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Is there a negative correlation between the mRNA produced by the cell and the time of extraction?

Is there a negative correlation between the mRNA produced by the cell and the time of extraction?


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I am doing some data analysis about gene expression time series. When I plot mRNA produced by P. Furiosus cells irradiated by gamma radiation against the time of extraction, it seems that there is a negative correlation between them. Is it a good result from a biological point of view ?


$gamma$-irradiation produces single- and double-strand DNA breaks, depending on the dosage, and activates DNA damage repair pathways like p53. During this time, the cell cycle arrests and most if not all mRNA production ceases. For sub-lethal doses of $gamma$ rays, I would expect to see newly-produced mRNA levels drop off fairly quickly with time following the initial dose, then possibly begin to ramp up again later as the damage is repaired and the cell cycle arrest checkpoints released.


The global dynamics of RNA stability orchestrates responses to cellular activation

Transcriptomics is used to quantify changes in accumulated levels of mRNAs following cellular activation. These changes arise from the opposing fluxes of transcription and mRNA decay, both of which affect the functional dynamics of global gene expression. A study published recently in BMC Genomics focuses on the contribution made by mRNA stability in shaping the kinetics of gene responses in mammalian cells.


Impact of lipid nanoparticle size on mRNA vaccine immunogenicity

Lipid nanoparticles (LNP) are effective delivery vehicles for messenger RNA (mRNA) and have shown promise for vaccine applications. Yet there are no published reports detailing how LNP biophysical properties can impact vaccine performance. In our hands, a retrospective analysis of mRNA LNP vaccine in vivo studies revealed a relationship between LNP particle size and immunogenicity in mice using LNPs of various compositions. To further investigate this, we designed a series of studies to systematically change LNP particle size without altering lipid composition and evaluated biophysical properties and immunogenicity of the resulting LNPs. While small diameter LNPs were substantially less immunogenic in mice, all particle sizes tested yielded a robust immune response in non-human primates (NHP).


Inherited Mutations in Cancer

To complicate matters, it is clear that the changes needed to create a cancer cell can be accomplished in many different ways. Although all cancers have to overcome the same spectrum of regulatory functions in order to grow and progress, the genes involved may differ. In addition, the order in which the genes become de-regulated or lost may also vary. As an example, colon cancer tumors from two different individuals may involve very different sets of tumor suppressors and oncogenes, even though the outcome (cancer) is the same.

The great heterogeneity seen in cancer, even those of the same organ, means that diagnosis and treatment are complicated. Current advances in the molecular classification of tumors should allow the rational design of treatment protocols based on the actual genes involved in any given case. New diagnostic tests may involve the screening of hundreds or thousands of genes to create a personalized profile of the tumor in an individual. This information should allow for the tailoring of cancer treatments geared to the individual. For more information on this see the Genomics/Proteomics section.

The genetic changes that lead to unregulated cell growth may be acquired in two different ways. It is possible that the mutation can occur gradually over a number of years, leading to the development of a 'sporadic' case of cancer. Alternatively, it is possible to inherit dysfunctional genes leading to the development of a familial form of a particular cancer type. Some examples of cancers with known hereditary components include:

  • Breast cancer- Inheritance of mutant versions of the BRCA1 and BRCA2 genes are known risk factors. Although many, if not most, individuals with breast cancer do not have detectable alterations in these genes, having a mutant form increases the likelihood of developing breast cancer.
  • Colon cancer- Defects in DNA repair genes such as MSH2 are known to predispose individuals to hereditary non-polyposis colorectal cancer ( HNPCC ).
  • Retinoblastoma - Defects in the Rb tumor suppressor gene are known to cause this eye cancer and several other types of cancers. More on this particular disease can be found in the section on Rb

This is an incomplete list of the known inherited cancer types, and it is certain that more inherited forms of cancer will identified as the genetics of various types of cancer are clarified.

More information on this topic may be found in Chapters 2 and 4 of The Biology of Cancer by Robert A. Weinberg.


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MRNA Uptake

To be translated and elicit an antigen-specific immune response, an mRNA-vaccine has to reach the cytosol of target cells. However, as opposed to DNA vaccines, RNA vaccines only have to cross the plasma membrane, but not the nuclear envelope which may improve the probability of successful in vivo transfection. 60 As early as 1990, the uptake of mRNA by mouse muscle cells upon simple injection, i.e., without any additional help from special delivery systems, was demonstrated. 61 Later on, numerous studies confirmed that locally administered naked mRNA is taken up by cells in target tissues. 8 , 62 - 65 The mechanism by which naked mRNA enters cells remained unclear initially. However, elucidating and understanding the uptake route is important to facilitate the development of more efficient mRNA-vaccines.

A plethora of studies investigated the cellular entry of nucleic acids. Most of them looked into the uptake routes of pDNA, DNA oligonucleotides, siRNA or long dsRNA and a complex picture emerged. The molecules entered cells by diffusion controlled mechanisms or diverse endocytic pathways, often strongly dependent on the respective cell type or species and frequently showed a vesicular localization, i.e., an entrapment in endocytic or lysosomal compartments. 66 - 73 However, mRNA differs from these types of molecules due to its unique combination of physico-chemical and structural parameters. In contrast to DNA, mRNA contains uridine instead of deoxythymidine, preferentially adopts a C3′-endo conformation and is hydroxylated at the 2′-position of the ribose. The single-stranded nature lets mRNA fold into complex secondary and tertiary structures, completely unknown from double-stranded DNA and RNA molecules, respectively. Finally, its length of a few hundred to several thousand nucleotides distinguishes mRNA from other single-stranded RNAs like antisense RNA or aptamers.

First insight into the uptake mechanism of naked mRNA was gained by a mouse study investigating intradermal administration by injection. 8 Local entry into cells of the dermis which were not exclusively professional antigen presenting cells (pAPCs) turned out to be saturable, improvable by calcium and associated with the movement of vesicles. More elaborate work in vitro revealed that uptake of naked mRNA is a widespread phenomenon among primary cells and cell lines of diverse types. 74 These efforts confirmed saturability of uptake and demonstrated that it is also temperature and dose dependent. Most of the mRNA appeared to enter cells via caveolae/lipid rafts, 74 most likely mediated by (a) scavenger-receptor(s) which are known to concentrate in caveolae and to preferentially recognize and facilitate internalization of negatively charged macromolecules. 75 - 78

To a minor degree, macropinocytosis also appeared to be involved in mRNA uptake of different primary cells and cell lines. 74 By contrast, macropinocytosis apparently predominates mRNA uptake by dendritic cells upon intranodal injection. 79 The picture becomes even more complicated when looking at formulated mRNA vaccines. For instance, a recently developed two-component vaccine consisting of naked and protamine-complexed mRNA reveals different routes and kinetics of uptake for the two components, albeit both are taken up via an endosomal pathway. 9 , 80

mRNA uptake and expression in vivo is quite efficient (much more efficient than spontaneous uptake by cells in vitro) and comparable even with cells transfected in vitro under optimal conditions. 8 , 61 In part, hydrodynamic pressure may contribute to target cell transfection in case of local injections 81 as it does upon intravenous administration. 82 However, the correlation between pressure and transfection efficiency/protein expression may not be linear but show an optimum. 83 Anyway, a large amount of the mRNA appears to stay trapped in endosomal vesicles. Hence, mRNA vaccines may profit strongly from approaches increasing the fraction of mRNA that reaches the cytosol.


