The role of strigolactone analog (GR24) in endogenous hormone metabolism and hormone-related gene expression in tobacco axillary buds.

KEY MESSAGE: Strigolactone has the potential to influence hormone metabolism, in addition to having a role in inhibiting axillary bud elongation, which could be regulated by the expression of phytohormones-related genes. The elongation of axillary buds affects the economic benefits of tobacco. In this study, it was investigated the effect of strigolactone (SL) on the elongation of tobacco axillary buds and its endogenous hormone metabolism and related gene expression by applying the artificial analog of SL, GR24, and an inhibitor of SL synthesis, TIS-108, to the axillary buds. The results showed that the elongation of axillary buds was significantly inhibited by GR24 on day 2 and day 9. Ultra-high-performance liquid-chromatography-mass spectrometry results further showed that SL significantly affected the metabolism of endogenous plant hormones, altering both their levels and the ratios between each endogenous hormone. Particularly, the levels of auxin (IAA), trans-zeatin-riboside (tZR), N6-(∆2-isopentenyl) adenine (iP), gibberellin A4 (GA4), jasmonic acid (JA), and jasmonoyl isoleucine (JA-Ile) were decreased after GR24 treatment on day 9, but the levels of 1-aminocyclopropane-1-carboxylic acid (ACC) and gibberellin A1 (GA1) were significantly increased. Further analysis of endogenous hormonal balance revealed that after the treatment with GR24 on day 9, the ratio of IAA to cytokinin (CTK) was markedly increased, but the ratios of IAA to abscisic acid (ABA), salicylic acid (SA), ACC, JAs, and, GAs were notably decreased. In addition, according to RNA-seq analysis, multiple differentially expressed genes were found, such as GH3.1, AUX/IAA, SUAR20, IPT, CKX1, GA2ox1, ACO3, ERF1, PR1, and HCT, which may play critical roles in the biosynthesis, deactivation, signaling pathway of phytohormones, and the biosynthesis of flavonoids to regulate the elongation of axillary buds in tobacco. This work lays the certain theoretical foundation for the application of SL in regulating the elongation of axillary buds of tobacco.

Comparative Transcriptomic Analysis of Genes in the 20-Hydroxyecdysone Biosynthesis in the Fern Microsorum scolopendria towards Challenges with Foliar Application of Chitosan.

Microsorum scolopendria is an important medicinal plant that belongs to the Polypodiaceae family. In this study, we analyzed the effects of foliar spraying of chitosan on growth promotion and 20-hydroxyecdysone (20E) production in M. scolopendria. Treatment with chitosan at a concentration of 50 mg/L in both young and mature sterile fronds induced the highest increase in the amount of accumulated 20E. Using RNA sequencing, we identified 3552 differentially expressed genes (DEGs) in response to chitosan treatment. The identified DEGs were associated with 236 metabolic pathways. We identified several DEGs involved in the terpenoid and steroid biosynthetic pathways that might be associated with secondary metabolite 20E biosynthesis. Eight upregulated genes involved in cholesterol and phytosterol biosynthetic pathway, five upregulated genes related to the methylerythritol 4-phosphate (MEP) and mevalonate (MVA) pathways, and several DEGs that are members of cytochrome P450s and ABC transporters were identified. Quantitative real-time RT-PCR confirmed the results of RNA-sequencing. Taken together, we showed that chitosan treatment increased plant dry weight and 20E accumulation in M. scolopendria. RNA-sequencing and DEG analyses revealed key enzymes that might be related to the production of the secondary metabolite 20E in M. scolopendria.

Construction and Validation of a Prognostic Gene-Based Model for Overall Survival Prediction in Hepatocellular Carcinoma Using an Integrated Statistical and Bioinformatic Approach.

