Population genomics reveals association of transposable elements variants with climatic adaptation in wild Amur grape

Domain: chemistry
Published: 2026-02-26
Editorial outcome: major_revision

Abstract
Amur grape (Vitis amurensis Rupr.) is widely recognized for its cold tolerance traits and serves as a valuable genetic resource for breeding climate-resilient grape cultivars. Here, we construct a graph pangenome reference (Vampan_V1.0) and generate a variant map comprising 48,308,434 short variants and 127,094 TE-associated structural variants (TEVs) using deep resequencing data from 330 samples across 31 natural populations covering the species’ distribution range. We discover a biased accumulation of SNPs around TEVs and identify 823 candidate adaptive genes associated with environmental variables. Using machine learning-based genetic offset models, we further show that putative adaptive TEVs significantly reduce genetic offsets by 7.3% to 8.2% under future climate scenarios. Our study shows the power of a graph-based pangenome to resolve complex variation and highlights the impact of TEVs on genetic diversity, local adaptation, and resilience to future climate change, providing insights into utilizing crop wild relatives in climate-resilient crop breeding.

Introduction
Crop wild relatives (CWRs) represent an essential reservoir of genetic diversity for crop improvement and climate-resilient breeding1-4. Grapevine (Vitis vinifera) is one of the earliest domesticated and most economically significant fruit crops5-8. Its wild relatives, species of the Vitis genus (Vitis L.), are recognized as important germplasm resources9,10, which play an essential role in grape breeding for resistance against both abiotic and biotic stresses, such as cold11-14, heat15, drought16,17, salt18,19, powdery mildew20,21, and Pierce’s disease22,23. Climate change has a significant impact on viticulture, with estimates suggesting that between 24% and 73% of current vineyard areas may no longer be suitable for grape growing by 207024-26. Consequently, incorporating adaptive traits from wild Vitis species through selective breeding will be crucial for the adaptability and sustainability of viticulture in the future. In this study, we investigate the local adaptation and genomic vulnerability to climate change of the wild Amur grape (V. amurensis Rupr.) (Fig. 1a). This species is distributed in the north temperate zone of East Asia, and exhibits remarkable cold and drought tolerance11,13,27,28. These traits have made V. amurensis a valuable genetic resource for grape breeding and an important system for studying stress tolerance in grapevine and other crops. However, despite its recognized agronomic and ecological importance, population-scale assessments of genetic diversity and adaptive potential across its natural range remain limited.

Understanding environmental responses in populations requires comprehensive characterization of genetic variation across the genome, including complex genomic variants. Transposable elements (TEs), first discovered in maize (Zea mays L.) by Barbara McClintock in 1948, are ubiquitous mobile genetic elements, constituting a substantial proportion of eukaryotic genomes and contribute to plant genome evolution29-35. In plants, TEs often account for a large fraction of genome content, including approximately 63.9% of the genome in cultivated grape (V. vinifera L.)36-38. The interaction between TEs and the plant's epigenetic system can lead to the retention of TEs, thereby causing an expansion of plant genome sizes39. A number of TEs have been found to affect agricultural traits and cause genetic diseases, which significantly impacts advancements in biology, medicine, and agriculture40-43. Additionally, TEs affect plant gene expression and function through cis- and trans-regulatory mechanisms44-52. As an important source of genetic diversity and contributor of large-effect mutations, TEs play an important role in promoting genomic structural variation, phenotypic divergence, rapid adaptation, trait evolution, and speciation53-61. Population genetic analyses of TE-associated structural variants (TEVs) have revealed that most TEVs might be slightly deleterious or neutral, with rarer alleles compared to nonsynonymous single nucleotide polymorphisms (SNPs) in plant populations59,62. It is particularly crucial to explore the role of TEVs in plant genome evolution at the population level as these evolutionary events are often recent and involve local adaptation63. However, the potential role of TEVs in shaping genetic diversity and their associations with climatic variables remain poorly understood.

Global climate change is an increasing threat to biodiversity and food security, potentially leading to extinctions of species, transformations of the global ecosystems, and reduction in crop production64,65. Species may persist under climate change through genetic mutations, increased environmental tolerance via phenotypic plasticity, or migration to suitable locations66-68. Due to the relatively weak dispersal ability of plants compared to animals, climatic adaptation appears to be more important for the evolution and species persistence in plants69. The rapid development of genomic technologies provides an unprecedented opportunity to comprehensively examine genetic loci, including SNPs, indels, and structural variants (SVs) that have involved in climatic adaptations70-75. Detecting potential environment-associated genetic variants, including TEVs, is critical for understanding the genetic mechanisms of local adaptation and for predicting plant adaptability to climate change72. In addition, in spatially heterogeneous environments, species exhibit sensitivity to various selective pressures, leading to the evolution of local adaptations2. Genotype-environment associations (GEA), integrating genetic offset and niche suitability modelling, provide valuable insights into predicting local adaptive potential and assessing vulnerability to climate change in plants, thereby facilitating effective management and conservation to address the loss of global biodiversity and agricultural security71,76-78. Past studies have primarily focused on the impact of SNPs on climatic adaptation and the assessment of future climate-induced vulnerability79,80, while the possible contribution of TEVs to local adaptation and response to future climate change remains rarely investigated.

In this study, we construct a graph-based pangenome reference from haplotype-resolved genomes of representative V. amurensis accessions, providing a comprehensive genomic framework for population-scale analyses. By integrating this pangenome with deep resequencing data of 330 accessions from 31 natural populations across the species’ distributional range, we generate a high-resolution variant map including 41,451,941 SNPs, 6,856,493 indels, and 127,094 TEVs. This genomic resource captures genetic diversity at the population scale and provides a robust framework for examining the association of TEVs with climate adaptation. Our study establishes a foundational dataset for investigating climate-related genomic patterns in V. amurensis and offers valuable insights into breeding and conservation strategies for this important crop wild relative in the context of rapid global climate change.

Results
Haplotype-resolved genome assemblies and TE landscape of V. amurensis
To capture the genetic diversity of V. amurensis, we selected four representative accessions based on their geographic distribution (see Methods). For de novo assembly of the four newly generated haplotype-resolved V. amurensis genomes, we integrated a total of 17.7-28 Gb (~35-56×) of PacBio HiFi CCS reads, and ~58-78Gb (~116-156×) of Hi-C reads. The final assembly of haplotype-resolved genomes resulted in total lengths ranging from 497.35 Mb to 512.02 Mb, with contig N50 ranging from 15.14 Mb to 24.15 Mb (Supplementary Data 1). The assembly consensus quality values (QVs) for haplotype-resolved genomes ranged from 67.29 to 69.11 based on HiFi data, indicating over 99.99% accuracy (Supplementary Data 1). More than 98.3% of the core embryophyte gene sets from the benchmarking universal single-copy orthologs (BUSCOs) assessment were detected in the haplotype-resolved genomes of each accession, supporting that our assemblies were of high quality and completeness (Supplementary Fig. 1). Furthermore, most of the centromeric and telomeric regions were identified on V. amurensis diploid genomes (Fig. 1b, Supplementary Fig. 2a, Supplementary Data 1). Approximately 63.13%-64.08% of the haplotype sequences were identified as repetitive elements, including 36.72%-37.76% retrotransposons, 16.79%-17.25% DNA transposons, and 0.18%-0.22% rolling-circle elements (Supplementary Table 1). TE insertion time analysis across the eight haplotype assemblies of V. amurensis revealed a broad peak around 5 million years ago (Mya) in the temporal distribution of TE insertions, followed by a sharp decline in more recent insertions, indicating that most TE insertions are relatively ancient (Supplementary Fig. 2b).

Population genetic dynamics of TEVs in V. amurensis populations
We collected 330 individuals from 31 populations across the main distribution areas of the V. amurensis. Whole-genome resequencing of these accessions generated ca. 7.37 Tb raw data, with an average coverage of ca. 40× per sample (ca. 22.33 Gb per individual; see Supplementary Data 2). We identified 41,451,941 SNPs and 6,856,493 indels based on a single-reference approach by mapping all resequencing reads to the Vam057 haplotype1 genome (see Methods).

We further filtered the SNP set using linkage disequilibrium (LD) pruning (see Methods), resulting in 344,087 independent SNPs for population genetic diversity analyses. The maximum-likelihood (ML) tree of V. amurensis supported an early divergence between populations of the southern range and those from the West-Center-Northeast range of the species, while populations distributed in the northeast represented later divergences (Fig. 1c and Supplementary Fig. 3). Population structure and principal component analysis (PCA) revealed four major geographic groups: West, Center, Northeast, and South (Fig. 1d-f, Supplementary Fig. 4a, Supplementary Data 2). We further confirmed that the four genomes (Vam057, Vam070, Vam106, and Vam113) were assigned to the Northeast, Center, West, and South groups, respectively (Fig. 1 and Supplementary Data 1).

To better detect structural variants, we utilized the PanGenome Graph Builder (PGGB)81 to build the V. amurensis pangenome graph (Vampan_V1.0) using the eight haplotype assemblies, which comprises 54.16 million nodes and 74.84 million edges, with a total sequence length of 832.77 Mb. The Vampan_V1.0 pangenome, generated from phased diploid assemblies, provides a pivotal framework for comprehensive analyses of haplotype diversity and the identification of TEVs on a population scale.

The utilization of a pangenome for V. amurensis, combined with the extensive resequencing data generated in this study, facilitated the identification of a broad spectrum of genomic variants. In total, 220,864 structural variants were identified, among which 127,094 were TEVs, which accounted for 57.54% of all structural variants (see Methods) (Fig. 2a). Population structure analysis based on TEVs yielded results consistent with those obtained from SNPs (Supplementary Fig. 4b). Statistics on the number of TEVs in each accession indicated that 91% of the accessions had at least 36,000 TEVs (Supplementary Fig. 5a). In addition, the main type of transposable elements in the V. amurensis population was retroelements (71.28%), including 16.66% Copia LTRs and 44.77% Gypsy LTRs, compared to DNA transposons (26.04%) (Fig. 2b). Based on high-quality 97,013 extensive TEVs and 4,114,075 SNPs (see Methods), the extents of population genetic diversity (π) of SNPs and TEVs over 1Mb non-overlapping windows were calculated, which demonstrated that there was a significant positive correlation between the two types of variants (P = 1.19e-13, Fig. 2c). Moreover, the number of SNPs in surrounding regions of TEVs was calculated, and our results suggested that SNP frequency around TEVs is higher than the average SNP frequency across the genome (Fig. 2d). A similar pattern was observed for SVs (including TEVs), with SNPs also exhibiting biased accumulation around structural variants (see Methods, Supplementary Fig. 5b).

The distribution of TEVs in the whole genome indicated that most TEVs were located at intergenic regions of the genome (79.21%). Approximately 26.11% TEVs were identified at the cis-regulatory regions (within 2 kb upstream and downstream of genes), and ca. 23.16% TEVs were distributed in genic regions, including 3’ untranslated region (UTR), 5’ UTR, introns, and coding sequences (CDSs) (Supplementary Table 2, Supplementary Fig. 5c). These reported percentages are not mutually exclusive, as individual TEVs may span multiple genomic features. The frequency of each TEV was calculated as the proportion of haplotypes carrying the variant allele in the population. Most TEVs were observed at allele frequency ≤ 0.5 across intergenic and genic regions, potentially due to either recent mobilization events or purifying selection which constrained the spread of TEVs throughout the genome (Fig. 2e). TEVs with a population frequency greater than 0.8 in the CDS regions are likely under positive selection in natural populations (Fig. 2e). To explore their potential functional impact, we performed gene ontology (GO) enrichment analysis on genes containing TEVs with an allele frequency > 0.8 in CDS regions. A total of 201 genes were annotated with GO biological processes terms and used for enrichment analysis. These genes showed significant enrichment in categories related to defense response (P = 0.0078), defense response to other organisms (P = 1.99e-4), and regulation of innate immune response (P = 0.0081) (Fig. 2f). We acknowledge that these gene categories may include members under relaxed selection or colocalized with TEVs without direct functional consequences.

TEVs can regulate gene expression by rearranging gene fragments or disrupting cis‐regulatory elements, which are generally considered deleterious. To estimate the effect of TEVs on the genome, we analyzed the unfolded site frequency spectra (SFS), which show how often newly derived variants occur at different frequencies in a population, using TEVs and SNPs across the genome from the four main groups (Supplementary Fig. 5d). This approach has been widely applied in population genomic studies of grapevine6,59. In each group, SNP frequencies showed a U-shaped distribution, whereas TEVs demonstrated a lower proportion of fixed variants compared with SNPs (Supplementary Fig. 5d). A total of 39,212 TEVs were shared among all four groups, and the South group contained the highest number of group-specific TEVs (8,852), which were not detected in the other groups (Supplementary Fig. 6).

Potential role of TEVs in the local adaptation of V. amurensis
To explore the association of TEVs to local adaptation, we identified high-quality 97,013 TEVs, 4,114,075 SNPs, and 677,438 indels (see Methods), and performed genotype–environment association (GEA) analyses for V. amurensis populations. Genomic variants associated with twenty environmental variables, including temperature, precipitation, and elevation-related variables, were identified using Latent factor mixed model (LFMM)82 (Supplementary Table 3, Supplementary Data 3). Moreover, the importance of each environmental variable for V. amurensis was assessed by the Gradient Forests (LGF) model (Supplementary Fig. 7a). Through the integration of environmental variable importance rankings and pairwise correlations among twenty variables, six variables with Spearman correlation coefficient |r| <0.8 were identified, including temperature seasonality (bio4), elevation (elev), precipitation seasonality (bio15), isothermality (bio3), precipitation of warmest quarter (bio18), and mean temperature of warmest quarter (bio10) (Supplementary Fig. 7). These six environmental variables can largely explain the adaptive environment-associated genomic variants across the distribution of V. amurensis based on the GF model. Temperature seasonality (bio4) was identified as the most important driver of local adaptation of natural populations distributed in the northeastern range (Supplementary Fig. 8). In contrast, precipitation seasonality (bio15) and mean temperature of warmest quarter (bio10) significantly influenced the putative adaptive genomic variants in most of the remaining natural populations of V. amurensis (Supplementary Fig. 8).

A total of 22,419 putative adaptive variants were identified using the LFMM analysis (False discovery rate, FDR < 0.01), including 1,098 TEVs and 21,321 short variants. Based on the linkage disequilibrium (LD) information of V. amurensis (Supplementary Fig. 9a), the upstream and downstream regions of 20kb of candidate adaptive variants were used to detect environment-associated genes. A total of 823 candidate genes were identified by the LFMM analysis of six environmental variables (Supplementary Data 4-9). We identified a set of precipitation-associated candidate adaptive genes that are homologous to genes in Arabidopsis, including TLP3 (Fig. 3, Supplementary Fig. 9b and 10). The V. amurensis homolog of TLP3 was associated with precipitation-related environmental variables, including precipitation seasonality (bio15) and precipitation of warmest quarter (bio18), and was involved in drought stress signaling (Fig. 3a and Supplementary Fig. 10c). A candidate adaptive TE insertion (TE5_3254586, with a length of 10,631 bp, and classified as a Gypsy LTR) was located in the putative cis-regulatory region of the gene TLP3 (ca. 1kb upstream of the 5’UTR) associated with precipitation seasonality (bio15), and there was relatively high linkage disequilibrium within the region surrounding this gene (Fig. 3a). This long TE insertion may affect a transcription factor binding site (chr5: 3255018) predicted using the PlantCARE database, suggesting a potential influence on the transcriptional regulation of TLP3 (Supplementary Fig. 9b). Our results indicated that there was a positive correlation (r = 0.67, P = 3e-5) between the precipitation seasonality and the allele frequency of the TE among 31 natural populations of V. amurensis (Fig. 3b). The geographic distribution of the allele frequencies demonstrated that a higher prevalence of the TE allele in populations of the Center and West groups. In contrast, this allele was relatively rare in populations of the South and Northeast groups (Fig. 3c). Numerous candidate genes were identified to be associated with temperature-related environmental variables, such as ICE1, CAMTA2, CBF1, HHP4, COR27, BAM3 and BAG7 (Fig. 4a and Supplementary Fig. 10). It is noteworthy that the gene TLP3 was also strongly associated with two temperature-related environmental variables, temperature seasonality (bio4) and mean temperature of warmest quarter (bio10) (Fig. 4a and Supplementary Fig. 10b). The tubby-like protein (TLP) gene family has also been reported to play a role in plant responses to extreme temperature stress83. Allelic variation in the gene HHP4 was associated with temperature seasonality (bio4) (Fig. 4a). The heptahelical protein (HHP) gene family has been implicated in multiple abiotic stresses, including extreme temperature and salt84. In addition, the gene ICE1, a transcriptional activator regulating the cold-induced transcription of CBF/DREB1 genes, was associated with variation in temperature seasonality (Fig. 4a). We selected the candidate adaptive SNP (SNP14_26191757, representing a G-to-A substitution), located in the putative cis-regulatory region (ca. 1kb upstream of the 5’UTR) of the gene ICE1, as the best example to illustrate the relationship between the allele frequency and the environmental variable (Fig. 4a). According to the PlantCARE database, this SNP is located within a predicted transcription factor binding site (chr14: 26191744), which may alter the transcriptional regulation of ICE1 (Supplementary Fig. 9c). Our results revealed a strong negative correlation (r = -0.83, P = 8e-9) between the temperature seasonality and the allele frequency of this SNP among 31 natural populations of V. amurensis (Fig. 4b). The distribution pattern of allele frequencies suggested that the reference allele was predominantly found in populations of Northeast Asia, characterized by relatively high values of temperature seasonality, whereas the alternative allele was fixed in populations located in areas with low values of temperature seasonality (Fig. 4c). In addition, thirteen genes were identified as candidates for elevation-related adaptation, associated with UV-B/photo-oxidative damage protection and freezing tolerance (Supplementary Fig. 10d). The identified candidate genes were subjected to GO enrichment analysis. These genes showed significant enrichment in biological processes such as response to cold (P = 5.92e-11), response to water deprivation (P = 9.87e-9), and response to abscisic acid (P = 1.1e-6) (Supplementary Table 4). These results suggest that some candidate genes may function within coordinated stress-response pathways, collectively contributing to environmental adaptation.

We estimated the proportion of environmental variance (PVE) associated with different genetic variants. TEVs were identified as associated with the largest proportion of variance in mean temperature of warmest quarter (bio10, 71.97%), precipitation of warmest quarter (bio18, 87.01%), and elevation (elev, 86.64%) (Supplementary Fig. 11). The unique contribution of TEVs to most environmental variables was larger than that of SNPs or indels when compared individually (Supplementary Fig. 11). TEVs uniquely explained 11.12% of the genetic variation associated with precipitation of warmest quarter, a much larger proportion than that explained by SNPs or indels (Supplementary Fig. 11e). Although the unique variance associated with TEVs was relatively lower than that of SNPs for isothermality (bio3), temperature seasonality (bio4), and precipitation seasonality (bio15), TEVs were still identified as associated with these environmental variables (Supplementary Fig. 11). These results suggest that TEVs may contribute to ecological adaptation in V. amurensis, representing a component of adaptive genetic diversity.

The role of TEVs in genomic adaptation to future climate change
To evaluate how populations of V. amurensis may respond to future climate change, shared putative adaptive variants identified by LFMM and the redundancy analysis (RDA) that were consistently associated with the six uncorrelated environmental variables (bio4, elev, bio15, bio3, bio18, and bio10) were defined as “core adaptive variants” for local adaptation (Supplementary Fig. 12). These variants were then further filtered (see Methods), resulting in 40 LD-pruned adaptive TEVs, along with 206 adaptive SNPs and indels. We predicted the genomic vulnerability of V. amurensis to future climate change using the LD-pruned adaptive SNPs and indels under SSP245 and SSP585 scenarios in two periods (Fig. 5 and Supplementary Fig. 13, see Methods). This involved estimating the genetic offset, representing the expected degree of allele frequency change required for populations to remain adapted under future climate scenarios. The SSP585 model was regarded as projecting higher emissions and more severe climate change than the SSP245 model. Genetic offset demonstrated an upward trajectory in correlation with escalating emissions when assessed across different emission scenarios (Fig. 5 and Supplementary Fig. 13). Populations with higher genetic offsets are particularly vulnerable to climate change because they require more substantial changes in adaptive allele frequencies to adapt to future climate.

