Chiang Mai Journal of Science

Transcriptomic Analysis and Molecular Mechanisms of Waterlogging Adaptation in Roots of Hippuris vulgaris L from Alpine Wetlands

1. INTRODUCTION

     Alpine wetlands represent unique and fragile ecosystems where plants face combined stresses of low temperature, hypoxia, and seasonal waterlogging [1,2]. Recent studies have begun to reveal the metabolic specializations that underpin survival in these demanding habitats [3] Hippuris vulgaris L., a dominant aquatic plant in these habitats, plays a crucial role in ecosystem stability and biogeochemical cycling due to its well-developed root system and physiological adaptations [4]. However, prolonged or extreme flooding-induced hypoxic stress can severely impair root respiratory metabolism, nutrient uptake, and antioxidant defense systems, ultimately threatening plant viability and ecological functions [5.6]. While previous studies have elucidated certain morphological and physiological responses of wetland plants to flooding [7-10], the transcriptome-level regulatory networks in Hippuris vulgaris L roots under alpine conditions remain poorly understood, particularly regarding metabolic reprogramming under hypoxia [11].
     Transcriptome analysis is a powerful tool for deciphering plant stress adaptation mechanisms [12]. Lowland plants under flooding exhibit conserved molecular responses, involving genes related to anaerobic metabolism and oxidative stress management [13-15]. However, these paradigms may not fully explain the adaptations in alpine species. During our 2021-2023 field studies in Zeku Wetland (Qinghai, 3,850 m elevation), Hippuris vulgaris L. exhibited remarkable physiological stability despite prolonged flooding at low water temperatures (4 °C), contrasting with typical lowland responses [16,17]. This suggests the existence of elevation-specific adaptive strategies. Approximately 31 % of the identified DEGs lacked functional annotation, hinting at potentially novel mechanisms. This study employs simulated alpine wetland flooding conditions, RNA-Seq, and bioinformatics to investigate the transcriptomic responses in Hippuris vulgaris L. roots. The objectives are to: (1) characterize flooding-induced transcriptional regulation;(2) decipher the molecular mechanisms of hypoxia tolerance and metabolic adaptation specific to alpine environments; (3) provide genetic resources for improving stress resistance in alpine plants and ecological restoration.

2. MATERIAL AND METHODS

2.1 Plant Materials and Experimental Design
     Hippuris vulgaris L. specimens were collected from Zeku Zequ National Wetland Park, Qinghai Province, China. The plants were acclimatized in a controlled greenhouse. For the flooding treatment (MW), the plants were submerged 3 cm above the soil for 40 days. The control plants (CK) were maintained without flooding. Root samples were collected, frozen in liquid nitrogen, and stored at - 80 °C. Three biological replicates per group were used, and each replicate consisted of three pooled plants.

2.2 RNA Extraction, Library Construction and Sequencing
     Total RNA was extracted from roots using a Polysaccharide & Polyphenol Plant RNA Extraction Kit (TIANGEN, DP441). The integrity of the RNA was verified. cDNA libraries were prepared and sequenced on the Illumina HiSeq 2000 platform following standard protocols [18].

2.3 Transcriptome Assembly and Quality Assessment
     Denovo transcriptome assembly was performed using Trinity (v2.8.5) with a minimum contig length of 200 bp and ak-mer size of 25. This yielded 112,170 transcripts (N50 = 1,542 bp) clustered into 62,602 unigenes. The sequencing data quality was high: 42.93 Gb of clean reads, with a Q30 value > 93.23% and a GC content of approximately 43.50%. BUSCO assessment showed an 89.7% coverage of conserved eukaryotic genes, confirming the completeness of the assembly.

2.4 Differential Gene Expression Analysis
     DEGs between MW and CK were identified using DESeq2 (v1.38.3). The pipeline involved the following steps: 1) Transcript quantification using Cufflinks (v2.2.1) with FPKM values；2) Statistical testing using DESeq2's negative binomial model on the FPKM - normalized expression matrix；3) Filtering with stringent criteria: |log₂FC| > 1 and FDR < 0.05.

2.5 Functional Annotation and Enrichment Analysis
     DEGs were annotated against KEGG, GO, EggNOG, NR, SWISS - PROT, and Pfam databases using blastx (e-value < 1e-5) and Blast2GO. Enrichment analysis was conducted using clusterProfiler (v4.6) with hypergeometric testing and FDR < 0.05.

