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Article

Comprehensive Analysis of the ARF Gene Family Reveals Their Roles in Chinese Chestnut (Castanea mollissima) Seed Kernel Development

by
Xili Liu
1,†,
Yun Li
2,†,
Manman Liang
3,
Dongsheng Wang
1,
Meng Wang
1,
Yi Lu
1,
Xia Liu
4,
Haie Zhang
1,
Xiangyu Wang
5,* and
Liyang Yu
1,6,*
1
Engineering Research Center of Chestnut Industry Technology, Ministry of Education, Hebei Normal University of Science and Technology, Qinhuangdao 066004, China
2
Research Center for Rural Vitalization, Hebei Normal University of Science and Technology, Qinhuangdao 066004, China
3
College of Horticulture Science and Technology, Hebei Normal University of Science and Technology, Qinhuangdao 066004, China
4
College of Smart Agriculture, Chongqing University of Arts and Sciences, Chongqing 402160, China
5
The Office of Scientific Research, Hebei Normal University of Science and Technology, Qinhuangdao 066004, China
6
Hebei Yanshan Special Industrial Technology Research Institute, Qinhuangdao 066004, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Biology 2025, 14(10), 1460; https://doi.org/10.3390/biology14101460
Submission received: 10 September 2025 / Revised: 14 October 2025 / Accepted: 16 October 2025 / Published: 21 October 2025
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Simple Summary

Yield and quality are the primary objectives in Castanea mollissima breeding. The kernel of C. mollissima primarily accumulates starch as its main storage substance, and the starch content is a key factor determining kernel quality and yield. The auxin response factor (ARF) gene family plays critical roles in plant growth, development, and nutrient accumulation. In this study, 18 ARF family members were identified in the C. mollissima genome and subjected to systematic bioinformatic analysis. By examining morphological traits, starch content, and amylase activity at five post-anthesis stages of kernel development, combined with correlation analysis and weighted gene co-expression network analysis (WGCNA), we revealed that specific ARF genes are crucial for coordinating seed kernel development and starch accumulation. Notably, we discovered that CmARF5a and CmARF18 may act as key repressors of starch accumulation. Key ARF genes potentially regulating starch metabolism were further screened, and their dynamic expression patterns during seed kernel development were validated via RT-qPCR. This study provides valuable insights into the molecular mechanisms underlying starch accumulation in C. mollissima and functional characterization of ARF genes.

Abstract

Auxin response factors (ARFs) are a class of significant transcription factors that play crucial roles in the regulation of plant growth and development. Although ARF genes have been extensively characterized in various plants, their functions in perennial woody crops, particularly their involvement in regulating starch accumulation—a key determinant of yield and quality in Castanea mollissima—remain largely unexplored. To address this knowledge gap, we conducted a comprehensive study of the ARF gene family in the C. mollissima. In this study, 18 CmARF members, exhibiting diverse physicochemical properties, were identified within the C. mollissima genome. These CmARFs were categorized into four groups. Dispersed duplication emerged as the primary mechanism driving the expansion of the CmARF gene family. As C. mollissima seed kernels developed, notable changes were observed in starch content and the activity of enzymes related to starch biosynthesis, particularly a significant decrease in GBSS activity, which corresponded with an increase in seed kernel size and starch content. Transcriptome analysis delineated the expression patterns of CmARF genes during the development of C. mollissima seed kernels. A key novel finding of our research is that CmARF5a and CmARF18 are hypothesized to act as pivotal repressors of starch accumulation. This hypothesis is based on their expression profiles, strong negative correlations with physiological indicators, and WGCNA. Notably, the lack of correlation between these CmARFs and the expression of core starch biosynthetic genes suggests a potential indirect regulatory mechanism, offering a new perspective on ARF function in storage organ development. This study not only provides the first comprehensive characterization of the CmARF family but also offers a theoretical framework and candidate genes for future functional research on C. mollissima seed kernel development and starch biosynthesis.

1. Introduction

Transcription factors (TFs) are proteins that play a pivotal role in regulating gene expression by either promoting or inhibiting transcription through their interaction with DNA sequences near specific genes [1]. Among these, auxin response factors (ARFs) are crucial TFs in plants [2]. An ARF typically comprises three conserved regions: the B3 DNA-binding domain (DBD) at the N-terminus, the C-terminal domain (CTD) featuring Phox and Bem1p (PB1) dimerization regions, and an intervening variable middle region (MR) [3]. The DBD is characterized by its specific DNA-binding activity, enabling it to regulate gene expression by binding to the auxin response element (AuxRE) within the target gene sequence TGTCTC [4,5]. The MR, positioned between the DBD and CTD, varies in composition and is categorized based on its transcriptional activation or inhibition activity, dependent on the amino acid content. The CTD, a relatively conserved domain, facilitates the interaction of Aux/IAA proteins with ARF domain III/IV through motifs such as Phe-Gly-Arg (FGR) and Ser-Thr/Ser-Ser (STS), or the interaction between ARF proteins themselves, thereby modulating the expression of genes responsive to auxin [6,7]. Dimerization within these regions is essential for the accurate regulation of gene transcription and the integration of various signaling pathways mediated by ARF proteins [7].
To date, ARF genes have been identified and characterized in a multitude of plant species, including Arabidopsis thaliana (23) [4], Oryza sativa (25) [8], Zea mays (31) [9], Ipomoea batatas (29) [10], Malus domestica (31) [11], Vitis vinifera (19) [12], Prunus mume (17) [13], Prunus persica (17) [14], and Cajanus cajan (17) [15]. Extensive research has explored the diverse biological functions of ARF genes in plant growth and development, as well as their responses to environmental stresses. In A. thaliana, specific ARFs such as AtARF5, AtARF6, AtARF7, AtARF8, and AtARF19 have been implicated in critical growth and developmental processes, including embryonic axis elongation, floral organ development, lateral root formation, and fruit development [16,17,18,19,20,21,22]. Notably, AtARF6 and AtARF8 are involved in the regulation of petal and stamen development through the induction of jasmonic acid synthesis, which subsequently activates the expression of R2R3-type MYB TFs MYB21 and MYB24, promoting cell elongation in petals and stamens [19]. Additionally, AtARF7 and AtARF19 play a significant role in lateral root formation by directly activating the LBD/ASL gene [20]. The LBD/ASL serves as a downstream effector in auxin signaling, with its functional loss or dominant inhibition resulting in defects in lateral root development [20]. OsARF6 and OsARF17 can bind to the ILA1 promoter and exert control over O. sativa leaf angle by regulating the biosynthesis of secondary cell walls at leaf junctions [23]. Additionally, OsARF6 is involved in the miR167a-OsARF6-OsAUX3 signaling pathway, which plays a critical role in determining the grain length and weight in O. sativa [24]. In P. mume, PmARF13 and PmARF17 are implicated in developmental processes and functional expression [13]. In M. domestica, MdARF13 influences anthocyanin biosynthesis through the Aux/IAA-ARF signaling pathway [25]. In Pinus koraiensis, two genes, Pkor01G00962.1 and Pkor07G00704.1, have been identified as central components of the PkorARF gene family, crucially regulating embryonic development [26]. Moreover, ARF genes also play a significant role in plant responses to environmental stress. For instance, OsARF6 and OsARF19 mitigate oxidative damage induced by salt stress by activating genes for antioxidant enzymes such as superoxide dismutase (SOD) and peroxidase (POD), and interact with the abscisic acid (ABA) signaling pathway to regulate the synthesis of osmoregulatory substances like proline and soluble sugars [27]. The complexity of the auxin response in plants highlights the ARF gene family as a pivotal element in the auxin signaling pathway, essential for elucidating the regulatory mechanisms underlying plant growth and development [2].
Despite extensive studies on ARF genes across numerous species, revealing their diverse roles in growth, development, and stress responses, their functions in perennial woody crops remain comparatively less explored. This is particularly true for their potential role in regulating economically critical traits such as starch accumulation. C. mollissima is a deciduous tree of significant agronomic value, whose seed kernels are a vital food source in the Northern Hemisphere due to their high starch content alongside minerals and vitamins [28,29,30]. However, while the ARF gene family has been systematically characterized in many herbaceous and woody plants, a comprehensive analysis in C. mollissima is still lacking. More specifically, a critical knowledge gap exists regarding whether CmARF genes participate in the regulation of starch biosynthesis during seed kernel development—a fundamental process that determines the yield and quality of this perennial crop. Elucidating this link is essential for advancing our understanding of carbon allocation in woody perennials and provides potential genetic targets for breeding improvement.
Therefore, this study aimed to identify and systematically characterize the ARF genes in the C. mollissima genome, including analyses of their physicochemical properties, gene structure, phylogenetic evolution, cis-acting elements, and duplication events. Furthermore, by measuring dynamic changes in seed kernel size, starch content, and the activity of starch biosynthesis-related enzymes across five developmental stages, and integrating these data with transcriptome profiles, we sought to decipher the molecular mechanisms governing seed kernel development. Our correlation analyses and WGCNA results suggest that CmARF5a and CmARF18 may play significant roles in the development of C. mollissima seed kernels and in starch biosynthesis. These findings provide crucial theoretical insights into the role of CmARF genes in seed kernel development and establish a foundation for future functional studies.

2. Materials and Methods

2.1. Identification and Phylogenetic Analysis of CmARFs

The genome data for Castanea mollissima was obtained from the Castanea Genome Database (http://castaneadb.net/#/) (accessed on 7 January 2025). Hidden Markov Model (HMM) files pertaining to ARF proteins were sourced from the Pfam database (http://pfam-legacy.xfam.org/) (accessed on 10 January 2025). Reference sequences of AtARF proteins were downloaded from the TAIR database and utilized to conduct a BLAST v.2.2.30+ search against the complete protein sequence database of C. mollissima. HMMER 3.0 facilitated the analysis of proteins selected in the preceding step to isolate candidate proteins. Validation of the ARF conserved domain in CmARF proteins was performed using NCBI-CDD and SMART. The physicochemical properties, subcellular localization, and motif arrangements of all CmARF proteins were predicted using tools such as Expasy, WoLF-PSORT, and MEME, with default parameters. The gene structure of CmARF, based on exon-intron positioning, was visualized using TBtools v. 2.313, employing data from gff3 files [31]. A phylogenetic tree, which included ARF proteins from both C. mollissima and A. thaliana, was constructed using MEGA 7.0 with the maximum likelihood method (Table S1). Secondary and three-dimensional structures of CmARF proteins were predicted using SOPMA and SWISS-MODEL, respectively.