To explore the role of TEVs in shaping the adaptive potential of V. amurensis under future climate change, we estimated genetic offsets using LD-pruned adaptive TEVs, SNPs, and indels. We specifically assessed the potential role of TEVs by comparing models that included LD-pruned adaptive TEVs alongside SNPs and indels (adaptive TEVs set) with those using only LD-pruned adaptive SNPs and indels (baseline set). Across all periods and emission scenarios, the baseline set showed significantly higher genetic offsets (P < 0.003), whereas inclusion of putative adaptive TEVs reduced offsets by 7.3–8.2% (Supplementary Table 5). Results from different datasets showed similar spatial and temporal genetic offset patterns (Fig. 5 and Supplementary Fig. 13-15). Populations of the Northeast group showed relatively high genetic offsets, indicating greater vulnerability to future climate change (Supplementary Fig. 15). As a control, the average genetic offsets based on random non-adaptive TEVs plus LD-pruned adaptive SNPs and indels (control set) showed no significant difference from the baseline set (Fig. 5c). These findings suggested that putative adaptive TEVs may play a role in future climate adaptation of V. amurensis by providing complementary adaptive genetic variation that reduces genetic offset.

To adapt to rapidly changing environments, plants must either change allele frequencies in situ or migrate to other suitable habitats. The role of migration in population level adaptability to climate change was considered and integrated into this study. We assessed the potential maladaptation based on forward genetic offsets under SSP585 models during the period 2081-2100, considering different maximum dispersal distances (100, 500, and 1000km), respectively (Supplementary Fig. 16). Restricting the maximum migration distances will increase forward offsets (Supplementary Fig. 16). Compared to analyses that do not consider migration, we inferred that migration benefits V. amurensis populations by facilitating TE exchange and enhancing their adaptation to the rapidly changing global climate.

Methods
Sample collection and genome sequencing
The V. amurensis is one of the most cold-tolerant grape species in the world, making it an important genetic resource for grape breeding. We selected four representative V. amurensis accessions for genome assemblies, which were collected from Heilongjiang province (voucher number Vam057), Shandong province (voucher number Vam070), Shanxi province (voucher number Vam106), and Zhejiang province (voucher number Vam113), China (Supplementary Data 1). The total genomic DNA was isolated from fresh leaves using the DNeasy Plant Mini Kit (Qiagen) following the manufacturer’s instructions. HiFi reads of four V. amurensis accessions were sequenced on PacBio Revio platform in a circular consensus sequencing (CCS) mode, using 15-20 kb SMRT cell library. For Hi-C sequencing, the library was prepared with chromatin extraction and digestion, and subsequently sequenced on the Illumina NovaSeq 6000 platform.

We collected 330 individuals from 31 natural populations across the natural range of V. amurensis (Supplementary Data 2). Each population was sampled with more than four individuals, and most populations (25 out of 31) included eight or more individuals. Individuals within each population were sampled after confirming that they were separated by at least 100 meters. The sampling strategy accounted for variants in latitude, altitude, and habitat to ensure a comprehensive representation of genetic diversity. Genomic DNA was extracted from fresh leaves with the DNeasy Plant Mini Kit (Qiagen). Each DNA sample was fragmented by sonication for library preparation, and whole-genome paired-end sequencing was generated using the Illumina NovaSeq 6000 platform with a coverage of ca. 40× per sample. The sequencing data of six accessions from North American wild grapes were downloaded from NCBI database9 (Supplementary Table 6).

Adapters were removed using Trimmomatic (v0.39) with default parameters131. All trimmed reads were filtered by fastp (v0.23.2) with default parameters132. After quality control, high-quality clean reads were obtained.

De novo genome assembly of four V. amurensis accessions
Following the genome assembly procedure described in ref.18,38, initial contigs level of genomes of the four V. amurensis accessions were assembled by Hifiasm (v0.15) with HiFi and HiC reads as input using default parameters (https://github.com/chhylp123/hifiasm)133. Furthermore, for chromosome-level scaffolding, Hi-C reads were filtered by fastp (v0.23.2) with default parameters132, and then used to anchor all contigs with Juicer (v1.6)134. The “scaffold” function of the software RagTag (v2.1.0)135 was used to adjust the order and orientation of the contigs with the genome of the domesticated grapevine (V. vinifera ssp. vinifera) cultivar PN40024 as reference38. Finally, the HiC contact map was visualized by 3d-dna (with the run-assembly-visualizer.sh script and -q 0 parameter)136, and manually adjusted in Juicebox (v1.11.08)137 to generate the two haplotype genomes of each accession. To complete the genome assembly, the HiFi reads were mapped on the genomes of each accessions using Minimap2 (v2.26)138. Gaps were filled and closed using the corresponding HiFi read sequences when more than 3 reads covered the gap region. The results were manually checked in Integrative Genomics Viewer (IGV) (v.2.13)139 to ensure the filling of gap was trustworthy. The k-mer-based consensus quality (QV) scores of the four V. amurensis accession genomes, which represent the log-scaled probability of error at each base (-10log10E), were calculated using Merqury (v1.3) with the k-mers counted by Meryl (v1.4.1) based on HiFi data140.

The genome assembly completeness was assessed by BUSCO (v5.2.2)141. To identify telomeric repeat units in the genome, the TIDK (v0.2.0) was used with parameters: -s TTTAGGG (https://github.com/tolkit/telomeric-identifier), according to the telomere repeat unit previously reported in grape 142. The centromere localizations were explored using TRF143 with parameters 2 7 7 80 10 80 500, and the results were transformed to gff3 format using TRF2GFF (https://github.com/Adamtaranto/TRF2GFF). The centromere region of each chromosome was confirmed based on protein-coding genes and transposable elements annotation, and the 107 bp unit of tandem repeats reported in the grape reference genome38. To analyze genome synteny and structural variations (SVs) among the PN_T2T genome38 and the eight haplotype assemblies of V. amurensis, we performed pairwise genome alignments between adjacent haplotypes using Minimap2 (v2.26)138. Genome rearrangement regions were identified using SyRI144. The synteny of these three genomes and the SVs were plotted using plotsr (v1.1.5)145.

Annotation of genes and transposable elements
A genome-wide annotation pipeline was used for the annotation of putative genes of V. amurensis haplotype genomes (https://github.com/unavailable-2374/Genome-Wide-Annotation-Pipeline). The genes were initially searched based on transcriptome data from previous studies38,146 and UniProt sequences (https://www.uniprot.org/) as evidence. Meanwhile, the gene prediction based on V. amurensis genome sequence was conducted using AUGUSTUS53 (v3.4.0)147. The predicted putative genes involved in duplicated regions, with total CDS regions length shorter than 90 or not supported by any evidence were filtered. The missing genes were attempted to be complemented and the complete genes were subjected to alternative splicing analysis. All the results were checked by hidden Markov models downloaded from the Pfam database to obtain the final gene models.

Transposable element families were identified by RepeatModeler2148 with a pan-Vitis database of repeat families38. The repeat elements were classified by using deepTE with the Plant model149. Finally, we annotated all repetitive sequences using RepeatMasker (v4.1.2)150. We estimated TE insertion times for the eight haplotype assemblies. The outputs generated by RepeatMasker were parsed using the Perl script parseRM.pl (https://github.com/4ureliek/Parsing-RepeatMasker-Outputs) with the parameters -l 50,1 -m 0.0025. The parameter -m specifies the substitution rate, following estimates from a previous study7.

Construction of the graph pangenome
To comprehensively represent the genetic diversity of V. amurensis, we utilized the PanGenome Graph Builder (PGGB) to construct the V. amurensis graph pangenome with the Vam057 haplotype1 genome serving as the reference, using the settings -s 5000 -l 25000 -p 80 -n 2 -K 19 -F 0.00181. All four genomes, encompassing eight haplotypes, were included in the construction of the graph pangenome. Through this approach, we aimed to capture the structural variants present within the V. amurensis populations effectively.

Variant maps of V. amurensis
To ensure the reliability of the short variant dataset and maintain analytical consistency across samples, we employed a widely used and well-validated linear reference-based approach. High-quality clean NGS reads were mapped to the Vam057 haplotype1 genome using BWA-MEM algorithm (v0.7.17)151. BAM files were filtered using samtools view -bS -F 0x100 -q 10 to remove secondary alignments and low-quality reads. The filtered BAM files were then sorted with SAMtools (v1.13)152, and PCR duplicates were identified and marked using Picard (v2.23.0) MarkDuplicates (http://broadinstitute.github.io/picard/). These steps ensured that only reliably mapped reads were retained for variant calling. The SNP and indel calling was performed using GATK (v4.2.6.0)153 functions of HaplotypeCaller, GenomicsDBImport, GenotypeGVCFs, SelectVariants and VariantFiltration. Specifically, variants were called per sample using HaplotypeCaller in GVCF mode to generate individual GVCF files. All GVCF files were combined with GenomicsDBImport to build a genomic database, and joint genotyping across all samples was performed using GenotypeGVCFs to produce cohort-level VCF files for each chromosome. The SNPs and indels were extracted from the joint VCF files using SelectVariants with the options -select-type SNP and -select-type INDEL, respectively. These variant sets were then filtered using VariantFiltration. SNPs were filtered based on “QD<2.0 || FS >60.0 || MQRankSum <- 12.5 || ReadPosRankSum <- 8.0 || SOR >3.0 || MQ<40.0”, while Indels were filtered using “QD<2.0 || FS >200.0 || MQRankSum <- 12.5 || ReadPosRankSum <- 8.0 || SOR >10.0”. The resulting variants were filtered by VCFtools (v0.1.15) using parameters: -max-missing 0.8154.

The PGGB pangenome was used to generate the structural variant set among V. amurensis populations. The re-genotyping tool PanGenie (v2.1.0) was used to determine unique k-mers in the graph with PanGenie index and generate structural variant VCF files of each accession, using the setting -r Vam057_haplotype1_genome along with default parameters155. These VCF files were further combined into a merged structural variant map using BCFtools (v1.13)156. We searched homologous nucleotide sequences of structural variants in the database of transposable element families mentioned above to identify TEVs using NCBI-BLASTN with default parameters (evalue < 1e-5, percentage identity > 70%)38,157. These TEVs were further filtered using VCFtools (v0.1.15) with parameters: -max-missing 0.8154.

Ecological niche modelling
To infer the potential geographic distribution of V. amurensis, we performed the ecological niche modeling using Maxent (v3.4.4) with 10% random test and 10 replicates158. The occurrence records of V. amurensis were collected from the Global Biodiversity Information Facility (GBIF, www. gbif.org) and fieldworks. A total of 20 current environmental factors, including 19 bioclimatic and 1 elevation variables, were used with resolution 2.5 arcmin from the WorldClim (www.worldclim.org)159.

Phylogenetic, principal component, and population structure analyses
Firstly, a linkage disequilibrium pruned SNP set was generated using PLINK (v1.90) software with parameters: --indep-pairwise 50 10 0.2 --vcf-min-gq 20 --maf 0.05 --geno 0.1160, which included 344,087 independent SNPs to be used in the following analyses. The phylogenetic relationship among populations of V. amurensis was reconstructed by IQ-TREE (v1.6.12)161 using the GTR model and 1000 bootstrap using six wild grapes from North America as outgroups. Principal component analysis was performed using the PLINK (v1.90) software with parameter “--pca”160. Population structures were examined to estimate individual admixture proportions from ancestors using ADMIXTURE (v1.3.0)162 with LD-pruned SNPs set and TEs set mentioned above, respectively. The number of clusters (K) was set from 2 to 6.

TEVs dynamics in V. amurensis population
To assess potential functional impact of TEVs, we conducted gene ontology (GO) analysis using the web tool DAVID (https://davidbioinformatics.nih.gov/) on genes containing TEVs with an allele frequency > 0.8 in their CDS regions. GO terms were assigned based on UniProt functional annotations (https://www.uniprot.org/).

To calculate the derived site frequency spectrum (SFS) of each population, we used the superSFS script (https://github.com/xhchauvet/superSFS) with six North American wild grapes as outgroups (Supplementary Table 6). Moreover, PopLDdecay (v3.42) was used to calculate the decay of linkage disequilibrium between SNPs and TEVs163.

Structural and SNP variants were further filtered by VCFtools (v0.1.15) with parameters “--maf 0.03” and “--maf 0.05”, respectively154. After filtering, we obtained high-quality 177,789 structural variants (including 97,013 TEVs) and 4,114,075 SNPs for the following analyses. Population genetic diversity (π) of SNPs and TEVs were generated by VCFTools (v0.1.15) in sliding windows of 1Mb, respectively154. Pearson correlation analysis was performed to evaluate the relationship between the π values of SNPs and TEVs. In order to explore the relationship between structural variants/TEVs and SNPs, the number of SNPs in surrounding regions of each filtered structural variant/TEV was counted with sliding windows of 15kb. And the average number of SNPs per 15 kb sliding window across the genome was calculated.

Detection of genotype-environment associations
To identify environment-associated genotype variants, including SNPs, indels, and TEVs, two different approaches were employed. TE variants were filtered with the parameter '--maf 0.03', while both indel and SNP variants were filtered with '--maf 0.05' using VCFtools (v0.1.15) 154. A total of 97,013 TEVs, 4,114,075 SNPs, and 677,438 indels were used in these analyses. Firstly, the univariate LFMM implemented in R package LEA (v3.11.4)82 was performed with the 19 bioclimatic and 1 elevation variables (resolution 2.5 arcmin). According to the population structures inferred by ADMIXTURE (v1.3.0)162, four latent factors were used in LFMM to account for the number of ancestry clusters in the genotype data. We performed five independent MCMC runs for each environmental variable, with a burn-in of 5000 iterations and followed by 10,000 iterations for sampling. The mean P values obtained from all five runs were subsequently utilized for further analysis and the significant cutoff was set at a False discovery rate (FDR) of 0.01. The LDBlockShow was used to show the regional linkage disequilibrium of candidate genes164. Gene ontology enrichment analysis of candidate genes was performed using the web tool DAVID (https://davidbioinformatics.nih.gov/). Secondly, six uncorrelated environmental variables (bio4, elev, bio15, bio3, bio18, and bio10) with Spearman correlation coefficient |r| <0.8 were selected to perform the redundancy analysis using the R package vegan (v2.6-4) to identify environment-associated genetic variants with a standard deviation cutoff of 3.5 along one or multiple RDA axes165.

To evaluate the potential regulatory impact of environment-associated variants, we analyzed the genomic regions surrounding these variants using the PlantCARE database (http://bioinformatics.psb.ugent.be/webtools/plantcare/html/). The predicted regulatory elements, including promoter motifs and transcription factor binding sites, were identified and compared between the reference and variant alleles. This analysis was used to assess whether the variants could disrupt or modify regulatory motifs, thereby potentially influencing the transcriptional regulation and expression of adjacent genes.

The contribution of environmental variables
The principal component analysis was performed with PLINK (v1.90) software using adaptive candidates of SNPs, indels, and TEVs based on the LFMM analysis (FDR < 0.01). The first two principal components were used for the variance partitioning analysis. The varpart function, using adjusted in RDA from the R package vegan (v2.6-4), was performed to estimate the proportion of environmental variance explained by different genetic variants165.

Genetic offset analysis
Future bioclimatic variables for the periods 2041–2060 and 2081–2100, under SSP245 and SSP585 scenarios, were obtained from three different climate models (ACCESS-CM2, CanESM5, and GISS-E2-1-G; resolution 2.5 arcmin), using the WorldClim version 2.1 database159. The overlapping candidate adaptive variants identified by both LFMM and RDA approaches were defined as “core adaptive variants” for local adaptation. To reduce biases arising from linkage disequilibrium, we employed PLINK (v1.90) software with the parameters (--indep-pairwise 50 10 0.5)160 based on core adaptive variants to obtain LD-pruned 40 adaptive TEVs, and 206 adaptive SNPs and indels for the following analyses. We used the gradient forest model, which is a machine learning regression tree approach, to predict the spatiotemporal adaptability of V. amurensis to future environmental change with calculating population genetic offset using the R package gradientForest166. Genetic offset represented the mismatch between contemporary and future spatial environment-associated genotype composition to quantify the maladaptation of populations. Populations with higher genetic offsets imply that they are greater vulnerability to future climate change due to the requirement for more pronounced changes in adaptive allele frequencies to ensure successful adaptation71,167. We incorporated predictions of genetic offsets from three different future climate models by considering distinctions of these models using the gradient forest approach. The mean genetic offset values from the three models were used to estimate local maladaptation under future climate conditions. Moreover, two different emission scenarios (SSP245 and SSP585) and two defined periods (2041–2060 and 2081–2100) were used for the predicted patterns of genetic offsets throughout the distribution range of V. amurensis.

To estimate the possible contribution of adaptive TEVs to the future climate adaptability of V. amurensis populations, we followed a systematic approach. First, we calculated the genetic offset using LD-pruned adaptive SNPs and indels as the baseline. Next, we included all LD-pruned adaptive variants in our genetic offset calculation. We then randomly selected the same number of non-adaptive TEVs along with the LD-pruned adaptive SNPs and indels to estimate genetic offsets, repeating this process 100 times to obtain average genetic offsets for a robust genetic vulnerability analysis. Finally, we compared these genetic offsets of 31 sampled populations to determine statistical significance.

