 500 μL of isopropanol (Shanghai Chemical Reagent, CN), mixing by inverting the tube approximately 30 times. The tube was left to stand at room temperature for 10 minutes, then centrifuged again under the same conditions for 10 minutes. The resulting supernatant was then removed, and the residue was washed with 1 mL of 75% ethanol (China National Medicines, CN), followed by a centrifuge run at 12,000 rpm for 5 minutes at 4 °C. After discarding the supernatant, the washing process was repeated twice. A final spin was conducted under the same conditions for one minute, after which the ethanol was carefully removed, and the RNA pellet was resuspended in a suitable amount of RNase-free water (Sigma, USA). We assessed the RNA’s concentration and purity using the NanoDrop2000 NC2000 (Thermo Scientific, USA) and evaluated its integrity with the 2100 Bioanalyzer (Agilent, USA). Samples with a total RNA quantity of ≥1 ug, an RNA Integrity Number (RIN) of ≥6.5, and an RNA concentration of ≥25 ng/μL passed quality control. RNA library construction and Illumina sequencing RNA purification, reverse transcription, library construction, and sequencing processes were conducted at Shanghai Personal Biotechnology Co., Ltd. (Shanghai, CN). For the library construction, we utilized the NEB Next Ultra II RNA Library Prep Kit (New England Biolabs, USA). We enriched mRNA with poly (A) tails via Oligo (dT) magnetic beads (Invitrogen, USA) and fragmented the enriched mRNA using divalent cations. This fragmentation served as a template for cDNA synthesis, which was initiated using random oligonucleotides as primers. Following the synthesis, the double-stranded cDNA underwent purification, end repair, addition of an “A” base at the 3’ end, and the ligation of sequencing adapters. We selected cDNA fragments ranging from 400–500 bp using AMPure XP beads (Beckman, USA). These selected fragments were then amplified through PCR and purified once more with AMPure XP beads, resulting in the final library. The quality of this library was evaluated using the Agilent 2100 Bioanalyzer (Agilent, USA) and the Agilent High Sensitivity DNA Kit (Agilent, USA). Library concentration was measured using the Quantifluor-ST fluorometer (Promega, USA) and the Quant-iT PicoGreen dsDNA Assay Kit (Invitrogen, USA). Effective library concentration for sequencing was quantified by QPCR (Thermo, USA). After normalization of multiplexed DNA libraries, samples were mixed in equal volumes. Finally, the mixed library was diluted stepwise for quantification and subjected to PE150 sequencing on an Illumina sequencing platform (Illumina, USA). Sequence data processing and transcriptome de novo assembly The sequencing platform’s built-in software was used to convert the sequenced image files into raw data in FASTQ format. We listed the three varieties of raw data for each specimen in Table [95]2, including their SRA accession numbers. Due to the presence of adapter sequences and low-quality reads in the raw data, further filtering was necessary. The fastp (v0.22.0) was employed to remove reads with adapters, lengths less than 50 bp, and average quality scores lower than Q20^[96]52. We then used the high-quality sequences for de novo assembly of transcript sequences, applying the De Bruijn Graph (DBG) assembly principle via Trinity software (v2.15.1)^[97]53. The specific steps are as follows: 1) Construction of a short sequence library of K-mer length from high-quality sequences, followed by extension of the short sequences using overlaps of length K-mer-1 between short sequences to obtain preliminary assembled Contig sequences. 