Integrated in silico MS-based phosphoproteomics and network enrichment analysis of RASopathy proteins

Abstract Background RASopathies are a group of syndromes showing clinical overlap caused by mutations in genes affecting the RAS-MAPK pathway. Consequent disruption on cellular signaling leads and is driven by phosphoproteome remodeling. However, we still lack a comprehensive picture of the different key players and altered downstream effectors. Methods An in silico interactome of RASopathy proteins was generated using pathway enrichment analysis/STRING tool, including identification of main hub proteins. We also integrated phosphoproteomic and immunoblotting studies using previous published information on RASopathy proteins and their neighbors in the context of RASopathy syndromes. Data from Phosphosite database (www.phosphosite.org) was collected in order to obtain the potential phosphosites subjected to regulation in the 27 causative RASopathy proteins. We compiled a dataset of dysregulated phosphosites in RASopathies, searched for commonalities between syndromes in harmonized data, and analyzed the role of phosphorylation in the syndromes by the identification of key players between the causative RASopathy proteins and the associated interactome. Results In this study, we provide a curated data set of 27 causative RASopathy genes, identify up to 511 protein–protein associations using pathway enrichment analysis/STRING tool, and identify 12 nodes as main hub proteins. We found that a large group of proteins contain tyrosine residues and their biological processes include but are not limited to the nervous system. Harmonizing published RASopathy phosphoproteomic and immunoblotting studies we identified a total of 147 phosphosites with increased phosphorylation, whereas 47 have reduced phosphorylation. The PKB signaling pathway is the most represented among the dysregulated phosphoproteins within the RASopathy proteins and their neighbors, followed by phosphoproteins implicated in the regulation of cell proliferation and the MAPK pathway. Conclusions This work illustrates the complex network underlying the RASopathies and the potential of phosphoproteomics for dissecting the molecular mechanisms in these syndromes. A combined study of associated genes, their interactome and phosphorylation events in RASopathies, elucidates key players and mechanisms to direct future research, diagnosis and therapeutic windows.

Download Full-text

Gene Expression Profile of Active HE4 Stimulation in Epithelial Ovarian Cancer Cells: Microarray Study and Comprehensive Bioinformatics Analysis

10.21203/rs.3.rs-46014/v1 ◽

2020 ◽

Author(s):

Liancheng Zhu ◽

Mingzi Tan ◽

Haoya Xu ◽

Bei Lin

Keyword(s):

Ovarian Cancer ◽

Epithelial Ovarian Cancer ◽

Clinical Significance ◽

Molecular Mechanisms ◽

Bioinformatics Analysis ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Pathway Enrichment Analysis ◽

Coexpression Analysis ◽

Pathway Enrichment

Abstract Background.Human Epididymis Protein 4 (HE4) is a novel serum biomarker for diagnosis of epithelial ovarian cancer (EOC) with high specificity and sensitivity compared with CA125, and the increasing researches have been carried out on its roles in promoting carcinogenesis and chemoresistance in EOC in recent years, however, its underlying molecular mechanisms remain poorly understood. The aim of this study was to elucidate the molecular mechanisms of HE4 stimulation and to identify the key genes and pathways mediating carcinogenesis in EOC using microarray and bioinformatics analysis.Methods. We established a stable HE4-silence ES-2 ovarian cancer cell line labeled as “S”, and its active HE4 protein stimulated cells labeled as “S4”. Human whole genome microarray analysis was used to identify deferentially expressed genes (DEGs) from triplicate samples of S4 and S cells. “clusterProfiler” package in R, DAVID, Metascape, and Gene Set Enrichment Analysis (GSEA) were used to perform gene ontology (GO) and pathway enrichment analysis, and cBioPortal for WFDC2 coexpression analysis. GEO dataset (GSE51088) and quantitative real-time polymerase chain reaction (qRT-PCR) was applied for validation. The protein–protein interaction (PPI) network and modular analyses were performed using Metascape and Cytoscape. Results.In total, 713 DEGs were found (164 up regulated and 549 down regulated) and further analyzed by GO, pathway enrichment and PPI analyses. We found that MAPK pathway accounted for a significant portion of the enriched terms. WFDC2 coexpression analysis revealed ten WFDC2 coexpressed genes (TMEM220A, SEC23A, FRMD6, PMP22, APBB2, DNAJB4, ERLIN1, ZEB1, RAB6B, and PLEKHF1) that were also dramatically changed in S4 cells and validated by dataset GSE51088. Kaplan–Meier survival statistics revealed clinical significance for all of the 10 target genes. Finally, PPI was constructed, sixteen hub genes and eight molecular complex detections (MCODEs) were identified, the seeds of five most significant MCODEs were subjected to GO and KEGG enrichment analysis and their clinical significance was evaluated.Conclusions.By applying microarray and bioinformatics analyses, we identified DEGs and determined a comprehensive gene network of active HE4 stimulation in EOC cells. We offered several possible mechanisms and identified therapeutic and prognostic targets of HE4 in EOC.