Additionally, migration plays an important role in allowing species to maintain populations under rapidly changing climate conditions and to avoid extirpation167. To consider the contribution of migration, we evaluated the sensitivity of the forward offset of 2080–2100 under SSP585 scenario by calculating the average values across all climate models with limiting the maximum allowable migration to different distance classes, such as 100, 500, and 1000 km.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this manuscript, Zhiyao Ma, Xiaodong Xu, et al performed several genomic analyses to evaluate the potential role of\ntransposable elements in the local adaptation of natural Vitis amurensis populations.\nAfter constructing a graph-based pangenome using four phased genomes they assembled, the authors extracted TEVs to\nevaluate their distribution and potential association with bioclimatic variables. They also performed a classic small variant\ncalling using a single linear reference genome and BWA-GATK tools. Their results were further analyzed in the context of\nclimate change projections using predictive models.\nAfter going through the manuscript, the reader is left primarily with predictive results, without a deeper exploration of\npotentially valuable intermediate findings. I recommend toning down the title, as the manuscript does not present direct\nfunctional evidence that transposable elements mediate adaptation in the studied model. To fully leverage both the graph-\nbased pangenome and the variant analyses, I suggest several major revisions outlined below.\nThe authors constructed a graph-based pangenome using representative genomes of their natural populations. Figure 1 is\ndedicated to the representativeness of such a graph and emphasizes its relevance for studying natural populations.\nUnfortunately, all subsequent SNPs/INDELs analyses were conducted by mapping resequencing reads to a single linear\nreference genome (Vam057 haplotype1) using BWA. In my opinion, this disrupts the methodological flow and gives the\nimpression of novelty with pangenomics while it is under-used. Once a graph-based pangenome is constructed, it is now\nstandard and more coherent to map short reads directly to the graph – particularly for diverse samples from natural\npopulations. I recommend the authors to fully leverage their graph-based pangenome and use it to perform the mapping of\nthe resequencing reads.\nMoreover, pangenome graphs built with PGGB typically capture abundant SNPs, it is unfortunate that this resource is not\nleveraged at all. The use of a linear reference genome could still serve as a comparative baseline but should not replace\ngraph-based analyses entirely. While one could argue that GATK enables de novo variant calling, whereas most graph tools\ngenotype existing variants, the authors applied stringent filtering afterwards, likely retaining common variants that would\nalready be present in the graph. This further undermines the rationale for using a linear mapping strategy.\nOn a positive note, I believe the integration of resequencing data with the graph through PanGenie was a strong decision\nand demonstrates efficient use of the available data.\nI found it highly counterintuitive that the authors repeatedly refer to their pangenome and variant map as central resources,\nVampan_v1.0 and Vamap_v1.0, yet do not release any of them. In addition, none of the HiFi data used for the genome\nassemblies has been made publicly available. This is far from ideal for the community and very disappointing for a genomic\nmanuscript. I strongly encourage the authors to release both the HiFi data and the pangenome, along with the associated\nvariant map, via a public repository such as Zenodo.\nRegarding the abbreviations used, while Vampan might stand for V. amurensis pangenome, I am not convinced that the\ncontraction Vamap is helpful. I also question whether the term V. am is specific enough to V. amurensis.\nThe description of the pangenome structure was very short. L167: “promoted the identification of genomic variant, especially\nTEVs that were usually ignored in previous research.”. Please expand this section to describe the types of\npolymorphisms/structural variants contained in the graph. What proportion TEVs represent compared to the total number of\nvariation bubbles? So far, it is not evident that TEVs are the most important variants while the paper is written along these\nlines.\nIn the results section describing the GEA (genotype–environment association) analyses, the functional consequences of\nSNPs and SVs on genes are unclear. It appears the authors simply intersected the VCFs with gene annotations. I\nrecommend performing variant impact predictions to assess whether the associated variants are likely to affect gene\nfunction. For example, you could run functional impact prediction if the TE is within the gene loci, regulatory region analysis\n(TF binding sites) if the TE is in the promoter, methylation site disruption...\nPlease see below my minor comments:\nL432: There are very few details on the hifi sequencing. There is no information on the library preparation nor on the\nsequencer, please add it.\nL456: I might be confused here. How do you use the contig level assembly of Cabernet Sauvignon from 2016 to orient and\norder your longer HiFi-based contigs?\nL139: This sentence might need some corrections. “our assembly was high quality and completeness” -> “our assemblies\nwere of high quality and complete”.\nL145: “To reduce reference bias” is a bit confusing here and was not introduced anywhere else, not even in the introduction.\nYou might want to introduce in a sentence first how using a single reference is biased and then show that you built a\npangenome.\nL474: There are only a few details on plotsr. Please described how the pairwise relationship between the haplotypes was\ncomputed.\nFig 1a: Please indicate that chromosome 1 was selected for the representation.\nL148: I feel calling Vampan_v1.0 as a genome is confusing, please use pangenome.\nL149-150: The choice of using a pangenome is justified but not well introduced. I would write 1 or 2 sentences in the\nintroduction so that the reader understands how pertinent it is in such case.\nL155: Here, it is very hard to know from the results section solely that these SNPs and INDELs all come from a single\nreference approach. This should be indicated and might be modified/removed after using the graph to do it. Also, the\nreasoning behind the selection of the genome for bwa-gatk is not explained.\nL170: I think it is pangenome instead of genome.\nL170: The abbreviation Vamap is not defined in the materials and methods, and as mentioned earlier if it uses Vam as Vitis\namurensis, then the remaining ap letters are not very intuitive.\nL509-510: Which genome was used for the -r flag (reference)? Based on my understanding, this parameter is mandatory.\nL510: How did you merge the VFCs? Which tool, which version?\nFig2a: How the TEs can be represented along a physical position? Is it the specific genome I was referencing earlier for\nPangenie? Is it the reference? Please be consistent, I see TEs (on plot) and TEVs (on legend).\nFig2c: Please add a description of r and what type of correlation? Also, the blue line should be defined.\nFig 2d: The blue line represents a fixed number for the average SNPs number in the genome. I am not sure in which window\nsize this was calculated. Also, it would be nice to have a second lineplot representing the number of SNPs in total SVs\nand/or non-TE SVs to support the accumulation.\nL180: The last sentence is quite redundant with the previous one, I would remove it.\nL182-186: I would construct the paragraph by descending order of percentages.\nFig 2e: I am not sure to understand how the frequency was calculated. Also, again regarding the structure of the paragraph\nL182, the flow goes from the TEVs to the TEs and goes back to the TEVs. I would start by the TEs and then focus on the\nTEVs.\nL491: Please give more details about the GO analysis. Which reference did you use? How GO were attributed in the\ngenomes/pangenome in the first place?\nL232: involving?\nL233: Please described the nature of the TE and its size.\nL246: I am not sure what the authors are referring to with “the tubby-like members”.\nL259: Could you please provide more details about this SNP? Does it interfere with a TF binding domain for example?\nL565: Is it variable or variance?\nL369: The way this sentence is structured could imply that the SNPs and INDELs were obtained with the graph while they\nwere identified using a single linear reference, please reformulate.\nL381: In my opinion, it is quite a shortcut to define these genes as “environment-associated candidate genes”. Based on my\nunderstanding, only the variants were statistically tested, and fall within 20 kb up-/down-stream of these gene loci (L226).\nL408-409: This sentence seems very general, how I am not sure what it implies specifically, please reformulate: “The\neffectiveness of conservation efforts is determined by the appropriateness of the strategies employed.”\nL617: Dara -> Data", "reviewer_2": "Through genomic analysis of a wide range of wild grape, the authors have provided convincing evidence that TEs have\nmade a significant contribution to stress adaptation in this species. They also use modeling to provide evidence for the\ncapacity of different populations to adapt to projected climate changes. This systematically addresses a question that has\nnot yet been comprehensively described to date: to what extent do TEs provide a unique capacity for populations to adapt to\nchanging conditions. Overall, I was impressed with the quality of the data, and I think the authors provide convincing\nevidence that TEs may indeed do so. I do, however, have some suggestions for improvement. First, although TEs are at the\ncenter of this analysis, I saw very little in the way of issues that commonly confront annotators. How, for instance, were very\nlow confidence TEs delt with? How about multiple duplicate copies of a recently active element? Were any of the TEs\nrecently active? How does the distribution of TEs compare with the population structure of this species? Largely\nconcordant? Second, although I do appreciate that there are space constraints, this journal is targeted to ta relatively broad\naudience, and most readers would benefit from short description of the various algorithms employed. As written, they are just\na series of black boxes that the reader must simply take on faith were used correctly. I am certain the authors put a great deal\nof thought into the tools they used, which often involves compromises. I would like to see more about those compromises.\nThe authors throughout should change the wording to reflect the fact that none of the evidence presented conclusively\ndemonstrates that any of the variation is causally related to stress response. Yes, we all use shorthand, but all we are doing\nin this kind of analysis is showing that various forms of genetic variation are genetically linked to certain phenotypes. With\nthat in mind, I would suggest that the author carefully consider alternative hypotheses, particularly in cases in which, for\ninstance, TEs, but not SNPs, predict future hypothetical responses to hypothetical changes in the environment. Finally, I\nwould strongly suggest that use one of the many AI-based grammar checkers available. Minor issues are inevitable, but in\nmany cases incorrect wording actually means something I suspect the authors did not mean.\nLine 63. “...role in promoting genomic structural variant...” promoting variation, not “variant”.\nLine 64. “traits variation” should be trait variation\nLine 73. Please don’t assume the conclusion. TEs may well play a “pivotal role”, but that has not been demonstrated, which\nis why this and other studies are being done. Frame this as a test of a hypothesis, not an effort to prove one.\nLine 77. “ In response to the threat of climate change, species adapt through genetic mutations...” I know this might sound\npedantic, but this manuscript is being framed in an evolutionary context, so it is important to be precise with respect to\nevolutionary processes. Plants aren’t responding to a threat, nor are species. Changing conditions favor the spread of new\nvariants or the extinction of species. There is no “response”. We need to stop using inaccurate shorthand like this.\nLine 80. How does one study of pine species address the comparison between plants and animals?\nLine 83. I would be careful of terms such as “climate associated variants”, since most of these end up being just so stories.\nLine 181. Not surprising, since these would be regions subject to lower levels of purifying selection.\nLine 186. These numbers don’t add up. 20% are in genic regions, 26% in regions outside of the genes but within 2kb, and\n76.84% are in intergenic regions (an odd distinction, since 2kb from a gene is, in many genomes, decidedly intergenic). That\nadds up to 112.84%. Also, why round for the 20% and the 26% but not the 76.84%?\nLine 187. “In different genomic regions, most TEs were observed at low frequency (≤ 0.5)” different from what? The 112.84%\nalready discussed? Also, why is less that 0.5 (50%?) low frequency?\nLine 193. “...functions for genes with a frequency more than 0.8 of TEVs in the CDS” here an throughout, the wording makes\nunderstanding difficult. Genes with of frequency of .8 of TEVs in the CDS means, I assume, that of the genes in the pan-\ngenome at syntenic positions, 80% of them have a TE inserted into them. But that is not the same as “genes with a frequency\nof .8 of TEVs”\nLine 196. Define “endemic.” Also, wording. The TEVs are not the most endemic, th\nLine 194. Likely because most of these are non-functional or pseudogenes, right?\nLine 199. “unfolded site frequency spectra (SFS)” Define briefly for the non-specialist.\nLine 202. The effect does not seem particularly strong, and it’s not clear that you know that the higher number of rare TE\nvariants is not due to low levels of TE insertions. Further, since few readers will know what the directional coordinates mean, it\nis not clear why there appears to be two classes of SNPs in the (0,1) group in the histogram. It is also not clear if the\nauthors mean SNPs within TEs (presumably from unique single copy SNPs) or SNPs adjacent to the TEs. The problem\nhere is that the authors are making a clear statement concerning selection with a poorly described experiment.\nLine 204. This is a strong claim that should be supported by the evidence.\nLine 216. Given that I am not familiar with these models, I am going to assume that these include Bonferoni corrections.\nLine 226. Given the dramatic differences in recombination frequency in different parts of plant genomes, I am surprised that a\nmap unit distance wasn’t used instead of a generic number of base pairs.\nLine 226. “adaptive candidate variants” should be candidate adaptive variants.\nLine 230. “The gene TLP3 was” You mean the grape homolog of this gene” (same below)\nLine 234. “in the putative cis-region of the gene TLP3”. It is demonstrably cis. I think you mean putative cis regulatory region.\nLine 247. “The gene HHP4” should be “allelic variation in”\nLine 248. “described in response to” should be “has been implicated in...” Also, does the reference actually show this?\nLine 251. “associated with variant in temperature seasonality” should be variation in. Here and throughout, please check\ngrammar.\nLine 252. “putative cis-region” Again, you mean putative cis regulatory region.\nLine 253. Again, grammar actually matters for meaning. “ illustrate the relationship between the allele frequency.” You did\nnot mean that in order to illustrate something, you identified this particular SNP, I assume, but that is what the text as written\nsays. I suspect you mean that you are talking about this SNP because it is the best candididate.\nLine 263. “We estimated the proportion of environmental variance (PVE) explained by...” Again, as written, this is incorrect.\nYou are not explaining the proportion of environmental variance by genetic variation because the environmental variance\noccurs independently of the genetic variance.\nLine 267. What is meant by “exclusive” here? That this is variance that would be wholly missed if only SNPs were used?\nLine 279. To be clear, does this mean that you picked variants that co-occurred under similar conditions, or that co-occurred\nin localized geographic regions? The first would be more interesting I would think because it would get around population\nstructure differences.\nLine 283. Here and throughout, just a bit of explanation would be helpful for many readers. Here I assume you are saying\nthat you looked at all the various local populations, the amount of variation that you surmise will be important for adaptation\nin response to two possible climate change (based on the relationship to that variation and current conditions), and then\nassessed the vulnerability of those populations. ]\nLine 287. “Populations with higher genetic offsets” I assume that means populations with lower frequencies of alleles that\nwould facilitate adaptation?\nLine 290. This kind of goes without saying doesn’t it? What would be more informative would be if the differences between\nvulnerable and more adaptive populations would be exacerbated.\nLine 304. I think you mean “may play and important role”. I am surprised that the adapative SNPs essentially made no\ndifference. Does this make sense based on the distribution of the effects of the SNPs? That is, since we know that many of\nthe SNPs are linked to potentially adaptive traits, is it those case that these are unlikely to be those that would facilitate\nadaptation to the particular conditions predicted by these models?\nLine 326. They do not reveal that TEVs significantly enhance the plants' adaptive potential to climate change, they show that\nyour model suggests this. The map, as they say, is not the terrain. Given that climate change as already significantly affected\ngrowing conditions, I wonder if you could make some retrospective predictions.\nLine 339. “ hotspots of genetic variant in” should be “variation”, not variant.\nLine 345. However is the beginning of a new sentence here. Also, please check throughout for the distinction between\nvariant and variation.\nLine 353. Is there evidence for recent mobilization of TEs in these populations? One thing of particular interest would be the\nway that the TEs were profiled. Most importantly, if there were multiple copies of identical TEs, were they all counted?\nI assume so, because you are looking for polymorphic insertions, but I am concerned about whether or not they were missed.\nGiven the centrality of TEs in this analysis, I am also surprised that the authors did not look carefully at the population\nstructure of the TEs themselves.\nLine 363. I would caution the authors to use correct terminology when discussion evolution. Species don’t adapt to anything\n“in order to” do anything. Evolution is simply what happens. Species don’t have agency.\nLine 397. These are all great examples, but given the large number of candidates that are just above the significance plot in\n3d, as is ICE, how is the reader to know that there are so many genes involved in various stress responses, the authors\nwould inevitably find at least some of them? I do understand that much of this is “under the hood” of the various algorithms,\nbut it might be helpful for the authors to carefully spell out why these candidates are unlikely to have been identified by\nchance?\nLine 419. These are excellent points.\nLine 486. In the text of the M&M, this is everything about how TEs were identified: Transposable element families were\nidentified by RepeatModeler with a pan-Vitis database of repeat families. The repeat elements were\nclassified by using deepTE with the Plant model. Finally, we annotated all repetitive sequences using RepeatMasker\n(v4.1.2). TEs are notoriously difficult to accurately identify and TEs are at the core of this analysis. I will return to this\ncomment later, but the process if far more involved that simply using a few out of the box tool sets.\nFigure 2, line 1009. The wording here is unclear. “based on genes with a frequency more than 80% of TEVs in the CDS\nregion” Does that mean genes that, on average, among all the accessions, 80% of them have a TE insertion into the CDS?\nPresumably, these are pseudogenes, TEs that look like genes, or just bad calls by annotation programs, which are often\ninaccurate. I would assume that these “genes” also have much higher rates of non-synonymous point mutations as well. Do\nthey?", "reviewer_3": "The authors present a pan-genome analysis of Vitis amurensis, offering novel insight into the role of transposable elements\nin environmental adaptation. While the study has clear potential, several key issues need to be addressed before it can be\nconsidered for publication:\n1. The population structure analysis indicates that the South clade possesses a distinct genetic background, as further\nsupported by syntenic and PCA analyses. These results suggest that the South clade may represent a separate evolutionary\nlineage, potentially not belonging to V. amurensis. The authors should provide additional taxonomic evidence (e.g.,\nmorphological traits) or molecular validation to clarify the classification of these accessions.\n2. Although the study identifies genes potentially associated with environmental adaptation, the analysis is based solely on\nLFMM modeling using climatic variables. To comprehensively identify genes involved in cold/drought stress responses,\nphenotypic data across all accessions should be incorporated. A phenotype-genotype association analysis would offer more\ndirect and biologically relevant evidence for adaptive traits.\n3. While the LFMM analysis identified numbers of significant genetic variants, the author only focused on a subset of genes\nrelated to cold and drought resistance. Genes linked to other significant loci should also be listed and considered.\nAdditionally, the wild habitats of the accessions should be clearly documented, along with the corresponding environmental\nfactors of those habitats, to ensure the accuracy and relevance of the analysis. It is also important to verify that the sampled\naccessions originate from natural populations rather than cultivated sources; such as individuals maintained in germplasm\nresource gardens should be traceable to their native habitat.\n4. The author state that “Transposable elements mediate adaptation to environment change” and identify several genes\nassociated with cold and drought resistance. However, the manuscript lacks a detailed genetic or regulatory model\nexplaining how these genes contribute to environmental adaption. The authors should provide mechanistic insights or\nsupporting evidence to support this conclusion."}, "author_response": "Response to Review Comments\nPart I: Summary of responses to the major questions:\nWe sincerely appreciate the interest shown by the editor and the three reviewers. Before\npresenting a detailed point-by-point response in Part II, we first address three major\nissues collectively:\nFirst, enhanced methodological clarity and balanced variant-calling strategy: We\nclarified our rationale for combining graph-based and linear-reference approaches.\nThe PGGB graph with PanGenie was used for structural variant discovery, while a\nlinear reference-based pipeline was employed for SNP/INDEL calling to ensure\nanalytical robustness.\nSecond, expanded functional interpretation of environment-associated variants:\nAdditional analyses using the PlantCARE database identified cis-regulatory motifs\npotentially affected by specific TE insertions or SNPs (e.g., a TE insertion upstream\nof TLP3 and a SNP upstream of ICE1), providing mechanistic hypotheses for how\nthese variants could influence gene regulation and contribute to adaptation.\nThird, clarification of taxonomy and gene lists: Morphological traits across all\nclades, including the South clade, were examined and showed no evidence of\ntaxonomic distinction, supporting their classification as V. amurensis. We also\nexpanded reporting to include all genes linked to significant loci, not just those\nassociated with cold/drought resistance.\nIn conclusion, we have provided detailed explanations in response to the reviewers'\ncomments. All updates in the manuscript are highlighted with tracked changes.\nAdditionally, we have revised the manuscript, supplemental figures and tables.\nPart II: Point by point response to specific comments:\n(The reviewer’s comments are shown in black, and the authors’ responses are\ndisplayed in blue.)\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nIn this manuscript, Zhiyao Ma, Xiaodong Xu, et al performed several genomic\nanalyses to evaluate the potential role of transposable elements in the local adaptation\nof natural Vitis amurensis populations.\nAfter constructing a graph-based pangenome using four phased genomes they\nassembled, the authors extracted TEVs to evaluate their distribution and potential\nassociation with bioclimatic variables. They also performed a classic small variant\ncalling using a single linear reference genome and BWA-GATK tools. Their results\nwere further analyzed in the context of climate change projections using predictive\nmodels.