2) Clustering of Contig sequences based on overlaps between them, followed by construction of Bruijn graphs for each cluster. 3) Processing of these Bruijn graphs to find paths based on reads and paired reads in the graphs, resulting in the transcripts. The longest transcripts were selected as Unigenes, and CD-HIT (v4.8.1)^[98]54 and CAP (v3.0)^[99]55 were used for final assembly by integrating sequence overlap and eliminating redundancy. Table 2. Summary of sample data information deposited in the SRA database. Sample ID Date collected Tissue Method Sex SRA accession NX1 2023-September-20 Blood, Liver, Muscle RNA-Seq Female SRR30171281,SRR30171280,SRR30171269 Gut contents MGS SRR29713752 NX2 2023-September-20 Blood, Liver, Muscle RNA-Seq Female SRR30171251,SRR30171240,SRR30171261 Gut contents MGS SRR29713751 NX3 2023-September-20 Blood, Liver, Muscle RNA-Seq Female SRR30171286,SRR30171285,SRR30171284 Gut contents MGS SRR29713740 NX6 2023-September-20 Blood, Liver, Muscle RNA-Seq Female SRR30171283,SRR30171279,SRR30171278 Gut contents MGS SRR29713739 NX7 2023-September-21 Blood, Liver, Muscle RNA-Seq Female SRR30171277, SRR30171276,SRR30171275 Gut contents MGS SRR29713738 NX8 2023-September-21 Blood, Liver, Muscle RNA-Seq Female SRR30171274,SRR30171273,SRR30171272 Gut contents MGS SRR29713737 NX9 2023-September-21 Blood, Liver, Muscle RNA-Seq Female SRR30171271,SRR30171270,SRR30171282 Gut contents MGS SRR29713736 NX13 2023-September-21 Blood, Liver, Muscle RNA-Seq Male SRR30171268,SRR30171267,SRR30171258 Gut contents MGS SRR29713735 NX15 2023-September-21 Blood, Liver, Muscle RNA-Seq Female SRR30171257,SRR30171256,SRR30171255 Gut contents MGS SRR29713734 NX16 2023-September-21 Blood, Liver, Muscle RNA-Seq Female SRR30171254,SRR30171253,SRR30171252 Gut contents MGS SRR29713733 NM1 2023-September-23 Blood, Liver, Muscle RNA-Seq Male SRR30171294,SRR30171293,SRR30171292 Gut contents MGS SRR29713750 NM2 2023-September-23 Blood, Liver, Muscle RNA-Seq Female SRR30171291,SRR30171290,SRR30171289 Gut contents MGS SRR29713749 NM3 2023-September-23 Blood, Liver, Muscle RNA-Seq Female SRR30171288,SRR30171287,SRR30171242 Gut contents MGS SRR29713748 NM7 2023-September-23 Blood, Liver, Muscle RNA-Seq Male SRR30171241,SRR30171239,SRR30171238 Gut contents MGS SRR29713747 NM8 2023-September-23 Blood, Liver, Muscle RNA-Seq Female SRR30171237, SRR30171236,SRR30171235 Gut contents MGS SRR29713746 NM10 2023-September-23 Blood, Liver, Muscle RNA-Seq Female SRR30171266,SRR30171265,SRR30171264 Gut contents MGS SRR29713745 NM12 2023-September-23 Blood, Liver, Muscle RNA-Seq Male SRR30171263,SRR30171262,SRR30171260 Gut contents MGS SRR29713744 NM14 2023-September-23 Blood, Liver, Muscle RNA-Seq Male SRR30171259,SRR30171250,SRR30171249 Gut contents MGS SRR29713743 NM15 2023-September-23 Blood, Liver, Muscle RNA-Seq Female SRR30171248,SRR30171247,SRR30171246 Gut contents MGS SRR29713742 NM18 2023-September-23 Blood, Liver, Muscle RNA-Seq Male SRR30171245,SRR30171244, SRR30171243 Gut contents MGS SRR29713741 [100]Open in a new tab Gene Annotation and expression analysis Functional annotation of Unigenes was performed. The databases utilized for this purpose included the NCBI non-redundant protein sequences (NR), Gene Ontology (GO)^[101]56, Kyoto Encyclopedia of Genes and Genome (KEGG)^[102]57, evolutionary genealogy of genes: Non-supervised Orthologous Groups (eggNOG)^[103]58, a manually annotated and reviewed protein sequence database (Swiss-Prot)^[104]59, and the protein family database (Pfam)^[105]60. Using the assembled Unigene sequences as references, RSEM (v2.15)^[106]61 was employed to align the clean reads