Dynamic N6 -methyladenosine RNA modification regulates peanut resistance to bacterial wilt.

N6 -methyladenosine (m6 A) is the most abundant mRNA modification in eukaryotes and is an important regulator of gene expression as well as many other critical biological processes. However, the characteristics and functions of m6 A in peanut (Arachis hypogea L.) resistance to bacterial wilt (BW) remain unknown. Here, we analyzed the dynamic of m6 A during infection of resistant (H108) and susceptible (H107) peanut accessions with Ralstonia solanacearum (R. solanacearum), the causative agent of BW. Throughout the transcriptome, we identified 'URUAY' as a highly conserved motif for m6 A in peanut. The majority of differential m6 A located within the 3' untranslated region (UTR) of the transcript, with fewer in the exons. Integrative analysis of RNA-Seq and m6 A methylomes suggests the correlation between m6 A and gene expression in peanut R. solanacearum infection, and functional analysis reveals that m6 A-associated genes were related to plant-pathogen interaction. Our experimental analysis suggests that AhALKBH15 is an m6 A demethylase in peanut, leading to decreased m6 A levels and upregulation of the resistance gene AhCQ2G6Y. The upregulation of AhCQ2G6Y expression appears to promote BW resistance in the H108 accession.

Natural resistance-associated macrophage proteins are involved in tolerance to heavy metal Cd2+ toxicity and resistance to bacterial wilt of peanut (Arachis hypogaea L.).

Peanut (Arachis hypogaea L.) is one of the most important oil and industrial crops. However, heavy-metal pollution and frequent soil diseases, poses a significant threat to the production of green and healthy peanuts. Herein, we investigated the effects of heavy metal Cd2+ toxicity to the peanuts, and screened out two peanut cultivars H108 and YZ 9102 with higher Cd2+-tolerance. RNA-seq revealed that Natural resistance-associated macrophage proteins (NRAMP)-like genes were involved in the Cd2+ stress tolerance in H108. Genome-wide identification revealed that 28, 13 and 9 Nramp-like genes existing in the A. hypogaea, A. duranensis and A. ipaensis, respectively. The 50 peanut NRAMP genes share conserved architectural characters, and they were classified into two groups. Expressions of AhNramps, particularly AhNramp4, AhNramp12, AhNramp19, and AhNramp25 could be greatly induced by not only cadmium toxicity, but also copper and zinc stresses. The expression profiles of AhNramp14, AhNramp16 and AhNramp25 showed significant differences in the H108 (resistance) and H107 (susceptible) under the infection of bacterial wilt. In addition, we found that the expression profiles of AhNramp14, AhNramp16, and AhNramp25 were greatly up- or down-regulated by the application of exogenous salicylic acid, methyl jasmonate, and abscisic acid. The AhNramp25, of which expression was affected by both heavy metal toxicity and bacterial wilt infection, were selected as strong candidate genes for peanut stress breeding. Our findings will provide an additional information required for further analysis of AhNramps involved in tolerance to heavy metal toxicity and resistance to bacterial wilt of peanut.

Coordinated Lipid Mobilization during Seed Development and Germination in Peanut (Arachis hypogaea L.).

Peanut (Arachis hypogaea L.) is one of the most important oil crops in the world due to its lipid-rich seeds. Lipid accumulation and degradation play crucial roles in peanut seed maturation and seedling establishment, respectively. Here, we utilized lipidomics and transcriptomics to comprehensively identify lipids and the associated functional genes that are important in the development and germination processes of a large-seed peanut variety. A total of 332 lipids were identified; triacylglycerols (TAGs) and diacylglycerols were the most abundant during seed maturation, constituting 70.43 and 16.11%, respectively, of the total lipids. Significant alterations in lipid profiles were observed throughout seed maturation and germination. Notably, TAG (18:1/18:1/18:2) and (18:1/18:2/18:2) peaked at 23386.63 and 23392.43 nmol/g, respectively, at the final stage of seed development. Levels of hydroxylated TAGs (HO-TAGs) increased significantly during the initial stage of germination. Accumulation patterns revealed an inverse relationship between free fatty acids and TAGs. Lipid degradation was determined to be regulated by diacylglycerol acyltransferase, triacylglycerol lipase, and associated transcription factors, predominantly yielding oleic acid, linoleic acid, and linolenic acid. Collectively, the results of this study provide valuable insights into lipid dynamics during the development and germination of large-seed peanuts, gene resources, and guiding future research into lipid accumulation in an economically important crop.