\nAfter going through the manuscript, the reader is left primarily with predictive results,\nwithout a deeper exploration of potentially valuable intermediate findings. I\nrecommend toning down the title, as the manuscript does not present direct functional\nevidence that transposable elements mediate adaptation in the studied model. To fully\nleverage both the graph-based pangenome and the variant analyses, I suggest several\nmajor revisions outlined below.\nThe authors constructed a graph-based pangenome using representative genomes of\ntheir natural populations. Figure 1 is dedicated to the representativeness of such a\ngraph and emphasizes its relevance for studying natural populations. Unfortunately,\nall subsequent SNPs/INDELs analyses were conducted by mapping resequencing\nreads to a single linear reference genome (Vam057 haplotype1) using BWA. In my\nopinion, this disrupts the methodological flow and gives the impression of novelty\nwith pangenomics while it is under-used. Once a graph-based pangenome is\nconstructed, it is now standard and more coherent to map short reads directly to the\ngraph – particularly for diverse samples from natural populations. I recommend the\nauthors to fully leverage their graph-based pangenome and use it to perform the\nmapping of the resequencing reads.\nMoreover, pangenome graphs built with PGGB typically capture abundant SNPs, it is\nunfortunate that this resource is not leveraged at all. The use of a linear reference\ngenome could still serve as a comparative baseline but should not replace graph-based\nanalyses entirely. While one could argue that GATK enables de novo variant calling,\nwhereas most graph tools genotype existing variants, the authors applied stringent\nfiltering afterwards, likely retaining common variants that would already be present in\nthe graph. This further undermines the rationale for using a linear mapping strategy.\nOn a positive note, I believe the integration of resequencing data with the graph\nthrough PanGenie was a strong decision and demonstrates efficient use of the\navailable data.\nResponse: We appreciate your insightful comments and careful evaluation of our\nwork. We understand the concern that the phrasing “Transposable Elements Mediate\nAdaptation” in the title may suggest a stronger causal relationship than our data\ndirectly support, since our study is primarily based on genomic associations and\npredictive modeling. To more accurately reflect the scope of our findings, we have\nrevised the title to: “Transposable Elements Contribute to Climate Change Adaptation\nin Wild Grapes”. This revision avoids overstating our findings and better aligns with\nthe goals and results of our research.\nWe appreciate your close attention to our analysis strategy, and we thank you for the\nconstructive comment. As you noted, we constructed a high-quality pangenome graph\nusing PGGB. Our primary motivation for building the graph was to enable\ncomprehensive detection of structural variants (SVs) with PanGenie. Graph-based\napproaches are known to clearly outperform linear references for such applications.\nThis allowed us to capture complex genomic variation, thereby providing critical\ninsights into the genomic architecture of natural populations and their potential\nadaptations to climate change.\nRegarding SNPs and INDELs detection, we agree that graph-based mapping can, in\nprinciple, provide a more coherent use of the pangenome framework. However, we\nfound that current tools for graph-based SNP calling still face challenges for\npopulation-scale resequencing, such as higher missing rates and potential false\npositives. For example, while graph-based SNP calling with the PGGB graph\nidentified 4,765,309 SNPs, only 1,717,081 SNPs (~36%) overlapped with those called\nusing a linear reference under the same filtering criteria. To ensure the reliability of\nour SNPs/INDELs dataset and maintain analytical consistency, we employed the\nwidely used and well-validated linear reference-based approach. This strategy helped\nminimize potential biases and ensured analytical robustness in this study.\nWe fully acknowledge the value of graph-based approaches for variants calling,\nespecially as these tools continue to improve. We believe our current choice\nrepresents a pragmatic and reliable compromise, and we look forward to incorporating\nmore advanced graph-based methods in future studies.\nI found it highly counterintuitive that the authors repeatedly refer to their pangenome\nand variant map as central resources, Vampan_v1.0 and Vamap_v1.0, yet do not\nrelease any of them. In addition, none of the HiFi data used for the genome\nassemblies has been made publicly available. This is far from ideal for the community\nand very disappointing for a genomic manuscript. I strongly encourage the authors to\nrelease both the HiFi data and the pangenome, along with the associated variant map,\nvia a public repository such as Zenodo.\nResponse: We truly appreciate the recommendations you provided for improvement.\nWe have submitted the HiFi data, HiC data, and the pangenome of Amur grape to\nNCBI and Zenodo. The eight haplotype-resolved genome assemblies of the wild\nAmur grape have been deposited in the National Center for Biotechnology\nInformation (NCBI) under the BioProject accession PRJNA1272278-PRJNA1272285\nand in Zendo (https://doi.org/10.5281/zenodo.16534920). The Amur grape\npangenome is also available in Zenodo at the same DOI. The associated HiFi and Hi-\nC sequencing data have been deposited in NCBI under BioProject accession\nPRJNA1308837. In addition, whole-genome resequencing data for the 330\nindividuals studied have been deposited in NCBI under BioProject accession\nPRJNA1089859.\nRegarding the abbreviations used, while Vampan might stand for V. amurensis\npangenome, I am not convinced that the contraction Vamap is helpful. I also question\nwhether the term V. am is specific enough to V. amurensis.\nResponse: We appreciate your comment regarding the clarity and appropriateness of\nthe abbreviations used in the manuscript. We have removed the abbreviation “Vamap”\nin the manuscript to avoid potential ambiguity.\nThe description of the pangenome structure was very short. L167: “promoted the\nidentification of genomic variant, especially TEVs that were usually ignored in\nprevious research.”. Please expand this section to describe the types of\npolymorphisms/structural variants contained in the graph. What proportion TEVs\nrepresent compared to the total number of variation bubbles? So far, it is not evident\nthat TEVs are the most important variants while the paper is written along these lines.\nResponse: Thank you for this constructive comment. We agree that our original\ndescription of the pangenome structure was too brief. In the revised manuscript, we\nhave expanded this section to provide a more detailed characterization of the\nstructural variants identified from the pangenome graph. Specifically, we now report\nthat a total of 220,864 structural variants were identified, among which 127,094 were\nTEVs, accounting for 57.54% of all structural variants. And we agree that the original\nphrasing might misleadingly suggest that TEVs are the most important variants. To\navoid overstatement, we have removed the wording “especially TEVs that were\nusually ignored in previous research”.\nIn the results section describing the GEA (genotype–environment association)\nanalyses, the functional consequences of SNPs and SVs on genes are unclear. I\nrecommend performing variant impact predictions to assess whether the associated variants\nare likely to affect gene function. For example, you could run functional impact\nprediction if the TE is within the gene loci, regulatory region analysis (TF binding\nsites) if the TE is in the promoter, methylation site disruption...\nResponse: Thank you for your valuable suggestion. In the revised manuscript, we\nhave conducted additional analyses to evaluate the potential functional influences of\nthe environment-associated variants. Specifically, we analyzed the genomic regions\nsurrounding candidate variants using the PlantCARE database, which predicts cis-\nregulatory elements such as promoter motifs and transcription factor binding sites. By\ncomparing the reference and variant alleles, we were able to assess whether the\nvariants could disrupt or modify regulatory motifs and thereby potentially influence\ngene regulation. For example, we identified an adaptive TE insertion upstream of\nTLP3 that may affect a predicted transcription factor binding site, and a candidate\nSNP upstream of ICE1 that lies within a predicted regulatory motif. These findings\nsuggest that environment-associated variants may indeed have functional\nconsequences through modulation of gene regulation (see revised Results and\nSupplementary Fig. S9).\nPlease see below my minor comments:\nL432: There are very few details on the hifi sequencing. There is no information on\nthe library preparation nor on the sequencer, please add it.\nResponse: Thank you for the helpful comment. We have added detail information on\nlibrary and sequencer in the Method section: “HiFi reads of four Amur grape\naccessions were sequenced on PacBio Revio platform in a circular consensus\nsequencing (CCS) mode, using 15-20 kb SMRT cell library”.\nL456: I might be confused here. How do you use the contig level assembly of\nCabernet Sauvignon from 2016 to orient and order your longer HiFi-based contigs?\nResponse: Thank you very much for your careful reading and valuable comment.\nThis was a citation error on our part. In fact, we used the genome of the domesticated\ngrapevine (V. vinifera ssp. vinifera) cultivar PN40024 from Shi et al. (2023) as the\nreference to orient and order the haplotigs. We have carefully rechecked our original\nassembly results and confirm that this was indeed the genome used. We sincerely\napologize for this oversight and have corrected both the citation and the\ncorresponding description in the revised manuscript to accurately reflect the reference\ngenome used.\nL139: This sentence might need some corrections. “our assembly was high quality\nand completeness” -> “our assemblies were of high quality and complete”.\nResponse: We thank you for your helpful suggestion. We have revised the sentence as\nrecommended: “our assemblies were of high quality and completeness.”\nL145: “To reduce reference bias” is a bit confusing here and was not introduced\nanywhere else, not even in the introduction. You might want to introduce in a sentence\nfirst how using a single reference is biased and then show that you built a pangenome.\nResponse: We appreciate your insightful comment. We agree that the phrase “To\nreduce reference bias” may be unclear without prior context. We have revised this\nsentence: “To better detect structural variants, we utilized …”. This revision provides\na clearer and more focused rationale for using a pangenome graph in our study.\nL474: There are only a few details on plotsr. Please described how the pairwise\nrelationship between the haplotypes was computed.\nResponse: Thank you for your helpful suggestion. We have added a detailed\ndescription of the methods used to compute the pairwise relationships between\nhaplotypes. Specifically, we used Minimap2 to perform whole-genome alignment\nbetween haplotype assemblies and SyRI to identify genome rearrangement regions.\nThe resulting data were then visualized using plotsr. These steps have been clarified in\nthe revised Materials and Methods section.\nFig 1a: Please indicate that chromosome 1 was selected for the representation.\nResponse: Thank you for the helpful comment. We have revised the caption of Figure\n1 to indicate that chromosome 1 was selected as a representative for the synteny\nanalysis: “Synteny among PN_T2T reference genome and eight haplotype genomes of\nAmur grape (shown for chromosome 1 as a representative)”.\nL148: I feel calling Vampan_v1.0 as a genome is confusing, please use pangenome.\nResponse: Thank you for the helpful suggestion. We have revised the term “genome”\nto \"pangenome\" to avoid confusion.\nL149-150: The choice of using a pangenome is justified but not well introduced. I\nwould write 1 or 2 sentences in the introduction so that the reader understands how\npertinent it is in such case.\nResponse: Thank you for the constructive suggestion. We have revised the\nintroduction to better highlight the motivation for constructing a graph-based\npangenome. Specifically, we added the following sentence: “To achieve more\ncomprehensive and accurate detection of structural variants, which are often missed or\nmischaracterized when using a single linear reference, we constructed a graph-based\npangenome reference using haplotype-resolved genomes of representative Amur\ngrape accessions”.\nL155: Here, it is very hard to know from the results section solely that these SNPs and\nINDELs all come from a single reference approach. This should be indicated and\nmight be modified/removed after using the graph to do it. Also, the reasoning behind\nthe selection of the genome for bwa-gatk is not explained.\nResponse: Thank you for your thoughtful comment. To clarify our approach, we have\nadded the following sentence to the Results section: “We identified 41,451,941 SNPs\nand 6,856,493 indels based on a single-reference approach by mapping all\nresequencing reads to the Vam057 haplotype1 genome (see material and methods)”.\nIn addition, to explain our rationale for choosing this approach, we have added the\nfollowing sentence to the Methods section: “To ensure the reliability of the short\nvariant dataset and maintain analytical consistency across samples, we employed the\nwidely used and well-validated linear reference-based approach”.\nL170: I think it is pangenome instead of genome.\nResponse: Thank you for the helpful suggestion. We have revised the term “genome”\nto \"pangenome\" to avoid confusion.\nL170: The abbreviation Vamap is not defined in the materials and methods, and as\nmentioned earlier if it uses Vam as Vitis amurensis, then the remaining ap letters are\nnot very intuitive.\nResponse: Thank you for pointing this out. We agree that the abbreviation “Vamap”\nmay cause confusion. We have removed this abbreviation from the manuscript.\nL509-510: Which genome was used for the -r flag (reference)? Based on my\nunderstanding, this parameter is mandatory.\nResponse: Thank you for your helpful comment. We have revised the Methods\nsection to specify that the Vam057 haplotype1 genome was used as the reference\ngenome with the -r Vam057_haplotype1_genome setting in PanGenie.\nL510: How did you merge the VFCs? Which tool, which version?\nResponse: We appreciate your suggestion. We used BCFtools (v1.13) to merge the\nVCF files. This information has now been added to the Materials and Methods section\nof the manuscript.\nFig2a: How the TEs can be represented along a physical position? Is it the specific\ngenome I was referencing earlier for Pangenie? Is it the reference? Please be\nconsistent, I see TEs (on plot) and TEVs (on legend).\nResponse: Thank you for pointing this out. TEVs were represented along the physical\nposition of the Vam057 haplotype1 genome, which was also used as the reference\ngenome for PanGenie (-r option). We have clarified this in the figure legend: “TEV\ndensity across the whole genome, based on the Vam057 haplotype1 reference”. To\navoid confusion, we have revised the figure and its legend to consistently use TEVs\nthroughout, as this term better reflects the variant-level information derived from\npopulation analysis.\nFig2c: Please add a description of r and what type of correlation? Also, the blue line\nshould be defined.\nResponse: Thank you for your helpful comment. We have clarified in both the\nMethods section and the figure legend that r represents the Pearson correlation\ncoefficient, which assesses the linear relationship between genetic diversity (π) values\nof SNPs and TEVs. We have also added a description in the figure legend stating that\nthe blue line represents the fitted linear regression line based on the least squares\nmethod.\nFig 2d: The blue line represents a fixed number for the average SNPs number in the\ngenome. I am not sure in which window size this was calculated. Also, it would be\nnice to have a second lineplot representing the number of SNPs in total SVs\nand/or non-TE SVs to support the accumulation.\nResponse: Thank you for your thoughtful comment. We have clarified in both the\nMethods section and the figure legend that the blue line represents the average\nnumber of SNPs per 15kb sliding windows across the genome. And following your\nadvice, we additionally calculated SNP density around total SVs. The results show\nthat SNPs also exhibit biased accumulation around SVs, consistent with the pattern\nobserved for TEVs. We have now included this analysis in the revised manuscript and\nadded the corresponding figure (Supplementary Figure S5b).\nL180: The last sentence is quite redundant with the previous one, I would remove it.\nResponse: Thank you for your helpful suggestion. We have removed it.\nL182-186: I would construct the paragraph by descending order of percentages.\nResponse: Thank you for the helpful suggestion. And we recalculated the numbers of\nTEVs in intergenic regions (including cis-regulatory regions) to avoid artificial\ndistinctions according to the suggest from other reviewers. We have revised the\nparagraph as follows: “The distribution of TEVs in whole genome indicated that most\nTEVs were located at intergenic regions of the genome (79.21%). Approximately\n26.11% of TEVs were identified at the cis-regulatory regions (within 2 kb upstream\nand downstream of genes), and about 23.16% of TEVs were distributed in genic\nregions, including 3’ untranslated region (UTR), 5’ UTR, introns, and coding\nsequences (CDS) (supplementary table S4 and Fig. S5c). These reported percentages\nare not mutually exclusive, as individual TEVs may span multiple genomic features”.\nFig 2e: I am not sure to understand how the frequency was calculated. Also, again\nregarding the structure of the paragraph L182, the flow goes from the TEVs to the\nTEs and goes back to the TEVs. I would start by the TEs and then focus on the\nTEVs.\nResponse: Thank you for your comment. We defined TEV frequency as the\nproportion of haplotypes carrying the variant allele in the population. We have\nclarified this definition in the Materials and Methods section of the revised\nmanuscript. We have revised the paragraph according to your suggestion.\nL491: Please give more details about the GO analysis. Which reference did you use?\nHow GO were attributed in the genomes/pangenome in the first place?\nResponse: Thank you for raising this point. We realize that our original wording\nmight have caused confusion, as it could be interpreted as if we performed GO\nenrichment on the whole genome. In fact, our GO enrichment analyses were\nconducted only on specific gene sets. To assess the potential functional impact of\nTEVs, we performed GO analysis on genes containing TEVs with an allele\nfrequency > 0.8 in their CDS regions, using the DAVID web tool\n(https://david.ncifcrf.gov/). To avoid ambiguity, we have removed the previous\nsentence “The gene ontology (GO) enrichment analysis was conducted using the\nDAVID website” and have clarified the analysis procedure and the specific gene set\nused in the revised manuscript (see subsection “TEVs dynamics in Amur grape\npopulation” in the Materials and Methods section).\nL232: involving?\nResponse: Thank you for pointing this out. We have revised this term to “involved”.\nL233: Please described the nature of the TE and its size.\nResponse: Thank you for your comment. We have now provided detailed information\nregarding this TE as follows: “Notably, a candidate adaptive TE insertion\n(TE5_3254586, with a length of 10,631 bp, and classified as a Gypsy LTR) was\nlocated in the putative cis-regulatory region of the gene TLP3…”.\nL246: I am not sure what the authors are referring to with “the tubby-like members”.\nResponse: Thank you for pointing this out. We apologize for the unclear sentence. To\nimprove clarity, we have revised the sentence as follows: “The tubby-like protein\n(TLP) gene family has also been reported to play a role in plant responses to extreme\ntemperature stress”.\nL259: Could you please provide more details about this SNP? Does it interfere with a\nTF binding domain for example?\nResponse: Thank you for your valuable suggestions. We have revised our manuscript\nto provide more details about this SNP. Specifically, we now state: “We selected the\ncandidate adaptive SNP (SNP14_26191757, representing a G-to-A substitution),\nlocated in the putative cis-regulatory region (ca. 1kb upstream of the 5’UTR) of the\ngene ICE1, as the best example to illustrate the relationship between the allele\nfrequency and the environmental variable (Fig. 3d). According to the PlantCARE\ndatabase, this SNP is located within a predicted transcription factor binding site\n(chr14:26191744), which may alter the transcriptional regulation of ICE1\n(supplementary Fig. S9c)”.\nL565: Is it variable or variance?\nResponse: Thank you for your careful reading. The correct term here should be\n\"variance\", not \"variable.\" We have revised this term. We appreciate your attention to\nthis detail.\nL369: The way this sentence is structured could imply that the SNPs and INDELs\nwere obtained with the graph while they were identified using a single linear\nreference, please reformulate.\nResponse: Thank you for your thoughtful comment and for pointing out the potential\nconfusion. To clarify the methods used, we have revised the sentence to distinguish\nbetween the approaches used for identifying different variant types. The revised\nsentence reads: “In this study, we identified SNPs and indels based on a single linear\nreference genome, and detected TEVs using a graph-based pangenome in Amur grape\npopulations”.\nL381: In my opinion, it is quite a shortcut to define these genes as “environment-\nassociated candidate genes”. Based on my understanding, only the variants were\nstatistically tested, and fall within 20 kb up-/down-stream of these gene loci (L226).\nResponse: Thank you for your valuable comment. In our study, these candidate genes\nwere defined as those located within 20 kb upstream or downstream of environment-\nassociated adaptive variants (selected with false discovery rate < 0.01), consistent\nwith linkage disequilibrium patterns in Amur grape. Therefore, we believe the term\n“environment-associated candidate genes” remains appropriate to describe genes\nputatively linked to adaptive variants in this context.\nL408-409: This sentence seems very general, how I am not sure what it implies\nspecifically, please reformulate: “The effectiveness of conservation efforts is\ndetermined by the appropriateness of the strategies employed.”\nResponse: Thank you for your helpful comment. We agree that the original sentence\nwas too general and could be clarified. To enhance clarity and precision, we have\nrevised it as follows: “Effective conservation requires strategies based on the genetic\nand ecological characteristics of the species and its populations”.\nL617: Dara -> Data\nResponse: Thank you for your careful reading. We have revised this term.\nReviewer #2 (Remarks to the Author):\nThrough genomic analysis of a wide range of wild grape, the authors have provided\nconvincing evidence that TEs have made a significant contribution to stress\nadaptation in this species. They also use modeling to provide evidence for the\ncapacity of different populations to adapt to projected climate changes. This\nsystematically addresses a question that has not yet been comprehensively described\nto date: to what extent do TEs provide a unique capacity for populations to adapt to\nchanging conditions. Overall, I was impressed with the quality of the data, and I think\nthe authors provide convincing evidence that TEs may indeed do so. I do, however,\nhave some suggestions for improvement. First, although TEs are at the center of this\nanalysis, I saw very little in the way of issues that commonly confront annotators.\nHow, for instance, were very low confidence TEs delt with? How about multiple\nduplicate copies of a recently active element? Were any of the TEs recently active?\nHow does the distribution of TEs compare with the population structure of this\nspecies? Largely concordant? Second, although I do appreciate that there are space\nconstraints, this journal is targeted to ta relatively broad audience, and most readers\nwould benefit from short description of the various algorithms employed. As written,\nthey are just a series of black boxes that the reader must simply take on faith were\nused correctly. I am certain the authors put a great deal of thought into the tools they\nused, which often involves compromises. I would like to see more about those\ncompromises.