MiR-221 Promotes Hepatocellular Carcinoma Cells Migration via Targeting PHF2

MicroRNAs (MiRNAs), which regulate the gene expression leading to translational inhibition or mRNA degradation, are involved in carcinogenesis and tumor progression. Previous studies have demonstrated that miR-221 was one of the most consistent overexpressed miRNAs in several types of cancer. However, the role of miR-221 in human liver cancer progression is not yet fully elucidated. Levels of miR-221 and plant homeodomain finger 2 (PHF2) expressions in human hepatocellular carcinoma (HCC) tissues and cell lines were detected using western blotting and quantitative real-time PCR (qRT-PCR). Cell migration was studied using the transwell assays. A dual-luciferase reporter system was used to validate the target gene of miR-221. The results indicated that miR-221 promoted HCC cell migration. By performing subsequent systematic bioinformatic analyses, we found PHF2 was the target gene of miR-221 and the direct binding relationship was further validated by dual-luciferase reporter assay. In addition, lower expression of PHF2 promoted HCC cell migration and linked to worse overall survival in HCC patients. Finally, the negative correlation between miR-221 and PHF2 expression levels in HCC specimens was further confirmed. Taken together, our findings implied that miR-221 could be a potential candidate for the therapeutics of HCC metastasis.

1. Introduction

Hepatocellular carcinoma (HCC) is the fifth common cancer and third leading cause of cancer-related deaths worldwide, and it is considered to be one of the most common cancers with poor prognosis [1]. Due to the rapid development of sequencing technology, there is a growing comprehension on the molecular mechanisms resulting in HCC carcinogenesis. Previous studies have considerably focused on study of DNA mutations and gene expression changes in HCC [2]. Moreover, RNA mutations and change of mRNA transcription are also correlated with the initiation and progression of HCC.

MicroRNAs (miRNAs) are small noncoding RNAs with approximately 22 nucleotides that could play important regulatory roles in plants and animals by targeting mRNAs for translational suppression [3]. In excess of 2,000 miRNAs have been identified to regulate a variety of protein coding transcription [4]. By modulating gene expression via posttranscriptional mechanisms, miRNAs are now known as vital players in cell cycle, differentiation, apoptosis, and oncogenesis [5]. Furthermore, miRNAs are identified to be correlated with the regulation of epithelial-mesenchymal transition and tumor metastasis by targeting important genes [6, 7]. MiR-221, which is encoded by human chromosome Xp11.3, is often abnormally expressed and associated with the regulation of oncogenes or tumor suppressive genes. Among numerous miRNAs, the upregulation of miR-221 has been recently recognized in numerous types of human cancers [8, 9]. Thus, identification of the function of miR-221 and its targets could make a new access to cancer treatment.

The aim of our study was to investigate miR-221 expression in HCC cells and tissues and to observe the changes in the migration ability of HCC cells following variation of the miR-221 expression. Our data, for the first time, revealed the tumorigenesis role of miR-221 in HCC and identified a target gene plant homeodomain finger 2 (PHF2). PHF2, which maps to human chromosome (Chr) 9q22 [10], is a member of the KDM7 histone demethylase family that contains a plant homeodomain (PHD) in the Jumonji-C and N-terminal domain [11]. Notably, previous studies indicated that PHF2 acts as a cancer suppressor by regulating p53 in colon cancer tissues [12]. However, the role of PHF2 in HCC remains to be investigated. In our study, we demonstrated that miR-221 promoted HCC cells migration via targeting PHF2 and could be a new target for HCC therapeutics. Taken together, our results may provide critical strategy for targeted therapy and prognosis of HCC.

2. Materials and Methods

2.1. Patients and Specimens

60 patients with hepatocellular carcinoma who underwent resection were collected from Affiliated Hospital of Yangzhou University between 2014 and 2017. The tissue microarray (TMA) consisted of 60 surgical cases produced by the National Engineering Centre for Biochip (Shanghai, China). The patients’ clinicopathologic parameters including tumor diameter, sex, age, TNM stage, lymph node metastasis, and depth of invasion. These data were acquired from the Medical Record of the Affiliated Hospital of Yangzhou University. 3-year clinical follow-up data were obtainable for 60 patients from the Yangzhou area. The median follow-up time is 20 months. And the cases of TMA include 2 lost follow-up patients. All the tissues were collected for the present study with patients’ informed consent. And the study of human specimens was approved by the Review Board of the Affiliated Hospital of Yangzhou University.

2.2. Tissue Samples

36 patients with histologically conformed hepatocellular carcinoma tissues (HCT) were obtained from the first Affiliated Hospital of Yangzhou University. The cancer tissues and adjacent cancerous tissues were collected from patients. All samples were acquired at the time of surgery and were frozen in liquid nitrogen immediately. This investigation was approved by the medical ethics committee of Yangzhou University and informed consent was gained from patients before recruitment.

2.3. Immunohistochemistry

Immunohistochemistry was performed with a standard avidin biotinylated–HRP complex (ABC) kit (Zhongshan biotech, Beijing, China) following the ABC method. The slips were incubated with anti-PHF2 antibody (1:1000) (abcam) overnight at 4°C, and diaminobenzidine (DAB Zhongshan Biotech, Beijing, China) was used to turn out a brown precipitation. The immunoreactivity was evaluated blindly by three pathologists using light microscopy (Olympus BX-51 light microscope), and the image was collected by Camedia Master C-3040 digital camera. The level of PHF2 was ranked as high when ≧5% of tumor cells showed immunopositivity. Biopsies with <5% tumor cells immunostaining were regarded as low.

2.4. Cell Lines and Culture

HCC cell lines SMMC-7721, Bel-7402, MHCC97, and HepG2 cells were obtained from the American Type Culture Collection (ATCC). Human normal hepatocyte HL-7702 cells were also obtained from the ATCC. HL-7702, SMMC-7721, and Bel-7402 cells were cultured in RPMI-1640 (GIBCO, US). HepG2 and MHCC97 cells were cultured in DMEM (GIBCO, US). The culture media were in humidified air with 5% CO2 at 37°C, supplemented with 10% fetal bovine serum and 1% streptomycin/penicillin.

2.5. Western Blot

After transfection, cells were harvested from the plates. Equivalent proteins were separated by 10% SDS polyacrylamide gel electrophoresis (SDS-PAGE) the proteins were then transferred onto PVDF membranes. After incubated overnight at 4°C with appropriate primary antibodies, the membranes were further probed with a horseradish peroxidase-conjugated secondary antibody (1:2000) for 2h at room temperature. The membrane was detected by enhanced chemiluminescence (ECL) solution and scanned on the chemiluminescence imaging analysis system (Tanon Biotechnology, Shanghai, China). Each western blot was repeated three times.

2.6. RNA Extraction and Quantitative Real-Time PCR (qRT-PCR)

After cell transfection, the cellular RNA was extracted from cell lines or tissues using TRIzol reagent (Sigma). Then the RNA transcribed into cDNA by PrimeScript RT master Mix (Takara, Dalian, China) following the corresponding protocols. qRT-PCR was carried out with a SYBR GREEN MIX kit (Promega, Madison, USA) conforming to the manufacturer’s instructions. The qRT-PCR detection was carried out using the ABI 7500 FAST Real-Time PCR System. GAPDH was used for loading control. The relative level was calculated using the relative quantification equation (RQ) =

2.7. Wound Healing Assays

SMMC-7721 cells were grown to 80% proportion in 6-well plates wounded by scratching the cell monolayer with a sterilized 200 μl pipette tip. Phase contrast images were collected in the same field at indicated time periods (0, 24, and 48 hours) using the Nikon Digital Microscope with magnification of × 100. Experiments were performed in thrice.

2.8. Cell Migration Assays

Cell migration assays were performed by modified two-chamber plates with a pore size of 8 μm. Cells were seeded in serum-free medium in the upper chamber at a density of 1 × 10 5 cells/well. After 24 h incubation in 37°C, the cells were fixed in methanol and stained with trypan blue. Cells in the upper chamber were carefully removed with a cotton swab and the cells that traversed the membrane were counted under a microscope in five fields. The analysis was performed thrice.