Download Full-text

Gene expression profiles following active HE4 stimulation in epithelial ovarian cancer cells: microarray study and comprehensive bioinformatics analysis

10.21203/rs.3.rs-100248/v1 ◽

2020 ◽

Author(s):

Liancheng Zhu ◽

Mingzi Tan ◽

Haoya Xu ◽

Bei Lin

Keyword(s):

Ovarian Cancer ◽

Epithelial Ovarian Cancer ◽

Molecular Mechanisms ◽

Bioinformatics Analysis ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Pathway Enrichment Analysis ◽

Ppi Network ◽

Coexpression Analysis ◽

Pathway Enrichment

Abstract Background: Human epididymis protein 4 (HE4) is a novel serum biomarker for diagnosing epithelial ovarian cancer (EOC) with high specificity and sensitivity, compared with CA125. Recent studies have focused on the roles of HE4 in promoting carcinogenesis and chemoresistance in EOC; however, the molecular mechanisms underlying its action remain poorly understood. This study was conducted to determine the molecular mechanisms underlying HE4 stimulation and identifying key genes and pathways mediating carcinogenesis in EOC by microarray and bioinformatics analysis.Methods: We established a stable HE4-silenced ES-2 ovarian cancer cell line labeled as “S”; the S cells were stimulated with the active HE4 protein, yielding cells labeled as “S4”. Human whole-genome microarray analysis was used to identify differentially expressed genes (DEGs) in S4 and S cells. The “clusterProfiler” package in R, DAVID, Metascape, and Gene Set Enrichment Analysis were used to perform gene ontology (GO) and pathway enrichment analysis, and cBioPortal was used for WFDC2 coexpression analysis. The GEO dataset (GSE51088) and quantitative real-time polymerase chain reaction were used to validate the results. Protein–protein interaction (PPI) network and modular analyses were performed using Metascape and Cytoscape, respectively. Results: In total, 713 DEGs were identified (164 upregulated and 549 downregulated) and further analyzed by GO, pathway enrichment, and PPI analyses. We found that the MAPK pathway accounted for a significant large number of the enriched terms. WFDC2 coexpression analysis revealed ten WFDC2-coexpressed genes (TMEM220A, SEC23A, FRMD6, PMP22, APBB2, DNAJB4, ERLIN1, ZEB1, RAB6B, and PLEKHF1) whose expression levels were dramatically altered in S4 cells; this was validated using the GSE51088 dataset. Kaplan–Meier survival statistics revealed that all 10 target genes were clinically significant. Finally, in the PPI network, 16 hub genes and 8 molecular complex detections (MCODEs) were identified; the seeds of the five most significant MCODEs were subjected to GO and KEGG enrichment analyses and their clinical relevance was evaluated.Conclusions: Through microarray and bioinformatics analyses, we identified DEGs and determined a comprehensive gene network following active HE4 stimulation in EOC cells. We proposed several possible mechanisms underlying the action of HE4 and identified the therapeutic and prognostic targets of HE4 in EOC.

Download Full-text

Quantitative Phosphoproteomic Comparison of Lens Proteins in Highly Myopic Cataract and Age-Related Cataract

BioMed Research International ◽

10.1155/2021/6668845 ◽

2021 ◽

Vol 2021 ◽

pp. 1-18

Author(s):

Shaohua Zhang ◽

Keke Zhang ◽

Wenwen He ◽

Yi Lu ◽

Xiangjia Zhu

Keyword(s):