\nResponse: Thank you for your insightful comments. First, we identified transposable\nelement families of Amur grape using RepeatModeler2 with a pan-Vitis repeat\nfamilies database (Shi et al., Horticulture Research, 2023). Second, to identify TEVs,\nwe searched homologous nucleotide sequences of structural variants identified by the\npangenome in the TE family database using NCBI-BLASTN, retaining only hits with\nevalue < 1e-5 and percentage identity > 70. Low confidence results, i.e., those with\nweak BLAST support, were excluded from downstream analyses of TEVs. For\nmultiple duplicate copies, each TEV was counted individually to accurately represent\nits frequency in the genome. Third, we added an analysis to estimated TE insertion\ntimes for the eight haplotype assemblies. The outputs generated by RepeatMasker\nwere parsed using the Perl script parseRM.pl (available at\nhttps://github.com/4ureliek/Parsing-RepeatMasker-Outputs) with the parameters -l\n50,1 -m 0.0025. The parameter -m specifies the substitution rate, following estimates\nfrom a previous study (Zhou et al., 2017, PNAS). Our results suggested that a broad\npeak around ~5 million years ago (Mya) in the temporal distribution of TE insertions,\nfollowed by a sharp decline in more recent insertions, indicating that most TE\ninsertions are relatively ancient (supplementary Fig. S2b). Finally, we have performed\na population structure analysis based on TEVs, and the results were consistent with\nthose obtained from SNPs (Fig. S4). To improve clarity for a broader readership, we\nhave also added brief descriptions of the algorithms employed and noted the main\ncompromises considered in their use. For example, although graph-based approaches\nmay capture more complex variation, we employed the widely used and well-\nvalidated linear reference-based approach to ensure the reliability of the short variant\ndataset and maintain analytical consistency across samples.\nThe authors throughout should change the wording to reflect the fact that none of the\nevidence presented conclusively demonstrates that any of the variation is causally\nrelated to stress response. Yes, we all use shorthand, but all we are doing in this kind\nof analysis is showing that various forms of genetic variation are genetically linked to\ncertain phenotypes. With that in mind, I would suggest that the author carefully\nconsider alternative hypotheses, particularly in cases in which, for instance, TEs,\nbut not SNPs, predict future hypothetical responses to hypothetical changes in the\nenvironment. Finally, I would strongly suggest that use one of the many AI-based\ngrammar checkers available. Minor issues are inevitable, but in many cases incorrect\nwording actually means something I suspect the authors did not mean.\nResponse: Thank you for your thoughtful and constructive comments. We agree that\nour analyses identify associations rather than causal relationships, and we have\ncarefully revised the manuscript to ensure that the wording reflects this distinction\n(e.g., replace “TEVs fuel local adaptation of Amur grape” with “Potential role of\nTEVs in local adaptation of Amur grape”). And we have carefully revised the\nlanguage of the manuscript and used an AI-based grammar checker to improve clarity\nand avoid unintended meanings. We hope that these changes address the reviewer’s\nconcerns.\nLine 63. “...role in promoting genomic structural variant...” promoting variation, not\n“variant”.\nResponse: Thank you for your careful reading. The term “variant” has been corrected\nto “variation”.\nLine 64. “traits variation” should be trait variation\nResponse: Thank you for pointing out this issue. We have revised this term.\nLine 73. Please don’t assume the conclusion. TEs may well play a “pivotal role”, but\nthat has not been demonstrated, which is why this and other studies are being done.\nFrame this as a test of a hypothesis, not an effort to prove one.\nResponse: Thank you for your insightful comment. We agree that it is important not\nto overstate conclusions. To address this, we have revised the sentence by changing\nthe term “pivotal” to “potential”, so that it reflects the exploratory nature of\nour study rather than implying a demonstrated outcome. This revision helps clarify\nthat the role of TEVs remains to be fully understood and is the subject of ongoing\ninvestigation.\nLine 77. “ In response to the threat of climate change, species adapt through genetic\nmutations...” I know this might sound pedantic, but this manuscript is being framed in\nan evolutionary context, so it is important to be precise with respect to evolutionary\nprocesses. Plants aren’t responding to a threat, nor are species. Changing conditions\nfavor the spread of new variants or the extinction of species. There is no “response”.\nWe need to stop using inaccurate shorthand like this.\nResponse: Thank you for pointing this out. We agree that the phrasing “in response to\nthe threat of climate change” can suggest active adaptation, which is misleading in an\nevolutionary context. To avoid this interpretation, we have revised the sentence to\nread: “Climate change can drive species adaptation through genetic mutations,\nincreased environmental tolerance via phenotypic plasticity, or migrating to suitable\nlocations”. In addition, we replaced the term “response” with “adaptability” in\nrelevant sentences. For example, “influencing plant populations’ response to rapid\nglobal climate change” was changed to “influencing plant populations’ adaptability to\nrapid global climate change”.\nLine 80. How does one study of pine species address the comparison between plants\nand animals?\nResponse: Thank you for pointing out this issue. We have removed the inappropriate\nquote.\nLine 83. I would be careful of terms such as “climate associated variants”, since most\nof these end up being just so stories.\nResponse: Thank you for your comment. We agree with your concern regarding the\npotential over-interpretation of “climate-associated variants.” To avoid implying a\ndirect causal relationship, we have revised the sentence to read: “Detecting potential\nenvironment-associated genetic variants, including TEVs, is critical for understanding\nthe genetic mechanisms of local adaptation and for predicting plant adaptability to\nclimate change.”\nLine 181. Not surprising, since these would be regions subject to lower levels of\npurifying selection.\nResponse: Thank you for your insightful comment. We agree with your interpretation\nthat the biased accumulation of SNPs around TEVs may result from relaxed purifying\nselection, as discussed in our Discussion section. To avoid redundancy with the\npreceding sentence and to maintain clarity, we have removed this sentence from the\nrevised manuscript.\nLine 186. These numbers don’t add up. 20% are in genic regions, 26% in regions\noutside of the genes but within 2kb, and 76.84% are in intergenic regions (an odd\ndistinction, since 2kb from a gene is, in many genomes, decidedly intergenic). That\nadds up to 112.84%. Also, why round for the 20% and the 26% but not the 76.84%?\nResponse: Thank you for your careful review and thoughtful comment. You are\nabsolutely correct in noting that the reported percentages (genic regions, cis-\nregulatory regions, and intergenic regions) add up to more than 100%. This is because\nTEVs are long structural variants, and some of them span multiple genomic features,\nleading to overlap among categories. For example, a single TEV may overlap both a\ngenic region and an intergenic region. As a result, the percentages are not mutually\nexclusive. To improve clarity, we have revised the corresponding paragraph in the\nmanuscript to clearly describe this point.\nWe also appreciate your note regarding the classification of cis-regulatory regions. We\nagree that regions within 2 kb of a gene are often considered part of the intergenic\nspace. In response, we recalculated the numbers of TEVs in intergenic regions\n(including cis-regulatory regions) to avoid artificial distinctions. The manuscript,\nfigure 2, supplementary figure S5, and supplementary table S4 have been revised\naccordingly.\nIn addition, we have revised all reported percentages to two decimal places to ensure\nclarity and uniformity.\nThe revised paragraph is as follows: “The distribution of TEVs in whole genome indicated that most\nTEVs were located at intergenic regions of the genome (79.21%).\nApproximately 26.11% of TEVs were identified at the cis-regulatory regions (within\n2 kb upstream and downstream of genes), and about 23.16% of TEVs were distributed\nin genic regions, including 3’ untranslated region (UTR), 5’ UTR, introns, and coding\nsequences (CDS) (supplementary table S4 and Fig. S5c). These reported percentages\nare not mutually exclusive, as individual TEVs may span multiple genomic features”.\nLine 187. “In different genomic regions, most TEs were observed at low frequency (≤\n0.5)” different from what? The 112.84% already discussed? Also, why is less that 0.5\n(50%?) low frequency?\nResponse: Thank you for pointing out this ambiguity. We have revised this sentence\nto avoid confusion: “Most TEVs were observed at allele frequency ≤ 0.5 across\nintergenic and genic regions”. In addition, we have already given a detailed response\nto the question about the percentage in the previous answer. To prevent possible\nmisinterpretation, we have chosen not to explicitly categorize allele frequency ≤0.5 as\n“low frequency” in the revised manuscript.\nLine 193. “...functions for genes with a frequency more than 0.8 of TEVs in the CDS”\nhere an throughout, the wording makes understanding difficult. Genes with of\nfrequency of .8 of TEVs in the CDS means, I assume, that of the genes in the pan-\ngenome at syntenic positions, 80% of them have a TE inserted into them. But that is\nnot the same as “genes with a frequency of .8 of TEVs”\nResponse: Thank you for pointing out this ambiguity. We agree that the original\nwording was unclear and could lead to misinterpretation. To improve clarity, we have\nrevised the sentence in the manuscript as follows: “TEVs with a population frequency\nmore than 0.8 in the CDS regions might be fixed within natural populations due to\ntheir beneficial effects on adaptation (Fig. 2e). To assess their potential functional\nimpact, we performed gene ontology (GO) analysis on genes containing TEVs with\nan allele frequency > 0.8 in their CDS regions”.\nLine 196. Define “endemic.” Also, wording. The TEVs are not the most endemic, th\nResponse: Thank you for your helpful comment. We apologize for the imprecise\nwording. In our original sentence, by “endemic TEVs” we meant TEVs that were\nuniquely present in a specific group and absent from the other three. To avoid\nconfusion, we have revised the sentence to improve clarity and remove the ambiguity\nword “endemic”. The revised sentence is: “A total of 39,212 TEVs were shared\namong all four groups, and the South group contained the highest number of group-\nspecific TEVs (8,852), which were not detected in the other groups”.\nLine 194. Likely because most of these are non-functional or pseudogenes, right?\nResponse: Thank you for your insightful comment. We agree that the high frequency\nof TEVs in CDS regions may partly result from relaxed selection in non-functional\ngenes or pseudogenes. However, our GO enrichment analysis revealed that these\ngenes are significantly enriched in defense-related functions, which are often fast-\nevolving and under diversifying selection. This suggests that some of these TEVs may\ncontribute to adaptive processes. Nonetheless, we acknowledge that functional\nvalidation is necessary to confirm their biological relevance.\nLine 199. “unfolded site frequency spectra (SFS)” Define briefly for the non-\nspecialist.\nResponse: Thank you for your comment. We have added a brief explanation for\nclarity: “To estimate the effect of TEVs on the genome, we analyzed the unfolded site\nfrequency spectra (SFS), which show how often newly derived variants occur at\ndifferent frequencies in a population, using TEVs and SNPs across the genome from\nthe four main groups”.\nLine 202. The effect does not seem particularly strong, and it’s not clear that you\nknow that the higher number of rare TE variants is not due to low levels of TE\ninsertions. Further, since few readers will know what the directional coordinates\nmean, it is not clear why there appears to be two classes of SNPs in the (0,1) group in\nthe histogram. It is also not clear if the authors mean SNPs within TEs (presumably\nfrom unique single copy SNPs) or SNPs adjacent to the TEs. The problem\nhere is that the authors are making a clear statement concerning selection with a poorly described experiment.\nResponse: Thank you for your valuable comments. We agree that the observed\ndifference in site frequency spectra (SFS) between TEVs and SNPs is modest. We\nperformed this analysis using both SNPs and TEVs across the entire genome. To\nimprove clarity and avoid overstating conclusions, we have revised both the\nmanuscript and the figure. In the revision, we now write: “To estimate the effect of\nTEVs on the genome, we analyzed the unfolded site frequency spectra (SFS), which\nshow how often newly derived variants occur at different frequencies in a population,\nusing TEVs and SNPs across the genome from the four main groups (Fig. 2g). In each\ngroup, SNP frequencies showed a U-shaped distribution, whereas TEVs\ndemonstrated a lower proportion of fixed variants compared with SNPs”. We have\nalso removed the direct inference that the observed patterns are due to purifying\nselection, to avoid overstating the conclusions based on the current evidence.\nLine 204. This is a strong claim that should be supported by the evidence.\nResponse: Thank you for your comment. We agree that our current evidence, based\non genotype–environment association (GEA) analyses, supports associations between\nTEVs and traits related to local adaptation, but does not provide direct experimental\nvalidation of their causal roles. To avoid overstating our conclusions, we have revised\nthe section title: Potential role of TEVs in local adaptation of Amur grape.\nLine 216. Given that I am not familiar with these models, I am going to assume that\nthese include Bonferoni corrections.\nResponse: Yes, these models include Bonferroni corrections, which are illustrated in\nfigure 3 and supplementary figure S10.\nLine 226. Given the dramatic differences in recombination frequency in different\nparts of plant genomes, I am surprised that a map unit distance wasn’t used instead of\na generic number of base pairs.\nResponse: Thank you for your valuable comment. We acknowledge that\nrecombination rates can vary significantly across different regions of plant genomes.\nIn our study, the 20 kb window upstream and downstream of adaptive candidate\nvariants was selected based on the linkage disequilibrium (LD) decay pattern\nobserved in Amur grape. Considering the observed LD decay, this physical distance\nwas deemed an appropriate proxy to capture genomic regions potentially linked to the\ncandidate variants. We chose this approach because a high-resolution genetic map for\nAmur grape is currently unavailable.\nLine 226. “adaptive candidate variants” should be candidate adaptive variants.\nResponse: Thank you for your valuable comment. We have revised this term in our\nmanuscript.\nLine 230. “The gene TLP3 was” You mean the grape homolog of this gene” (same\nbelow)\nResponse: Thank you for your valuable comment. We have revised the manuscript\naccordingly. Specifically, we now refer to “The Amur grape homolog of TLP3 …” to\nclarify this point.\nLine 234. “in the putative cis-region of the gene TLP3”. It is demonstrably cis. I think\nyou mean putative cis regulatory region.\nResponse: Thank you for your insightful comment. We agree that “putative cis-\nregulatory region” is a more precise term, and we have revised the manuscript\naccordingly.\nLine 247. “The gene HHP4” should be “allelic variation in”\nResponse: Thank you for your insightful comment. We have revised the manuscript\nto specify “allelic variation in the gene HHP4” to more accurately reflect the\nassociation.\nLine 248. “described in response to” should be “has been implicated in...” Also, does\nthe reference actually show this?\nResponse: Thank you for your valuable comment. We agree that “has been implicated\nin” is a more accurate expression than “has been described in response to.” We have\nrevised the sentence accordingly. Regarding the reference, we have carefully\nrechecked it and confirmed that it supports the involvement of the HHP gene family\nin response to temperature and salt stress.\nLine 251. “associated with variant in temperature seasonality” should be variation in.\nHere and throughout, please check grammar.\nResponse: Thank you for your helpful comment. We have corrected “associated with\nvariant in temperature seasonality” to “associated with variation in temperature\nseasonality” and have carefully checked the manuscript for similar grammatical issues\nthroughout.\nLine 252. “putative cis-region” Again, you mean putative cis regulatory region.\nResponse: Thank you for your valuable comment. We have revised this term in our\nmanuscript.\nLine 253. Again, grammar actually matters for meaning. “ illustrate the relationship\nbetween the allele frequency.” You did not mean that in order to illustrate something,\nyou identified this particular SNP, I assume, but that is what the text as written says. I\nsuspect you mean that you are talking about this SNP because it is the best candidate.\nResponse: Thank you for pointing out the ambiguity in our original sentence. To\nclarify, we have revised the sentence: “We selected the candidate adaptive SNP\n(SNP14_26191757, representing a G-to-A substitution), located in the putative cis-\nregulatory region (ca. 1kb upstream of the 5’UTR) of the gene ICE1, as the best\nexample to illustrate the relationship between the allele frequency and the\nenvironmental variable”.\nLine 263. “We estimated the proportion of environmental variance (PVE) explained\nby...” Again, as written, this is incorrect. You are not explaining the proportion of\nenvironmental variance by genetic variation because the environmental variance\noccurs independently of the genetic variance.\nResponse: Thank you for your valuable comment. We agree that environmental\nvariance and genetic variance are independent. We have revised the sentence as:\n“We estimated the proportion of environmental variance (PVE) associated with different\ngenetic variants”.\nLine 267. What is meant by “exclusive” here? That this is variance that would be\nwholly missed if only SNPs were used?\nResponse: Thank you for your comment. By “exclusive contribution”, we refer to the\nproportion of the environmental variance uniquely attributable to TEVs, which would\nnot be captured if only SNPs and indels were considered. We have revised the text to\nclarify this as follows: “Notably, the unique contribution of TEVs to most\nenvironmental variables was larger than that of both SNPs and indels in this study”.\nLine 279. To be clear, does this mean that you picked variants that co-occurred under\nsimilar conditions, or that co-occurred in localized geographic regions? The first\nwould be more interesting I would think because it would get around population\nstructure differences.\nResponse: Thank you for your valuable comment. We mean variants that were\nconsistently identified as associated with the same six uncorrelated environmental\nvariables (bio4, elev, bio15, bio3, bio18, and bio10) by both LFMM and redundancy\nanalysis (RDA). These variants were considered “core adaptive variants” for local\nadaptation. We have clarified this in the revised manuscript.\nLine 283. Here and throughout, just a bit of explanation would be helpful for many\nreaders. Here I assume you are saying that you looked at all the various local\npopulations, the amount of variation that you surmise will be important for adaptation\nin response to two possible climate change (based on the relationship to that variation\nand current conditions), and then assessed the vulnerability of those populations. ]\nResponse: Thank you for your insightful comment. We assessed the genomic\nvulnerability of Amur grape populations by analyzing adaptive genetic variants linked\nto current environments. We then projected how these populations might respond to\nfuture climate scenarios (SSP245 and SSP585) in two periods by estimating the\ngenetic offset, which reflects the mismatch between current adaptation and future\nconditions. We have added a brief explanation in the revised manuscript for clarity:\n“This involved estimating the genetic offset, which measures the mismatch between\ncurrent adaptive genetic variation and the genetic composition needed under future\nclimate conditions, to assess potential population maladaptation.”\nLine 287. “Populations with higher genetic offsets” I assume that means populations\nwith lower frequencies of alleles that would facilitate adaptation?\nResponse: Thank you for your comment. To clarify, populations with higher genetic\noffsets have a larger mismatch between their current genetic composition and that\nrequired for future climate adaptation. This does not necessarily mean they currently\nhave lower frequencies of adaptive alleles, but rather that greater changes in allele\nfrequencies will be necessary to achieve adaptation. We hope this clarifies the\nconcept.\nLine 290. This kind of goes without saying doesn’t it? What would be more\ninformative would be if the differences between vulnerable and more adaptive\npopulations would be exacerbated.\nResponse: Thank you for your insightful comment. We agree that the increase in\ngenetic vulnerability over time is expected and may be somewhat redundant to state.\nTherefore, we have removed this sentence from the revised manuscript to improve\nclarity.\nLine 304. I think you mean “may play and important role”. I am surprised that the\nadapative SNPs essentially made no difference. Does this make sense based on the\ndistribution of the effects of the SNPs? That is, since we know that many of the\nSNPs are linked to potentially adaptive traits, is it those case that these are unlikely to be those that would facilitate\nadaptation to the particular conditions predicted by these models?\nResponse: Thank you for your valuable comment. We agree that “may play an\nimportant role” is a more cautious expression, and we have revised the manuscript\naccordingly. In our study, we focused on evaluating the potential role of TEVs in\nfuture climate adaptation. The baseline set of adaptive SNPs and indels served as a\nreference for comparison, but this does not imply that SNPs and indels have little\nimportance in predicting future adaptation. Our results highlight that TEVs provide\nadditional, complementary adaptive variation that further reduces genetic offset\nbeyond what is captured by SNPs and indels alone. We have revised the manuscript\nas follows: “To explore the role of TEVs in shaping the adaptive potential of Amur\ngrape under future climate change, we estimated genetic offsets using LD-pruned\nadaptive TEVs, SNPs, and indels. We specifically assessed the potential role of TEVs\nby comparing models that included LD-pruned adaptive TEVs alongside SNPs and\nindels (adaptive TEVs set) with those using only LD-pruned adaptive SNPs and indels\n(baseline set). Across all periods and emission scenarios, the baseline set showed\nsignificantly higher genetic offsets (all Ps < 0.003), whereas inclusion of TEVs\nreduced offsets by 7.3–8.2% (Supplementary Table S13). Results from different\ndatasets showed similar spatial and temporal genetic offset patterns (Fig. 4 and\nSupplementary Figs. S13-S15). Populations of the Northeast group showed relatively\nhigh genetic offsets, indicating greater vulnerability to future climate change\n(supplementary Fig. S15). As a control, the average genetic offsets based on random\nnon-adaptive TEVs plus LD-pruned adaptive SNPs and indels (control set) showed no\nsignificant difference from the baseline set (Fig. 4c). These findings suggested that\nadaptive TEVs may play an important role in future climate adaptation of Amur grape\nby providing complementary adaptive genetic variation that reduces genetic offset”.\nLine 326. They do not reveal that TEVs significantly enhance the plants' adaptive\npotential to climate change, they show that your model suggests this. The map, as\nthey say, is not the terrain. Given that climate change as already significantly affected\ngrowing conditions, I wonder if you could make some retrospective predictions.\nResponse: Thank you for pointing this out. You are correct that our results are based\non model predictions rather than direct empirical evidence, and our wording may have\noverstated this point. We have revised the sentence to clarify that our findings indicate\nthat TEVs may enhance the adaptive potential of Amur grape under future climate\nscenarios, as suggested by our modeling results: “Secondly, using a machine learning\nmodel, we predicted the spatiotemporal adaptability of Amur grape populations to\nfuture climate change, with the results suggesting that TEVs may enhance their\nadaptive potential to climate change”.\nRegarding retrospective predictions, we agree this would be an interesting approach to\nfurther validate the model, but such an analysis would require detailed historical\ngenomic datasets as control, which are currently unavailable for Amur grape. We\nacknowledge this as a potential avenue for future research.\nLine 339. “ hotspots of genetic variant in” should be “variation”, not variant.\nResponse: Thank you for pointing this out. We agree with your suggestion and have\nchanged “hotspots of genetic variant” to “hotspots of genetic variation” in the revised\nmanuscript.