2.9. Dual-Luciferase Report Assay

MiRNA-binding regions of PHF2 for miR-221-3p in the 3′-UTR were subcloned into the GP-miRGLO luciferase miRNA vector. SMMC-7721 cells were seeded in 24-well plates at a density of 5 × 10 4 cells per well and transfected with wild type or mutant luciferase reporter plasmids at the concentration of 50 nmol/L. After 24 hours of incubation, luciferase activity was measured by a dual-luciferase reporter system (Promega, Fitchburg, WI, USA).

2.10. Statistical Analysis

Statistical analysis was executed by SPSS 16.0 software and images were obtained with GraphPad Prism 5 Software. The grayscale detection software is Image J. The data are shown as the mean ± standard deviation (SD). The correlation analyses were using Pearson’s correlation analyses, and the between-group differences were evaluated using Student’s T test or one-way ANOVA. For TMA, the relationship between PHF2 and the clinicopathologic factors of the HCC patients was evaluated by χ2 test. The Kaplan-Meier method and log-rank test were employed to assess the correlation between PHF2 expression and patient survival. P<0.05 is identified as statistically significant (

3. Results

3.1. MiR-221 Expression Is Increased in HCC Cell Lines and Tissues

To investigate the mRNA level of miR-221 in HCC cell lines, qRT-PCR was performed and results demonstrated that the miR-221 mRNA levels in HCC cell lines were higher than human normal hepatocyte (HL-7702) (Figure 1(a)). Moreover, we also found that miR-221 levels were higher in HCC tissues (T) than in adjacent noncancerous tissues (N) (n=36, Figure 1(b), Supplemental Figure 1). Results demonstrated that higher miR-221 expression was evidently associated with HCC. Thus, we used HCC cells to examine the role of miR-221 on cell migration. Furthermore, we demonstrated the correlation of the relative expression of miR-221 with the clinicopathological features of HCC patients in Table 1. Results showed that miR-221 expression in HCC patients with tumor size (≤7cm), pN status (p

) or TNM stage II was evidently lower than that with tumor size (>7cm), pN2-pN3 or TNM stage III-IV (p < 0.05). The relative expression of miR-221 was not found to be associated with age, gender, pT status, or serum AFP levels of HCC patients (p > 0.05). Therefore, miR-221 expression is increased in HCC tissues and had an evident relationship with the HCC patients’ characteristics.

3.2. MiR-221 Promotes the HCC Cell Lines Migration

To validate whether miR-221 was associated with the migration of HCC cells, we examine the effect of miR-221 on the cell wound healing. We found that miR-221-transfected cells showed a longer distance of shift whereas anti-miR-221-transfected groups showed shorter shift when compared with relevant negative control group (Figure 1(c)). The data showed that miR-221 promoted HCC cells wound healing capability. We then investigated the effect of miR-221 on HCC cell lines migration and found that miR-221-transfected SMMC-7721 cells enhanced the number of cells penetrating the inserts. In contrast, miR-221 inhibition decreased the abilities of SMMC-7721 cells to penetrate the inserts (Figure 1(d)). The results revealed that miR-221 could promote cell migration in SMMC-7721 cells.

3.3. MiR-221 Influences Metastasis-Related Genes

To investigate the effect of miR-221 on cell migration, we carried molecular analyses to detect the expression of some type metastasis-related genes. Our results showed that miR-221 downregulated the expression of an epithelial marker (E-cadherin) in mRNA levels in SMMC-7721 cells, whereas anti-miR-221 led to the opposite results (Figure 2(a)). Meanwhile, the mRNA level of a mesenchymal marker (N-cadherin) was increased when the HCC cells was transfected with miR-221. And anti-miR-221 decreased the expression of N-cadherin in mRNA levels (Figure 2(b)). Furthermore, miR-221 had the same effect on E-cadherin and N-cadherin in protein levels (Figures 2(c) and 2(d)). It is identified that the epithelial-to-mesenchymal transition (EMT) transcription factors play a crucial role in the process of EMT of cancer cells. We performed western blot to verify which EMT transcription factors miR-221 regulates. And the results demonstrated that miR-221 positively regulated the EMT transcription factors Snail and Slug (supplemental Figure 2). These data suggested that miR-221 significantly influenced the expression of EMT transcription factors and biomarkers. Taken together, miR-221 has a crucial impact on HCC cell migration.

3.4. PHF2 Is a Target Gene of miR-221

The mechanism of the migration regulated by miR-221 has not been well indicated. Then we found putative genes that miR-221 might regulate by bioinformatics systems. Bioinformatics analysis was performed in two online predicting algorithms miRDB (http://www.mirdb.org/) and TargetScan (http://www.targetscan.org/) to identify miR-221 target genes. Among these genes PHF2 gene was our applicant target. The results showed that miR-221 remarkably reduced the protein PHF2 expression in SMMC-7721 cells. Conversely, anti-miR-221 significantly increased the protein level of PHF2 (Figure 3(a)). In qPCR, our data indicated that miR-221 downregulated PHF2 mRNA levels in SMMC-7721 cells, whereas anti-miR-221 led to the opposite results (Figure 3(b)). To verify whether PHF2 is a target gene of miR-221, we established the dual-luciferase reporter vectors containing mutation type (Mut) or wild type (WT) fragments of PHF2 3′-UTR. The dual-luciferase reporter assay results showed that miR-221 suppressed the activity of luciferase in WT-transfected HCC cells (Figure 3(c)). These results demonstrated the PHF2 3′-UTR is a target of miR-221 in HCC cells.

3.5. PHF2 Inhibits HCC Cell Migration

To investigate the role of PHF2 in HCC cell lines, transwell analyses were used to examine the cell migration. The data showed that knockdown of PHF2 increased the number of migratory cells in contrast to negative control group. Meanwhile, following overexpression of PHF2 transfection, the number of migratory cells was decreased compared to negative control group (Figure 3(d)). Thus, PHF2 could inhibit the cell migration of HCC cells. For that PHF2 is a direct target of miR-221 in HCC cells, this is supported by our previous study that miR-221 promoted the migration of HCC cells and anti-miR-221 suppressed the migration of HCC cells in vitro. To investigate the role of miR-221-PHF2 pathway in HCC tumorigenesis, we performed the restoration of PHF2 in miR-221 overexpression cells. Notably, PHF2 rescued miR-221 mediated promotion of migration in SMMC-7721 cells (Figure 3(e)). Collectively, these data indicated that miR-221 could promote cell migration of HCC cells by downregulating PHF2.

3.6. The Role of miR-221 and PHF2 Expression in Human HCC

We performed immunohistochemistry staining of TMA slide containing HCC/adjacent cancerous tissues and found that PHF2 protein was situated in the cytoplasmic (Figure 4(a)). In adjacent cancerous tissues, high PHF2 staining was recorded in 66.6% (40 of 60 cases). In HCC tissues, high expression of PHF2 was observed in 38.3% (23 of 60 cases). Higher expression of PHF2 was detected in adjacent cancerous tissues compared to the carcinoma tissues (P< 0.05, paired χ2 test). TNM stage is the most important prognostic indicator for HCC patients, so we investigated whether the protein level of PHF2 was correlated with TNM stage. Our results showed that PHF2 staining was increased in TNM stages II compared with stages III-IV (P < 0.05, paired χ2 test) (Figure 4(b)). Furthermore, the protein level of PHF2 was also correlated with lymph node metastasis-pN status, depth of invasion and serum AFP (P < 0.05, paired χ2 test) (Table 2). Nevertheless, we did not find the correlation between PHF2 and other clinicopathologic factors including tumor size, age, gender, microvascular invasion, and portal vein tumor thrombus. Overall survival was used for survival analysis and the overall mortality events were 50. The period of the follow-up is 36 months. Kaplan-Meier survival analysis illustrated a higher overall survival in HCC patients with high PHF2 expression than those with low PHF2 expression (P = 0.0437, log-rank test) (Figure 4(c)). To investigate the miR-221 and PHF2 protein levels in vivo, 12 human HCC tissues were detected by the qRT-PCR. The results indicated a negative correlation between the expressions of PHF2 and miR-221 (Figure 4(d)). Furthermore, we performed qRT-PCR and results showed that the PHF2 mRNA levels in HCC cell lines were lower than human normal hepatocyte (HL-7702) (supplemental Figure 4). These suggested that PHF2 was negatively correlated with miR-221 and had an evident relationship with clinicopathological parameters in HCC tissues.