Molecular Mechanisms ◽

Kegg Pathway ◽

Phosphoglycerate Kinase ◽

Enrichment Analysis ◽

Glutathione Metabolism ◽

Pathway Enrichment Analysis ◽

Phosphorylation Sites ◽

Pathway Enrichment ◽

Age Related ◽

Go Enrichment

Purpose. To investigate and compare the lens phosphoproteomes in patients with highly myopic cataract (HMC) or age-related cataract (ARC). Methods. In this study, we undertook a comparative phosphoproteome analysis of the lenses from patients with HMC or ARC. Intact lenses from ARC and HMC patients were separated into the cortex and nucleus. After protein digestion, the phosphopeptides were quantitatively analyzed with TiO2 enrichment and liquid chromatography-mass spectrometry. The potential functions of different phosphopeptides were assessed by Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Results. In total, 522 phosphorylation sites in 164 phosphoproteins were identified. The number of phosphorylation sites was significantly higher in the cortex than in the nucleus, in both ARC and HMC lenses. The differentially phosphorylated peptides in the lens cortex and nucleus in HMC eyes were significantly involved in the glutathione metabolism pathway. The KEGG pathway enrichment analysis indicated that the differences in phosphosignaling mediators between the ARC and HMC lenses were associated with glycolysis and the level of phosphorylated phosphoglycerate kinase 1 was lower in HMC lenses than in ARC lenses. Conclusions. We provide an overview of the differential phosphoproteomes of HMC and ARC lenses that can be used to clarify the molecular mechanisms underlying their different phenotypes.

Download Full-text

Utilizing network pharmacology to explore the underlying mechanism of Radix Salviae in diabetic retinopathy

Chinese Medicine ◽

10.1186/s13020-019-0280-7 ◽

2019 ◽

Vol 14 (1) ◽

Cited By ~ 3

Author(s):

Chun-Li Piao ◽

Jin-Li Luo ◽

De Jin ◽

Cheng Tang ◽

Li Wang ◽

...

Keyword(s):

Diabetic Retinopathy ◽

Chinese Medicine ◽

Growth Factor ◽

Traditional Chinese Medicine ◽

Molecular Mechanisms ◽

Enrichment Analysis ◽

Network Pharmacology ◽

Pathway Enrichment Analysis ◽

Pathway Enrichment ◽

Underlying Mechanisms

Abstract Introduction Radix Salviae (Dan-shen in pinyin), a classic Chinese herb, has been extensively used to treat diabetic retinopathy in clinical practice in China for many years. However, the pharmacological mechanisms of Radix Salviae remain vague. The aim of this study was to decrypt the underlying mechanisms of Radix Salviae in the treatment of diabetic retinopathy using a systems pharmacology approach. Methods A network pharmacology-based strategy was proposed to elucidate the underlying multi-component, multi-target, and multi-pathway mode of action of Radix Salviae against diabetic retinopathy. First, we collected putative targets of Radix Salviae based on the Traditional Chinese Medicine System Pharmacology database and a network of the interactions among the putative targets of Radix Salviae and known therapeutic targets of diabetic retinopathy was built. Then, two topological parameters, “degree” and “closeness certainty” were calculated to identify the major targets in the network. Furthermore, the major hubs were imported to the Database for Annotation, Visualization and Integrated Discovery to perform a pathway enrichment analysis. Results A total of 130 nodes, including 18 putative targets of Radix Salviae, were observed to be major hubs in terms of topological importance. The results of pathway enrichment analysis indicated that putative targets of Radix Salviae mostly participated in various pathways associated with angiogenesis, protein metabolism, inflammatory response, apoptosis, and cell proliferation. The putative targets of Radix Salviae (vascular endothelial growth factor, matrix metalloproteinases, plasminogen, insulin-like growth factor-1, and cyclooxygenase-2) were recognized as active factors involved in the main biological functions of treatment, which implied that these were involved in the underlying mechanisms of Radix Salviae on diabetic retinopathy. Conclusions Radix Salviae could alleviate diabetic retinopathy via the molecular mechanisms predicted by network pharmacology. This research demonstrates that the network pharmacology approach can be an effective tool to reveal the mechanisms of traditional Chinese medicine from a holistic perspective.

Download Full-text

Network Pharmacology-Based Investigation For Predicting Active Ingredients And Potential Targets Of Strychnos Nux-Vomica L. In Treating Multiple Myeloma

10.21203/rs.3.rs-97494/v1 ◽

2020 ◽

Author(s):

Yan Zhou ◽

Jianping Shen ◽

Keting Jin ◽

Chenjun Lin ◽

Zirui Hong ◽

...