Regional odontodysplasia diagnosed and treated via multidisciplinary approach: a case report.

Regional odontodysplasia (RO) is a rare non-hereditary dental anomaly associated with dysplasia. Its etiology remains unclear but is known to affect both the mesodermal and ectodermal dental components, as well as deciduous and permanent dentitions. Its young age of onset and complexity has great physical and psychological impact on the affected patients. However, the clinical management of RO remains unified without standardized treatment guidelines. Thus, this study aimed to report an RO case, the first from Jiangxi Province, China, and discuss its clinical diagnosis and treatment to provide a reference to treat similar cases more effectively in the future.

The preliminary in vitro study and application of deep learning algorithm in cone beam computed tomography image implant recognition.

To properly repair and maintain implants, which are bone tissue implants that replace natural tooth roots, it is crucial to accurately identify their brand and specification. Deep learning has demonstrated outstanding capabilities in analysis, such as image identification and classification, by learning the inherent rules and degrees of representation of data models. The purpose of this study is to evaluate deep learning algorithms and their supporting application software for their ability to recognize and categorize three dimensional (3D) Cone Beam Computed Tomography (CBCT) images of dental implants. By using CBCT technology, the 3D imaging data of 27 implants of various sizes and brands were obtained. Following manual processing, the data were transformed into a data set that had 13,500 two-dimensional data. Nine deep learning algorithms including GoogleNet, InceptionResNetV2, InceptionV3, ResNet50, ResNet50V2, ResNet101, ResNet101V2, ResNet152 and ResNet152V2 were used to perform the data. Accuracy rates, confusion matrix, ROC curve, AUC, number of model parameters and training times were used to assess the efficacy of these algorithms. These 9 deep learning algorithms achieved training accuracy rates of 100%, 99.3%, 89.3%, 99.2%, 99.1%, 99.5%, 99.4%, 99.5%, 98.9%, test accuracy rates of 98.3%, 97.5%, 94.8%, 85.4%, 92.5%, 80.7%, 93.6%, 93.2%, 99.3%, area under the curve (AUC) values of 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00. When used to identify implants, all nine algorithms perform satisfactorily, with ResNet152V2 achieving the highest test accuracy, classification accuracy, confusion matrix area under the curve, and receiver operating characteristic curve area under the curve area. The results showed that the ResNet152V2 has the best classification effect on identifying implants. The artificial intelligence identification system and application software based on this algorithm can efficiently and accurately identify the brands and specifications of 27 classified implants through processed 3D CBCT images in vitro, with high stability and low recognition cost.

Dynamic DNA methylation modification in peanut seed development.

Cytosine methylation is an important epigenetic modification involved in regulation of plant development. However, the epigenetic mechanisms governing peanut seed development remain unclear. Herein, we generated DNA methylation profiles of developmental seeds of peanut H2014 and its smaller seed mutant H1314 at 15 and 60 days after pegging (DAP, S1, S4). Accompanying seed development, globally elevated methylation was observed in both lines. The mutant had a higher methylation level of 31.1% than wild type at S4, and 27.1-35.9% of the differentially methylated regions (DMRs) between the two lines were distributed in promoter or genic regions at both stages. Integrated methylome and transcriptome analysis revealed important methylation variations closely associated with seed development. Furthermore, some genes showed significantly negative correlation of expression with the methylation level within promoter or gene body. The results provide insights into the roles of DNA methylation in peanut seed development.

CD4+ T-Cell Subsets in Aplastic Anemia, Myelodysplastic Syndrome, and Acute Myelogenous Leukemia Patients: a Comparative Analysis.