\nLine 345. However is the beginning of a new sentence here. Also, please check\nthroughout for the distinction between variant and variation.\nResponse: Thank you for your valuable comment. We have corrected the sentence.\nAdditionally, we carefully checked the manuscript and ensured consistent and\nappropriate use of “variant” and “variation” throughout the text.\nLine 353. Is there evidence for recent mobilization of TEs in these populations? One\nthing of particular interest would be the way that the TEs were profiled. Most\nimportantly, if there were multiple copies of identical TEs, were they all counted?\nI assume so, because you are looking for polymorphic insertions, but I am concerned\nabout whether or not they were missed. Given the centrality of TEs in this analysis, I\nam also surprised that the authors did not look carefully at the population structure of\nthe TEs themselves.\nResponse: Thank you for raising these important points. To address the possibility of\nrecent mobilization, we estimated TE insertion times based on RepeatMasker outputs.\nOur results indicated a broad peak of TE activity around ~5 Mya, followed by a sharp\ndecline in more recent insertions, suggesting that most TE insertions are relatively\nancient rather than recently active (Supplementary Fig. S2b). Regarding duplicate\ncopies, each TEV was indeed counted individually so that no polymorphic insertions\nwere missed. Finally, we performed a population structure analysis based on TEVs,\nand the results were highly concordant with those obtained from SNPs (Fig. S4),\nconfirming that the TE dataset reliably reflects the underlying population history.\nLine 363. I would caution the authors to use correct terminology when discussion\nevolution. Species don’t adapt to anything “in order to” do anything. Evolution is\nsimply what happens. Species don’t have agency.\nResponse: Thank you for your valuable comment. We agree that the original wording\nis inappropriate. To address this, we have removed the sentence “To avoid extinction,\nspecies employ adaptive strategies in response to climate change, e.g. undergoing\ngenetic evolution or moving to new suitable habitats” from the manuscript.\nLine 397. These are all great examples, but given the large number of candidates that\nare just above the significance plot in 3d, as is ICE, how is the reader to know that\nthere are so many genes involved in various stress responses, the authors would\ninevitably find at least some of them? I do understand that much of this is “under the\nhood” of the various algorithms, but it might be helpful for the authors to carefully\nspell out why these candidates are unlikely to have been identified by chance?\nResponse: Thank you for your insightful comment. To address the concern about the\npossibility of false positives among candidate genes, we would like to clarify two\npoints. First, we applied a stringent statistical significance threshold (False Discovery\nRate, FDR < 0.01), which is more rigorous than the commonly used FDR < 0.05 in\nmany other studies, thereby minimizing the chance of false positives in our analyses.\nSecond, many of the identified genes have well-documented functional roles in stress\nresponses, supported by previous experimental evidence. Specifically, some adaptive\ngenes we identified, such as BAM3, ICE1, and CBF, have been functionally validated\nin Amur grape, providing strong cross-study support for our findings.\nLine 419. These are excellent points.\nResponse: Thank you for your kind words. We appreciate your recognition of our\nfindings on the role of TEVs in adaptation.\nLine 486. In the text of the M&M, this is everything about how TEs were identified:\nTransposable element families were identified by RepeatModeler with a pan-Vitis\ndatabase of repeat families. The repeat elements were\nclassified by using deepTE with the Plant model. Finally, we annotated all repetitive\nsequences using RepeatMasker (v4.1.2). TEs are notoriously difficult to accurately\nidentify and TEs are at the core of this analysis. I will return to this comment later, but\nthe process if far more involved that simply using a few out of the box tool sets.\nResponse: Thank you for your insightful comment regarding the identification of\nTEs. We acknowledge that TE annotation is a challenging and complex process. In\nour study, we employed a comprehensive and widely used pipeline combining\nRepeatModeler, deepTE classification, and RepeatMasker annotation, which together\nprovide high accuracy and reliability. We believe that the quality and depth of our TE\nidentification are sufficient to support the analyses and conclusions presented in this\nmanuscript.\nFigure 2, line 1009. The wording here is unclear. “based on genes with a frequency\nmore than 80% of TEVs in the CDS region” Does that mean genes that, on average,\namong all the accessions, 80% of them have a TE insertion into the CDS?\nPresumably, these are pseudogenes, TEs that look like genes, or just bad calls by\nannotation programs, which are often inaccurate. I would assume that these “genes”\nalso have much higher rates of non-synonymous point mutations as well. Do they?\nResponse: Thank you for pointing this out. We realize that our original wording in the\nfigure legend was ambiguous. To clarify, what we mean is: we performed gene\nontology (GO) analysis on genes containing TEVs with an allele frequency > 0.8 in\ntheir CDS regions. It does not mean that 80% of the CDS is occupied by TE\nsequences, nor that 80% of the genes have such insertions. We have revised the figure\nlegend to read: “Gene ontology (GO) analysis of genes containing TEVs (allele\nfrequency > 0.8) in their CDS regions”.\nReviewer #3 (Remarks to the Author):\nThe authors present a pan-genome analysis of Vitis amurensis, offering novel insight\ninto the role of transposable elements in environmental adaptation. While the study\nhas clear potential, several key issues need to be addressed before it can be considered\nfor publication:\n1. The population structure analysis indicates that the South clade possesses a distinct\ngenetic background, as further supported by syntenic and PCA analyses. These results\nsuggest that the South clade may represent a separate evolutionary lineage, potentially\nnot belonging to V. amurensis. The authors should provide additional taxonomic\nevidence (e.g., morphological traits) or molecular validation to clarify the\nclassification of these accessions.\nResponse: Thank you for this important comment. We have examined the\nmorphological traits of accessions from all four V. amurensis clades, including the\nSouth clade, and have added representative images in Supplementary Figure S3. No\nobvious morphological differences were observed among the four clades based on the\ntaxonomic characteristics of V. amurensis as described in the Flora of China.\nTherefore, based on morphological evidence, the South clade is morphologically\nconsistent with V. amurensis.\n2. Although the study identifies genes potentially associated with environmental\nadaptation, the analysis is based solely on LFMM modeling using climatic variables.\nTo comprehensively identify genes involved in cold/drought stress responses,\nphenotypic data across all accessions should be incorporated. A phenotype-genotype\nassociation analysis would offer more direct and biologically relevant evidence for\nadaptive traits.\nResponse: Thank you for this valuable suggestion. We fully agree that incorporating\nphenotype–genotype association analyses using cold- and drought-tolerance\nphenotypic data across all accessions would provide more direct and biologically\nrelevant evidence for adaptive traits. However, such comprehensive phenotypic\ndatasets are currently unavailable for the full set of accessions included in this study\nand obtaining them would require extensive multi-year common garden and\ncontrolled-environment experiments. In this work, we therefore focused on\ngenotype–environment associations as a well-established approach to identify\nputative adaptive loci in the absence of large-scale phenotypic measurements. We\nagree that integrating phenotype–genotype analyses in future research will be an\nimportant next step to further validate the adaptive roles of the identified genes.\n3. While the LFMM analysis identified numbers of significant genetic variants, the\nauthor only focused on a subset of genes related to cold and drought resistance. Genes\nlinked to other significant loci should also be listed and considered. Additionally, the\nwild habitats of the accessions should be clearly documented, along with the\ncorresponding environmental factors of those habitats, to ensure the accuracy and\nrelevance of the analysis. It is also important to verify that the sampled accessions\noriginate from natural populations rather than cultivated sources; such as individuals\nmaintained in germplasm resource gardens should be traceable to their native habitat.\nResponse: We appreciate your valuable suggestions. Following your\nrecommendation, we have now listed all genes linked to significant loci, not only\nthose related to cold and drought resistance, in Supplementary Tables S7–S12. We\nalso confirm that all accessions analyzed in this study were collected from wild\npopulations in their natural habitats. Detailed information on the wild habitats of these\naccessions, along with the corresponding environmental factors, is now provided in\nSupplementary Table S6.\n4. The author state that “Transposable elements mediate adaptation to environment\nchange” and identify several genes associated with cold and drought resistance.\nHowever, the manuscript lacks a detailed genetic or regulatory model explaining how\nthese genes contribute to environmental adaption. The authors should provide\nmechanistic insights or supporting evidence to support this conclusion.\nResponse: We appreciate your valuable suggestions. In the revised manuscript, we\nfurther explored the potential functional relevance of environment-associated variants.\nWe examined the genomic context of candidate loci using the PlantCARE database to\npredict cis-regulatory elements, including promoter motifs and transcription factor\nbinding sites. This allowed us to evaluate whether sequence variants might alter\nregulatory motifs and thereby influence gene expression. For instance, we found a TE\ninsertion upstream of TLP3 that overlaps a predicted transcription factor binding site,\nas well as a SNP upstream of ICE1 located within a putative regulatory motif. These\nfindings suggest that environment-associated variants could contribute to adaptation\nby modulating regulatory networks (see revised Results and Supplementary Fig. S9).\nOur findings provide a valuable set of testable hypotheses and candidate loci for\nfuture functional studies.\nIn addition, we tone down the title to avoid overinterpretation, we have revised the\ntitle to: Transposable Elements Contribute to Climate Change Adaptation in Wild\nGrapes,” which more accurately reflects the content of our analyses.\n** See Nature Portfolio’s author and referees' website at www.nature.com/authors for\ninformation about policies, services and author benefits."}, {"round": 2, "reviewer_comments": {"reviewer_1": "I appreciate the detailed answer to my comments from the authors.\nI only have a few minor comments remaining:\nL151-156: I am still not sure whether the pangenome graph is well introduced or well placed. The first section of the results\ndescribes the genome assemblies and suddenly switches to “To better detect structural variants” while no population\ngenetic results were presented yet. Also, the title of the section is “Graph-based pangenome reference for Amur grape\n(Vampan_V1.0)” while it is only briefly presented in the last two sentences. Same for the Figure 1, it is entitled “Graph-based\npangenome reference (Vampan_V1.0) for Amur grape” while none of the results presented are related to the pangenome\ngraph. Please rename the first results section and the Figure 1 so they do not refer to the pangenome. For better logical flow,\nplease move the 2 sentences about the pangenome L151-156 into the next section when you use the structural variant\nanalysis to complement your linear genome SNP analysis right before “The utilization of a pangenome for Amur grape”\nL174.\nL531: How the bam files were filtered?\nL533: Please give more details about the variant call, which GATK function did you use? How did you merge the results\nfrom all the samples? How did you extract SNP and indel from the variant call?", "reviewer_2": "Thanks to the authors for their thoughtful responses to my previous comments.\nI think my ongoing concerns have much more to do with framing and assertions of what has been shown to be the case than\nthe quality of the data. I don’t know if this is a language think or a scientific culture thing, but by my lights you can’t say\nyou’ve demonstrated a conclusion just because you provide evidence supporting that conclusion. For instance, the authors\nstate that: “We evaluate the role of TEVs in facilitating local adaptation of Amur grape to current and future climates, with\nimplications for its survival in a rapidly changing environment.” But they did not really evaluate the role of TEVs in future\nclimates, they provided evidence that they may play a role in facilitating adaptation to future changes. They did not detect\n246 adaptive variants; they detected 246 putative adaptive variants.\n“Our results demonstrate that adaptive TEVs significantly reduce genetic offsets of populations with 7.3% to 8.2% reduction\namplitude, indicating these variants play an important role in enhancing local adaptation to future climate change.” The\nlanguage here is difficult to follow. The putative, adaptive TEVs reduce genetic offsets of populations that that ave 7.3% to\n8.2% amplitude, or they reduce the genetic offsets by 7.3 to 8.2%. An X% reduction amplitude is not a meaningful phrase.\nLine 124. Might be a good place to carefully define “genetic offset” here, since it’s important for your overall argument.\nLine 201. As I understand it, “fixed” means 100%.\nLine 206. These are also classes of genes that are often present in relatively large numbers, many members of which may\nnot be functional, subject to relaxed selection and colocalized with TE insertions because the insertions don’t have an effect\non phenotype. Also, in figure 2g, the results are somewhat underwhelming. The actual total number of genes is very small,\nand the largest class has a very low p value. Honestly, if I am reading the figure correctly, these dots (whose color I cannot\nsee) represent less than seven genes; how could you end up with anything like a significant result from so few genes? Am I\nmissing something? And why are you focusing on insertions in the CDS regions? Because knockouts would be more likely\nto contribute to adaptation?\nFigure 1c. What are the colors here? What do they correspond to? I get that they correspond to the various haplotypes but\nplease make it obvious for the non-specialist.\nLine 212. Reference for use of this? I’m sure it’s a common measure, but it would be nice to see an example of how this was\nused in a similar situation.\nLine 235. As below, please refer to these as “potentially adaptive” variants.\nLine 279. Here and throughout, consider avoiding this kind of passive wording. “Were considered” can mean that someone\nelse in the past thought this, or that the authors did, or that they do. Here I assume that the authors are hypothesizing that\nthese are good candidates based on linkage and their presumed function.\nLine 286. Both together or individually?\nLine 288. Again, the language is a real problem here and must be addressed. The TEVs didn’t “account” for variance in the\nprecipitation of “warmest quarter”; I’m not even certain what this means. Genetic variation associated with the population in\nthe warmest quarter?\nLine 292. Again, the wording suggests that you know that these variants are responsible for “adaptation”. You don’t know\nthat. All you have is linkage and a hypothesis.\nLine 306. This is pure supposition. The authors, indeed, no one knows what variation will prove to be adaptive. I would\naccept a far more watered down version of this that spelled out very carefully the logic, which, I assume, is that if we accept\nthat the variation present now is associated with and required for current adaptation, this might tell us about what kinds of\nvariation are currently available for future adaptation.\nLine 349. I’m afraid I am unconvince that the authors have shown “the critical role of TEVs in adaptive evolution and suggest\nthat comprehensive population genomics research is essential for developing effective breeding and conservation strategies\nfor crops and their wild relatives in the face of climate change.” They have provided some interesting evidence for this, but\nthat’s it.\nLine 359. They may have also been purged from regions with higher levels of conservation.\nLine 418. These are all interesting possibilities, but so far, I am concerned that these are simply “just so” stories.", "reviewer_3": "The authors have partially addressed my concerns. However, they have evaded several key issues, which continues to\nundermine the ability to effectively guarantee the article’s accuracy and scientific rigor. Given this incomplete resolution, I\nstill have the following unresolved concerns and questions that require further clarification:\n1. we have located the images in Supplementary Figure S3. While the figures are accessible, the authors should provide\nstandardized, professional images for different groups, as the currently provided materials appear to have been captured in a\nrelatively casual and unstandardized manner. Such non-professional imagery may introduce ambiguity and ultimately affect\nthe reliability of the visual evidence presented.\n2. Upon examining the phenotypes of these germplasms, we identified a substantial number of identical environmental\nphenotypes. This observation strongly suggests that either the phenotypic data separation is unreliable or the samples were\ncollected within an overly restricted geographical range. Compounding this issue, the bio5 (Max Temperature of the\nWarmest Month) values range from 23℃ to 31℃, which is inconsistent with prevailing climatic conditions. Therefore, the\nauthors must first provide a detailed explanation for this discrepancy and then offer empirical evidence to verify the data’s\naccuracy. Furthermore, despite the authors’ previous explanation regarding environmental factors, these factors alone\ncannot adequately reflect the true adaptability of these germplasm resources. To ensure the validity of the analysis,\nintrinsic grape phenotypes that are directly indicative of environmental adaptability should therefore be supplemented.\n3. While the authors have presented some preliminary analyses of transposons, the relationship between transposons and\nresistance or environmental adaptation remains inadequately defined. As this constitutes the core proposition of the\nmanuscript, further in-depth analysis and supplementary data are imperative to strengthen the argument. Moreover, the\nmanuscript lacks a detailed genetic or regulatory model that elucidates how the identified genes mediate environmental\nadaptation. Building on this gap, the authors are therefore urged to provide mechanistic insights or additional corroborative\nevidence to substantiate this key conclusion and enhance the overall persuasiveness of the study."}, "author_response": "Response to Review Comments\nPart I: Summary of responses to the major questions:\nWe sincerely appreciate the interest shown by the editor and the three reviewers. Before\npresenting a detailed point-by-point response in Part II, we first address three major\nissues collectively:\nFirst, enhanced result organization and methodological clarity: We reorganized\nthe Results section to introduce the pangenome graph in a more logical order, aligning\nits presentation with subsequent analyses. The Methods section was expanded with\ndetailed descriptions of BAM file filtering and GATK-based variant-calling\nprocedures to enhance analytical reproducibility.\nSecond, clarified TEVs’ potential role using cautious language: We revised the\nmanuscript to use cautious phrasing, replacing assertive terms (e.g., “adaptive\nvariants”) with cautious phrasing (“putative adaptive variants”) and clarifying TEVs’\npotential role in adaptation to future climates.\nThird, improvements in figure quality and data validation: We updated\nSupplementary Figure S3 with standardized, high-resolution leaf images and scale\nbars to improve visual clarity and comparability across population groups. We also\nclarified our sampling design, environmental data resolution, and the rationale for\npopulation-level analyses to address concerns regarding data reliability and accuracy.\nIn conclusion, we have provided detailed explanations in response to the reviewers'\ncomments. All updates in the manuscript are highlighted with tracked changes.\nAdditionally, we have revised the manuscript and supplemental figures.\nPart II: Point by point response to specific comments:\n(The reviewer’s comments are shown in black, and the authors’ responses are\ndisplayed in blue.)\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nI appreciate the detailed answer to my comments from the authors.\nI only have a few minor comments remaining:\nL151-156: I am still not sure whether the pangenome graph is well introduced or well\nplaced. The first section of the results describes the genome assemblies and suddenly\nswitches to “To better detect structural variants” while no population genetic results\nwere presented yet. Also, the title of the section is “Graph-based pangenome reference\nfor Amur grape (Vampan_V1.0)” while it is only briefly presented in the last two\nsentences. Same for the Figure 1, it is entitled “Graph-based pangenome reference\n(Vampan_V1.0) for Amur grape” while none of the results presented are related to the\npangenome graph. Please rename the first results section and the Figure 1 so they do\nnot refer to the pangenome. For better logical flow, please move the 2 sentences about\nthe pangenome L151-156 into the next section when you use the structural variant\nanalysis to complement your linear genome SNP analysis right before “The utilization\nof a pangenome for Amur grape” L174.\nResponse: Thank you for pointing out this issue of logical flow and section labeling.\nFollowing your suggestion, we renamed the first Results section to “Haplotype-\nresolved genome assemblies and TE landscape of Amur grape” and retitled Figure 1\nas “Morphology, genome assemblies, and population structure of Amur grape”.\nWe also moved the two sentences describing the pangenome graph (L151–156 in the\noriginal version) to the beginning of the next section (“The utilization of a pangenome\nfor Amur grape…”).\nL531: How the bam files were filtered?\nResponse: Thank you for this question. To clarify, BAM files were filtered to retain\nonly high-quality primary alignments before downstream processing. Specifically, we\nused the following command: samtools view -bS -F 0x100 -q 10, which removes\nsecondary alignments and retains reads with a mapping quality greater than 10. The\nresulting BAM files were then sorted using samtools, and PCR duplicates were\nidentified and marked with Picard MarkDuplicates.\nThe revised sentence now reads: “BAM files were filtered using samtools view -bS -F\n0x100 -q 10 to remove secondary alignments and low-quality reads. The filtered\nBAM files were then sorted with SAMtools (v1.13), and PCR duplicates were\nidentified and marked using Picard (v2.23.0) MarkDuplicates\n(http://broadinstitute.github.io/picard/).”\nL533: Please give more details about the variant call, which GATK function did you\nuse? How did you merge the results from all the samples? How did you extract SNP\nand indel from the variant call?\nResponse: Thank you for your valuable comments. The revised sentence now reads:\n“The SNP and indel calling was performed using GATK (v4.2.6.0) functions of\nHaplotypeCaller, GenomicsDBImport, GenotypeGVCFs, SelectVariants and\nVariantFiltration. Specifically, variants were called per sample using HaplotypeCaller\nin GVCF mode to generate individual GVCF files. All GVCF files were combined\nwith GenomicsDBImport to build a genomic database, and joint genotyping across all\nsamples was performed using GenotypeGVCFs to produce cohort-level VCF files for\neach chromosome. The SNPs and indels were extracted from the joint VCF files using\nSelectVariants with the options -select-type SNP and -select-type INDEL,\nrespectively. These variant sets were then filtered using VariantFiltration. SNPs were\nfiltered based on “QD<2.0 || FS >60.0 || MQRankSum <- 12.5 || ReadPosRankSum <-\n8.0 || SOR >3.0 || MQ<40.0”, while Indels were filtered using “QD<2.0 || FS >200.0 ||\nMQRankSum <- 12.5 || ReadPosRankSum <- 8.0 || SOR >10.0”. The resulting\nvariants were filtered by VCFtools (v0.1.15) using parameters: -max-missing 0.8”.\nReviewer #2 (Remarks to the Author):\nThanks to the authors for their thoughtful responses to my previous comments.