2.2. Chromosomal Distribution and Collinear Analysis of CmARFs

Chromosomal locations of the CmARF genes were extracted from the C. mollissima gff3 file and visualized using TBtools v. 2.313 [31]. Genomic data for A. thaliana, Q. robur, S. lycopersicum, V. vinifera, Z. mays, O. sativa, and P. bretschneideri were downloaded from the Phytozome database (https://phytozome-next.jgi.doe.gov/) (accessed on 3 March 2025). Collinear among these species and C. mollissima was analyzed using MCScanX v1.0 [32]. Non-synonymous (Ka) and synonymous (Ks) substitution rates of homologous genes within homologous fragments were calculated using the “add_ka_and_ks_to_collinearity” function in MCScanX v1.0. Dot-plots illustrating homologous collinear within the C. mollissima genome were generated using TBtools v. 2.313, and the median Ks values were calculated using a custom script [33]. Duplication types of CmARFs were identified using the “duplicate_gene_classifier” in MCScanX v1.0, and WGD or segmental duplications were differentiated using a previously described method [34].

2.3. Cis-Acting Elements, GO/KEGG Enrichment, Transcription Factors (TFs) Regulatory and Protein-Protein Interaction (PPI) Networks Analysis

The upstream 2000 bp sequences of the CmARF genes were uploaded to Plant-CARE to predict cis-acting elements [35]. TBtools v. 2.313 facilitated the GO and KEGG enrichment analyses [36]. The regulatory roles of TFs within the 2000 bp upstream regions of the CmARF genes were inferred using the Plant Transcriptional Regulatory Map (PTRM). Visualization of these predictions was accomplished using Cytoscape software (v3.9.1) [37]. The potential interactions between CmARF proteins were predicted using the STRING database (https://cn.string-db.org/) (accessed on 13 March 2025) [38].

2.4. Plant Materials and Phenotypic Determination

The cultivar ‘Xinglong No. 4’ of C. mollissima has demonstrated notable agronomic characteristics, particularly due to its high starch content in the kernels. These were observed at the experimental base of Hebei Normal University of Science and Technology, located in Qinhuangdao City, Hebei Province (39°66′ N, 119°22′ E). Samples from ‘Xinglong No. 4’ C. mollissima were harvested at 60, 70, 80, 90, and 100 days (S1 to S5) post-flowering. The mass and dimensions of the kernels—longitudinal, transverse, and lateral diameters—were measured using an analytical balance and a vernier caliper, respectively. Amylose content was determined using the Amylose Content Assay Kit (Cominbio, Suzhou, China) based on an enzymatic method. In this assay, amylose was separated and hydrolyzed to glucose, which was then quantified using the GOPOD reagent at 505 nm. The kit-provided standard (a defined amylose solution) was used for calibration. Amylopectin content was quantified using the Amylopectin Content Assay Kit (Cominbio, Suzhou, China) based on the dual-wavelength method (measuring absorbance at 550 nm and 743 nm). The calculation was performed using the standard curve equation (y = 0.0607x + 0.0076) provided and validated by the kit manufacturer, which is established using purified amylopectin standards. Enzyme activities for adenosine diphosphate glucose pyrophosphorylase (AGPase), soluble starch synthase (SSS), granule-bound starch synthase (GBSS), and starch branching enzyme (SBE) were assessed using a kit provided by Keming Biotechnology Co., Ltd., Suzhou, China [39].

2.5. RNA-Sequencing, DEG and WGCNA, RT-qPCR Validation

The RNAprep Pure Plant Kit (Tiengen, Beijing, China) was utilized for RNA extraction from C. mollissima kernels. RNA purity was evaluated using the Agilent 2100 Bioanalyzer (Agilent, San Diego, CA, USA). Subsequently, the NEBNext® Ultra™ RNA Library Prep Kit for Illumina (San Diego, CA, USA) facilitated the construction of cDNA libraries. Sequencing was performed on the NovaSeq 6000 system (Illumina, San Diego, CA, USA), and the RNA-seq data have been deposited in the NCBI database (BioProject number: PRJNA1268182). Hisat2 v2.0.5 aligned the clean data to the reference genome, specifically the published N-11 C. mollissima genome, which was obtained from the Castanea genome database [40]. FeatureCounts and DESeq2 v1.4.5 were employed to calculate the FPKM (Fragments Per Kilobase of transcript per Million mapped reads) of genes and to identify DEGs [41,42]. WGCNA was conducted using WGCNAshiny in TBtools v. 2.313 [31]. Pearson correlation analysis identified modules significantly correlated with the physiological and biological traits of C. mollissima seed kernels of ‘Xinglong No. 4’ at various developmental stages. RT-qPCR analysis was performed using RNA isolated from seed kernels at five developmental stages to validate the temporal expression patterns of selected CmARF genes. The PrimeScript RT Master Mix (Takara Biotechnology Co., Dalian, China) was used for the reverse transcription of total RNA. Primer Premier designed specific primers for the CmARF genes [43]. The ABI 7500 Real-Time PCR system (Applied Biosystems Inc., Foster City, CA, USA) conducted RT-qPCR experiments using TB Green Premix Ex Taq (Takara). As in our previous extensive research [28,44,45,46,47,48], the 18S gene was selected as a reference gene due to its relatively stable expression levels in various cell types and physiological conditions of C. mollissima. Additionally, the 2−ΔΔCt method was used to analyze the relative gene expression levels. Information about the primers is available in Table S2.

2.6. Statistical Analysis

All experiments were performed with three independent biological replicates. Data are presented as the mean ± standard deviation (SD). Statistical comparisons were conducted by one-way analysis of variance (ANOVA) followed by Tukey’s post hoc test. Differences were considered statistically significant at a p-value < 0.05. All statistical analyses were performed using GraphPad Prism 9.5. Additionally, Pearson correlation analysis was performed using IBM SPSS Statistics 26 to assess: (1) the relationships between CmARF genes expressions (FPKM) and phenotypic indicators, and (2) the correlation between RNA-seq (FPKM) and RT-qPCR results for CmARF genes. A p-value < 0.05 was considered statistically significant.

3. Results

3.1. Identification and Phylogenetic Analysis of the CmARFs

In the genome of C. mollissima, a total of 18 ARF genes were identified based on sequence similarity and the presence of conserved domains (Table S3). The molecular weights of the CmARFs range from 63.15 kDa (CmARF17) to 119.12 kDa (CmARF5b). Their GRAVY values span from −0.669 (CmARF2a) to −0.249 (CmARF16b), confirming the hydrophilic nature of CmARF proteins. The theoretical isoelectric points (pI) of these proteins vary from 5.26 (CmARF5a) to 8.71 (CmARF6a), with only CmARF10 and CmARF6a possessing pI above seven, indicating that the majority of CmARF proteins are acidic (pI < 7). The aliphatic index ranges from 64.04 (CmARF2a) to 77.00 (CmARF6a), while the instability index ranges from 45.51 (CmARF10) to 66.25 (CmARF5c), suggesting that these proteins are generally unstable. Predictions of subcellular localization reveal that while CmARF6b is localized to both the nucleus and cytoplasm, other members are exclusively nuclear. Phylogenetic analysis comparing ARF genes from C. mollissima, A. thaliana, and O. sativa grouped the CmARF genes into four distinct clusters: Group Ia and Ib each contain six CmARF members; Group III includes four; and Group II comprises just two, CmARF3 and CmARF4 (Figure 1). In the cross-species phylogenetic tree, as expected from their closer evolutionary relationship as dicots, all CmARF members, except CmARF5b, CmARF5c, CmARF10, and CmARF16b, exhibit closer genetic relationships with AtARF members than with OsARF members.

3.2. Characterization of the CmARFs

Each of the CmARF proteins incorporates B3 DBDs and ARF. Notably, ARF members in Groups II and III, along with CmARF6a and CmARF5c from Group Ib, lack C-terminal AUX/IAA domains (CTD), whereas other CmARF members possess the complete ARF domain architecture (DBD-ARF-CTD) (Figure 2A,B). The exon-intron structures of CmARF members vary among groups (Figure 2C): those in Groups Ia and Ib typically contain 13–15 introns, whereas members in Group III have between 2 and 8 introns. CmARF3 and CmARF4 from Group II contain 10 and 11 introns, respectively. These structural features align with the phylogenetic relationships previously described (Figure 1). Motifs 1, 4, and 10 were identified in all CmARF members, underscoring their significant roles within the gene family (Figure 2D). Additionally, some members display a motif arrangement that deviates from others within the same group but matches that of members in different groups. For instance, CmARF6a and CmARF3, belonging to Groups Ib and II, respectively, share identical motif orders, differing from other members within their respective groups. This suggests evolutionary changes in non-motif sequences among some CmARF members, leading to the retention of identical motif orders across different groups.