BACKGROUND: Systematic and comparative studies on CD4+ T-lymphocytes in aplastic anemia (AA), myelodysplastic syndrome (MDS), and acute myelogenous leukemia (AML) are scarce. This study aimed to investigate the importance of CD4+ T-cells in bone marrow (BM) failure. METHODS: The proportions of Th1, Th2, Th17, and Treg cells in peripheral blood mononuclear cells (PBMCs) were examined by flow cytometry (FCM). The mRNA expression levels of transcription factors were measured using real-time PCR. RESULTS: The proportions of Th1, Th17 cells, and Th1/Th2 in the AA group were higher, whereas Th2 and Tregs were lower compared to controls. The proportions of Th17 and Treg cells accompanied by RORγt, and Foxp3 expression were significantly higher in the MDS group. The proportions of Th1, Th17, and Th1/Th2 were higher, whereas Th2 cells and GATA3 expression were significantly lower in MDS-multilineage dysplasia group, than in control group. The proportions of Th1, Th17, and Th1/Th2 were lower in MDS-excess blasts, and AML groups, than in controls, whereas that of Th2 and Treg cells accompanied by GATA3, and Foxp3 expression were significantly higher. CONCLUSIONS: Imbalance in CD4+ T-cell subsets may play a critical role in the pathogenesis and BM failure in the investigated diseases.

PSW1, an LRR receptor kinase, regulates pod size in peanut.

Pod size is a key agronomic trait that greatly determines peanut yield, the regulatory genes and molecular mechanisms that controlling peanut pod size are still unclear. Here, we used quantitative trait locus analysis to identify a peanut pod size regulator, POD SIZE/WEIGHT1 (PSW1), and characterized the associated gene and protein. PSW1 encoded leucine-rich repeat receptor-like kinase (LRR-RLK) and positively regulated pod stemness. Mechanistically, this allele harbouring a 12-bp insertion in the promoter and a point mutation in the coding region of PSW1 causing a serine-to-isoleucine (S618I) substitution substantially increased mRNA abundance and the binding affinity of PSW1 for BRASSINOSTEROID INSENSITIVE1-ASSOCIATED RECEPTOR KINASE 1 (BAK1). Notably, PSW1HapII (super-large pod allele of PSW1) expression led to up-regulation of a positive regulator of pod stemness PLETHORA 1 (PLT1), thereby resulting in larger pod size. Moreover, overexpression of PSW1HapII increased seed/fruit size in multiple plant species. Our work thus discovers a conserved function of PSW1 that controls pod size and provides a valuable genetic resource for breeding high-yield crops.

Identification of ORM1, vWF, SPARC, and PPBP as immune-related proteins involved in immune thrombocytopenia by quantitative LC-MS/MS.

BACKGROUND: Immune thrombocytopenia (ITP) is a common autoimmune disease characterized by loss of immune tolerance to platelet autoantigens leading to excessive destruction and insufficient production of platelets. METHOD: Quantitative liquid chromatography tandem mass spectrometry (LC-MS/MS) was performed to detect the differentially expressed proteins in bone marrow samples from active ITP patients and normal controls. RESULT: Our bioinformatic analysis identified two upregulated proteins (ORM1 and vWF) and two downregulated proteins (PPBP and SPARC) related to immune function. The four proteins were all found to be related to the tumor necrosis factor (TNF) -α signalling pathway and involved in the pathogenesis of ITP in KEGG pathway analysis. CONCLUSION: Bioinformatics analysis identified differentially expressed proteins in bone marrow that are involved in the TNF-α signalling pathway and are related to the activation of immune function in ITP patients. These findings could provide new ideas for research on the loss of immune tolerance in ITP patients.

Advances in the Research on Plant WRKY Transcription Factors Responsive to External Stresses.

The WRKY transcription factors are a class of transcriptional regulators that are ubiquitous in plants, wherein they play key roles in various physiological activities, including responses to stress. Specifically, WRKY transcription factors mediate plant responses to biotic and abiotic stresses through the binding of their conserved domain to the W-box element of the target gene promoter and the subsequent activation or inhibition of transcription (self-regulation or cross-regulation). In this review, the progress in the research on the regulatory effects of WRKY transcription factors on plant responses to external stresses is summarized, with a particular focus on the structural characteristics, classifications, biological functions, effects on plant secondary metabolism, regulatory networks, and other aspects of WRKY transcription factors. Future research and prospects in this field are also proposed.

Prognostic Value of Transglutaminase 2 in Patients with Solid Tumors: A Meta-analysis.