Keyword(s):

Multiple Myeloma ◽

Molecular Mechanisms ◽

Chinese Herb ◽

Enrichment Analysis ◽

Network Pharmacology ◽

Pathway Enrichment Analysis ◽

Pathway Enrichment ◽

Topological Parameters ◽

Pharmacological Potential ◽

Underlying Mechanisms

Abstract Background: Strychnos nux-vomica L. (SN)，a classic Chinese herb, have long been used for the treatment of cancer for many years, However, the pharmacological mechanisms of SN in treatment of Multiple myeloma L.remain vague.The aim of this study was to examine the network pharmacological potential effects of SN on Multiple myeloma using a systems pharmacology approach.Methods: we collected putative targets of SN based on the Traditional Chinese Medicine System Pharmacology database，and oral bioavailability and drug-likeness was screened using absorption, distribution, metabolism, and excretion (ADME) criteria. the network of the interactions among the putative targets of SN and known therapeutic targets of Multiple myeloma was built by using the STITCH database. Then, topological parameters, “Degree” ,“Closeness” and“Betweenness” were calculated to identify the hub targets in the network. Furthermore, the hub targets were imported to the Database for Annotation, Visualization and Integrated Discovery to perform a pathway enrichment analysis.Results: 60 of the identified potential targets of the SN were also Multiple Myeloma- related targets, including 14 putative targets of SN were observed to be major hubs in terms of topological importance.Additionally,the results of pathway enrichment analysis indicated that targets of SN in treating Multiple Myeloma were mainly clustered into multiple biological processes by activating on several signaling pathways(PI3K-Akt, p38-MAPK, Ras/Raf/MEK/ERK pathways), which implied that these were involved in the underlying mechanisms of SN on Multiple Myeloma. Conclusions: Our works successfully explain the potential effects of SN for Multiple Myeloma treatment via the molecular mechanisms predicted by network pharmacology.Moreover,our present outcomes might shed light on the further clinical application of SN in treating Multiple Myeloma.

Download Full-text

Proteomic signature of the Dravet syndrome in the genetic Scn1a-A1783V mouse model

10.1101/2021.04.27.441099 ◽

2021 ◽

Author(s):

Nina Miljanovic ◽

Stefanie M. Hauck ◽

R. Maarten van Dijk ◽

Valentina Di Liberto ◽

Ali Rezaei ◽

...

Keyword(s):

Mouse Model ◽

Enrichment Analysis ◽

Dravet Syndrome ◽

Pathway Enrichment Analysis ◽

Data Set ◽

Pathophysiological Mechanisms ◽

Nitric Oxide Signaling ◽

Pathway Enrichment ◽

Proteomic Data ◽

Genetic Deficiency

AbstractBackgroundDravet syndrome is a rare, severe pediatric epileptic encephalopathy associated with intellectual and motor disabilities. Proteomic profiling in a mouse model of Dravet syndrome can provide information about the molecular consequences of the genetic deficiency and about pathophysiological mechanisms developing during the disease course.MethodsA knock-in mouse model of Dravet syndrome with Scn1a haploinsufficiency was used for whole proteome, seizure and behavioral analysis. Hippocampal tissue was dissected from two-(prior to epilepsy manifestation) and four-(following epilepsy manifestation) week-old male mice and analyzed using LC-MS/MS with label-free quantification. Proteomic data sets were subjected to bioinformatic analysis including pathway enrichment analysis. The differential expression of selected proteins was confirmed by immunohistochemical staining.ResultsThe findings confirmed an increased susceptibility to hyperthermia-associated seizures, the development of spontaneous seizures, and behavioral alterations in the novel Scn1a-A1873V mouse model of Dravet syndrome. As expected, proteomic analysis demonstrated more pronounced alterations following epilepsy manifestation. In particular, proteins involved in neurotransmitter dynamics, receptor and ion channel function, synaptic plasticity, astrogliosis, neoangiogenesis, and nitric oxide signaling showed a pronounced regulation in Dravet mice. Pathway enrichment analysis identified several significantly regulated pathways at the later time point, with pathways linked to synaptic transmission and glutamatergic signaling dominating the list.ConclusionIn conclusion, the whole proteome analysis in a mouse model of Dravet syndrome demonstrated complex molecular alterations in the hippocampus. Some of these alterations may have an impact on excitability or may serve a compensatory function, which, however, needs to be further confirmed by future investigations. The proteomic data indicate that, due to the molecular consequences of the genetic deficiency, the pathophysiological mechanisms may become more complex during the course of the disease. Resultantly, the management of Dravet syndrome may need to consider further molecular and cellular alterations. Ensuing functional follow-up studies, this data set may provide valuable guidance for the future development of novel therapeutic approaches.