3.3. Chromosomal Location and Duplication Type Analysis of CmARFs

The C. mollissima genome encodes 17 CmARF genes situated on various chromosomes, with the exception of CmARF2a, which is located on a scaffold (Figure 3A). Chromosome 1, the longest chromosome, harbors four CmARF members: CmARF1, CmARF8, CmARF9, and CmARF5a. Chromosomes 2, 5, 6, 11, and 12 each contain two CmARF genes. In contrast, Chromosomes 3, 4, and 9 each support a single CmARF member, while Chromosomes 7, 8, and 10 have no CmARF genes. Notably, there appears to be no correlation between the length of a chromosome and the presence of CmARF members. Gene duplication constitutes a pivotal mechanism underpinning the evolution of gene families and the diversification of gene functions [49]. Utilizing collinear analysis within the C. mollissima genome, and extending this analysis to include homologous collinear dot plots, we previously classified the duplication types of CmARF genes (Figure 3B,C) [28,34]. Our findings reveal that dispersed duplication accounts for approximately 66.67% (12 genes) of all CmARF genes, suggesting this duplication type plays a significant role in the expansion of the CmARF gene family. Additionally, CmARF10, CmARF16a, and CmARF16b are derived from the core eudicot common hexaploidization (ECH) event [33]. Segmental duplication is observed in CmARF2a and CmARF2b, while CmARF8 is associated with proximal duplication. Furthermore, the non-synonymous (Ka) to synonymous (Ks) substitution rate ratios for the gene pairs within the CmARF family are all below 1, indicating that these genes have been subject to purifying selection throughout their evolutionary history (Table S4).

3.4. Collinear Analysis of ARFs in Several Plant Species

A gene family is generally defined as a collection of genes descended from a common ancestor, comprising two or more copies of a gene that have arisen through gene duplication [50]. In this study, seven representative species were selected to examine their collinear with the C. mollissima genome, building upon our previous research [28]. The analysis identified varying numbers of orthologous gene pairs containing CmARFs distributed across the collinear regions of C. mollissima and several other species: A. thaliana (14), Q. robur (15), S. lycopersicum (24), V. vinifera (22), Z. mays (6), O. sativa (10), and P. bretschneideri (31) (Figure 4A,B; Tables S5–S11). Additionally, the collinear regions between C. mollissima and these species contained 11, 10, 13, 15, 5, 8, and 14 CmARF genes, respectively (Tables S5–S11). It is evident that additional orthologous gene pairs have been retained between C. mollissima and dicotyledonous plants relative to monocotyledonous plants, suggesting independent duplication events in the ARF gene family during the evolution of dicotyledonous plants. Notably, CmARF10 is associated with three orthologous gene pairs in Q. robur, S. lycopersicum, and V. vinifera, which indicates a higher preservation of homologous genes of CmARF in these species (Figure 4A,B; Tables S5–S11). Similarly, both CmARF16a and CmARF16b are linked to at least three orthologous genes in the genomes of V. vinifera, S. lycopersicum, and P. bretschneideri, respectively. The gene dosage hypothesis posits that genes integral to complex regulatory networks are more likely to be conserved through evolution due to their sensitivity to changes in gene dosage (i.e., gene copy number), suggesting a crucial role for CmARF10, CmARF16a, and CmARF16b in the evolutionary trajectory of the CmARF gene family [51,52]. A diverse array of collinear blocks containing CmARFs was observed between C. mollissima and A. thaliana (14), Q. robur (16), S. lycopersicum (24), V. vinifera (23), Z. mays (7), O. sativa (10), and P. bretschneideri (36), with median block lengths of 20.5, 13.5, 13.0, 25.0, 7.0, 7.5, and 16, respectively (Figure 4C; Tables S12–S18). These findings illuminate the evolutionary relationships among these species and underscore the retention of the ARF gene family within collinear regions.

3.5. Cis-Acting Elements and TFs Regulatory Network

Promoters are DNA regulatory sequences situated upstream of genes [53]. Located within the promoter region, specific sequences initiate transcription by binding to RNA polymerase and TFs, thereby regulating the spatiotemporal specificity and intensity of gene expression [54]. In the promoters of 18 CmARFs, a total of 417 cis-acting elements were predicted (Figure 5A–C; Table S19). Notably, a substantial assortment of cis-acting elements associated with development and hormones was found across nearly all CmARFs’ promoter regions. These include the auxin-responsive element (TGA-element), gibberellin-responsive element (P-box), MeJA-responsive cis-acting regulatory element (TGACG-motif), salicylic acid responsive cis-acting element (TCA-element), and a cis-regulatory element involved in endosperm expression, suggesting a broad involvement of CmARFs in the growth and development of C. mollissima. Additionally, other cis-acting elements related to light responses and environmental pressures were identified, including the light-responsive element (GT1-motif), MYB binding site involved in drought response (MBS), elements involved in defense and stress responses (TC-rich repeats), wound responsive element (WUN motif), and cis-acting element involved in low-temperature responsiveness (LTR). These findings align with the extensively documented role of ARF genes in plant growth, development, and environmental stress response [2]. Moreover, TFs potentially acting on the CmARFs were also predicted (Figure 5D,E; Table S20). A total of 308 TFs from 39 gene families were identified as likely regulators of CmARFs expression. Notable among these TFs were ERF (39), MYB (38), NAC (31), and WRKY (23), indicating their significant roles in the regulation of CmARFs expression. Additionally, certain TF families were represented by only one member potentially influencing CmARF gene expression, such as GRAS, TALE, CAMTA, and STS.

3.6. Protein Interaction Network and GO/KEGG Enrichment Analysis

The secondary and tertiary structures of proteins encoded by the CmARF gene were predicted to characterize these proteins. The secondary structure predominantly consists of random coils, ranging from 70.6% to 79.6%, alpha helices from 10.8% to 17.8%, and extended strands from 8.2% to 13.2%, with an absence of beta-turns (Table S21). The tertiary structure predictions indicate that CmARF proteins within the same subfamily exhibit similar conformations (Figure S1). Additionally, the interaction network of CmARF proteins was inferred using the String online tool [38] (Figure 6A). Consistent with expectations, IAA proteins were identified at the core of this network. This centrality is attributable to the ability of ARF proteins, which are TFs, to recognize and bind IAA, thereby regulating the expression of genes responsive to growth hormones [2]. Notably, CmARF5b and CmARF5c occupy central positions within this protein-interaction network, suggesting their pivotal roles in the regulation of growth and development in C. mollissima. Furthermore, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses revealed that CmARF genes are primarily enriched in pathways related to plant hormone signal transduction, transcriptional regulator activity, and the regulation of RNA biosynthetic processes (Figure 6B,C). These findings align with the known role of ARF as a key component of the auxin signaling pathway [55].

3.7. Phenotypic Changes During the Development of C. mollissima Seed Kernels

The variety ‘Xinglong No. 4’ of C. mollissima exhibited significant morphological changes in both the thorn bracts and seed kernels during development, primarily characterized by an increase in size. The thorn bracts opened 100 days post-flowering, denoting the onset of maturity (Figure 7A). An essential trait under investigation was the size of the seed kernels. Initially, at stage S1, the fresh weight of the seed kernel was a mere 0.28 g; subsequently, it progressively increased, reaching a peak of 6.52 g at stage S5 (Figure 7B). Similarly, the dimensions of the seed kernels expanded over time, with their longitudinal, transverse, and lateral diameters starting at 5.60 mm, 10.40 mm, and 8.76 mm, respectively, at stage S1 and culminating at 21.45 mm, 28.85 mm, and 19.44 mm at maturity (stage S5). The growth rate of the seed kernel size was notably accelerated between stages S3 and S4. Starch constitutes the primary nutrient in the seed kernels of C. mollissima [56]. At the initial stage S1, the total starch content was recorded at 548.25 mg/g, representing 54.83% of the dry weight of the seed kernels (Figure 7B). This starch content increased, reaching its highest at 640.74 mg/g at stage S5. Within the starch composition, amylopectin was predominant, while amylose was less abundant. The content of amylopectin and amylose initially was 419.38 mg/g and 128.87 mg/g, respectively, at stage S1, escalating to their respective maxima of 500.25 mg/g and 140.49 mg/g at stage S5. Key enzymes implicated in starch biosynthesis in C. mollissima, such as granule-bound starch synthase (GBSS), starch branching enzyme (SBE), soluble starch synthase (SSS), and adenosine diphosphate glucose pyrophosphatase (AGPPase), have been identified [29,30,56]. GBSS activity was significantly higher during the initial stages (S1 and S2) and thereafter decreased to its lowest level at stage S5 (Figure 7B). Conversely, the activities of AGPPase and SBE generally increased with development, though SBE activity notably declined at stage S5. Statistical analysis confirmed that SSS activity was significantly lower at stage S3 compared to S1, S2, S4, and S5, before increasing as development progressed.

3.8. RNA-Sequencing and DEG Identification

mRNA was extracted from the seed kernels of ‘Xinglong No. 4’ C. mollissima across five developmental stages, leading to the construction of 15 cDNA libraries (three replicates per stage). Transcriptome sequencing yielded a total of 878.66 Mb of clean reads, characterized by high base quality (Q20 ≥ 98.01%, Q30 ≥ 94.23%) and a consistent GC content ranging between 43.62% and 44.32% (Table S22). The alignment rates to the reference genome varied from 88.82% to 90.15% (Table S23). PCA distinctly differentiated the RNA-seq data from various developmental stages, highlighting significant differences in gene expression profiles among the groups (Figure 8A).
To investigate the underlying mechanisms of kernel development and starch biosynthesis in C. mollissima, DEGs were identified across various developmental stages using stringent criteria: an absolute log2 FC of at least 2 and an FDR below 0.05. Relative to stage S1, there was a significant down-regulation in the expression of 1191, 2101, 3480, and 4712 genes at stages S2, S3, S4, and S5, respectively. Concurrently, 840, 1782, 3226, and 4478 genes demonstrated significant up-regulation at these stages (Figure 8B,C). Notably, the number of DEGs increased progressively as the C. mollissima kernels matured, with the number of down-regulated genes consistently surpassing that of up-regulated genes. The predominance of down-regulated genes during development may reflect a coordinated shutdown of specific metabolic and biosynthetic pathways as cells transition to more specialized states [57]. Enrichment analyses via KEGG and GO pathways revealed that these DEGs were predominantly associated with the biosynthesis of secondary metabolites, plant hormone signal transduction, starch and sucrose metabolism, and the MAPK signaling pathway (Figure 8D; Figures S2 and S3).
WGCNA was employed to identify core genes critical to the development of C. mollissima seed kernels, utilizing RNA-seq data from five developmental stages alongside corresponding phenotypic data (Figure 9A). Analysis of the 27 identified modules revealed varied correlations with different phenotypic aspects of the seed kernel (Figure 9B). Noteworthy were the significant correlations observed in some modules with kernel size and starch content. For instance, the turquoise module displayed significant negative correlations with fresh weight (r = −0.87, p = 2.5 × 10−5), longitudinal diameter (r = −0.97, p = 2.3 × 10−9), transverse diameter (r = −0.85, p = 6 × 10−5), lateral diameter (r = −0.95, p = 6.1 × 10−8), total starch (r = −0.93, p = 5.2 × 10−7), and amylopectin content (r = −0.91, p = 2.5 × 10−6). Conversely, the green module exhibited significant positive correlations with fresh weight (r = 0.93, p = 5.2 × 10−7), longitudinal diameter (r = 0.89, p = 8.8 × 10−6), transverse diameter (r = 0.87, p = 2.5 × 10−5), and lateral diameter (r = 0.89, p = 8.8 × 10−6). These findings suggest that the gene sets and their expression regulatory networks within these modules are intricately linked to kernel development and starch biosynthesis in C. mollissima. Intriguingly, ten of the eighteen CmARF genes were located within the turquoise module, implying a potential role in kernel development and starch accumulation (Table S24). GO and KEGG functional enrichment analyses of the turquoise module indicated significant enrichment in cellular developmental processes, biosynthesis of various secondary metabolites, and positive regulation of macromolecule metabolic processes, underscoring the module’s relevance to C. mollissima seed kernel development (Figure 9C,D).