Background: Transglutaminase 2 (TG2), a member of the transglutaminase family, also known as tissue transglutaminase, plays a fundamental role in cancer growth and progression. In this study, we aimed to comprehensively review the evidence of TG2 as a prognostic biomarker in solid tumors. Methods: PubMed, Embase, and Cochrane databases were searched for human studies with clearly described cancer types if they presented the relationship between TG2 expression and prognostic indicators from inception to February 2022. Two authors independently screened the eligible studies and extracted the relevant data. The association between TG2 and overall survival (OS), disease-free survival (DFS), and relapse-free survival (RFS) were described as hazard ratios (HR) and their corresponding 95% confidence intervals (CIs). Statistical heterogeneity was assessed using Cochrane Q-test and Higgins I-squared statistic. A sensitivity analysis was conducted by sequentially omitting the impact of each study. Publication bias was assessed by Egger's funnel plot. Results: A total of 2864 patients with various cancers from 11 individual studies were enrolled. Results showed that elevated TG2 protein expression and mRNA expression predicted a shorter OS, with a combined HR of 1.93 (95% CI: 1.41-2.63) or HR of 1.95 (95% CI: 1.27-2.99), respectively. Moreover, data suggested that elevated TG2 protein expression was correlated with a shorter DFS (HR = 1.76, 95% CI: 1.36-2.29); whereas elevated TG2 mRNA expression was associated with a shorter DFS (HR = 1.71, 95% CI: 1.30-2.24). Conclusions: Our meta-analysis indicated that TG2 might serve as a promising biomarker of cancer prognosis.

Surface modification of cellulose via photo-induced click reaction.

To pursue highly functional ecologically friendly materials, functionalized cellulose has gained increasing number of research interests within the last decades. Compared with other cellulose modification approaches, surface modification of cellulose via photo-induced click reaction is a particular attractive cellulose modification strategy as it integrates the advantages of photochemistry, click reaction and surface modification. High efficiency, quantitative conversion and spatial temporal control are achieved. The physical and chemical properties of various cellulose surfaces are also effectively regulated by using this strategy, which significantly expands their applications. In this mini-review, we summarized the recent progress in cellulose surface modification via photo-induced click reactions. The principles and the merits of photo-induced thiol-X and cycloaddition reactions were compared and highlighted. Photo-induced thiol-ene reaction, thiol-yne reaction, tetrazole-ene cycloaddition, Diels-Alder cycloaddition, and azide-alkyne cycloaddition applied in modification of various cellulose surfaces were introduced in order. At last, the challenges and outlook of photo-induced click reaction in cellulose surface modification were discussed. We hope this review will provide insights into photo-induced click chemistry, cellulose surface modification and novel sustainable materials.

Morphological and biochemical studies of Salvia guaranitica St. Hil. under simulated deposition with different amounts of dust.

The collection of many environmental pollutants from road dust is harmful to living things and their surroundings. Previous studies have confirmed that road dust affects plant pigmentation, pollination, and biochemical properties. However, there are no comprehensive studies on multi-level dust pollution levels and multifaceted physiological properties of plants, and more importantly, there are no studies on atmospheric dust pollution monitors. In this experiment, the effect of road dust on the morphology and biochemistry of Salvia guaranitica St.Hil.was investigated by simulated deposition of different amounts of dust, and the changes of their physiological morphology under different pollution levels were also explored. A control group CK (0.00 g/plant), four experimental groups S1 (0.015 g/plant), S2 (0.030 g/plant), S3 (0.045 g/plant) and S4 (0.060 g/plant) were sprayed with the same dust samples every other day for 30 days. It was found that after 30 days of dust exposure, different degrees of morphological changes and damage occurred in Salvia. The different pollution levels also resulted in different degrees of biochemical characteristics of Salvia. With the increase of pollution, chlorophyll content, photosynthetic and evaporation rates decreased significantly, but the activity of SOD and the content of MDA increased significantly in different experimental groups. Especially, the experiments also revealed that severe road dust pollution caused damage and deformation to stomata, as well as a significant reduction in stomatal and glandular density. In addition, the regression curves of the different physiological responses of Salvia to road dust can be used as a preliminary basis for plant monitoring of dust pollution degrees, thus provided a scientific basis for the use of plant biomonitors in the field of pollution biology.

Survival prediction model for right-censored data based on improved composite quantile regression neural network.