4. Discussion

HCC has the highest mortality as a primary cancer for its strong malignant proliferation and migration [13]. The advance diagnosis of HCC largely improves the therapeutic efficacy in patients, thereupon, a sensitive and specific marker is extremely critical [14]. Previous research identified biomarkers mainly focused on proteins [15] however, miRNAs have absorbed the attention from investigators for the low cost of validation as new molecular markers [16]. Furthermore, dysregulation of miRNAs is a general incident that influences cell invasion, migration, apoptosis, and proliferation in tumor progression [17]. The aim of our study is to investigate the role of miR-221 in HCC and the candidate as diagnostic and therapeutic indicator. Our study demonstrated that the level of miR-221 in HCC is higher than that in adjacent cancerous tissues and cell lines. These indicated that miR-221 could be regarded as a biomarker in early diagnosis and thereby establishing new treatment strategies for HCC.

MiR-221 has been identified to be abnormally regulated in various tumors and involved in cancer cell proliferation and EMT transition in breast cancers [18–21]. Hence, we explored the biological role of miR-221 in HCC cell lines, and our results demonstrate that miR-221 could evidently promote HCC cell migration, while inhibition of miR-221 suppressed the cell migration. It is identified that epithelial-to-mesenchymal transition (EMT) which could induce stem cell features was accompanied by a stable increase in EMT-associated mesenchymal markers and a decrease in epithelial markers [22, 23]. Previous studies have showed that miR-221 increased E-cadherin level in an EMT-induced cell line [23] and miR-221 was downregulated by EMT transcription factor Slug in human breast cancer cells [19]. However, the correlation of miR-221 and N-cadherin in HCC remains to be clarified. In our study, the mRNA and protein level of N-cadherin was increased when the HCC cells were transfected with miR-221. Meanwhile, miR-221 could downregulate the E-cadherin expression in protein and mRNA levels. Although miR-221 could regulate the protein level of E-cadherin, Nassirpour et al. were unable to identify the matching sequence in the 3′UTR [24]. Therefore, E-cadherin could not be a target gene of miR-221. Then we determined the potential target genes of miR-221 with miRDB databases and TargetScan. The genes calculated by algorithms were selected as potential genes of miR-221. PHF2 is found to be most promising among the candidates.

Recently, PHF2 has been shown to act as a tumor repressor associated with p53 in colon and stomach tumor development [12, 25]. Meanwhile, PHF2 are overexpressed in esophageal squamous cell carcinoma (ESCC) and was associated with decreased overall survival of ESCC patients [26]. However, association of PHF2 with the underlying molecular mechanisms in HCC cells is poorly understood. In the current study, our findings showed a negative correlation between PHF2 and miR-221. MiR-221 decreased the expression of PHF2 in both mRNA and protein levels. As shown in luciferase reporter assay, miR-221 inhibited the activity of luciferase in WT-PHF2-3′-UTR transfected HCC cells. These demonstrated that PHF2 3′-UTR was a target of miR-221-3p in HCC cells. We also found PHF2 inhibited HCC cell migration and an evident lower expression of PHF2 was detected in the carcinoma tissues compared with adjacent cancerous tissues. Meanwhile, the protein level of PHF2 was correlated with depth of invasion, TNM stage and lymph node metastasis-pN status. Furthermore, we verified the negative correlation between the mRNA level of miR-221 and PHF2 in clinical HCC patients by Pearson's correlation coefficient analysis. To the best of our knowledge, this is the first study that investigated the role of PHF2 as a target gene of miR-221 in HCC development.

Taken together, this study showed that miR-221 was upregulated in HCC cells and tissues and revealed the tumorigenesis role in HCC cells. Moreover, we also identified PHF2 as a target gene of miR-221 in experimental and clinical levels. Our data indicated that miR-221 participated in HCC cell migration and played its biological roles via regulating the PHF2 gene in HCC. Our characterization of this signaling pathway may provide novel therapeutic targets for the future treatment of HCC.

Abbreviations

miRNAs:MicroRNAs
PHF2:Plant homeodomain finger 2
HCC:Hepatocellular carcinoma
PHD:Plant homeodomain
TMA:Tissue microarray
HCT:Hepatocellular carcinoma tissues
EMT:Epithelial-to-mesenchymal transition
ESCC:Esophageal squamous cell carcinoma.

Data Availability

All relevant data used to support the findings of this study are included within the article.

Conflicts of Interest

The authors have declared that no conflicts of interest exist.

Acknowledgments

This work was supported by the project of Soochow Science and Technology Plan (SYS201676) and Natural Science Fund for Colleges and Universities in Jiangsu Province (09KJB310017).

Supplementary Materials

Supplemental Figure 1: quantitative PCR results of miR-221 mRNA levels in HCC tissues (T) and in adjacent noncancerous tissues (N). MiR-221 mRNA levels are higher in the HCC tissues (T) than in adjacent noncancerous tissues (N) (n=24). Each bar represents the mean ± SD of three independent experiments. P<0.01. Supplemental Figure 2: western blot was used to detect Snail and Slug proteins level in miR-221-overexpression and miR-221-knockdown SMMC-7721 cells. Supplemental Figure 3: the histograms that express grayscale of Figures 2C-D. Each bar represents the mean ± SD of three independent experiments. P<0.01. Supplemental Figure 4: qRT-PCR results of PHF2 mRNA levels in HCC cell lines and human normal hepatocyte. Each bar represents the mean ± SD of three independent experiments. P<0.01. (Supplementary Materials)

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Copyright

Copyright © 2019 Yi Fu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Contents

In 1982, Nobel laureate James P. Allison first discovered the T-cell receptor. [6] Then, Tak Wah Mak [7] and Mark M. Davis [8] identified the cDNA clones encoding the human and mouse TCR respectively in 1984. These findings allowed the entity and structure of the elusive TCR, known before as the "Holy Grail of Immunology", to be revealed. This allowed scientists from around the world to carry out studies on the TCR, leading to important studies in the fields of CAR-T, cancer immunotherapy and checkpoint inhibition.

The TCR is a disulfide-linked membrane-anchored heterodimeric protein normally consisting of the highly variable alpha (α) and beta (β) chains expressed as part of a complex with the invariant CD3 chain molecules. T cells expressing this receptor are referred to as α:β (or αβ) T cells, though a minority of T cells express an alternate receptor, formed by variable gamma (γ) and delta (δ) chains, referred as γδ T cells. [9]

Each chain is composed of two extracellular domains: Variable (V) region and a Constant (C) region, both of Immunoglobulin superfamily (IgSF) domain forming antiparallel β-sheets. The Constant region is proximal to the cell membrane, followed by a transmembrane region and a short cytoplasmic tail, while the Variable region binds to the peptide/MHC complex.

The variable domain of both the TCR α-chain and β-chain each have three hypervariable or complementarity-determining regions (CDRs). There is also an additional area of hypervariability on the β-chain (HV4) that does not normally contact antigen and, therefore, is not considered a CDR. [ citation needed ]

The residues in these variable domains are located in two regions of the TCR, at the interface of the α- and β-chains and in the β-chain framework region that is thought to be in proximity to the CD3 signal-transduction complex. [10] CDR3 is the main CDR responsible for recognizing processed antigen, although CDR1 of the alpha chain has also been shown to interact with the N-terminal part of the antigenic peptide, whereas CDR1 of the β-chain interacts with the C-terminal part of the peptide.