3.9. Correlation Between CmARF Genes and Phenotypic Indicators, and RT-qPCR Validation

The significant roles of the ARF gene family in plant growth and development have been extensively documented [2,24,25,26]. Notably, certain members of the CmARF family exhibit pronounced fluctuations in expression during the development of C. mollissima seed kernels (Figure 10A). For instance, the expression levels of CmARF9 and CmARF5a were elevated at the S1 stage, with FPKM values of 59.88 and 31.32, respectively. These levels subsequently declined markedly, reaching their nadir at the maturation stage (S5) with values of only 7.58 and 4.52, respectively (Figure 10A). Additionally, an analysis was conducted to assess the correlation between the expression levels of CmARF genes and associated phenotypic indicators, aiming to elucidate the potential roles of CmARF genes in seed kernel development (Figure 10B). Genes that exhibited significant associations with seed kernel size and starch content were of particular interest. This focus stems from the observation that seed kernel size represents the most conspicuous change during development, and starch constitutes the primary nutrient in seed kernels, typically comprising about 50% of their dry weight [58,59]. Notably, as detailed in Table S25, the expression of CmARF5a (r = −9.78, p < 0.01) and CmARF18 (r = −9.81, p < 0.01) were highly significantly negatively correlated with total starch content. Conversely, CmARF9, CmARF5b, CmARF2c, and CmARF4 demonstrated significant negative correlations with traits related to seed kernel size (fresh weight, longitudinal diameter, transverse diameter, and measured diameter), indicating their associations with seed kernel development. Selected CmARF genes, which displayed substantial expression differences during the development phases of C. mollissima seed kernels, were chosen for RT-qPCR experiments to validate the transcriptome data (Figure 10C). The outcomes of these RT-qPCR experiments exhibited robust consistency and correlation with the RNA-seq data, thereby confirming the dynamic expression of CmARF genes throughout seed kernel development.

4. Discussion

4.1. Identification and Molecular Characteristics of ARF Genes in C. mollissima

In this study, 18 CmARF genes were identified in the C. mollissima genome. The encoded proteins were all predicted to be hydrophilic and unstable, a common characteristic of ARF proteins across plant species [2,11,13,15,26]. These properties could potentially explain their generally low expression in heterologous systems and may present challenges for functional validation in C. mollissima [60]. Phylogenetic analysis classified the CmARF genes into four distinct groups, consistent with the categorization in other plants [61] (Figure 1). All CmARF proteins possess the conserved B3 DNA-binding domain (DBD) and Auxin Responsive Domain (ARD), which are considered essential for their functional [62]. Gene structure analysis revealed that members within the same phylogenetic group generally share similar exon-intron patterns (Figure 2). Notably, groups Ia and Ib contained more introns (13–15) than group III (2–8), a structural variation that might be relevant to gene evolution and expression regulation [63,64,65]. Furthermore, CmARF proteins within the same group exhibited similar three-dimensional structures (Figure S1), implying potential functional conservation. This structural similarity could aid in predicting their roles by analogy with characterized ARF proteins from model species like A. thaliana [66].

4.2. Evolution and Expansion of CmARFs

The number of ARF genes in C. mollissima (18) is similar to that in V. vinifera (19) [12], P. mume (17) [13], and P. persica (17) [14]. Collinear analysis suggested that C. mollissima exhibited closer evolutionary relationships with dicotyledonous plants than monocots (Figure 4, Tables S5–S18). Some CmARF members, such as CmARF16a and CmARF16b, possessed multiple orthologous genes in several dicot species, which might indicate potential functional importance or diversification. Conversely, CmARF2a and CmARF8 lacked clear orthologous genes in the seven surveyed plant genomes, hinting at potential functional specificity in C. mollissima. Analysis of duplication events indicated that dispersed duplication was the primary mechanism (66.67% of genes) for the expansion of the CmARF gene family (Figure 3), aligning with its recognized role as a major driver of gene family evolution in plants [67,68].

4.3. Promoter Analysis and Protein Interaction Network Analysis

Cis-acting element analysis revealed that the promoter regions of CmARF16a and CmARF16b were particularly enriched with elements related to seed development, including those involved in endosperm and meristem expression, and gibberellin response (Figure 5; Table S19). This finding is consistent with their potential role in seed development, especially given that their A. thaliana orthologous genes, AtARF16, inhibits embryonic axis elongation [69]. Protein interaction network analysis predicted CmARF5b and CmARF5c as central hubs (Figure 6A), analogous to the critical role of AtARF5 in embryonic development [2]. Additionally, the promoter of CmARF10 was predicted to be regulated by multiple TFs (e.g., MYB, NAC, TCP) (Figure 5D,E), suggesting its involvement in complex regulatory networks, potentially in processes like lateral root formation as seen with AtARF10 [70].

4.4. Potential Functional Analysis of CmARFs During C. mollissima Seed Kernel Development

The role of ARF genes in fruit and seed development is well-established [21,22,70]. In this study, we investigated their involvement in the development of C. mollissima seed kernels, which are notable for their high starch content [29,30]. We observed significant negative correlations between the expression of several CmARF genes (e.g., CmARF9, CmARF5b, CmARF2c) and seed kernel size (Figure 10), which aligns with the known functions of ARFs like SlARF7 and AtARF8 in limiting organ growth [17,71].
Our most intriguing finding pertains to CmARF5a and CmARF18. Their expression showed highly significant negative correlations with total starch, amylopectin, and amylose content. However, a critical and novel observation was the lack of significant correlation between their expression and the key genes encoding starch biosynthetic enzymes (SSS, GBSS, AGPase, SBE). The absence of a direct correlation with the expression of core starch biosynthetic genes suggests that CmARF5a and CmARF18 may act as indirect repressors, potentially modulating upstream processes like carbon partitioning or photosynthetic efficiency. This proposed indirect mechanism represents a testable hypothesis for future research.
We hypothesize that these TFs might modulate upstream processes such as photosynthesis or carbohydrate transport. This interpretation is supported by: (1) the abundance of photosynthesis-related cis-elements in their promoter regions (Figure 5), and (2) their membership in the WGCNA turquoise module, which is significantly correlated with starch content and enriched in pathways like “biosynthesis of secondary metabolites” and “positive regulation of macromolecule metabolic processes,” but not directly in starch biosynthesis itself (Figure 9). The role of AtARF5 as a transcriptional activator in complex developmental processes provides a plausible precedent for CmARF5a acting through multifaceted pathways [70,72]. The pronounced decrease in CmARF5a and CmARF18 expression during seed kernel maturation, as confirmed by RT-qPCR, is consistent with a potential role as repressors of starch accumulation.
In conclusion, this study provides the first systematic characterization of the CmARF gene family in C. mollissima. Beyond cataloging these genes, our integrated analysis points to a potential novel regulatory paradigm where specific CmARF genes (CmARF5a and CmARF18) may control starch accumulation in perennial woody crops through indirect mechanisms, distinct from the direct transcriptional regulation of biosynthetic genes often reported in other systems. These findings propose a framework for future functional studies aimed at validating these mechanisms and improving starch-related traits in C. mollissima.

4.5. Limitations and Future Perspectives

While this study provides comprehensive insights into the CmARF gene family, several limitations should be considered when interpreting the results. First, the associations between CmARF genes expressions and phenotypic traits are primarily correlative. Although these correlations provide valuable clues about potential gene functions, they do not establish causal relationships. Second, functional validation of the proposed roles of key CmARF genes, particularly CmARF5a and CmARF18, through transgenic experiments in model plants or C. mollissima itself is ultimately required to confirm their biological functions. Such experiments remain challenging in perennial woody crops like C. mollissima due to long life cycles and transformation difficulties, but they represent a crucial next step. Third, our study focused on a single elite cultivar, ‘Xinglong No. 4’. Consequently, the generalizability of the observed expression patterns and correlations to other C. mollissima cultivars with different genetic backgrounds requires further investigation. Future work should include multiple cultivars to assess the broader applicability of these findings. Furthermore, the identification of gene family members based on domain and motif structure, while standard, may exclude atypical members; future functional studies could clarify the status of such genes.
Furthermore, the observed decrease in GBSS activity during later developmental stages, which coincided with a marked increase in total starch content (Figure 7B), may initially seem paradoxical. However, this pattern can be explained by the concurrent dynamic of a significantly increasing amylopectin-to-amylose ratio. As GBSS is primarily responsible for amylose synthesis, its reduced activity likely contributes to a relative enrichment of amylopectin, the major component of starch. The substantial increase in the activities of AGPase and SBE, key enzymes for amylopectin synthesis and branching, during these stages (Figure 7B) supports this interpretation and accounts for the net accumulation of total starch.
Despite these limitations, the current study establishes a solid foundation and identifies specific candidate genes for such functional studies, which will be vital for elucidating the precise regulatory mechanisms and for genetic improvement of C. mollissima.