Hepatocellular carcinoma (HCC) is one of the most common lethal cancers worldwide and is often related to late diagnosis and poor survival outcome. More evidence is demonstrating that gene-based prognostic models can be used to predict high-risk HCC patients. Therefore, our study aimed to construct a novel prognostic model for predicting the prognosis of HCC patients. We used multivariate Cox regression model with three hybrid penalties approach including least absolute shrinkage and selection operator (Lasso), adaptive lasso and elastic net algorithms for informative prognostic-related genes selection. Then, the best subset regression was used to identify the best prognostic gene signature. The prognostic gene-based risk score was constructed using the Cox coefficient of the prognostic gene signature. The model was evaluated by Kaplan-Meier (KM) and receiver operating characteristic curve (ROC) analyses. A novel four-gene signature associated with prognosis was identified and the risk score was constructed based on the four-gene signature. The risk score efficiently distinguished the patients into a high-risk group with poor prognosis. The time-dependent ROC analysis revealed that the risk model had a good performance with an area under the curve (AUC) of 0.780, 0.732, 0.733 in 1-, 2- and 3-year prognosis prediction in The Cancer Genome Atlas (TCGA) dataset. Moreover, the risk score revealed a high diagnostic performance to classify HCC from normal samples. The prognosis and diagnosis prediction performances of risk scores were verified in external validation datasets. Functional enrichment analysis of the four-gene signature and its co-expressed genes involved in the metabolic and cell cycle pathways was constructed. Overall, we developed a novel-gene-based prognostic model to predict high-risk HCC patients and we hope that our findings can provide promising insight to explore the role of the four-gene signature in HCC patients and aid risk classification.

[A bioinformatics analysis of differentially expressed genes associated with liver cancer].

Objective: To investigate differentially expressed genes associated with liver cancer using bioinformatics methods, and to screen out molecular markers for early diagnosis of liver cancer and potential molecular targets for immunotherapy. Methods: The microarray data associated with liver cancer were downloaded from Gene Expression Omnibus. JMP software was used for correlation analysis of GSE datasets, Limma program in R language was used to screen out differentially expressed genes, and the Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway analysis were performed for differentially expressed genes. A protein-protein interaction (PPI) network was also established for analysis. An analysis of specific expression associated with liver cancer was performed with reference to RNA-seq transcriptome data for other tumors obtained from TCGA to further identify specific differentially expressed genes in liver cancer, and a survival curve analysis was performed for patients with liver cancer. Results: A total of 92 differentially expressed genes were identified, with 21 upregulated genes and 71 downregulated genes. Through the GO, KEGG, and PPI analyses, RNA-seq data verified that only glypican 3 (GPC3) was upregulated in liver cancer, and MBL2, SDS, SLCO1B3, TDO2, SAA4, and SPP2 were downregulated. Conclusions: GPC3 might act as a target for immunotherapy, and other molecular markers may become molecular markers for early detection of liver cancer and potential targets for immunotherapy.

Identification of differentially expressed genes to predict the risk of heart failure in older patients with hypertrophic cardiomyopathy.

AIM: Hypertrophic cardiomyopathy (HCM) is a common heart disease. Old people with HCM are at high risk of heart failure (HF). This study aimed to identify differentially expressed genes (DEGs) to evaluate the risk of HF in older patients with HCM. METHODS: GSE89714 and GSE116250 were downloaded from Gene Expression Omnibus (GEO) database, and DEGs were identified by using limma R package with P < 0.05 and logFC> 1 as cut off. Protein-protein interaction (PPI) network, Genome Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed for the identified DEGs. NetworkAnalyst online tool was applied for Gene Set Enrichment Analysis (GSEA) analysis. RESULTS: We identified 124 overlap DEGs from the 2 datasets. PPI network showed that COL1A1, COL3A1, COL1A2, BGN, COL5A1, LUM, TGFB2, FMOD, ASPN, and COL14A1 were the top ten genes related to HCM and HF compared with control. Functional and pathway analyses showed that the overlap genes were mainly related to ECM-receptor interaction, ECM organization, Focal adhesion, PI3K-Akt signaling, TGF-beta signaling, and Platelet activation signaling and aggregation. Among the overlap genes, COL5A1 and LUM were significantly upregulated, while TGFB2, FMOD, ASPN, and COL14A1 were significantly downregulated in HF dataset compared with HCM dataset. CONCLUSIONS: Bioinformatics-based analysis revealed potential genes associated with HCM and HF, which could be utilized to evaluate the risk of HF in older patients with HCM.