CDR2 is thought to recognize the MHC. CDR4 of the β-chain is not thought to participate in antigen recognition, but has been shown to interact with superantigens.

The constant domain of the TCR consists of short connecting sequences in which a cysteine residue forms disulfide bonds, which form a link between the two chains.

The TCR is a member of the immunoglobulin superfamily, a large group of proteins involved in binding, recognition, and adhesion the family is named after antibodies (also called immunoglobulins). The TCR is similar to a half-antibody consisting of a single heavy and single light chain, except the heavy chain is without its crystallisable fraction (Fc). The two subunits of TCR are twisted together. Whereas the antibody uses its Fc region to bind to Fc Receptors on leukocytes, TCR is already docked onto the cell membrane. However, it is not able to mediate signal transduction itself due to its short cytoplasmic tail, so TCR still requires CD3 and zeta to carry out the signal transduction in its place [ citation needed ] , just as antibodies require binding to FcRs to initiate signal transduction. In this way the MHC-TCR-CD3 interaction for T cells is functionally similar to the antigen(Ag)-immunoglobulin(Ig)-FcR interaction for myeloid leukocytes, and Ag-Ig-CD79 interaction for B cells.

The generation of TCR diversity is similar to that for antibodies and B-cell antigen receptors. It arises mainly from genetic recombination of the DNA-encoded segments in individual somatic T cells by somatic V(D)J recombination using RAG1 and RAG2 recombinases. Unlike immunoglobulins, however, TCR genes do not undergo somatic hypermutation, and T cells do not express activation-induced cytidine deaminase(AID). The recombination process that creates diversity in BCR (antibodies) and TCR is unique to lymphocytes (T and B cells) during the early stages of their development in primary lymphoid organs (thymus for T cells, bone marrow for B cells).

Each recombined TCR possess unique antigen specificity, determined by the structure of the antigen-binding site formed by the α and β chains in case of αβ T cells or γ and δ chains on case of γδ T cells. [11]

  • The TCR alpha chain is generated by VJ recombination, whereas the beta chain is generated by VDJ recombination (both involving a random joining of gene segments to generate the complete TCR chain).
  • Likewise, generation of the TCR gamma chain involves VJ recombination, whereas generation of the TCR delta chain occurs by VDJ recombination.

The intersection of these specific regions (V and J for the alpha or gamma chain V, D, and J for the beta or delta chain) corresponds to the CDR3 region that is important for peptide/MHC recognition (see above).

It is the unique combination of the segments at this region, along with palindromic and random nucleotide additions (respectively termed "P-" and "N-"), which accounts for the even greater diversity of T-cell receptor specificity for processed antigenic peptides.

Later during development, individual CDR loops of TCR can be re-edited in the periphery outside thymus by reactivation of recombinases using a process termed TCR revision (editing) and change its antigenic specificity.

In the plasma membrane the TCR receptor chains α and β associate with six additional adaptor proteins to form an octameric complex. The complex contains both α and β chains, forming the ligand-binding site, and the signaling modules CD3δ, CD3γ, CD3ε and CD3ζ in the stoichiometry TCR α β - CD3εγ - CD3εδ - CD3ζζ. Charged residues in the transmembrane domain of each subunit form polar interactions allowing a correct and stable assembly of the complex. [12] The cytoplasmic tail of the TCR is extremely short, hence the CD3 adaptor proteins contain the signalling motifs needed for propagating the signal from the triggered TCR into the cell. The signalling motifs involved in TCR signalling are tyrosine residues in the cytoplasmic tail of these adaptor proteins that can be phosphorylated in the event of TCR-pMHC binding. The tyrosine residues reside in a specific amino acid sequence of the signature Yxx(L/I)x6-8Yxx(L/I), where Y, L, I indicate tyrosine, leucine and isoleucine residues, x denotes any amino acids, the subscript 6-8 indicates a sequence of 6 to 8 amino acids in length. This motif is very common in activator receptors of the non-catalytic tyrosine-phosphorylated receptor (NTR) family and is referred to as immunoreceptor tyrosine-based activation motif (ITAM). [5] CD3δ, CD3γ and CD3ε each contain a single ITAM, while CD3ζ contains three ITAMs. In total the TCR complex contains 10 ITAMs. [12] Phosphorylated ITAMs act as binding site for SH2-domains of additionally recruited proteins.

Each T cell expresses clonal TCRs which recognize a specific peptide loaded on a MHC molecule (pMHC), either on MHC class II on the surface of antigen-presenting cells or MHC class I on any other cell type. [13] A unique feature of T cells is their ability to discriminate between peptides derived from healthy, endogenous cells and peptides from foreign or abnormal (e.g. infected or cancerous) cells in the body. [14] Antigen presenting cells do not discriminate between self and foreign peptides and typically express a large number of self derived pMHC on their cell surface and only a few copies of any foreign pMHC. For example, it has been shown that cells infected with HIV have only 8-46 HIV specific pMHCs next to 100000 total pMHC per cell. [15] [16]

Because T cells undergo positive selection in the thymus there is a non-negligible affinity between self pMHC and the TCR, nevertheless, the T-cell receptor signalling should not be activated by self pMHC such that endogenous, healthy cells are ignored by T cells. However, when these very same cells contain even minute quantities of pathogen derived pMHC, T cells must get activated and initiate immune responses. The ability of T cells to ignore healthy cells but respond when these same cells express a small number of foreign pMHC is known as antigen discrimination. [17] [18]

To do so, T cells have a very high degree of antigen specificity, despite the fact that the affinity to the peptide/MHC ligand is rather low in comparison to other receptor types. [19] The affinity, given as the dissociation constant (Kd), between a TCR and a pMHC was determined by surface plasmon resonance (SPR) to be in the range of 1-100 μM, with an association rate (kon) of 1000 -10000 M −1 s −1 and a dissociation rate (koff) of 0.01 -0.1 s −1 . [20] In comparison, cytokines have an affinity of KD = 10-600 pM to their receptor. [21] It has been shown that even a single amino acid change in the presented peptide that affects the affinity of the pMHC to the TCR reduces the T cell response and cannot be compensated by a higher pMHC concentration. [22] A negative correlation between the dissociation rate of the pMHC-TCR complex and the strength of the T cell response has been observed. [23] That means, pMHC that bind the TCR for a longer time initiate a stronger activation of the T cell. Furthermore, T cells are very sensitive. Interaction with a single pMHC is enough to trigger activation. [24] Also, the decision whether a T cell response to an antigen is made quickly. T cells rapidly scan pMHC on an antigen presenting cell to increase the chance of finding a specific pMHC. On average, T cell encounter 20 APCs per hour. [25]

Different models for the molecular mechanisms that underlie this highly specific and highly sensitive process of antigen discrimination have been proposed. The occupational model simply suggests that the TCR response is proportional to the number of pMHC bound to the receptor. Given this model, a shorter lifetime of a peptide can be compensated by higher concentration such that the maximum response of the T cell stays the same. However, this cannot be seen in experiments and the model has been widely rejected. [23] The most accepted view is that the TCR engages in kinetic proofreading. The kinetic proofreading model proposes that a signal is not directly produced upon binding but a series of intermediate steps insure a time delay between binding and signal output. Such intermediate "proofreading" steps can be multiple rounds of tyrosine phosphorylation. These steps require energy and therefore do not happen spontaneously, only when the receptor is bound to its ligand. This way only ligands with high affinity that bind the TCR for a long enough time can initiate a signal. All intermediate steps are reversible, such that upon ligand dissociation the receptor reverts to its original unphosphorylated state before a new ligand binds. [26] This model predicts that maximum response of T cells decreases for pMHC with shorter lifetime. Experiments have confirmed this model. [23] However, the basic kinetic proofreading model has a trade-off between sensitivity and specificity. Increasing the number of proofreading steps increases the specificity but lowers the sensitivity of the receptor. The model is therefore not sufficient to explain the high sensitivity and specificity of TCRs that have been observed. (Altan Bonnet2005) Multiple models that extend the kinetic proofreading model have been proposed, but evidence for the models is still controversial. [14] [27] [28]