2.6 Validation of Differentially Expressed Genes by RT-qPCR
     To validate the reliability of the RNA-seq results, a total of eight differentially expressed genes (DEGs)were selected for RT-qPCR analysis. The selection aimed to cover key functional categoriesidentified in the enrichment analysis, including transcription factors, anaerobic metabolism, and cellular growth regulation. Given the critical role of upregulated genes in active stress responses (e.g., anaerobic fermentation), we prioritized their representation, selecting four up-regulated and four down-regulated genes. Gene-specific primers were designed using Primer Premier 5.0. The RT-qPCR reactions were performed using SYBR Green Premix Pro Taq HS on a QuantStudio 5 Real-Time PCR system. β-actin was employed as an internal control, and all reactions were run in three technical replicates. The relative expression levels were calculated using the 2^(-^ΔΔCt) method.

3. RESULTS AND ANALYSIS

3.1 Transcriptome Sequencing, Assembly, and Quality Control
     RNA-seq of six root samples (3 MW, 3 CK) generated 42.93 Gb of high-quality clean data. All samples showed high quality (Q30 > 93.23%, GC content > 47.42%). (Table 1) Pearson correlation coefficients indicated strong reproducibility within groups. The sequencing depth saturation was sufficient. (Figure 1)

3.2 Identification of Differentially Expressed Genes (DEGs)
     Transcriptomic profiling identified 123 significantly differentially expressed genes (DEGs)in Hippuris vulgaris L. roots following 40-day waterlogging stress (|log₂FC| ≥ 1, p < 0.05), comprising 27 upregulated and 96 downregulated genes (Figure 2). The pronounced downregulation bias (78% of DEGs) suggests systemic metabolic suppression under prolonged hypoxia, mirroring an energy-conservation strategy commonly observed in flood-tolerant species. DESeq2-based analysis revealed: (1) sinificant enrichment in anaerobic respiration pathways; (2) coordinated downregulation of aerobic respiration-related genes; (3) activation of stress-responsive transcription factor families. The volcano plot demonstrates robust separation between treatment groups, with the majority of significant DEGs exhibiting substantial expression Changes.

3.3 Functional Annotation of the Transcriptome and DEGs
     Comprehensive annotation of the entire transcriptome (62,602 unigenes) revealed extensive coverage across databases (e.g., NR: 33,042; GO: 27,957). This high annotation rate provides a solid foundation for functional inference. Subsequently, the 123 differentially expressed genes (DEGs) were mapped to these annotations for enrichment analysis. (Table 2)

3.4 GO and KEGG Enrichment Analysis of DEGs
     GO enrichment analysis revealed that the DEGs were significantly over - represented in "metabolic process" and "catalytic activity" (Figure 3A), indicating a major shift in primary metabolism. KEGG pathway analysis highlighted enrichment in "Carbohydrate metabolism", "Plant hormone signal transduction," and "Biosynthesis of secondary metabolites" (Figure 3B). Key genes involved in anaerobic fermentation, such as pyruvate decarboxylase (PDC) and alcohol dehydrogenase (ADH), were upregulated, facilitating energy production under hypoxia [19,20].

3.6 Transcription Factor Analysis
     Differential expression analysis identified key transcription factor (TF) families (Figure 5) involved in the waterlogging stress response, including MYB, AP2/ERF, bHLH, WRKY, and NAC. The analysis of all DEGs assigned to TF families revealed that MYB (32%) and AP2/ERF (28%) were the most prevalent, indicating their central roles in coordinating the transcriptional reprogramming. Among the differentially expressed AP2/ERF genes, a representative member was down regulated, which may reflect the complex regulation of ethylene signaling under prolonged stress. Conversely, members of other TF families such as MYB, bHLH, and WRKY were up regulated, potentially contributing to processes like phenylpropanoid metabolism and general stress response. Furthermore, critical functional genes were identified, including up regulated pyruvate decarboxylase (PDC)for anaerobic fermentation and down regulated cytochrome c oxidase (COX)and expansin (EXP), highlighting a strategic shift in energy metabolism and suppression of energy-consuming growth processes (Table 3).

3.7 Validation of RNA-Seq Results by RT-qPCR
     The expression patterns of eight selected DEGs (four up-regulated and four down-regulated) were confirmed using RT-qPCR. The results demonstrated a high degree of consistency between the RNA-seq and RT-qPCR data. As shown in Figure 6, the directional trend (up- or down-regulation) for all eight validated genes was consistent between the two methods. Furthermore, the relative fold changes measured by both techniques showed a significant positive correlation, thereby robustly validating the accuracy and reliability of our transcriptomic analysis. This high concordance underscores the credibility of the identified DEGs and the subsequent biological interpretations.