With the development of the field of survival analysis, statistical inference of right-censored data is of great importance for the study of medical diagnosis. In this study, a right-censored data survival prediction model based on an improved composite quantile regression neural network framework, called rcICQRNN, is proposed. It incorporates composite quantile regression with the loss function of a multi-hidden layer feedforward neural network, combined with an inverse probability weighting method for survival prediction. Meanwhile, the hyperparameters involved in the neural network are adjusted using the WOA algorithm, integer encoding and One-Hot encoding are implemented to encode the classification features, and the BWOA variable selection method for high-dimensional data is proposed. The rcICQRNN algorithm was tested on a simulated dataset and two real breast cancer datasets, and the performance of the model was evaluated by three evaluation metrics. The results show that the rcICQRNN-5 model is more suitable for analyzing simulated datasets. The One-Hot encoding of the WOA-rcICQRNN-30 model is more applicable to the NKI70 data. The model results are optimal for k=15 after feature selection for the METABRIC dataset. Finally, we implemented the method for cross-dataset validation. On the whole, the Cindex results using One-Hot encoding data are more stable, making the proposed rcICQRNN prediction model flexible enough to assist in medical decision making. It has practical applications in areas such as biomedicine, insurance actuarial and financial economics.

Increased BMI and Blood Lipids Are Associated With a Hypercoagulable State in the Moli-sani Cohort.

The coagulation system can be assessed by the thrombin generation (TG) assay, and increased TG peak height, endogenous thrombin potential (ETP), and velocity index are associated with an increased risk of thrombosis. Obesity had been reported to increase TG and is associated with dyslipidemia, which also predisposes to atherosclerotic cardiovascular disease (CVD). However, the effect of the blood lipid profile on TG has not been studied extensively. To gain more insight into the associations of TG, body mass index (BMI) and lipid profile, we studied TG in relation to these parameters in a large Italian population cohort, the Moli-sani study (N = 22,546; age ≥ 35 years; 48% men). TG was measured in plasma samples collected at the enrollment of subjects in the Moli-sani study. TG was triggered with 1 or 5 pM tissue factor, and TG parameters lag time, peak, ETP, time-to-peak (TTP) and velocity index (VI). Additionally, thrombomodulin was added to assess the function of the activated protein C system during TG. In both women and men, overweight (BMI 25-30 kg/m2) and obesity (BMI > 30 kg/m2) were significantly associated with higher ETP, peak and VI (all p < 0.001). High total cholesterol, triglycerides and LDL-cholesterol levels were significantly associated with increased ETP and peak (all p < 0.001). Linear regression analysis revealed that the ETP is positively associated with both plasma LDL and HDL cholesterol levels, whereas the velocity index is positively associated with HDL cholesterol. Additionally, ETP, peak and VI were significantly associated with the plasma triglycerides content. In conclusion, our study shows significant associations of high BMI and blood lipid levels with increased TG parameters, and this hypercoagulability may partly explain the increased risk of CVD in individuals with obesity and/or dyslipidemia.

Quality and Metabolomics Analysis of Houttuynia cordata Based on HS-SPME/GC-MS.

Houttuynia cordata is a medicinal and edible plant with a wide biological interest. Many parts were discarded due to various modes of consumption, resulting in resource waste. In this study, a comprehensive study was conducted on various edible indicators and medicinal components of Houttuynia cordata to understand its edible and medicinal value. The edible indexes of each root, stem, and leaf were determined, and the metabolites of different parts were investigated using the headspace solid-phase micro-extraction technique (HS-SPME-GC-MS). The differential metabolites were screened by orthogonal partial least squares discriminant analysis (OPLS-DA) and clustering analysis. The results of the study showed that the parts of Houttuynia cordata with high edibility values as a vegetable were mainly the roots and leaves, with the highest vitamin C content in the roots and the highest total flavonoids, soluble sugars, and total protein in the leaves. The nutrient content of all the stems of Houttuynia cordata was lower and significantly different from the roots and leaves (p < 0.05). In addition, 209 metabolites were isolated from Houttuynia cordata, 135 in the roots, 146 in the stems, 158 in the leaves, and 91 shared metabolites. The clustering analysis and OPLS-DA found that the parts of Houttuynia cordata can be mainly divided into above-ground parts (leaves and stems) and underground parts (roots). When comparing the differential metabolites between the above-ground parts and underground parts, it was found that the most important medicinal component of Houttuynia cordata, 2-undecanone, was mainly concentrated in the underground parts. The cluster analysis resulted in 28 metabolites with up-regulation and 17 metabolites with down-regulation in the underground parts. Most of the main components of the underground part have pharmacological effects such as anti-inflammatory, anti-bacterial and antiviral, which are more suitable for drug development. Furthermore, the above-ground part has more spice components and good antioxidant capacity, which is suitable for the extraction of edible flavors. Therefore, by comparing and analyzing the differences between the edible and medicinal uses of different parts of Houttuynia cordata as a medicinal and food plant, good insights can be obtained into food development, pharmaceutical applications, agricultural development, and the hygiene and cosmetic industries. This paper provides a scientific basis for quality control and clinical use.