5. Conclusions

In this study, a total of 18 ARF gene family members were identified in the C. mollissima genome. Phylogenetic analysis classified them into four distinct subfamilies. Further examination revealed that all CmARF genes contain typical ARF conserved domains and exhibit high conservation in gene structure and motif composition. Integrated analyses involving collinearity assessment, promoter cis-acting element screening, transcription factor binding prediction, functional enrichment, and protein interaction networks collectively suggested potential roles of CmARF genes in C. mollissima growth, development, and starch accumulation. Transcriptomic profiling and RT-qPCR validation demonstrated that all 18 CmARF genes were differentially expressed across five developmental stages of C. mollissima seed kernels, showing significant spatiotemporal specificity. Among these, CmARF5a and CmARF18 were identified as key regulators potentially involved in seed kernels development and starch metabolism. However, the precise molecular mechanisms through which these genes mediate seed kernels development warrant further investigation. This study provides a foundation for functional characterization of ARF gene families in woody plants and offers theoretical insights and candidate genes for future research on the regulation of seed development and starch metabolism in C. mollissima.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/biology14101460/s1, Figure S1: The predicted three-dimensional structure of CmARF proteins; Figure S2: KEGG enrichment of DEGs in S2 vs. S1, S3 vs. S1; Figure S3: GO enrichment of DEGs in S2 vs. S1, S3 vs. S1, S4 vs. S1, and S5 vs. S1; Table S1: ARF genes list from C. mollissima, A. thaliana, O. sativa; Table S2: Primers used for RT-qPCR analysis. Table S3: Accession numbers and characteristics of 18 CmARF genes in Castanea mollissima; Table S4: The duplication model and calculation of Ka and Ks ratios of duplicated CmARF gene pairs; Table S5: The orthologous gene pairs containing CmARF in the cross of chestnut and Arabidopsis; Table S6: The orthologous gene pairs containing CmARF in the cross of chestnut and grape; Table S7: The orthologous gene pairs containing CmARF in the cross of chestnut and oak; Table S8: The orthologous gene pairs containing CmARF in the cross of chestnut and rice; Table S9: The orthologous gene pairs containing CmARF in the cross of chestnut and tomato; Table S10: The orthologous gene pairs containing CmARF in the cross of chestnut and pear; Table S11: The orthologous gene pairs containing CmARF in the cross of chestnut and maize; Table S12: The collinear blocks containing CmARF identified between chestnut and grape; Table S13: The collinear blocks containing CmARF identified between chestnut and Arabidopsis thaliana; Table S14: The collinear blocks containing CmARF identified between chestnut and oak; Table S15: The collinear blocks containing CmARF identified between chestnut and rice; Table S16: The collinear blocks containing CmARF identified between chestnut and tomato; Table S17: The collinear blocks containing CmARF identified between chestnut and pear; Table S18: The collinear blocks containing CmARF identified between chestnut and maize; Table S19: Information of cis-acting elements predicted from 2000 bp upstream regions of 21 CmARF genes; Table S20: Predictive transcription factor (TFs) analysis of CmARF genes; Table S21: CmARF protein secondary structure; Table S22: Summary of transcriptome sequencing; Table S23: Mapping rate range information statistics with reference genome; Table S24: The distribution of CmARF genes in color modules in WGCNA; Table S25: Correlation analysis between CmARF gene expression level and phenotype indicators.