4. DISCUSSION

     This study reveals the transcriptomic basis of waterlogging adaptation in Hippuris vulgaris L. roots under alpine wetland conditions. The relatively small number of DEGs (123) identified here, compared to lowland species such as rice or poplar [21] under similar stress duration, likely reflects an inherent pre-adaptation to the hypoxic environment of alpine wetlands. This restrained transcriptional profile aligns with observations in other pre-adapted alpine species, which often rely on constitutive mechanisms to minimize energetic costs of stress responses [22]. The pronounced bias toward gene downregulation (78% of DEGs) further supports a systemic energy-conservation strategy under prolonged oxygen deprivation [23,24], representing a key adaptation for survival in predictably hypoxic  niches [25].
     We identified core adaptive mechanisms activated in Hippuris vulgaris L roots, integrating the DEG results with specific physiological traits:
1. Metabolic Shift toward Anaerobic Energy Production: Enrichment in carbohydrate metabolism pathways and the marked upregulation of key anaerobic genes—including pyruvate decarboxylase (PDC) and alcohol dehydrogenase (ADH)—underscore a critical shift to fermentative metabolism. This transition is essential for maintaining basal energy supply under hypoxia and represents a conserved, yet vital, response among waterlogging-tolerant plants [26,27].
2. Reinforcement of Protein Homeostasis: The significant enrichment of DEGs in "Posttranslational modification, protein turnover, chaperones" (EggNOG category O) indicates a robust cellular effort to maintain protein function. This involves refolding misfolded proteins and clearing damaged ones, a crucial mechanism for mitigating proteotoxicity under stress-induced cellular disturbances [28].
3. Coordinated Stress Signaling and Transcriptional Regulation: Differential expression of transcription factors, particularly the hypoxia-sensing AP2/ERF-VII family members, along with MYB and WRKY TFs, points to a multi-layered regulatory network fine-tuning the hypoxia response. Similar coordinated regulatory patterns have been reported in other stress-tolerant plants, including drought-adapted rice varieties from Northern Thailand [29-31] and pre-adapted alpine species [32-34].
     A distinctive feature of the alpine response in Hippuris vulgaris L is the involvement of phenylpropanoid metabolism genes. This enrichment suggests a role for phenolic compounds in antioxidant defense and possibly cell wall reinforcement, which may be particularly advantageous under the combined hypoxia and low-temperature stress characteristic of alpine wetlands, a form of metabolic specialization observed in these environments [35-38]. Furthermore, the significant portion (~31%) of DEGs that lacked functional annotation warrants targeted investigation, as these may represent novel, species-specific genetic elements underlying alpine adaptation.

     In conclusion, Hippuris vulgaris L. demonstrates a composite and refined adaptation strategy. It combines conserved hypoxia responses with apparent alpine-specific features, centering on metabolic efficiency, robust protein homeostasis, and specialized secondary metabolism. This study bridges alpine-specific molecular responses with conserved lowland mechanisms, providing a transcriptional framework for understanding plant adaptation to waterlogging stress across elevations [39-41]. The following conceptual model summarizes the key mechanisms identified in this study (Figure 7).

5. CONCLUSIONS

     We identified 123 DEGs in Hippuris vulgaris L roots responding to 40-day waterlogging stress, characterized by a predominant down regulatory trend. The transcriptomic response highlights adaptive strategies centered on metabolic reprogramming towards anaerobic energy production, enhanced protein homeostasis, and activation of specific regulatory networks. The relatively limited scale of transcriptional changes suggests inherent pre-adaptation to hypoxic conditions in this alpine wetland species. These findings deepen our understanding of plant adaptation to combined hypoxia and low-temperature stress and offer genetic insights for the conservation and utilization of alpine wetland plants.

ACKNOWLEDGEMENTS

     This study was supported by the Qinghai University Research Ability Enhancement Project (2025KTST06).

AUTHOR CONTRIBUTIONS

Xiaoyan Su: Writing - Original Draft, Writing - Review & Editing, Formal analysis. Yu Zhan: Resources. Changhui Li: Conceptualization, Supervision.

CONFLICT OF INTEREST

     All authors declare that there are no competing financial interests or personal relationships that could have influenced the work reported in this paper.

DECLARATION OF USE OF GENERATIVE AI

     During the preparation of this work, the authors used QuillBot Premium to improve the readability and language of the manuscript. After using this tool, the authors reviewed and edited the content as necessary and takes full responsibility for the publication's content.

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