Multilocation proteins in organelle communication: Based on protein-protein interactions.

Protein-protein interaction (PPI) plays a crucial role in most biological processes, including signal transduction and cell apoptosis. Importantly, the knowledge of PPIs can be useful for identification of multimeric protein complexes and elucidation of uncharacterized protein functions. Arabidopsis thaliana, the best-characterized dicotyledonous plant, the steadily increasing amount of information on the levels of its proteome and signaling pathways is progressively enabling more researchers to construct models for cellular processes for the plant, which in turn encourages more experimental data to be generated. In this study, we performed an overview analysis of the 10 major organelles and their associated proteins of the dicotyledonous model plant Arabidopsis thaliana via PPI network, and found that PPI may play an important role in organelle communication. Further, multilocation proteins, especially phosphorylation-related multilocation proteins, can function as a "needle and thread" via PPIs and play an important role in organelle communication. Similar results were obtained in a monocotyledonous model crop, rice. Furthermore, we provide a research strategy for multilocation proteins by LOPIT technique, proteomics, and bioinformatics analysis and also describe their potential role in the field of plant science. The results provide a new view that the phosphorylation-related multilocation proteins play an important role in organelle communication and provide new insight into PPIs and novel directions for proteomic research. The research of phosphorylation-related multilocation proteins may promote the development of organelle communication and provide an important theoretical basis for plant responses to external stress.

Two-stage feature selection for classification of gene expression data based on an improved Salp Swarm Algorithm.

Microarray technology has developed rapidly in recent years, producing a large number of ultra-high dimensional gene expression data. However, due to the huge sample size and dimension proportion of gene expression data, it is very challenging work to screen important genes from gene expression data. For small samples of high-dimensional biomedical data, this paper proposes a two-stage feature selection framework combining Wrapper, embedding and filtering to avoid the curse of dimensionality. The proposed framework uses weighted gene co-expression network (WGCNA), random forest and minimal redundancy maximal relevance (mRMR) for first stage feature selection. In the second stage, a new gene selection method based on the improved binary Salp Swarm Algorithm is proposed, which combines machine learning methods to adaptively select feature subsets suitable for classification algorithms. Finally, the classification accuracy is evaluated using six methods: lightGBM, RF, SVM, XGBoost, MLP and KNN. To verify the performance of the framework and the effectiveness of the proposed algorithm, the number of genes selected and the classification accuracy was compared with the other five intelligent optimization algorithms. The results show that the proposed framework achieves an accuracy equal to or higher than other advanced intelligent algorithms on 10 datasets, and achieves an accuracy of over 97.6% on all 10 datasets. This shows that the method proposed in this paper can solve the feature selection problem related to high-dimensional data, and the proposed framework has no data set limitation, and it can be applied to other fields involving feature selection.

Genome-wide identification and expression analysis of auxin response factors in peanut (Arachis hypogaea L.).

Auxin response factors (ARFs) are transcription factors that regulate the expression of auxin response genes, and have important functions in plant growth and development. In this study, available genome data for peanut (Arachis hypogaea L.) were used to identify AhARF genes. In total, 61 AhARFs and 23 AtARFs were divided into six groups (I-VI). Molecular structural analysis revealed that the protein members of AhARF contain at least two domains, the B3 domain and the Auxin-resp domain, and that some have a C-terminal dimerisation domain. Screening of the transcriptome data of 22 tissues of A. hypogaea cv. Tifrunner in a public database showed high expression levels of AhARF2 and AhARF6. AhARF6 was expressed more highly in the stem and branch than in the root and leaf of the wild species Arachis monticola (A. mon) and cultivated species H103. After treatment with exogenous auxin (NAA), the expression of AhARF6 was inhibited, and this inhibition was greater in A. mon than in H103. The transcriptome map revealed that the expression of AhARF6 was higher in the larger pods of H8107 and ZP06 than in the medium pods of H103 and small pods of A. mon. Moreover, AhARF6-5 was proven to be localised in the nucleus, consistent with the location of AtARF6. These results suggest that AhARF6 may play an important role in pod development in peanut.