The antigen sensitivity is higher in antigen-experienced T cells than in naive T cells. Naive T cells pass through the process of functional avidity maturation with no change in affinity. It is based on the fact that effector and memory (antigen-experienced) T cell are less dependent on costimulatory signals and higher antigen concentration than naive T cell. [29]

The essential function of the TCR complex is to identify specific bound antigen derived from a potentially harmful pathogen and elicit a distinct and critical response. At the same time it has to ignore any self-antigen and tolerate harmless antigens such as food antigens. The signal transduction mechanism by which a T cell elicits this response upon contact with its unique antigen is termed T-cell activation. Upon binding to pMHC, the TCR initiates a signalling cascade, involving transcription factor activation and cytoskeletal remodelling resulting in T cell activation. Active T cells secrete cytokines, undergo rapid proliferation, have cytotoxic activity and differentiate into effector and memory cells. When the TCR is triggered, T cells form an immunological synapse allowing them to stay in contact with the antigen presenting cell for several hours. [30] On a population level, T cell activation depends on the strength of TCR stimulation, the dose–response curve of ligand to cytokine production is sigmoidal. However, T cell activation on a single cell level can be characterised by a digital switch-like response, meaning the T cell is fully activated if the stimulus is higher than a given threshold, otherwise the T cell stay in its non-activated state. There is no intermediate activation state. The robust sigmoid dose-response curve on population level results from individual T cells having slightly different thresholds. [22]

T cells need three signals to become fully activated. Signal 1 is provided by the T-cell receptor when recognising a specific antigen on a MHC molecule. Signal 2 comes from co-stimulatory receptors such as CD28, presented on the surface of other immune cells. It is expressed only when an infection was detected by the innate immune system, it is a "Danger indicating signal". This two-signal system makes sure that T cells only respond to harmful pathogens and not to self-antigens. An additional third signal is provided by cytokines, which regulate the differentiation of T cells into different subsets of effector T cells. [30] There are myriad molecules involved in the complex biochemical process (called trans-membrane signaling) by which T-cell activation occurs. Below, the signalling cascade is described in detail.

Receptor activation Edit

The initial triggering follows the mechanism common for all NTR receptor family members. Once the TCR binds a specific pMHC, the tyrosine residues of the Immunoreceptor tyrosine-based activation motifs (ITAMs) in its CD3 adaptor proteins are phosphorylated. The residues serve as docking sites for downstream signalling molecules, which can propagate the signal. [31] [32] Phosphorylation of ITAMs is mediated by the Src kinase Lck. Lck is anchored to the plasma membrane by associating with the co-receptor CD4 or CD8, depending on the T cell subtype. CD4 is expressed on helper T cells and regulatory T cells, and is specific for MHC class II. CD8, on the other hand, specific for MHC class I, is expressed on cytotoxic T cells. Binding of the co-receptor to the MHC bring Lck in close proximity to the CD3 ITAMs. It has been shown that 40% of Lck is active even before the TCR binds pMHC and therefore has the ability to constantly phosphorylate the TCR. [33] Tonic TCR signalling is avoided by the presence of phosphatase CD45 that removes phosphorylation from tyrosine residues and inhibits signal initiation. Upon binding the balance of kinase activity to phosphatase activity is perturbed, leading to a surplus of phosphorylation and initiation of the signal. How such perturbation is accomplished by TCR binding is still debated. Mechanisms involving conformational change of TCR, TCR aggregation and kinetic segregation have been suggested. [31] Tyrosine kinase Fyn might be involved in ITAM phosphorylation but is not essential for TCR signalling. [34] [35]

Proximal TCR signaling Edit

Phosphorylated ITAMs in the cytoplasmic tails of CD3 recruit protein tyrosine kinase Zap70 that can bind to the phosphorylated tyrosine residues with its SH2 domain. This brings Zap70 into close proximity to Lck which results to its phosphorylation and activation by Lck. [36] Lck phosphorylates a number of different proteins in the TCR pathway. [37] Once activated, Zap70 is able to phosphorylate multiple tyrosine residues of the transmembrane protein LAT. LAT is a scaffold protein associated with the membrane. It itself does not have any catalytic activity but it provides binding sites for signalling molecules via phosphorylated tyrosine residues. LAT associates with another scaffolding protein Slp-76 via the Grap2 adaptor protein, which provides additional binding sites. Together LAT and Slp-76 provide a platform for the recruitment of many downstream signalling molecules. By bringing these signalling molecules into close proximity, they can then be activated by Lck, Zap70 and others kinases. Therefore, the LAT/Slp76 complex act as a highly cooperative signalosome. [36]

Molecules that bind the LAT/Slp76 complex include: Phospholipase Cγ1 (PLCγ1), SOS via a Grb2 adaptor, Itk, Vav, Nck1 and Fyb. [36]

Signal transduction to the nucleus Edit

PLCγ is a very important enzyme in the pathway as it generates second messenger molecules. It is activated by the tyrosine kinase Itk which is recruited to the cell membrane by binding to Phosphatidylinositol (3,4,5)-trisphosphate (PIP3). PIP3 is produced by the action of Phosphoinositide 3-kinase(PI-3K), which phosphorylates Phosphatidylinositol 4,5-bisphosphate (PIP2) to produce PIP3. It is not known that PI-3K is activated by the T cell receptor itself, but there is evidence that CD28, a co-stimulatory receptor providing the second signal, is able to activate PI-3K. The interaction between PLCγ, Itk and PI-3K could be the point in the pathway where the first and the second signal are integrated. Only if both signals are present, PLCγ is activated. [30] Once PLCγ is activated by phosphorylation, it hydrolyses PIP2 into two secondary messenger molecules, namely the membrane-bound diacyl glycerol(DAG) and the soluble inositol 1,4,5-trisphosphate (IP3). [38]

These second messenger molecules amplify the TCR signal and distribute the prior localised activation to the entire cell and activate protein cascades that finally lead to the activation of transcription factors. Transcription factors involved in T cell signalling pathway are the NFAT, NF-κB and AP1, a heterodimer of proteins Fos and Jun. All three transcription factors are needed to activate the transcription of interleukin-2(IL2) gene. [30]

NFAT Edit

NFAT activation depends on calcium signaling. IP3 produced by PLC-γ is no longer bound to the membrane and diffuses rapidly in the cell. Binding of IP3 to calcium channel receptors on the endoplasmic reticulum (ER) induces the release of calcium (Ca 2+ ) into the cytosol. The resulting low Ca 2+ concentration in the ER causes STIM1 clustering on the ER membrane, which in turn leads to activation of cell membrane CRAC channels that allows additional calcium to flow into the cytosol from the extracellular space. Therefore, levels of Ca 2+ are strongly increased in the T cell. This cytosolic calcium binds calmodulin, inducing a conformational change of the protein such that it can then bind and activate calcineurin. Calcineurin, in turn, dephosphorylates NFAT. In its deactivated state, NFAT cannot enter the nucleus as its nuclear localisation sequence (NLS) cannot be recognised by nuclear transporters due to phosphorylation by GSK-3. When dephosphorylated by Calcineurin translocation of NFAT into the nucleus is possible. [30] Additionally, there is evidence that PI-3K via signal molecules recruits the protein kinase AKT to the cell membrane. AKT is able to deactivate GSK3 and thereby inhibiting the phosphorylation of NFAT, which could contribute to NFAT activation. [36]