\nI think my ongoing concerns have much more to do with framing and assertions of\nwhat has been shown to be the case than the quality of the data. I don’t know if this is\na language think or a scientific culture thing, but by my lights you can’t say\nyou’ve demonstrated a conclusion just because you provide evidence supporting that\nconclusion. For instance, the authors state that: “We evaluate the role of TEVs in\nfacilitating local adaptation of Amur grape to current and future climates, with\nimplications for its survival in a rapidly changing environment.” But they did not\nreally evaluate the role of TEVs in future climates, they provided evidence that they\nmay play a role in facilitating adaptation to future changes. They did not detect 246\nadaptive variants; they detected 246 putative adaptive variants.\nResponse: We sincerely thank you for this thoughtful and important comment. In the\nrevised manuscript, we have carefully refined the language throughout to more\naccurately reflect the strength and scope of our evidence. For example, we now state\nthat we “evaluated the potential role of TEVs in facilitating local adaptation of V.\namurensis to current and projected future climates” and that “TEVs may play a role in\nfacilitating adaptation of populations to future climate change”. In addition, we now\nrefer to the identified loci as “putative adaptive variants” rather than “adaptive\nvariants.” Similar revisions have been made across the Abstract, Introduction, Results,\nand Discussion to ensure cautious and appropriate framing.\n“Our results demonstrate that adaptive TEVs significantly reduce genetic offsets of\npopulations with 7.3% to 8.2% reduction amplitude, indicating these variants play an\nimportant role in enhancing local adaptation to future climate change.” The language\nhere is difficult to follow. The putative, adaptive TEVs reduce genetic offsets of\npopulations that that ave 7.3% to 8.2% amplitude, or they reduce the genetic offsets\nby 7.3 to 8.2%. An X% reduction amplitude is not a meaningful phrase.\nResponse: Thank you for pointing out this ambiguity. We have revised the sentence to\nclarify its meaning and improve readability. The revised version now reads: “Our\nresults indicate that putative adaptive TEVs significantly reduce population genetic\noffsets by 7.3% to 8.2%, suggesting that these variants may contribute to enhancing\nlocal adaptation to future climate change.”\nLine 124. Might be a good place to carefully define “genetic offset” here, since it’s\nimportant for your overall argument.\nResponse: Thank you for this helpful suggestion. We agree that defining “genetic\noffset” will improve clarity for readers unfamiliar with this concept. We have\ntherefore revised the text to include a brief definition. The revised sentence now\nreads: “quantify and estimate the vulnerability of populations by assessing genetic\noffset (i.e., the predicted genomic mismatch between current and future climate\nscenarios)”.\nLine 201. As I understand it, “fixed” means 100%.\nResponse: Thank you for this clarification. Our intention was to indicate that these\nTEVs show very high frequencies within populations. We have revised the sentence\nto: “TEVs with a population frequency great than 0.8 in the CDS regions are likely\nunder positive selection in natural populations”.\nLine 206. These are also classes of genes that are often present in relatively large\nnumbers, many members of which may not be functional, subject to relaxed selection\nand colocalized with TE insertions because the insertions don’t have an effect on\nphenotype. Also, in figure 2g, the results are somewhat underwhelming. The actual\ntotal number of genes is very small, and the largest class has a very low p value.\nHonestly, if I am reading the figure correctly, these dots (whose color I cannot\nsee) represent less than seven genes; how could you end up with anything like a significant\nresult from so few genes? Am I missing something? And why are you focusing on\ninsertions in the CDS regions? Because knockouts would be more likely to contribute\nto adaptation?\nResponse: We appreciate your insightful comment. Regarding Figure 2f, the GO\nenrichment analysis was performed on a total of 201 genes annotated in GO\nbiological processes. Although the number of genes in each category is relatively\nsmall, the enrichment results remain statistically significant. For example, the largest\nclass, defense response, showed significant at P = 0.0078.\nWe focused on TEVs located in CDS regions because these regions are highly\nconserved, and TEVs (including insertions or deletions) within them can alter amino\nacid coding and potentially affect protein function, which may have more direct\nimplications for environmental adaptation. While TEVs in non-coding regions may\nalso be functionally relevant, our focus on CDS regions reflects their higher\nlikelihood of producing measurable functional effects.\nWe agree that these genes classes may be subject to relaxed selection or may\ncolocalize with TE insertions without having direct phenotypic effects. To address\nthis, we have revised the text to adopt more cautious and precise language, avoiding\nany implication of causality.\nThe revised sentence now reads: “TEVs with a population frequency greater than 0.8\nin the CDS regions are likely under positive selection in natural populations (Fig. 2e).\nTo explore their potential functional impact, we performed gene ontology (GO)\nenrichment analysis on genes containing TEVs with an allele frequency > 0.8 in CDS\nregions. A total of 201 genes were annotated in GO biological processes and used for\nenrichment analysis. These genes showed significant enrichment in categories related\nto defense response (P = 0.0078), defense response to other organism (P = 1.99e-4),\nand regulation of innate immune response (P = 0.0081) (Fig. 2f). We acknowledge\nthat these gene categories may include members under relaxed selection or\ncolocalized with TEVs without direct functional consequences”.\nFigure 1c. What are the colors here? What do they correspond to? I get that they\ncorrespond to the various haplotypes but please make it obvious for the non-specialist.\nResponse: Thank you for your suggestions. We revised the figure legend to:\n“Maximum Likelihood phylogenetic relationship of 330 accessions of Amur grape\nwith whole-genome LD-pruned SNPs. Four major geographic groups were identified:\nNortheast (red), Center (orange), West (blue) and South (green).”\nLine 212. Reference for use of this? I’m sure it’s a common measure, but it would be\nnice to see an example of how this was used in a similar situation.\nResponse: Thank you for this helpful suggestion. We have added relevant references\nto illustrate the application of the unfolded site frequency spectrum (SFS) in similar\npopulation genomic analyses of grapevine (Zhou et al., Nature Plant 2019; Xiao et al.,\nPNAS 2023). These references provide examples of similar comparable analytical\napproaches used in related genomic contexts.\nLine 235. As below, please refer to these as “potentially adaptive” variants.\nResponse: Thank you for this constructive comment. We have revised the manuscript\naccordingly and now refer to these as “putative adaptive variants” to more accurately\ndescribe their inferred adaptive potential.\nLine 279. Here and throughout, consider avoiding this kind of passive wording. “Were\nconsidered” can mean that someone else in the past thought this, or that the authors\ndid, or that they do. Here I assume that the authors are hypothesizing that these are\ngood candidates based on linkage and their presumed function.\nResponse: Thank you for this valuable suggestion. We agree that “were considered”\nmay imply ambiguity regarding who made the judgment. In the revised manuscript,\nwe have replaced it with more precise expressions such as “were defined as,” or\n“were identified as,” where appropriate. We have revised the sentence to use a more\ndirect and active construction: “In addition, thirteen genes were identified as\ncandidates for elevation-related adaptation, associated with UV-B/photo-oxidative\ndamage protection and freezing tolerance”.\nLine 286. Both together or individually?\nResponse: Thank you for pointing out this ambiguity. We have clarified the sentence\nto specify the comparison. The revised version now reads: “Notably, the unique\ncontribution of TEVs to most environmental variables was larger than that of SNPs or\nindels when compared individually.”\nLine 288. Again, the language is a real problem here and must be addressed. The\nTEVs didn’t “account” for variance in the precipitation of “warmest quarter”; I’m not\neven certain what this means. Genetic variation associated with the population in the\nwarmest quarter?\nResponse: Thank you for this valuable comment and for catching the imprecise\nphrasing. What we intended to express is that TEVs explained a portion of the genetic\nvariation associated with the environmental variable (bio18, precipitation of the\nwarmest quarter). We have revised the sentence accordingly for clarity.\nThe revised version now reads: “TEVs uniquely explained 11.12% of the genetic\nvariation associated with precipitation of the warmest quarter, a much larger\nproportion than that explained by SNPs or indels.”\nLine 292. Again, the wording suggests that you know that these variants are\nresponsible for “adaptation”. You don’t know that. All you have is linkage and a\nhypothesis.\nResponse: Thank you for this important clarification. To avoid overstating our\nconclusions, we have revised the text to use more cautious wording.\nThe revised sentences now read: “TEVs were still identified as associated with these\nenvironmental variables (Supplementary Fig. S11). These results suggest that TEVs\nmay contribute to ecological adaptation in Amur grape, representing a previously\noverlooked component of adaptive genetic diversity.”\nLine 306. This is pure supposition. The authors, indeed, no one knows what variation\nwill prove to be adaptive. I would accept a far more watered down version of this that\nspelled out very carefully the logic, which, I assume, is that if we accept that the\nvariation present now is associated with and required for current adaptation, this\nmight tell us about what kinds of variation are currently available for future\nadaptation.\nResponse: Thank you for this valuable comment. We acknowledge the inherent\ndifficulty in determining which variants will truly be adaptive in the future. In our\nanalysis, we used currently identified, potentially adaptive loci to estimate the\nmagnitude of allele frequency change (genetic offset) required for populations to\nremain adapted under projected future climates. This provides an indirect but\ninformative assessment of potential adaptive capacity. In the revised version, we have\nalso removed the sentence “which measures the mismatch between current adaptive\ngenetic variation and the genetic composition needed under future climate conditions,\nto assess potential population maladaptation”.\nThe revised text now reads: “This involved estimating the genetic offset, representing\nthe expected degree of allele frequency change required for populations to remain\nadapted under future climate scenarios.”\nLine 349. I’m afraid I am unconvince that the authors have shown “the critical role of\nTEVs in adaptive evolution and suggest that comprehensive population genomics\nresearch is essential for developing effective breeding and conservation strategies for\ncrops and their wild relatives in the face of climate change.” They have provided\nsome interesting evidence for this, but that’s it.\nResponse: We agree that our conclusions should more cautiously reflect the strength\nof the evidence provided by our analyses. Accordingly, we have revised the\nmanuscript to adopt more precise and appropriately cautious wording. The revised\nversion now reads: “Overall, we provide new evidence supporting the potential role of\nTEVs in adaptive evolution and highlight the importance of integrating population\ngenomics approaches in future studies on the breeding and conservation of crops and\ntheir wild relatives under climate change.”\nLine 359. They may have also been purged from regions with higher levels of\nconservation.\nResponse: We appreciate this insightful comment. We agree that the observed SNP\naccumulation around TEVs could also reflect the purging of variants from more\nconserved genomic regions. We have revised the sentence to acknowledge this\nalternative explanation. The revised version now reads: “Reduced recombination rates\naround TEVs may lead to relaxed purifying selection, resulting in the biased\naccumulation of SNPs in these regions. Alternatively, the observed pattern may result\nfrom variant purging in more conserved genomic regions.”\nLine 418. These are all interesting possibilities, but so far, I am concerned that these\nare simply “just so” stories.\nResponse: Thank you for this comment. We agree that the functional roles of the\ncandidate genes we identified remain putative and should be interpreted cautiously.\nTo address this, we have revised the manuscript to avoid definitive statements and\nnow describe these genes as “candidate genes potentially associated with\nenvironmental adaptation” based on their known homologs and prior studies.\nReviewer #3 (Remarks to the Author):\nThe authors have partially addressed my concerns. However, they have evaded\nseveral key issues, which continues to undermine the ability to effectively guarantee\nthe article’s accuracy and scientific rigor. Given this incomplete resolution, I still have\nthe following unresolved concerns and questions that require further clarification:\n1. we have located the images in Supplementary Figure S3. While the figures are\naccessible, the authors should provide standardized, professional images for different\ngroups, as the currently provided materials appear to have been captured in a\nrelatively casual and unstandardized manner. Such non-professional imagery may\nintroduce ambiguity and ultimately affect the reliability of the visual evidence\npresented.\nResponse: Thank you for this helpful suggestion. We have replaced the previous\nimages in Supplementary Figure S3 with standardized photographs of representative\nleaves from different population groups. Each image was captured under consistent\nbackground conditions, with a scale bar added to indicate size. These revisions ensure\na more professional and comparable presentation of morphological characteristics\nacross groups, thereby improving the clarity and reliability of the visual evidence.\nThank you once again for your constructive feedback.\n2. Upon examining the phenotypes of these germplasms, we identified a substantial\nnumber of identical environmental phenotypes. This observation strongly suggests\nthat either the phenotypic data separation is unreliable or the samples were\ncollected within an overly restricted geographical range. Compounding this issue, the\nbio5 (Max Temperature of the Warmest Month) values range from 23℃ to 31℃, which is\ninconsistent with prevailing climatic conditions. Therefore, the authors must first\nprovide a detailed explanation for this discrepancy and then offer empirical evidence\nto verify the data’s accuracy. Furthermore, despite the authors’ previous explanation\nregarding environmental factors, these factors alone cannot adequately reflect the true\nadaptability of these germplasm resources. To ensure the validity of the analysis,\nintrinsic grape phenotypes that are directly indicative of environmental adaptability\nshould therefore be supplemented.\nResponse: Thank you for this important comment. We would like to clarify several\npoints regarding our sampling strategy, environmental data, and analytical rationale.\nOur sampling strategy was designed to comprehensively cover the full geographical\nand climatic range of the Amur grape, encompassing 330 individuals from 31 natural\npopulations spanning over 20 degrees in latitude (Fig. 1f). Within each population,\nindividuals were sampled at a minimum distance of 100 meters to minimize the\ncollection of clones or closely related individuals.\nAll environmental variables were obtained from WorldClim (www.worldclim.org) at a\nresolution of 2.5 arcmin (~5 km). Because this dataset assigns identical environmental\nvalues to sampling locations within a single grid cell, individuals from the same\npopulation often share identical environmental data. This is a known characteristic of\nthe dataset and has been widely adopted in population genomic studies using\ngenotype–environment association analyses (e.g., Sang et al., Nat. Commun., 2022;\nZhang et al., Mol. Plant, 2024). Since our analyses are conducted at the population\nlevel, rather than at the level of individual microenvironments, this does not\ncompromise the validity of our results.\nRegarding the reviewer’s concern about the bio5 (Max Temperature of the Warmest\nMonth), we have carefully verified these values and confirmed their accuracy in\nWorldClim. The observed range reflects our inclusion of high-latitude and high-\naltitude forest populations (Fig 1f and supplementary tables S6), where maximum\nsummer temperatures are naturally moderated relative to surrounding lowland areas.\nWe agree with the reviewer that experimental validation using intrinsic plant\nphenotypes would provide the most direct evidence of environmental adaptation. In\nthis study, we employed a genotype–environment association approach based on the\nrationale that the survival of wild populations in specific ecological niches reflects\nunderlying local adaptation. By correlating genomic variation with environmental\nvariables, we identified candidate loci potentially involved in adaptation. The\neffectiveness of this strategy is supported by our identification of several genes with\nknown roles in abiotic stress tolerance (e.g., ICE1 and BAM3, which were confirmed\nas confirmed\nas key regulators of freezing tolerance in Amur grape), indicating that our approach\ncaptures biologically relevant signals. While phenotypic data would strengthen our\nconclusions, generating such data for 330 accessions would require extensive multi-\nyear experiments beyond the current scope. This study provides a comprehensive\nadaptive landscape for the Amur grape, offering a foundation for future functional\nvalidation and breeding efforts. We believe your constructive suggestion represents a\nvaluable direction for subsequent research.\nReference:\nSang, Y. P. et al. Genomic insights into local adaptation and future climate-induced\nvulnerability of a keystone forest tree in East Asia. Nat Commun. 13, 6541 (2022).\nZhang, F. et al. Evolutionary genomics of climatic adaptation and resilience to climate\nchange in alfalfa. Mol Plant 17, 867-883 (2024).\n3. While the authors have presented some preliminary analyses of transposons, the\nrelationship between transposons and resistance or environmental adaptation remains\ninadequately defined. As this constitutes the core proposition of the manuscript,\nfurther in-depth analysis and supplementary data are imperative to strengthen the\nargument. Moreover, the manuscript lacks a detailed genetic or regulatory model that\nelucidates how the identified genes mediate environmental adaptation. Building on\nthis gap, the authors are therefore urged to provide mechanistic insights or additional\ncorroborative evidence to substantiate this key conclusion and enhance the overall\npersuasiveness of the study.\nResponse: Thank you for this important comment. We agree that fully establishing\nthe causal relationship between TEVs and environmental adaptation would require\nextensive functional validation experiments, which are beyond the scope of the\ncurrent study. In this work, we have focused on robust population genomic analyses to\nidentify putative adaptive TEVs and explore their potential roles in local adaptation\nand responses to future climate change.\nTo provide additional mechanistic insights, we further analyzed the genomic context\nof environment-associated variants to develop a preliminary regulatory framework\nlinking TEVs with potential modulation of gene expression. Using the PlantCARE\ndatabase, we predicted cis-regulatory elements, including promoter motifs and\ntranscription factor binding sites (TFBS). This framework suggests that TEV\ninsertions or linked variants near adaptive genes may alter local transcription factor\nbinding landscapes, thereby influencing gene expression responsiveness under\nenvironmental stress. For example, we identified a long TE insertion upstream of\nTLP3 that overlaps a predicted transcription factor binding site, as well as a SNP\nupstream of ICE1 located within a putative regulatory motif. These findings suggest\nthat environment-associated variants could potentially modulate regulatory networks\n(see revised Results and Supplementary Fig. S9). Our findings provide a valuable set\nof testable hypotheses and candidate loci for future functional studies.\nWe have also toned down the wording in the manuscript to emphasize that our\nfindings provide evidence supporting a potential role of TEVs in local adaptation,\nrather than definitive proof. While experimental validation is required to fully confirm\ncausality, the combined genomic and regulatory analyses presented here offer a\nvaluable resource and a set of candidate loci for future functional studies.\nSupplementary Figure 9. b, Candidate adaptive TEV potentially affecting predicted\ntranscription factor binding site in the putative regulatory region of TLP3. The dashed\narrow represents the assumed regulation. The predicted transcription factor binding\nsite is depicted as red triangle. c, Candidate adaptive variants located within predicted\ntranscription factor binding site in the putative regulatory region of ICE1."}, {"round": 3, "reviewer_comments": {"reviewer_1": "I do not understand the change in the title. Adding the word “jumping” does not tone down the title and does not align with the manuscript content.\nL71: to “be” present\nL97: “the” biodiversity\nL107: “the” adaptability\nL136: “their” geographic distribution\nL174: I would remove “in summary”, there is only one sentence about the pangenome.\nL194: “the” whole genome\nL216: considered as > considered (remove as)\nL223: in “the” local adaptation\nL228: elevation-related\nL249: there were > there was\nL384: Our findings support the (remove “to”)\nL386: “the” environmental adaptation\nL390: exploring of the effects (remove “of”)\nL407: “the” regulation", "reviewer_2": "The authors have adequately addressed my concerns. I still have some quibbles about some of the wording, but I suspect that is due to differences between fields. My feeling is that authors, well, really, all of us, can forget that models are just models and linkage is just linkage. That being said, I congratulate the authors on what was almost certainly a difficult and complex task. One final caveat is the title: Transposable Element Jumping Facilitates Climate Change Adaptation in Wild Grapes. After all that back and forth, why make an unproven claim in the title? I would adjust the title to reflect the actual conclusions. Also, \"jumping\" is too informal, and really, the paper did not show that transposition caused anything. They showed that TE insertion variants are linked to loci that are associated with climate variability. I know, not as flashy, but best to stick to what we know.", "reviewer_3": "Thank you for the authors’ thoughtful response. However, these replies still do not fully address my concerns. I remain skeptical regarding certain aspects of the data and the overall presentation of the article. Thus, additional work and further explanations are still required.\n1. The authors explained that the environmental variables were sourced from WorldClim, with a resolution of 2.5 arcmin (approximately 5 km), and many samples share identical environmental data. I still have three key questions regarding this approach: First, are genotypic differences within a population also reflective of the population’s environmental adaptability? If so, can the impact of completely consistent environmental data on the analysis be effectively controlled? Second, according to the data provided by the authors, some samples exhibit elevation differences of up to 200 m or more (e.g., ZY407 – ZY416), which can induce notable microclimatic variations. Genotypic differences within the population are likely adaptive responses to these microclimatic differences. However, the use of identical environmental data for such samples in this study may introduce biases that affect the reliability of the analysis results. Finally, the authors stated that the data were downloaded from WorldClim, but it is critical to verify whether these data are representative of the specific year(s) when the samples were collected. Additionally, the WorldClim data used (covering 1970–2000) are over two decades old, and significant changes in current environmental conditions will inevitably compromise the accuracy of the analysis. For these reasons, I still maintain that environmental adaptability parameters inherent to the plants themselves should be adopted and incorporated into the analysis.