Author Contributions

Conceptualization, X.W. and L.Y.; methodology, X.L. (Xili Liu), Y.L. (Yun Li) and M.L.; software, X.L. (Xili Liu), D.W., M.W., Y.L. (Yi Lu) and X.L. (Xia Liu); validation, H.Z. and X.W.; formal analysis, X.L. (Xili Liu), Y.L. (Yun Li) and M.L.; investigation, H.Z. and X.W.; resources, H.Z., X.W. and L.Y.; data curation, X.W. and L.Y.; writing—original draft preparation, X.L. (Xili Liu), Y.L. (Yun Li) and M.L.; writing—review and editing, X.W. and L.Y.; visualization, X.L. (Xili Liu), Y.L. (Yun Li) and M.L.; supervision, X.W. and L.Y.; project administration, X.W. and L.Y.; funding acquisition, X.W. and L.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Science Research Project of Hebei Education Department, grant number QN2025036, and the Hebei Natural Science Foundation, grant number C2024407040.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in this study are included in the article and Supplementary Materials. The RNA-seq data generated by this study were uploaded to NCBI and can be accessed through the BioProject login number PRJNA1268182.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Singh, K.B.; Foley, R.C.; Oñate-Sánchez, L. Transcription factors in plant defense and stress responses. Curr. Opin. Plant Biol. 2002, 5, 430–436. [Google Scholar] [CrossRef]
  2. Li, S.B.; Xie, Z.Z.; Hu, C.G.; Zhang, J.Z. A Review of Auxin Response Factors (ARFs) in Plants. Front. Plant Sci. 2016, 7, 47. [Google Scholar] [CrossRef]
  3. Li, Y.; Han, S.; Qi, Y. Advances in structure and function of auxin response factor in plants. J. Integr. Plant Biol. 2023, 65, 617–632. [Google Scholar] [CrossRef] [PubMed]
  4. Guilfoyle, T.J.; Hagen, G. Auxin response factors. Curr. Opin. Plant Biol. 2007, 10, 453–460. [Google Scholar] [CrossRef]
  5. Tiwari, S.B.; Hagen, G.; Guilfoyle, T. The Roles of Auxin Response Factor Domains in Auxin-Responsive Transcription. Plant Cell 2003, 15, 533–543. [Google Scholar] [CrossRef]
  6. Konishi, M.; Donner, T.J.; Scarpella, E.; Yanagisawa, S. MONOPTEROS directly activates the auxin-inducible promoter of the Dof5.8 transcription factor gene in Arabidopsis thaliana leaf provascular cells. J. Exp. Bot. 2014, 66, 283–291. [Google Scholar] [CrossRef]
  7. Krogan, N.T.; Marcos, D.; Weiner, A.I.; Berleth, T. The auxin response factor MONOPTEROS controls meristem function and organogenesis in both the shoot and root through the direct regulation of PIN genes. New Phytol. 2016, 212, 42–50. [Google Scholar] [CrossRef]
  8. Wang, D.; Pei, K.; Fu, Y.; Sun, Z.; Li, S.; Liu, H.; Tang, K.; Han, B.; Tao, Y. Genome-wide analysis of the auxin response factors (ARF) gene family in rice (Oryza sativa). Gene 2007, 394, 13–24. [Google Scholar] [CrossRef] [PubMed]
  9. Xing, H.; Pudake, R.N.; Guo, G.; Xing, G.; Hu, Z.; Zhang, Y.; Sun, Q.; Ni, Z. Genome-wide identification and expression profiling of auxin response factor (ARF) gene family in maize. BMC Genom. 2011, 12, 178. [Google Scholar] [CrossRef] [PubMed]
  10. Mathura, S.R.; Sutton, F.; Bowrin, V. Genome-wide identification, characterization, and expression analysis of the sweet potato (Ipomoea batatas [L.] Lam.) ARF, Aux/IAA, GH3, and SAUR gene families. BMC Plant Biol. 2023, 23, 622. [Google Scholar] [CrossRef]
  11. Luo, X.C.; Sun, M.H.; Xu, R.R.; Shu, H.R.; Wang, J.W.; Zhang, S.Z. Genomewide identification and expression analysis of the ARF gene family in apple. J. Genet. 2014, 93, 785–797. [Google Scholar] [CrossRef] [PubMed]
  12. Wan, S.; Li, W.; Zhu, Y.; Liu, Z.; Huang, W.; Zhan, J. Genome-wide identification, characterization and expression analysis of the auxin response factor gene family in Vitis vinifera. Plant Cell Rep. 2014, 33, 1365–1375. [Google Scholar] [CrossRef]
  13. Song, J.; Gao, Z.; Huo, X.; Sun, H.; Xu, Y.; Shi, T.; Ni, Z. Genome-wide identification of the auxin response factor (ARF) gene family and expression analysis of its role associated with pistil development in Japanese apricot (Prunus mume Sieb. et Zucc). Acta Physiol. Plant. 2015, 37, 145. [Google Scholar] [CrossRef]
  14. Diao, D.; Hu, X.; Guan, D.; Wang, W.; Yang, H.; Liu, Y. Genome-wide identification of the ARF (auxin response factor) gene family in peach and their expression analysis. Mol. Biol. Rep. 2020, 47, 4331–4344. [Google Scholar] [CrossRef] [PubMed]
  15. Arpita, K.; Sharma, S.; Srivastava, H.; Kumar, K.; Mushtaq, M.; Gupta, P.; Jain, R.; Gaikwad, K. Genome-wide survey, molecular evolution and expression analysis of Auxin Response Factor (ARF) gene family indicating their key role in seed number per pod in pigeonpea (C. cajan L. Millsp.). Int. J. Biol. Macromol. 2023, 253, 126833. [Google Scholar] [CrossRef]
  16. Hardtke, C.S.; Ckurshumova, W.; Vidaurre, D.P.; Singh, S.A.; Stamatiou, G.; Tiwari, S.B.; Hagen, G.; Guilfoyle, T.J.; Berleth, T. Overlapping and non-redundant functions of the Arabidopsis auxin response factors MONOPTEROS and NONPHOTOTROPIC HYPOCOTYL 4. Development 2004, 131, 1089–1100. [Google Scholar] [CrossRef]
  17. Nagpal, P.; Ellis, C.M.; Weber, H.; Ploense, S.E.; Barkawi, L.S.; Guilfoyle, T.J.; Hagen, G.; Alonso, J.M.; Cohen, J.D.; Farmer, E.E.; et al. Auxin response factors ARF6 and ARF8 promote jasmonic acid production and flower maturation. Development 2005, 132, 4107–4118. [Google Scholar] [CrossRef]
  18. Tabata, R.; Ikezaki, M.; Fujibe, T.; Aida, M.; Tian, C.-e.; Ueno, Y.; Yamamoto, K.T.; Machida, Y.; Nakamura, K.; Ishiguro, S. Arabidopsis AUXIN RESPONSE FACTOR6 and 8 Regulate Jasmonic Acid Biosynthesis and Floral Organ Development via Repression of Class 1 KNOX Genes. Plant Cell Physiol. 2009, 51, 164–175. [Google Scholar] [CrossRef]
  19. Reeves, P.H.; Ellis, C.M.; Ploense, S.E.; Wu, M.-F.; Yadav, V.; Tholl, D.; Chételat, A.; Haupt, I.; Kennerley, B.J.; Hodgens, C.; et al. A Regulatory Network for Coordinated Flower Maturation. PLoS Genet. 2012, 8, e1002506. [Google Scholar] [CrossRef]
  20. Okushima, Y.; Fukaki, H.; Onoda, M.; Theologis, A.; Tasaka, M. ARF7 and ARF19 regulate lateral root formation via direct activation of LBD/ASL genes in Arabidopsis. Plant Cell 2007, 19, 118–130. [Google Scholar] [CrossRef]
  21. Reed, J.W.; Wu, M.F.; Reeves, P.H.; Hodgens, C.; Yadav, V.; Hayes, S.; Pierik, R. Three Auxin Response Factors Promote Hypocotyl Elongation. Plant Physiol. 2018, 178, 864–875. [Google Scholar] [CrossRef]
  22. Goetz, M.; Vivian-Smith, A.; Johnson, S.D.; Koltunow, A.M. AUXIN RESPONSE FACTOR8 is a negative regulator of fruit initiation in Arabidopsis. Plant Cell 2006, 18, 1873–1886. [Google Scholar] [CrossRef]
  23. Huang, G.; Hu, H.; van de Meene, A.; Zhang, J.; Dong, L.; Zheng, S.; Zhang, F.; Betts, N.S.; Liang, W.; Bennett, M.J.; et al. AUXIN RESPONSE FACTORS 6 and 17 control the flag leaf angle in rice by regulating secondary cell wall biosynthesis of lamina joints. Plant Cell 2021, 33, 3120–3133. [Google Scholar] [CrossRef] [PubMed]
  24. Qiao, J.; Jiang, H.; Lin, Y.; Shang, L.; Wang, M.; Li, D.; Fu, X.; Geisler, M.; Qi, Y.; Gao, Z.; et al. A novel miR167a-OsARF6-OsAUX3 module regulates grain length and weight in rice. Mol. Plant 2021, 14, 1683–1698. [Google Scholar] [CrossRef] [PubMed]
  25. Wang, Y.C.; Wang, N.; Xu, H.F.; Jiang, S.H.; Fang, H.C.; Su, M.Y.; Zhang, Z.Y.; Zhang, T.L.; Chen, X.S. Auxin regulates anthocyanin biosynthesis through the Aux/IAA-ARF signaling pathway in apple. Hortic. Res. 2018, 5, 59. [Google Scholar] [CrossRef]
  26. Zhang, Y.; Wu, W.; Shen, H.; Yang, L. Genome-wide identification and expression analysis of ARF gene family in embryonic development of Korean pine (Pinus koraiensis). BMC Plant Biol. 2024, 24, 267. [Google Scholar] [CrossRef] [PubMed]
  27. Song, X.; Xiong, Y.; Kong, X.; Huang, G. Roles of auxin response factors in rice development and stress responses. Plant Cell Environ. 2023, 46, 1075–1086. [Google Scholar] [CrossRef]
  28. Yu, L.; Tian, Y.; Wang, X.; Cao, F.; Wang, H.; Huang, R.; Guo, C.; Zhang, H.; Zhang, J. Genome-wide identification, phylogeny, evolutionary expansion, and expression analyses of ABC gene family in Castanea mollissima under temperature stress. Plant Physiol. Biochem. 2025, 219, 109450. [Google Scholar] [CrossRef]
  29. Huang, R.; Peng, F.; Wang, D.; Cao, F.; Guo, C.; Yu, L.; Zhang, J.; Yang, Y. Transcriptome analysis of differential sugar accumulation in the developing embryo of contrasting two Castanea mollissima cultivars. Front. Plant Sci. 2023, 14, 1206585. [Google Scholar] [CrossRef]
  30. Zhang, P.; Liu, J.; Jia, N.; Wang, M.; Lu, Y.; Wang, D.; Zhang, J.; Zhang, H.; Wang, X. Genome-wide identification and characterization of the bZIP gene family and their function in starch accumulation in Chinese chestnut (Castanea mollissima Blume). Front. Plant Sci. 2023, 14, 1166717. [Google Scholar] [CrossRef]
  31. Chen, C.; Chen, H.; Zhang, Y.; Thomas, H.R.; Frank, M.H.; He, Y.; Xia, R. TBtools: An integrative toolkit developed for interactive analyses of big biological bata. Mol. Plant. 2020, 13, 1194–1202. [Google Scholar] [CrossRef] [PubMed]
  32. Wang, Y.; Tang, H.; DeBarry, J.D.; Tan, X.; Li, J.; Wang, X.; Lee, T.-h.; Jin, H.; Marler, B.; Guo, H.; et al. MCScanX: A toolkit for detection and evolutionary analysis of gene synteny and collinearity. Nucleic Acids Res. 2012, 40, e49. [Google Scholar] [CrossRef]
  33. Yu, L.; Hui, C.; Huang, R.; Wang, D.; Fei, C.; Guo, C.; Zhang, J. Genome-wide identification, evolution and transcriptome analysis of GRAS gene family in Chinese chestnut (Castanea mollissima). Front. Genet. 2022, 13, 1080759. [Google Scholar] [CrossRef]
  34. Yu, L.; Diao, S.; Zhang, G.; Yu, J.; Zhang, T.; Luo, H.; Duan, A.; Wang, J.; He, C.; Zhang, J. Genome sequence and population genomics provide insights into chromosomal evolution and phytochemical innovation of Hippophae rhamnoides. Plant Biotechnol. J. 2022, 20, 1257–1273. [Google Scholar] [CrossRef]
  35. Lescot, M.; Déhais, P.; Thijs, G.; Marchal, K.; Moreau, Y.; Van de Peer, Y.; Rouzé, P.; Rombauts, S. PlantCARE, a database of plant cis-acting regulatory elements and a portal to tools for in silico analysis of promoter sequences. Nucleic Acids Res. 2002, 30, 325–327. [Google Scholar] [CrossRef]
  36. Chen, C.; Wu, Y.; Li, J.; Wang, X.; Zeng, Z.; Xu, J.; Liu, Y.; Feng, J.; Chen, H.; He, Y.; et al. TBtools-II: A “one for all, all for one” bioinformatics platform for biological big-data mining. Mol. Plant 2023, 16, 1733–1742. [Google Scholar] [CrossRef]
  37. Kohl, M.; Wiese, S.; Warscheid, B. Cytoscape: Software for visualization and analysis of biological networks. Methods Mol. Biol. 2011, 696, 291–303. [Google Scholar] [CrossRef]