Investigation of cold-resistance mechanisms in cryophylactic yeast Metschnikowia pulcherrima based on comparative transcriptome analysis.

Introduction: Low temperature inhibits the growth of most microorganisms. However, some microbes can grow well in a low temperature, even a freezing temperature. Methods: In this study, the mechanisms conferring cold resistance in the cryophylactic yeast Metschnikowia (M.) pulcherrima MS612, an isolate of the epidermis of ice grapes, were investigated based on comparative transcriptome analysis. Results: A total of 6018 genes and 374 differentially expressed genes (> 2-fold, p < 0.05) were identified using RNA-Seq. The differentially expressed genes were mainly involved in carbohydrate and energy metabolism, transport mechanisms, antifreeze protection, lipid synthesis, and signal transduction. M. pulcherrima MS612 maintained normal growth at low temperature (5°C) by enhancing energy metabolism, sterol synthesis, metal ion homeostasis, amino acid and MDR transport, while increased synthesis of glycerol and proline transport to improve its resistance to the freezing temperature (-5°C). Furthermore, cAMP-PKA and ERAD signaling pathways contribute to resist the low temperature and the freezing temperature, respectively. Conclusion: This study provides new insights into cold resistance in cryophylactic microorganisms for maneuvering various metabolism to resist different cold environment.

Applying multi-omics data to study the genetic background of bovine respiratory disease infection in feedlot crossbred cattle.

Bovine respiratory disease (BRD) is the most common and costly infectious disease affecting the wellbeing and productivity of beef cattle in North America. BRD is a complex disease whose development is dependent on environmental factors and host genetics. Due to the polymicrobial nature of BRD, our understanding of the genetic and molecular mechanisms underlying the disease is still limited. This knowledge would augment the development of better genetic/genomic selection strategies and more accurate diagnostic tools to reduce BRD prevalence. Therefore, this study aimed to utilize multi-omics data (genomics, transcriptomics, and metabolomics) analyses to study the genetic and molecular mechanisms of BRD infection. Blood samples of 143 cattle (80 BRD; 63 non-BRD animals) were collected for genotyping, RNA sequencing, and metabolite profiling. Firstly, a genome-wide association study (GWAS) was performed for BRD susceptibility using 207,038 SNPs. Two SNPs (Chr5:25858264 and BovineHD1800016801) were identified as associated (p-value <1 × 10-5) with BRD susceptibility. Secondly, differential gene expression between BRD and non-BRD animals was studied. At the significance threshold used (log2FC>2, logCPM>2, and FDR<0.01), 101 differentially expressed (DE) genes were identified. These DE genes significantly (p-value <0.05) enriched several immune responses related functions such as inflammatory response. Additionally, we performed expression quantitative trait loci (eQTL) analysis and identified 420 cis-eQTLs and 144 trans-eQTLs significantly (FDR <0.05) associated with the expression of DE genes. Interestingly, eQTL results indicated the most significant SNP (Chr5:25858264) identified via GWAS was a cis-eQTL for DE gene GPR84. This analysis also demonstrated that an important SNP (rs209419196) located in the promoter region of the DE gene BPI significantly influenced the expression of this gene. Finally, the abundance of 31 metabolites was significantly (FDR <0.05) different between BRD and non-BRD animals, and 17 of them showed correlations with multiple DE genes, which shed light on the interactions between immune response and metabolism. This study identified associations between genome, transcriptome, metabolome, and BRD phenotype of feedlot crossbred cattle. The findings may be useful for the development of genomic selection strategies for BRD susceptibility, and for the development of new diagnostic and therapeutic tools.

Bioinformatics Approach to Identify Significant Biomarkers, Drug Targets Shared Between Parkinson's Disease and Bipolar Disorder: A Pilot Study.