Download Full-text

A Comparative Study of Topology-based Pathway Enrichment Analysis Methods

10.1101/814145 ◽

2019 ◽

Cited By ~ 2

Author(s):

Jing Ma ◽

Ali Shojaie ◽

George Michailidis

Keyword(s):

Comparative Study ◽

Differential Expression ◽

Enrichment Analysis ◽

Superior Performance ◽

Systematic Evaluation ◽

Pathway Enrichment Analysis ◽

Data Set ◽

Metabolomics Data ◽

Expression Levels ◽

Pathway Enrichment

AbstractBackgroundPathway enrichment analysis is extensively used in the analysis of Omics data for gaining biological insights into the functional roles of pre-defined subsets of genes, proteins and metabolites. A large number of methods have been proposed in the literature for this task. The vast majority of these methods use as input expression levels of the biomolecules under study together with their membership in pathways of interest. The latest generation of pathway enrichment methods also leverages information on the topology of the underlying pathways, which as evidence from their evaluation reveals, lead to improved sensitivity and specificity. Nevertheless, a systematic empirical comparison of such methods is still lacking, making selection of the most suitable method for a specific experimental setting challenging. This comparative study of nine network-based methods for pathway enrichment analysis aims to provide a systematic evaluation of their performance based on three real data sets with different number of features (genes/metabolites) and number of samples.ResultsThe findings highlight both methodological and empirical differences across the nine methods. In particular, certain methods assess pathway enrichment due to differences both across expression levels and in the strength of the interconnectedness of the members of the pathway, while others only leverage differential expression levels. In the more challenging setting involving a metabolomics data set, the results show that methods that utilize both pieces of information (with NetGSA being a prototypical one) exhibit superior statistical power in detecting pathway enrichment.ConclusionThe analysis reveals that a number of methods perform equally well when testing large size pathways, which is the case with genomic data. On the other hand, NetGSA that takes into consideration both differential expression of the biomolecules in the pathway, as well as changes in the topology exhibits a superior performance when testing small size pathways, which is usually the case for metabolomics data.

Download Full-text

A comparative study of topology-based pathway enrichment analysis methods

BMC Bioinformatics ◽

10.1186/s12859-019-3146-1 ◽

2019 ◽

Vol 20 (1) ◽

Cited By ~ 8

Author(s):

Jing Ma ◽

Ali Shojaie ◽

George Michailidis

Keyword(s):

Comparative Study ◽

Differential Expression ◽

Enrichment Analysis ◽

Superior Performance ◽

Systematic Evaluation ◽

Pathway Enrichment Analysis ◽

Data Set ◽

Metabolomics Data ◽

Expression Levels ◽

Pathway Enrichment

Abstract Background Pathway enrichment extensively used in the analysis of Omics data for gaining biological insights into the functional roles of pre-defined subsets of genes, proteins and metabolites. A large number of methods have been proposed in the literature for this task. The vast majority of these methods use as input expression levels of the biomolecules under study together with their membership in pathways of interest. The latest generation of pathway enrichment methods also leverages information on the topology of the underlying pathways, which as evidence from their evaluation reveals, lead to improved sensitivity and specificity. Nevertheless, a systematic empirical comparison of such methods is still lacking, making selection of the most suitable method for a specific experimental setting challenging. This comparative study of nine network-based methods for pathway enrichment analysis aims to provide a systematic evaluation of their performance based on three real data sets with different number of features (genes/metabolites) and number of samples. Results The findings highlight both methodological and empirical differences across the nine methods. In particular, certain methods assess pathway enrichment due to differences both across expression levels and in the strength of the interconnectedness of the members of the pathway, while others only leverage differential expression levels. In the more challenging setting involving a metabolomics data set, the results show that methods that utilize both pieces of information (with NetGSA being a prototypical one) exhibit superior statistical power in detecting pathway enrichment. Conclusion The analysis reveals that a number of methods perform equally well when testing large size pathways, which is the case with genomic data. On the other hand, NetGSA that takes into consideration both differential expression of the biomolecules in the pathway, as well as changes in the topology exhibits a superior performance when testing small size pathways, which is usually the case for metabolomics data.