  38. Szklarczyk, D.; Franceschini, A.; Kuhn, M.; Simonovic, M.; Roth, A.; Minguez, P.; Doerks, T.; Stark, M.; Muller, J.; Bork, P.; et al. The STRING database in 2011: Functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res. 2011, 39, D561–D568. [Google Scholar] [CrossRef] [PubMed]
  39. Luo, J.; Li, Z.; Mo, F.; Liao, Y.; Liu, Y. Removal of superior wheat kernels promotes filling of inferior kernels by changing carbohydrate metabolism and sink strength. Crop J. 2021, 9, 1375–1385. [Google Scholar] [CrossRef]
  40. Kim, D.; Langmead, B.; Salzberg, S.L. HISAT: A fast spliced aligner with low memory requirements. Nat. Methods 2015, 12, 357–360. [Google Scholar] [CrossRef] [PubMed]
  41. Liao, Y.; Smyth, G.K.; Shi, W. featureCounts: An efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 2014, 30, 923–930. [Google Scholar] [CrossRef]
  42. Love, M.I.; Huber, W.; Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014, 15, 550. [Google Scholar] [CrossRef]
  43. Singh, V.K.; Mangalam, A.K.; Dwivedi, S.; Naik, S. Primer premier: Program for design of degenerate primers from a protein sequence. BioTechniques 1998, 24, 318–319. [Google Scholar] [CrossRef] [PubMed]
  44. Yu, L.; Tian, Y.; Wang, J.; Wang, D.; Wang, X.; Zhang, H.; Zhang, J.; Wang, X. Characterization of DnaJ gene family in Castanea mollissima and functional analysis of CmDnaJ27 under cold and heat stresses. BMC Plant Biol. 2025, 25, 778. [Google Scholar] [CrossRef] [PubMed]
  45. Tian, Y.; Wang, J.; Wang, X.; Wang, D.; Wang, X.; Liu, J.; Zhang, H.; Zhang, J.; Yu, L. Genome-wide identification, phylogeny, and expression analysis of PEBP gene family in Castanea mollissima. Front. Genet. 2025, 16, 1530910. [Google Scholar] [CrossRef]
  46. Tian, Y.; Huang, J.; Wang, J.; Wang, D.; Huang, R.; Liu, X.; Zhang, H.; Zhang, J.; Wang, X.; Yu, L. Genome-Wide Identification, Evolutionary Expansion, and Expression Analyses of Aux/IAA Gene Family in Castanea mollissima During Seed Kernel Development. Biology 2025, 14, 806. [Google Scholar] [CrossRef]
  47. Yu, L.; Wang, D.; Huang, R.; Cao, F.; Guo, C.; Zhang, J. Genome-wide identification, characterization and expression profile analysis of BBX gene family in Chinese chestnut (Castanea mollissima). Plant Biotechnol. Rep. 2023, 18, 129–142. [Google Scholar] [CrossRef]
  48. Yu, L.; Fei, C.; Wang, D.; Huang, R.; Xuan, W.; Guo, C.; Jing, L.; Meng, W.; Yi, L.; Zhang, H.; et al. Genome-wide identification, evolution and expression profiles analysis of bHLH gene family in Castanea mollissima. Front. Genet. 2023, 14, 1193953. [Google Scholar] [CrossRef]
  49. Ren, R.; Wang, H.; Guo, C.; Zhang, N.; Zeng, L.; Chen, Y.; Ma, H.; Qi, J. Widespread whole genome duplications contribute to genome complexity and species diversity in angiosperms. Mol. Plant 2018, 11, 414–428. [Google Scholar] [CrossRef]
  50. Domazet-Lošo, M.; Široki, T.; Šimičević, K.; Domazet-Lošo, T. Macroevolutionary dynamics of gene family gain and loss along multicellular eukaryotic lineages. Nat. Commun. 2024, 15, 2663. [Google Scholar] [CrossRef]
  51. Birchler, J.; Veitia, R. The gene balance hypothesis: From classical genetics to modern genomics. Plant Cell. 2007, 19, 395–402. [Google Scholar] [CrossRef]
  52. Veitia, R.A. Gene Dosage Balance in Cellular Pathways: Implications for Dominance and Gene Duplicability. Genetics 2004, 168, 569–574. [Google Scholar] [CrossRef]
  53. Danino, Y.M.; Even, D.; Ideses, D.; Juven-Gershon, T. The core promoter: At the heart of gene expression. Biochim. Biophys. Acta (BBA)-Gene Regul. Mech. 2015, 1849, 1116–1131. [Google Scholar] [CrossRef]
  54. Roeder, R.G. The role of general initiation factors in transcription by RNA polymerase II. Trends Biochem. Sci. 1996, 21, 327–335. [Google Scholar] [CrossRef] [PubMed]
  55. Feng, S.; Li, N.; Chen, H.; Liu, Z.; Li, C.; Zhou, R.; Zhang, Y.; Cao, R.; Ma, X.; Song, X. Large-scale analysis of the ARF and Aux/IAA gene families in 406 horticultural and other plants. Mol. Hortic. 2024, 4, 13. [Google Scholar] [CrossRef]
  56. Li, S.; Shi, Z.; Zhu, Q.; Tao, L.; Liang, W.; Zhao, Z. Transcriptome sequencing and differential expression analysis of seed starch accumulation in Chinese chestnut Metaxenia. BMC Genom. 2021, 22, 617. [Google Scholar] [CrossRef]
  57. Rojas, C.M.; Senthil-Kumar, M.; Tzin, V.; Mysore, K.S. Regulation of primary plant metabolism during plant-pathogen interactions and its contribution to plant defense. Front. Plant Sci. 2014, 5, 17. [Google Scholar] [CrossRef] [PubMed]
  58. Silva, A.P.; Oliveira, I.; Silva, M.E.; Guedes, C.M.; Borges, O.; Magalhães, B.; Gonçalves, B. Starch characterization in seven raw, boiled and roasted chestnuts (Castanea sativa Mill.) cultivars from Portugal. J. Food Sci. Technol. 2016, 53, 348–358. [Google Scholar] [CrossRef] [PubMed]
  59. Zhang, L.; Liu, T.; Hu, G.; Guo, K.; Wei, C. Comparison of Physicochemical Properties of Starches from Nine Chinese Chestnut Varieties. Molecules 2018, 23, 3248. [Google Scholar] [CrossRef]
  60. Chen, J.-P.; Gong, J.-S.; Su, C.; Li, H.; Xu, Z.-H.; Shi, J.-S. Improving the soluble expression of difficult-to-express proteins in prokaryotic expression system via protein engineering and synthetic biology strategies. Metab. Eng. 2023, 78, 99–114. [Google Scholar] [CrossRef]
  61. Liu, N.; Dong, L.; Deng, X.; Liu, D.; Liu, Y.; Li, M.; Hu, Y.; Yan, Y. Genome-wide identification, molecular evolution, and expression analysis of auxin response factor (ARF) gene family in Brachypodium distachyon L. BMC Plant Biol. 2018, 18, 336. [Google Scholar] [CrossRef]
  62. Freire-Rios, A.; Tanaka, K.; Crespo, I.; van der Wijk, E.; Sizentsova, Y.; Levitsky, V.; Lindhoud, S.; Fontana, M.; Hohlbein, J.; Boer, D.R.; et al. Architecture of DNA elements mediating ARF transcription factor binding and auxin-responsive gene expression in Arabidopsis. Proc. Natl. Acad. Sci. USA 2020, 117, 24557–24566. [Google Scholar] [CrossRef] [PubMed]
  63. Shaul, O. How introns enhance gene expression. Int. J. Biochem. Cell Biol. 2017, 91, 145–155. [Google Scholar] [CrossRef]
  64. Castillo-Davis, C.I.; Mekhedov, S.L.; Hartl, D.L.; Koonin, E.V.; Kondrashov, F.A. Selection for short introns in highly expressed genes. Nat. Genet. 2002, 31, 415–418. [Google Scholar] [CrossRef]
  65. Liu, H.; Lyu, H.M.; Zhu, K.; Van de Peer, Y.; Max Cheng, Z.M. The emergence and evolution of intron-poor and intronless genes in intron-rich plant gene families. Plant J. 2021, 105, 1072–1082. [Google Scholar] [CrossRef]
  66. Koehler Leman, J.; Szczerbiak, P.; Renfrew, P.D.; Gligorijevic, V.; Berenberg, D.; Vatanen, T.; Taylor, B.C.; Chandler, C.; Janssen, S.; Pataki, A.; et al. Sequence-structure-function relationships in the microbial protein universe. Nat. Commun. 2023, 14, 2351. [Google Scholar] [CrossRef]
  67. Qiao, X.; Li, Q.; Yin, H.; Qi, K.; Li, L.; Wang, R.; Zhang, S.; Paterson, A.H. Gene duplication and evolution in recurring polyploidization–diploidization cycles in plants. Genome Biol. 2019, 20, 38. [Google Scholar] [CrossRef] [PubMed]
  68. Chen, H.; Zhang, Y.; Feng, S. Whole-genome and dispersed duplication, including transposed duplication, jointly advance the evolution of TLP genes in seven representative Poaceae lineages. BMC Genom. 2023, 24, 290. [Google Scholar] [CrossRef]
  69. Dai, X.; Lu, Q.; Wang, J.; Wang, L.; Xiang, F.; Liu, Z. MiR160 and its target genes ARF10, ARF16 and ARF17 modulate hypocotyl elongation in a light, BRZ, or PAC-dependent manner in Arabidopsis: miR160 promotes hypocotyl elongation. Plant Sci. 2021, 303, 110686. [Google Scholar] [CrossRef] [PubMed]
  70. Okushima, Y.; Overvoorde, P.J.; Arima, K.; Alonso, J.M.; Chan, A.; Chang, C.; Ecker, J.R.; Hughes, B.; Lui, A.; Nguyen, D.; et al. Functional genomic analysis of the AUXIN RESPONSE FACTOR gene family members in Arabidopsis thaliana: Unique and overlapping functions of ARF7 and ARF19. Plant Cell 2005, 17, 444–463. [Google Scholar] [CrossRef]
  71. Varaud, E.; Brioudes, F.; Szécsi, J.; Leroux, J.; Brown, S.; Perrot-Rechenmann, C.; Bendahmane, M. AUXIN RESPONSE FACTOR8 regulates Arabidopsis petal growth by interacting with the bHLH transcription factor BIGPETALp. Plant Cell 2011, 23, 973–983. [Google Scholar] [CrossRef] [PubMed]
  72. Shen, C.; Wang, S.; Bai, Y.; Wu, Y.; Zhang, S.; Chen, M.; Guilfoyle, T.J.; Wu, P.; Qi, Y. Functional analysis of the structural domain of ARF proteins in rice (Oryza sativa L.). J. Exp. Bot. 2010, 61, 3971–3981. [Google Scholar] [CrossRef] [PubMed]
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Figure 1. The phylogenetic tree of ARF genes of C. mollissima, A. thaliana, and O. sativa. (A) Rooted phylogenetic tree of ARF genes of C. mollissima, A. thaliana, and O. sativa. (B) Unrooted phylogenetic tree of ARF genes of C. mollissima, A. thaliana, and O. sativa. (C) Rooted phylogenetic tree of ARF genes of C. mollissima. MEGA 7.0 was used to construct the phylogenetic tree based on the protein sequences with maximum likelihood method.
Figure 1. The phylogenetic tree of ARF genes of C. mollissima, A. thaliana, and O. sativa. (A) Rooted phylogenetic tree of ARF genes of C. mollissima, A. thaliana, and O. sativa. (B) Unrooted phylogenetic tree of ARF genes of C. mollissima, A. thaliana, and O. sativa. (C) Rooted phylogenetic tree of ARF genes of C. mollissima. MEGA 7.0 was used to construct the phylogenetic tree based on the protein sequences with maximum likelihood method.
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Figure 2. The conserved domains, gene structure, and conserved motifs distribution of CmARF genes. (A) Phylogenetic analysis of CmARF genes. (B) The conserved domains of CmARF genes. (C) Gene structure of CmARF genes. (D) The conserved motifs of CmARF proteins. (E) The sequence of ten conserved motifs in CmARF proteins.
Figure 2. The conserved domains, gene structure, and conserved motifs distribution of CmARF genes. (A) Phylogenetic analysis of CmARF genes. (B) The conserved domains of CmARF genes. (C) Gene structure of CmARF genes. (D) The conserved motifs of CmARF proteins. (E) The sequence of ten conserved motifs in CmARF proteins.