\n2. In this study, the authors identified candidate genes corresponding to bio3, 4, 10, 15, and 18. As noted, V. amurensis is considered as one of the most cold-tolerant grape species; however, this study lacks dedicated analysis of its cold resistance mechanisms—a trait that constitutes a key adaptive feature of V. amurensis and thus warrants inclusion in the research framework. Additionally, while the authors reported a large set of candidate genes, the absence of further analysis hinders the elucidation of how these genes collectively contribute to environmental adaptability. Further investigations are therefore required to validate and quantify the specific roles of these candidate genes in mediating the adaptive traits of V. amurensis. Moreover, although several genes associated with cold and drought tolerance were selected for detailed analysis, the study failed to clarify the specific impacts of transposon insertions on the structure or function of these genes. Without such mechanistic insights, the link between these genes and the adaptive phenotypes of V. amurensis remains insufficiently substantiated.\n3. The title and research objective of this study focus on \"Transposable Element Jumping Facilitates Climate Change Adaptation in Wild Grapes.\" However, it is clear that the authors have not provided sufficient evidence to support this core claim. The authors only demonstrated that TEVs are closely associated with several resistance-related genes. This association, however, does not directly confirm a causal link between TEVs and environmental adaptability. If the authors wish to argue that TEVs are key drivers of environmental adaptation, they must provide additional evidence to show that the overall pattern of TEV insertions enhances the adaptability of plants to their respective growing environments. Alternatively, if the authors propose that specific genes play a major role in TEV-mediated adaptation, they need to clarify how TEV insertions alter the expression of these target genes, and elucidate the mechanism by which such expression changes enable adaptation to different environmental conditions. Ultimately, the authors should provide a concise model to illustrate how TEV insertions influence environmental adaptability. This step is not only necessary to strengthen the study’s conclusions but also the core of this article.\n4. The title of this article is \"Transposable Element Jumping Facilitates Climate Change Adaptation in Wild Grapes\"; however, the study primarily focuses on analyzing the environmental adaptation mechanisms of V. amurensis. Since this proposed mechanism has not been validated or extended to other wild grape species, the title’s broad claim regarding \"wild grapes\" is somewhat overgeneralized and thus inappropriate. To address this discrepancy, the authors should either conduct comparable analyses on additional wild grape taxa to verify the universality of the mechanism, or revise the title to more accurately reflect the study’s focal species and avoid overextrapolation."}, "author_response": "Response to Review Comments\nPart I: Summary of responses to the major questions:\nWe sincerely appreciate the careful evaluation by the editor and the three reviewers.\nBefore presenting a detailed point-by-point response in Part II, we first address four\nmajor issues collectively:\nFirst, revision of the manuscript title based on the combined feedback from all\nthree reviewers: In response to the reviewers’ collective concerns regarding\noverstatement, we revised the title to: “Transposable Elements Contribute to Climate\nChange Adaptation in the Wild Amur Grape (Vitis amurensis)”.\nSecond, comprehensive language editing to improve clarity and readability: We\nhave carefully revised the manuscript according to reviewer’s suggestions and we have\nfurther reviewed and refined the entire manuscript to improve consistency, clarity, and\ngrammatical accuracy.\nThird, clarification of the appropriateness of the environmental data: We clarified\nthat the population-level GEA framework used in this study targets broad-scale and\nlong-term environmental gradients among populations. The suitability of data from\nWorldClim for this purpose, particularly for long-lived perennial species, was justified\nwith reference to its widespread use in population genomic studies.\nFourth, expansion of functional interpretation and clarification of study scope: We\nperformed a GO enrichment analysis of 823 candidate genes, revealing significant\nenrichments in cold- and drought-response pathways. We also expanded the discussion\nof cold resistance mechanisms by integrating literature-supported evidence and\nconsidering the potential impact of TEVs. We clarified that experimental validation\nremains beyond the scope of this study.\nIn conclusion, we have provided detailed explanations in response to the reviewers'\ncomments. All updates in the manuscript are highlighted with tracked changes.\nAdditionally, we have revised the manuscript and supplemental tables.\nPart II: Point by point response to specific comments:\n(The senior editor and reviewer’s comments are shown in black, and the authors’\nresponses are displayed in blue.)\nSenior Editor\nThank you again for submitting your manuscript \"Transposable Element Jumping\nFacilitates Climate Change Adaptation in Wild Grapes\" to Nature Communications. We\nhave now received reports from 3 reviewers and, based on their comments, we have\ndecided to invite a revision of your work. Your revision should address all the points\nraised by our reviewers (see their reports below). In particular, as specified by all three\nreviewers, we ask to remove all overstatements about the causative effect relationships\nif the observations are only supported by linkage or association.\nResponse: We sincerely thank you for the opportunity to revise our manuscript. We are\ngrateful to you and the three reviewers for their careful evaluation and constructive\ncomments, which have been invaluable in improving the quality and clarity of our work.\nWe have carefully considered all the points raised by the reviewers and have revised\nthe manuscript accordingly. In particular, we have thoroughly modified the text and\nremoved or toned down any overstatements regarding causative relationships where the\nevidence is based solely on linkage or association analyses.\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nI do not understand the change in the title. Adding the word “jumping” does not tone\ndown the title and does not align with the manuscript content.\nResponse: We appreciate your time and valuable insights. Thank you for pointing this\nissue out. We have revised the title to: “Transposable Elements Contribute to Climate\nChange Adaptation in the Wild Amur Grape (Vitis amurensis)”.\nWe have carefully revised the manuscript accordingly. In addition, we have further\nreviewed and refined the entire manuscript to improve consistency, clarity, and\ngrammatical accuracy.\nL71: to “be” present\nResponse: Thank you for the helpful suggestion. We have revised the text to “to be\npresent”.\nL97: “the” biodiversity\nResponse: We appreciate your suggestion. We have revised the text to “the\nbiodiversity”.\nL107: “the” adaptability\nResponse: Thank you for your suggestion. We have revised the text to “the\nadaptability”.\nL136: “their” geographic distribution\nResponse: Thank you for the helpful suggestion. We have revised the text to “their\ngeographic distribution”.\nL174: I would remove “in summary”, there is only one sentence about the pangenome.\nResponse: Thank you for pointing this out. We have removed it.\nL194: “the” whole genome\nResponse: Thank you for your valuable suggestions. We have revised the text to “the\nwhole genome”.\nL216: considered as > considered (remove as)\nResponse: Thank you for pointing this out. We have removed it.\nL223: in “the” local adaptation\nResponse: We appreciate your suggestion. We have revised the text to “the local\nadaptation”.\nL228: elevation-related\nResponse: Thank you for your careful reading. We have revised the text to “elevation-\nrelated variables”.\nL249: there were > there was\nResponse: Thank you for your helpful suggestion. We have removed this term and\nrevised the sentence to “We identified a set of precipitation-associated candidate\nadaptive genes that are homologous to genes in Arabidopsis, including TLP3”.\nL384: Our findings support the (remove “to”)\nResponse: Thank you for your helpful suggestion. We have removed it.\nL386: “the” environmental adaptation\nResponse: We appreciate your suggestion. We have revised the text to “the\nenvironmental adaptation”.\nL390: exploring of the effects (remove “of”)\nResponse: Thank you for your helpful suggestion. We have removed it.\nL407: “the” regulation\nResponse: We appreciate your suggestion. We have revised the text to “the regulation”.\nReviewer #2 (Remarks to the Author):\nThe authors have adequately addressed my concerns. I still have some quibbles about\nsome of the wording, but I suspect that is due to differences between fields. My feeling\nis that authors, well, really, all of us, can forget that models are just models and linkage\nis just linkage. That being said, I congratulate the authors on what was almost certainly\na difficult and complex task. One final caveat is the title: Transposable Element\nJumping Facilitates Climate Change Adaptation in Wild Grapes. After all that back and\nforth, why make an unproven claim in the title? I would adjust the title to reflect the\nactual conclusions. Also, \"jumping\" is too informal, and really, the paper did not show\nthat transposition caused anything. They showed that TE insertion variants are linked\nto loci that are associated with climate variability. I know, not as flashy, but best to stick\nto what we know.\nResponse: We sincerely thank you for the positive assessment of our revisions and for\nyour constructive feedback throughout the review process. In response to your\ncomments on the title, we have revised it to more accurately reflect our conclusions and\nto avoid overstating causality. The new title is: “Transposable Elements Contribute to\nClimate Change Adaptation in the Wild Amur Grape (Vitis amurensis)”.\nReviewer #3 (Remarks to the Author):\nThank you for the authors’ thoughtful response. However, these replies still do not fully\naddress my concerns. I remain skeptical regarding certain aspects of the data and the\noverall presentation of the article. Thus, additional work and further explanations are\nstill required.\nResponse: We sincerely thank you for the careful evaluation of our manuscript and for\nproviding thoughtful and constructive comments. In response, we have made analysis\nto further clarify, revise, and strengthen the manuscript. We are grateful for your time\nand valuable insights, which have helped us improve the quality of our work.\n1. The authors explained that the environmental variables were sourced from\nWorldClim, with a resolution of 2.5 arcmin (approximately 5 km), and many samples\nshare identical environmental data. I still have three key questions regarding this\napproach: First, are genotypic differences within a population also reflective of the\npopulation’s environmental adaptability? If so, can the impact of completely consistent\nenvironmental data on the analysis be effectively controlled? Second, according to the\ndata provided by the authors, some samples exhibit elevation differences of up to 200\nm or more (e.g., ZY407 – ZY416), which can induce notable microclimatic variations.\nGenotypic differences within the population are likely adaptive responses to these\nmicroclimatic differences. However, the use of identical environmental data for such\nsamples in this study may introduce biases that affect the reliability of the analysis\nresults. Finally, the authors stated that the data were downloaded from WorldClim, but\nit is critical to verify whether these data are representative of the specific year(s) when\nthe samples were collected. Additionally, the WorldClim data used (covering 1970–\n2000) are over two decades old, and significant changes in current environmental\nconditions will inevitably compromise the accuracy of the analysis. For these reasons,\nI still maintain that environmental adaptability parameters inherent to the plants\nthemselves should be adopted and incorporated into the analysis.\nResponse: Thank you for your insightful comments. We agree that micro-\nenvironmental differences, including elevation, may contribute to microclimatic\nvariation within a population. However, our analyses focus on broad-scale\nenvironmental gradients among populations rather than at the scale of individual\nmicrohabitats. Consistent with established practice in population genomics, our\ngenotype–environment association (GEA) analyses were performed at the population\nlevel, which is widely considered the appropriate scale for environmental association\nstudies using datasets such as WorldClim. This approach is well supported by previous\npopulation genomic studies (e.g., Fior et al.,2025; Zhang et al., 2025; Lazic et al., 2024;\nSang et al., 2022). Under this framework, identical environmental values within a\npopulation do not bias the analysis, because signals of selection are inferred from\ncorrelations between allele-frequency variation and environmental gradients among\npopulations, rather than from micro-scale environmental differences among individuals.\nConsidering the scope of our study, the environmental data used are appropriate for\naddressing our research questions.\nWe also appreciate the concern regarding the temporal resolution of WorldClim (1970–\n2000). WorldClim provides long-term climatic averages that capture the broad and\nrelatively stable environmental gradients shaping genetic structure in long-lived\nperennial species such as V. amurensis. Although local climates have changed in recent\ndecades, environmental association approaches are designed to detect persistent\nselective pressures rather than short-term climatic fluctuations. WorldClim is the most\nwidely used and benchmark dataset in environmental genomic analyses, including\nmany recent studies (Karunarathne et al., 2024; Hung et al., 2023; Moreira & Smith,\n2023; Aguirre-Liguori et al., 2021; Lovell et al., 2021; Exposito-Alonso et al., 2019).\nReference:\n1. Fior, S. et al. Ancient alleles drive contemporary climate adaptation in an alpine\nplant. Science. 390, 59-64 (2025):\n2. Zhang, S. et al. Hybridization mitigates climate change risk in mountainous birds.\nNat Clim Change. 1-10 (2025)\n3. Lazic, D. et al. Genomic variation of European beech reveals signals of local\nadaptation despite high levels of phenotypic plasticity. Nat Commun. 15, 8553\n(2024).\n4. Sang, YP. et al. Genomic insights into local adaptation and future climate-induced\nvulnerability of a keystone forest tree in East Asia. Nat Commun. 13, 6541 (2022).\n5. Karunarathne, P. et al. Hybridization mediated range expansion and climate change\nresilience in two keystone tree species of boreal forests. Global Change Biol. 30,\ne17262 (2024).\n6. Hung TH, et al. Range-wide differential adaptation and genomic offset in critically\nendangered Asian rosewood. PNAS,120, e2301603120 (2023).\n7. Moreira, LR. & Smith, BT. Convergent genomic signatures of local adaptation\nacross a continental-scale environmental gradient. Sci. Adv. 9, eadd0560 (2023).\n8. Aguirre-Liguori, J. et al. The evolutionary genomics of species’ responses to\nclimate chang. Nat. Ecol. Evol. 5, 1350-1360 (2021).\n9. Lovell, JT. et al. Genomic mechanisms of climate adaptation in polyploid bioenergy\nswitchgrass. Nature 590, 438-444 (2021).\n10. Exposito-Alonso, M. et al. Natural selection on the Arabidopsis thaliana genome\nin present and future climates. Nature 573, 126-129 (2019).\n2. In this study, the authors identified candidate genes corresponding to bio3, 4, 10, 15,\nand 18. As noted, V. amurensis is considered as one of the most cold-tolerant grape\nspecies; however, this study lacks dedicated analysis of its cold resistance\nmechanisms—a trait that constitutes a key adaptive feature of V. amurensis and thus\nwarrants inclusion in the research framework. Additionally, while the authors reported\na large set of candidate genes, the absence of further analysis hinders the elucidation of\nhow these genes collectively contribute to environmental adaptability. Further\ninvestigations are therefore required to validate and quantify the specific roles of these\ncandidate genes in mediating the adaptive traits of V. amurensis. Moreover, although\nseveral genes associated with cold and drought tolerance were selected for detailed\nanalysis, the study failed to clarify the specific impacts of transposon insertions on the\nstructure or function of these genes. Without such mechanistic insights, the link\nbetween these genes and the adaptive phenotypes of V. amurensis remains insufficiently\nsubstantiated.\nResponse: Thank you for this thoughtful and constructive comment. We further\nperformed a gene ontology (GO) enrichment analysis on the identified candidate genes.\nThese genes revealed significant enrichment in biological processes such as response\nto cold (P = 5.92e-11), response to water deprivation (P = 9.87e-9), and response to\nabscisic acid (P = 1.1e-6) (Supplementary Table S13). These results suggest that some\ncandidate genes may function within coordinated stress-response pathways,\ncollectively contributing to environmental adaptation.\nWe agree that cold tolerance represents a key adaptive trait of Vitis amurensis, and we\nfully acknowledge the importance of elucidating the underlying molecular mechanisms.\nSome cold resistance mechanisms of genes identified in this study have been previously\ncharacterized in V. amurensis. For example, ICE1 homologs in the Amur grape have\nbeen confirmed as key regulator acting in the early steps of the transcriptional cascade\ncontrolling cold tolerance, modulating the expression of multiple cold-related genes in\nthe CBF pathway (Xu et al., 2014; Ren et al., 2021; Ma et al., 2022). In addition, BAM3\nhas been shown to enhance cold resistance by regulating starch decomposition in Amur\ngrape (Meng et al., 2025). We now present these functional links more clearly in the\nrevised manuscript and provide additional literature-supported functional annotations\nto strengthen the interpretation of the potential roles of these candidate genes in cold\nadaptation.\nWe acknowledge that the potential functional impacts of TE insertions require further\ninvestigation. In this study, we identified a long TE insertion located between a\npredicted TFBS and the 5' regulatory region of the gene TLP3. The long insertion may\ninterfere with the interaction between the predicted TFBS and the promoter region of\nTLP3 (supplementary Fig. S9b). The positional pattern suggests a potential regulatory\nor structural impact. However, elucidating the precise molecular mechanisms through\nwhich putative environment-associated genes contribute to environmental adaptability\nwould require comprehensive experimental validation, such as molecular functional\nassays, gene expression profiling, and physiological experiments under controlled\nenvironmental conditions. These analyses are beyond the scope of this population-\ngenomic study. We have clarified this limitation in the revised manuscript and\nhighlighted these directions as important priorities for future functional research.\nThe revised manuscript now reads: “We identified numerous putative environment-\nassociated genes and generated testable hypotheses for future functional studies. We\nacknowledge that the present work does not include experimental validation of these\nmolecular processes, and that such efforts remain essential. Future studies should\nprioritize functional analyses to clarify the biological roles of these genes in the\nenvironmental adaptability of the Amur grape.”\n3. The title and research objective of this study focus on \"Transposable Element\nJumping Facilitates Climate Change Adaptation in Wild Grapes.\" However, it is clear\nthat the authors have not provided sufficient evidence to support this core claim. The\nauthors only demonstrated that TEVs are closely associated with several resistance-related\ngenes. This association, however, does not directly confirm a causal link\nbetween TEVs and environmental adaptability. If the authors wish to argue that TEVs\nare key drivers of environmental adaptation, they must provide additional evidence to\nshow that the overall pattern of TEV insertions enhances the adaptability of plants to\ntheir respective growing environments. Alternatively, if the authors propose that\nspecific genes play a major role in TEV-mediated adaptation, they need to clarify how\nTEV insertions alter the expression of these target genes, and elucidate the mechanism\nby which such expression changes enable adaptation to different environmental\nconditions. Ultimately, the authors should provide a concise model to illustrate how\nTEV insertions influence environmental adaptability. This step is not only necessary to\nstrengthen the study’s conclusions but also the core of this article.\nResponse: Thank you for your insightful comments. We fully agree that establishing a\ndirect causal link between TEVs and environmental adaptability would require\nextensive functional validation experiments, which are beyond the scope of the current\nstudy. To provide preliminary mechanistic insights, we analyzed the genomic context\nof environment-associated TEVs using the PlantCARE database to predict cis-\nregulatory elements, including promoter motifs and transcription factor binding sites\n(TFBS). For example, we identified a long insertion (more than 10000bp) upstream of\nTLP3, located between a predicted TFBS and the 5' regulatory region of the gene. Such\na long insertion may disrupt the physical proximity between the predicted TFBS and\nthe promoter region of TLP3, potentially hindering transcription factor access or\nbinding (supplementary Fig. S9b). This disruption could impair normal regulatory\ninteractions and lead to altered TLP3 expression. Previous studies have shown that\nTLP3 homologs are involved in drought and cold stress responses in plants, suggesting\nthat this insertion may contribute to the environmental adaptation of the Amur grape.\nSimilarly, SNPs upstream of ICE1, an established key regulator of the ICE–CBF–COR\ncold-response pathway and a known determinant of freezing tolerance in Vitis\namurensis, was located directly within a predicted TFBS (supplementary Fig. S9).\nThis positional relationship suggests that TEVs and associated variants may modulate\ntranscription factor binding and thereby influence gene expression. These findings\nprovide testable hypotheses for future experimental validation.\nWhile we cannot yet definitively demonstrate causality, the observed genomic\ncorrelations between TEVs and environmental variables, together with the predicted\nregulatory context, support the idea that TEVs may contribute to local adaptation.\nWe have clarified in the manuscript that our study provides evidence for a potential role of\nTEVs in adaptation rather than definitive proof. We also revised the title to:\n“Transposable Elements Contribute to Climate Change Adaptation in the Wild Amur\nGrape (Vitis amurensis)” to avoid overextrapolation.\n4. The title of this article is \"Transposable Element Jumping Facilitates Climate Change\nAdaptation in Wild Grapes\"; however, the study primarily focuses on analyzing the\nenvironmental adaptation mechanisms of V. amurensis. Since this proposed mechanism\nhas not been validated or extended to other wild grape species, the title’s broad claim\nregarding \"wild grapes\" is somewhat overgeneralized and thus inappropriate. To\naddress this discrepancy, the authors should either conduct comparable analyses on\nadditional wild grape taxa to verify the universality of the mechanism, or revise the title\nto more accurately reflect the study’s focal species and avoid overextrapolation.\nResponse: Thank you for this valuable comment. We agree that the previous title may\nnot be appropriate. To accurately reflect the study’s content and avoid overextrapolation,\nwe have revised the title to: “Transposable Elements Contribute to Climate Change\nAdaptation in the Wild Amur Grape (Vitis amurensis)”.\nREVIEWERS' COMMENTS\nReviewer #3 (Remarks to the Author):\nI would like to express my gratitude to the authors for their detailed response to my\nquestions, which has addressed some of my concerns. However, their response does not\nfundamentally alter my doubts regarding the core content of this paper. While the\nauthors have identified a large number of TEVs adjacent to environment-related genes,\nthey have failed to provide direct evidence demonstrating the contribution of these\nTEVs to environmental adaptability, nor have they clarified the specific mechanisms\nby which these genes enhance grape environmental adaptability through transposition.\nSince this constitutes the central argument of the paper, I still recommend that the\nauthors supplement or revise the content to ensure consistency between the title and the\nsubstance of the work. It is inadvisable to treat unsubstantiated conclusions as the\ncore of the paper.\nResponse: We sincerely thank you for your time and thoughtful comments. We have\nrevised the manuscript to remove any implication of a causal relationship between\ntransposable element variants (TEVs) and environmental adaptation.\nIn addition, we have substantially revised the Introduction and Discussion to\ndeemphasize transposable elements as adaptive drivers and to avoid mechanistic or\ncausal interpretations."}]

Decision letter