NF-κB Edit

NF-κB activation is initiated by DAG, the second, membrane bound product of PLCγ hydrolysation of PIP2. DAG binds and recruits Protein kinase C θ (PKCθ) to the membrane where it can activated the membrane bound scaffold protein CARMA1. CARMA1 then undergoes a conformational change which allow it to oligomerise and bind the adapter proteins BCL10, CARD domain and MALT1. This multisubunit complex binds the Ubiquitin ligase TRAF6. Ubiquitination of TRAF6 serves as scaffold to recruit NEMO, IκB kinase (IKK) and TAK1. [30] TAK 1 phosphorylates IKK, which in turn phosphorylates the NF-κB inhibitor I-κB, leading to the ubiquitination and subsequent degradation of I-κB. I-κB blocks the NLS of NF-κB therefore preventing its translocation to the nucleus. Once I-κB is degraded, it cannot bind to NF-κB and the NLS of NF-κB becomes accessible for nuclear translocation. [30]

AP1 Edit

Activation of AP1 involves three MAPK signalling pathways. These pathway use a phosphorylation cascade of three successive acting protein kinases to transmit a signal. The three MAPK pathways in T cells involve kinases of different specificities belonging to each of the MAP3K, MAP2K, MAPK families. Initial activation is done by the GTPase Ras or Rac which phosphorylate the MAP3K. [30] A cascade involving the enzymes Raf, MEK1, ERK results in the phosphorylation of Jun, conformational change allows Jun to bind to Fos and hence AP-1 to form. AP-1 then acts as transcription factor. Raf is activated via the second messenger DAG, SOS, and Ras. DAG recruits among other proteins the RAS guanyl nucleotide-releasing protein (RasGRP), a guanine nucleotide exchange factor (GEF), to the membrane. RasGRP activates the small GTPase Ras by exchanging Guanosine diphosphate (GDP) bound to Ras against Guanosine triphosphate (GTP). Ras can also be activated by the guanine nucleotide exchange factor SOS which binds to the LAT signalosom. Ras then initiates the MAPK cascade. [36] The second MAPK cascade with MEKK1, JNKK, JNK induces protein expression of Jun. Another cascade, also involving MEKK1 as MAPK3, but then activating MKK3 /6 and p38 induces Fos transcription. Activation of MEKK1, additionally to being activated by Ras, involves Slp-76 recruiting the GEF Vav to the LAT signalosom, which then activates the GTPase Rac. Rac and Ras activate MEKK1 and thereby initiate the MAPK cascade. [36]


Results

Transcript RNA sequence dataset of pear and apple libraries

In this study, RNA of different types of materials (including two species and three developmental stages) were pooled to provide a broad gene library associated with fruit growth and finally sixteen libraries were generated including the fruits and leaves (Fig. 1, Table 1). A total of 14,937,456—30,370,082 reads were obtained from eight libraries of pear, 57.8%—71.8% of which could be mapped to ‘Dangshansuli’ reference genome and concordant-pairs value is 50.5%—66.2% [14]. A total of 16,219,542—34,807,619 reads were obtained from eight libraries of apple, 61.0%—76.3% of which could be mapped to the ‘Golden Delicious’ genome and concordant-pairs value is 54.2%—72.5% [17]. We obtained 84,055 transcripts and 40,402 genes with N50 length 2306 bp and median length 1643. 95 bp in pear we obtained 95,600 transcripts and 56,781 genes with N50 length 2117 bp and median length 1470.5 bp in apple (Table 2).

Plant materials at three developmental stages. t1, t2 and t3 represent the young fruit stage, the expansion stage and the mature stage respectively. Each square represents 1 cm

Physiological index variation at developmental stages of pear and apple fruits

To better understand the physiological variation among growth, transverse and longitudinal diameter, single fruit weight and sugar acid content from the developmental stages were observed (Fig. 2, Table 3). The transverse and vertical diameter of wild pear at maturity were 1.46 and 1.23 times of that at young fruit stage, respectively (Fig. 2A). The growth rate of wild pear was slow and the fruit size had no obvious change during the fruit development process. The transverse and vertical diameter of cultivar pear at maturity stage were 4.3 and 2.9 times of that at young fruit stage, respectively. The lateral growth rate of cultivar pear is higher than that of longitudinal growth, and the growth rate of fruit size is much faster than that of wild. The transverse and longitudinal diameters of wild apple at the young fruit stage were 12.87 mm and 13.1 mm respectively, while at the mature stage they were 25.14 mm and 23.77 mm, which were about 1.95 and 1.48 times that at young fruit stage (Fig. 2B). The fruit grew slowly and the size did not change significantly. In contrast, the transverse and longitudinal diameters of cultivar apple at maturity were 74.25 mm and 67.22 mm respectively, which were about 3.27 and 2.72 times that at the young fruit stage. The weight at maturity stage of cultivar pear is about 447 times of that of wild (Fig. 2C). The weight of cultivar apple at maturity stage was 8.12 times that of wild (Fig. 2D). There are obvious differences in volume and weight between the wild and cultivar, with both fruit characteristics being greater in the cultivar.

Fruit size and single fruit weight of wild and cultivar fruits at three stages. A&B: fruit size, ordinate represents transverse and longitudinal diameter value (mm), blue represents transverse diameter, green represents longitudinal diameter. C&D: fruit weight, ordinate represents the value of fruit weight (g) solid represents the wild apple slant represents the cultivated apple abscissa t1, t2, t3 represents the young fruit stage, expansion stage and mature stage respectively

The soluble sugars in pear and apple mainly includes sorbitol, fructose, glucose and sucrose [27]. The organic acids are mainly include quinic acid, citric acid, malic acid, oxalic acid and shikimic acid (Table 3) [28]. The total sugar content of cultivar pear at maturity reached 43.6 (mathrmullet mathrm) −1 , and 20.66 (mathrmullet mathrm) −1 in wild, which showed that the total sugar content of cultivar pear was higher than that of wild. The content of fructose in cultivar pear was 26.24 (mathrmullet mathrm) −1 at maturity stage, about 13 times that of sucrose. The contents of glucose, fructose and sucrose increased with fruit development, and were positively correlated with total sugar content. There was a negative correlation between sorbitol and total sugar content. The content of citric acid in wild pear is higher than that of other acids. The content of organic acids in wild pear maturity stage is higher than that in cultivar.

The fructose content was higher than other sugars and in cultivar apple at maturity was about 2.6 times of that in wild apple. The total sugar content at maturity of cultivar apple reached 131.23 (mathrmullet mathrm) −1 , and wild apple reached 61.45 (mathrmullet mathrm) −1 , which showed that the total sugar content in cultivar apple was significantly higher than in wild. The contents of glucose, fructose and sucrose increased during fruit development, and were positively correlated with total sugar content. The content of citric acid and malic acid were higher than the other acids. The highest acid content of wild apple at maturity was citric acid 8.18 (mathrmullet mathrm) −1 , followed by malic acid content of 6.36 (mathrmullet mathrm) −1 . In contrast to sugar content, the organic acid content decreased during fruit maturation. The total acid content in wild was higher than in cultivar apple.

Identification of differentially expressed transcripts and genes in wild and cultivar fruit at three stages

According to P < 0.001 and |Fold change|> 2, the significant differential expressed transcripts were identified (Fig. 3). There were 3339, 4005 and 4070 differential expressed transcripts of the two pear varieties at young fruit stage, expanding stage and mature stage, respectively (Fig. 3A). 7051 transcripts were left after removing duplicate transcripts in all three stages, which corresponded to 5921 genes. There are 1228 transcripts that were differentially expressed at all stages this corresponds to 1068 genes. There were 2261 (1228 + 1033) differential expressed transcripts existing simultaneously at the expanding stage and the mature stage, and 1699 (1228 + 471) and 1631 (1228 + 403) at the young fruit stage and expanding stage, the young fruit stage and mature stage, respectively (Fig. 3A).