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Figure 3. Chromosome distribution and duplication type analysis of CmARF genes. (A) Chromosome distribution of CmARF genes. The color of segments in the chromosomes shows the gene density of the corresponding region. (B) Collinear relationship within C. mollissima genome. (C) Homologous collinear dot-plot within C. mollissima genome. The collinear blocks from WGD containing CmARF genes are marked in the gray boxes of the figure, and the blocks length and median Ks of the collinear blocks are marked. The genes highlighted in green are identified as from WGD events. The pie chart in the bottom right corner of the dot-plot shows the proportion of CmARFs for different duplication types.
Figure 3. Chromosome distribution and duplication type analysis of CmARF genes. (A) Chromosome distribution of CmARF genes. The color of segments in the chromosomes shows the gene density of the corresponding region. (B) Collinear relationship within C. mollissima genome. (C) Homologous collinear dot-plot within C. mollissima genome. The collinear blocks from WGD containing CmARF genes are marked in the gray boxes of the figure, and the blocks length and median Ks of the collinear blocks are marked. The genes highlighted in green are identified as from WGD events. The pie chart in the bottom right corner of the dot-plot shows the proportion of CmARFs for different duplication types.
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Figure 4. Collinear analyses between the CmARF genes and genes in seven representative plant species (Q. robur, P. bretschneideri, V. vinifera, A. thaliana, S. lycopersicum, O. sativa, and Z. mays). (A) The dual collinear plot between C. mollissima and seven representative plant species. Gray lines in the background indicate collinear blocks within C. mollissima and other plant genomes, while red lines highlight collinear ARF gene pairs. (B) The orthologous gene pair number between C. mollissima and other seven plant genomes. (C) The block number and median block length between C. mollissima and other seven plant genomes.
Figure 4. Collinear analyses between the CmARF genes and genes in seven representative plant species (Q. robur, P. bretschneideri, V. vinifera, A. thaliana, S. lycopersicum, O. sativa, and Z. mays). (A) The dual collinear plot between C. mollissima and seven representative plant species. Gray lines in the background indicate collinear blocks within C. mollissima and other plant genomes, while red lines highlight collinear ARF gene pairs. (B) The orthologous gene pair number between C. mollissima and other seven plant genomes. (C) The block number and median block length between C. mollissima and other seven plant genomes.
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Figure 5. Prediction of cis-acting elements and transcription factor regulatory network analysis of CmARFs. (A) Cis-acting elements in the promoters of CmARFs. Various color symbols present different elements, and their position in the figure indicates their relative position on the promoter. (B) The number of various cis-acting elements in the promoters of each CmARFs. (C) The relative proportions of different cis-acting elements in the promoters of CmARFs are indicated in the chart. (D) The putative TFs regulatory network analysis of CmARF genes. (E) Word-cloud of predicted TFs interacting with CmARF genes. The font size is positively correlated with the number of corresponding TFs.
Figure 5. Prediction of cis-acting elements and transcription factor regulatory network analysis of CmARFs. (A) Cis-acting elements in the promoters of CmARFs. Various color symbols present different elements, and their position in the figure indicates their relative position on the promoter. (B) The number of various cis-acting elements in the promoters of each CmARFs. (C) The relative proportions of different cis-acting elements in the promoters of CmARFs are indicated in the chart. (D) The putative TFs regulatory network analysis of CmARF genes. (E) Word-cloud of predicted TFs interacting with CmARF genes. The font size is positively correlated with the number of corresponding TFs.
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Figure 6. Protein interaction network and GO/KEGG analysis of CmARFs. (A) Protein interaction network of CmARF proteins. (B) GO function enrichment analysis of CmARF genes. (C) KEGG function enrichment analysis of CmARF genes.
Figure 6. Protein interaction network and GO/KEGG analysis of CmARFs. (A) Protein interaction network of CmARF proteins. (B) GO function enrichment analysis of CmARF genes. (C) KEGG function enrichment analysis of CmARF genes.
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Figure 7. Dynamic changes in morphology and physiological indicators of different developmental stages of ‘Xinglong No. 4’ C. mollissima. (A) The thorns bract, seeds, and seed kernels of ‘Xinglong No. 4’ C. mollissima at different developmental stages. (B) The fresh weight, longitudinal diameter, transverse diameter, lateral diameter, total starch content, amylopectin content, amylose content, SSS, GBSS, SBE, and AGGPase activity of seed kernels of ‘Xinglong No. 4’ C. mollissima at different developmental stages. The small letters indicate the significance of differences between physiological indicators in five stages.
Figure 7. Dynamic changes in morphology and physiological indicators of different developmental stages of ‘Xinglong No. 4’ C. mollissima. (A) The thorns bract, seeds, and seed kernels of ‘Xinglong No. 4’ C. mollissima at different developmental stages. (B) The fresh weight, longitudinal diameter, transverse diameter, lateral diameter, total starch content, amylopectin content, amylose content, SSS, GBSS, SBE, and AGGPase activity of seed kernels of ‘Xinglong No. 4’ C. mollissima at different developmental stages. The small letters indicate the significance of differences between physiological indicators in five stages.
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Figure 8. Transcriptome analysis results. (A) PCA analysis of transcriptome data. (B) DEGs distribution in four comparisons at different developmental stages. (C) DEGs of S2 vs. S1, S3 vs. S1, S4 vs. S1, and S5 vs. S1, respectively. (D) KEGG enrichment of DEGs in S4 vs. S1. (E) KEGG enrichment analysis of DEGs in S5 vs. S1.
Figure 8. Transcriptome analysis results. (A) PCA analysis of transcriptome data. (B) DEGs distribution in four comparisons at different developmental stages. (C) DEGs of S2 vs. S1, S3 vs. S1, S4 vs. S1, and S5 vs. S1, respectively. (D) KEGG enrichment of DEGs in S4 vs. S1. (E) KEGG enrichment analysis of DEGs in S5 vs. S1.
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Figure 9. WGCNA and functional enrichment analysis of the turquoise module. (A) The clustering dendrogram of genes identifying the WGCNA modules. (B) The correlation of the identified modules with the phenotypic traits at different developmental stages. (C) GO enrichment analysis of the turquoise modules. (D) KEGG enrichment analysis of the turquoise modules.
Figure 9. WGCNA and functional enrichment analysis of the turquoise module. (A) The clustering dendrogram of genes identifying the WGCNA modules. (B) The correlation of the identified modules with the phenotypic traits at different developmental stages. (C) GO enrichment analysis of the turquoise modules. (D) KEGG enrichment analysis of the turquoise modules.
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Figure 10. The expression profiles of CmARF genes, RT-qPCR at different developmental stages of ‘Xinglong No. 4’ C. mollissima seed kernels, and their correlation with phenotypic indicators. (A) The expression profiles of CmARFs at different developmental stages of ‘Xinglong No. 4’ C. mollissima seed kernels. (B) The correlation between phenotypic indicators and CmARFs expression levels at different developmental stages of ‘Xinglong No. 4’ C. mollissima seed kernels. ***, ** and * denote genes and physiological indicators exhibiting significant differences (p < 0.001, p < 0.01, p < 0.05 respectively) (C) RT-qPCR analysis of CmARFs in C. mollissima at different developmental stages of ‘Xinglong No. 4’ C. mollissima seed kernels. The lowercase letters indicate the significance of differences between FPKM of transcriptome data from five stages, while uppercase letters indicate the significance of differences between RT-qPCR results from five stages.
Figure 10. The expression profiles of CmARF genes, RT-qPCR at different developmental stages of ‘Xinglong No. 4’ C. mollissima seed kernels, and their correlation with phenotypic indicators. (A) The expression profiles of CmARFs at different developmental stages of ‘Xinglong No. 4’ C. mollissima seed kernels. (B) The correlation between phenotypic indicators and CmARFs expression levels at different developmental stages of ‘Xinglong No. 4’ C. mollissima seed kernels. ***, ** and * denote genes and physiological indicators exhibiting significant differences (p < 0.001, p < 0.01, p < 0.05 respectively) (C) RT-qPCR analysis of CmARFs in C. mollissima at different developmental stages of ‘Xinglong No. 4’ C. mollissima seed kernels. The lowercase letters indicate the significance of differences between FPKM of transcriptome data from five stages, while uppercase letters indicate the significance of differences between RT-qPCR results from five stages.
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Liu, X.; Li, Y.; Liang, M.; Wang, D.; Wang, M.; Lu, Y.; Liu, X.; Zhang, H.; Wang, X.; Yu, L. Comprehensive Analysis of the ARF Gene Family Reveals Their Roles in Chinese Chestnut (Castanea mollissima) Seed Kernel Development. Biology 2025, 14, 1460. https://doi.org/10.3390/biology14101460

AMA Style

Liu X, Li Y, Liang M, Wang D, Wang M, Lu Y, Liu X, Zhang H, Wang X, Yu L. Comprehensive Analysis of the ARF Gene Family Reveals Their Roles in Chinese Chestnut (Castanea mollissima) Seed Kernel Development. Biology. 2025; 14(10):1460. https://doi.org/10.3390/biology14101460

Chicago/Turabian Style

Liu, Xili, Yun Li, Manman Liang, Dongsheng Wang, Meng Wang, Yi Lu, Xia Liu, Haie Zhang, Xiangyu Wang, and Liyang Yu. 2025. "Comprehensive Analysis of the ARF Gene Family Reveals Their Roles in Chinese Chestnut (Castanea mollissima) Seed Kernel Development" Biology 14, no. 10: 1460. https://doi.org/10.3390/biology14101460

APA Style

Liu, X., Li, Y., Liang, M., Wang, D., Wang, M., Lu, Y., Liu, X., Zhang, H., Wang, X., & Yu, L. (2025). Comprehensive Analysis of the ARF Gene Family Reveals Their Roles in Chinese Chestnut (Castanea mollissima) Seed Kernel Development. Biology, 14(10), 1460. https://doi.org/10.3390/biology14101460

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