Download Full-text

Novel Insight Into the Association Between Obesity and Hepatocellular Carcinoma Occurrence and Recurrence: High-Throughput Microarray Data Set Analysis of Differentially Expressed Genes

JCO Clinical Cancer Informatics ◽

10.1200/cci.21.00094 ◽

2021 ◽

pp. 1169-1180

Author(s):

Seyedeh Faezeh Hassani ◽

Masoud Sayaf ◽

Seyedeh Sara Danandeh ◽

Zahra Nourollahzadeh ◽

Mahshid Shahmohammadi ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Differentially Expressed Genes ◽

Microarray Data ◽

Molecular Mechanisms ◽

Interaction Network ◽

Enrichment Analysis ◽

Differentially Expressed ◽

Pathway Enrichment Analysis ◽

Data Set ◽

Potential Biomarkers

PURPOSE This study aims to identify potential biomarkers of hepatocellular carcinoma (HCC) occurrence/recurrence and obesity, along with the molecular mechanisms that involve these biomarkers. METHODS Three microarray data sets, namely GSE18897, GSE25097, and GSE36376 (genetic suppressor elements associated with obesity, tumor, and recurrence, respectively), were downloaded from Gene Expression Omnibus database to be investigated for their expression as differentially expressed genes (DEGs) in HCC and obesity. The functional and pathway enrichment analysis of these DEGs were identified by the Database for Annotation Visualization and Integrated Discovery. The protein-protein interaction network analysis was performed with STRING online tool and Cytoscape software. RESULTS One hundred sixty common DEGs were screened. We found that these genes were associated with certain pathways such as metabolic pathways, terpenoid backbone biosynthesis, and adipocytokine signaling pathway. The involvements of 10 genes, including RPS16, RPS7, CCT3, HNRNPA2B1, EIF4G1, PSMC4, NHP2, EGR1, FDPS, and MCM4, were identified in the subnetwork. HNRNPA2B1 and RPS7 in the GSE18897 data set, RPS16, RPS7, CCT3, HNRNPA2B1, PSMC4, NHP2, FDPS, and MCM4 in the GSE25097 data set, and RPS16, RPS7, CCT3, HNRNPA2B1, EIF4G1, PSMC4, NHP2, FDPS, and MCM4 in the GSE36376 data set exhibited positive fold changes. CONCLUSION These DEGs and pathways could be of diagnostic value as potential biomarkers involved in the pathogenesis of HCC, pertaining to both obesity and HCC occurrence/recurrence.

Download Full-text

Investigating the molecular basis for heterophylly in the aquatic plant Potamogeton octandrus (Potamogetonaceae) with comparative transcriptomics

PeerJ ◽

10.7717/peerj.4448 ◽

2018 ◽

Vol 6 ◽

pp. e4448 ◽

Cited By ~ 3

Author(s):

Dingxuan He ◽

Pin Guo ◽

Paul F. Gugger ◽

Youhao Guo ◽

Xing Liu ◽

...

Keyword(s):

Differentially Expressed Genes ◽

High Throughput ◽

Molecular Mechanisms ◽

High Throughput Sequencing ◽

Kegg Pathway ◽

Enrichment Analysis ◽

Differentially Expressed ◽

Pathway Enrichment Analysis ◽

Pathway Enrichment ◽

Floating Leaves

Many plant species exhibit different leaf morphologies within a single plant, or heterophylly. The molecular mechanisms regulating this phenomenon, however, have remained elusive. In this study, the transcriptomes of submerged and floating leaves of an aquatic heterophyllous plant, Potamogeton octandrus Poir, at different stages of development, were sequenced using high-throughput sequencing (RNA-Seq), in order to aid gene discovery and functional studies of genes involved in heterophylly. A total of 81,103 unigenes were identified in submerged and floating leaves and 6,822 differentially expressed genes (DEGs) were identified by comparing samples at differing time points of development. KEGG pathway enrichment analysis categorized these unigenes into 128 pathways. A total of 24,025 differentially expressed genes were involved in carbon metabolic pathways, biosynthesis of amino acids, ribosomal processes, and plant-pathogen interactions. In particular, KEGG pathway enrichment analysis categorized a total of 70 DEGs into plant hormone signal transduction pathways. The high-throughput transcriptomic results presented here highlight the potential for understanding the molecular mechanisms underlying heterophylly, which is still poorly understood. Further, these data provide a framework to better understand heterophyllous leaf development in P. octandrus via targeted studies utilizing gene cloning and functional analyses.

Download Full-text