The number of differentially expressed transcripts of wild and cultivar samples at three stages. t1, t2 and t3 represent the young fruit stage, the expansion stage and the mature stage respectively

The numbers of differential expression transcripts were 3188, 2975, and 3918 at apple young fruit stage, expansion stage and mature stage respectively (Fig. 3B). The intersection and union of the three sets were 1065 and 6381 respectively, corresponding to 969 and 5744 genes respectively. The common differentially expressed transcripts between expansion and mature stage, young and expansion stage, young and mature stage were 1586, 1653 and 1526 respectively. The independent differentially expressed transcripts at young fruit stage, expansion stage and mature stage were 1074, 801 and 1871 respectively these were only expressed in a single stage.

Expression trend analysis of differential expression genes at three periods

5921 and 5744 different expression genes were further clustered using Short Time-series Expression Miner (STEM) to analyze the expression trend in pear and apple respectively [29]. It identified 16 model expression profiles both in pear and apple (Fig. 4). Colored profiles are statistically significant assigned with P < 0.05. Among the 7 colored profiles of cultivar pear, profile 13 (397 genes), 15 (239 genes), 12 (208 genes) and 11 (319 genes) were up-regulated, profile 0 (381 genes) and 3 (179 genes) were down-regulated and profile 14 (484 genes) was first up-regulated and then down-regulated (Fig. 4A1). Four profiles (2007 genes) with significantly colored in wild pear and all of them were down-regulated (Fig. 4A2).

Expression trend profiles of differentially expressed genes at three stages of cultivar pear (A1), wild pear (A2), cultivar apple (B1) and wild apple (B2) respectively. The number of differential expression genes in pear and apple was 5921, 5744 respectively. Each box represents a different expression profile, colored profiles have a statistically significant number of genes assigned with P < 0.05, the upper left corner of the digital is profile ID, the lower right corner represents P value

There were 6 colored profiles are significantly at three stages of cultivar apple (Fig. 4B1) and 3 profiles in wild apple (Fig. 4B2). In cultivar apple, profile 7 (539 genes), 2 (345 genes), 3 (298 genes) and 0 (382 genes) were down-regulated, profile 8 (692 genes) was up-regulated, and profile 9 (623 genes) was up-regulated at the beginning and then down-regulated (Fig. 4B1). In wild apple, profile 8 (726 genes) and profile 12 (174 genes) were up-regulated, and profile 0 (299 genes) was down-regulated (Fig. 4B2).

Go functional annotation enrichment analysis of differential expression genes

GO enrichment analysis was carried out on 5921 and 5744 different expression genes in pear and apple respectively, of which 4332 and 4378 genes could be annotated to 3261 and 3190 GO term respectively. GO functional enrichment accorded with a hypergeometric distribution, in which P < 0.05 was significantly enriched to 855 and 533 terms (Supplementary Fig S1) in pear and apple respectively. GO terms with -log (P-value) > 6 were further enriched to 36 biological pathways, 16 cell components and 18 molecular functions in pear different expression genes (Fig. 5A), 17 biological pathways, 16 cell components and 6 molecular functions in apple different expression genes (Fig. 5B). The common GO term with -log (P-value) > 6 in pear and apple different expression genes were enriched into 8 biological pathways, 4 cell components and 4 molecular functions (Fig. 5C). The main enriched terms in biological processes were glycolytic process, fatty acid biosynthetic process, response to stress, response to oxidative stress, chorismate biosynthetic process, response to cytokinin, photosynthesis and response to cadmium ion (Fig. 5C).

Functional enrichment of all differentially expressed genes with –log (p-value) > 6. 36 biological pathways, 16 cell components and 18 molecular functions in pear (A), 17 biological pathways, 16 cell components and 6 molecular functions in apple (B). 8 biological pathways, 4 cell components and 4 molecular functions (C) both enriched to pear and apple

Differential expression genes pathway enrichment analysis of pear and apple at three stages

The Kyoto encyclopedia of genes and genomes (KEGG) pathway can help to further determine biological functions and interactions of genes [30]. Based on a comparison against the KEGG database, of the 40,402 genes in pear, 13,102 (32.43%) genes had significant matches and were assigned to 133 KEGG pathways of the 56,781 genes in apple, 18,459 (32.51%) genes had significant matches and were assigned to 134 KEGG pathways. Among the 5921 different expression genes in pear, 1929 genes were assigned to 123 KEGG pathways, and 50 pathways with P < 0.05 were significantly enrichment (Supplementary Fig S2). Among the 5744 apple different expression genes, 2578 genes participated in 128 pathways, and 30 pathways with P < 0.05 were significantly enrichment (Supplementary Fig S3). 24 pathways were significantly enrichment both in pear and apple, which mainly involved in Metabolic pathways (578 genes in pear, 911 genes in apple), Biosynthesis of secondary metabolites (348 genes in pear, 553 genes in apple), Carbon metabolism (85 genes in pear, 161 genes in apple) and Biosynthesis of amino acids (72 genes in pear, 136 genes in apple) etc. (Fig. 6).

KEGG pathway enrichment both in pear and apple differential expression genes with P < 0.05. The ordinate represents the common 24 KEGG pathways

Co-expression modules analysis of differential expression genes of pear and apple at three stages

1068 and 969 genes expressed differently through all development stages in pear and apple respectively. According to the correlation between gene expression, these 1068 and 969 genes been divided into 9 models respectively using WGCNA with Power = 14 (Fig. 7) [31]. The grey module, which is a non-functional module, has 4 and 2 genes in pear and apple respectively. The correlations between modules and six samples were examined. Most of the 9 modules were negatively correlated with each other in six samples, but the positive correlation was especially significant at certain stages (P < 0.05). Function classification indicated that yellow in WP_t1 (r = 0.97, 84 genes) associated with flavonoid biosynthetic process, response to cadmium ion etc. red in WP_t3 (r = 0.90, 78 genes) response to transcription, high light intensity, heat, salt stress and abscisic acid blue in CP_t1 (r = 0.90, 112 genes) associated with transcription, glycolytic process, photosynthesis brown in CP_t2 (r = 0.98, 85 genes) also focuses on response to water deprivation, abscisic acid, cold and salt stress green in CP_t3 (r = 0.96, 78 gens) focus on plant-type cell wall organization, malate metabolic process etc. (Fig. 7A, Additional file 1). Module red in WA_t1 (r = 0.93, 62 gens) associated with flavonoid biosynthetic process, anthocyanin-containing compound biosynthetic process, response to stress brown in WA_t3 (r = 0.93, 117 genes) associated with transcription, defense response, response to cold pink in CA_t1 (r = 0.85, 35 genes) associated with response to light stimulus, ATP hydrolysis coupled proton transport black in CA_t2 (r = 0.86, 41 genes) associated with response to response to water deprivation, photosynthesis, response to cytokinin yellow in CA_t3 (r = 0.90, 72 genes) associated with translation, defense response, fatty acid biosynthetic process (Fig. 7B, Additional file 1). Combined with the gene interactions within each module, 10 key genes significantly expressed in each module of pear and apple were selected to conduct the expression level analysis (Additional file 2, Supplementary Fig S4).

Co-expression module of 1068 and 969 co-differential expression genes at three stages of pear (A) and apple (B) respectively. Screening of soft-threshold power as 14 at R 2 = 0.8(red line), which divided genes into 9 color modules. Correlation between each module and six samples showed with the correlation coefficient and P value, red indicates positive correlation and blue means negative correlation


Affiliations

State key laboratory of tree genetics and breeding, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091, China

Cai-yun He, Kai Cui, Jian-guo Zhang, Ai-guo Duan & Yan-fei Zeng

Key Laboratory of Tree Breeding and Cultivation, State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091, China

Cai-yun He, Kai Cui, Jian-guo Zhang, Ai-guo Duan & Yan-fei Zeng

Research Institute of Resources Insects, Chinese Academy of Forestry, Kunming, 650224, China


Watch the video: Protocol 1 - DNA Extraction Part 1 (September 2022).


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