Parkinson's disease (PD) is a neurodegenerative disorder responsible for shaking, rigidity, and trouble in walking and patients' coordination ability and physical stability deteriorate day by day. Bipolar disorder (BD) is a psychiatric disorder which is the reason behind extreme shiftiness in mood, and frequent mood inversion may reach too high called mania. People with BD have a greater chance of developing PD during the follow-up period. A lot of work has been done to understand the key factors for developing these 2 diseases. But the molecular functionalities that trigger the development of PD in people with BD are not clear yet. In our study, we are intended to identify the molecular biomarkers and pathways shared between BD and PD. We have investigated the RNA-Seq gene expression data sets of PD and BD. A total of 45 common unique genes (32 up-regulated and 13 down-regulated) abnormally expressed in both PD and BD were identified by applying statistical methods on the GEO data sets. Gene ontology (GO) and BioCarta, KEGG, and Reactome pathways analysis of these 45 common dysregulated genes identified numerous altered molecular pathways such as mineral absorption, Epstein-Barr virus infection, HTLV-I infection, antigen processing, and presentation. Analysis of protein-protein interactions revealed 9 significant hub-proteins, namely RPL21, RPL34, CKS2, B2M, TNFRSF10A, DTX2, HLA-B, ATP2A3, and TAPBP. Significant transcription factors (IRF8, SPI1, RUNX1, and FOXA1) and posttranscriptional regulator microRNAs (hsa-miR-491-3p and hsa-miR-1246) are also found by analyzing gene-transcription factors and gene-miRNAs interactions, respectively. Protein-drug interaction analysis revealed hub-protein B2M's interaction with molecular drug candidates like N-formylmethionine, 3-indolebutyric acid, and doxycycline. Finally, a link between pathological processes of PD and BD is identified at transcriptional level. This study may help us to predict the development of PD among the people suffering from BD and gives some clue to understand significant pathological mechanisms.

A bioinformatics framework to identify the biomarkers and potential drugs for the treatment of colorectal cancer.

Colorectal cancer (CRC), a common malignant tumor, is one of the main causes of death in cancer patients in the world. Therefore, it is critical to understand the molecular mechanism of CRC and identify its diagnostic and prognostic biomarkers. The purpose of this study is to reveal the genes involved in the development of CRC and to predict drug candidates that may help treat CRC through bioinformatics analyses. Two independent CRC gene expression datasets including The Cancer Genome Atlas (TCGA) database and GSE104836 were used in this study. Differentially expressed genes (DEGs) were analyzed separately on the two datasets, and intersected for further analyses. 249 drug candidates for CRC were identified according to the intersected DEGs and the Crowd Extracted Expression of Differential Signatures (CREEDS) database. In addition, hub genes were analyzed using Cytoscape according to the DEGs, and survival analysis results showed that one of the hub genes, TIMP1 was related to the prognosis of CRC patients. Thus, we further focused on drugs that could reverse the expression level of TIMP1. Eight potential drugs with documentary evidence and two new drugs that could reverse the expression of TIMP1 were found among the 249 drugs. In conclusion, we successfully identified potential biomarkers for CRC and achieved drug repurposing using bioinformatics methods. Further exploration is needed to understand the molecular mechanisms of these identified genes and drugs/small molecules in the occurrence, development and treatment of CRC.

Comparative transcriptomic analyses of powdery mildew resistant and susceptible cultivated cucumber (Cucumis sativus L.) varieties to identify the genes involved in the resistance to Sphaerotheca fuliginea infection.

BACKGROUND: Cucumber (Cucumis sativus L.) is a widely cultivated vegetable crop, and its yield and quality are greatly affected by various pathogen infections. Sphaerotheca fuliginea is a pathogen that causes powdery mildew (PM) disease in cucumber. However, the genes involved in the resistance to PM in cucumber are largely unknown. METHODS: In our study, a cucumber PM resistant cultivated variety "BK2" and a susceptible cultivated variety "H136" were used to screen and identify differential expressed genes (DEGs) under the S. fuliginea infection. RESULTS: There were only 97 DEGs between BK2 and H136 under the control condition, suggesting a similarity in the basal gene expression between the resistant and susceptible cultivated varieties. A large number of hormone signaling-related DEGs (9.2% of all DEGs) between resistant and susceptible varieties were identified, suggesting an involvement of hormone signaling pathways in the resistance to PM. In our study, the defense-related DEGs belonging to Class I were only induced in susceptible cultivated variety and the defense-related DEGs belonging to Class II were only induced in resistant cultivated variety. The peroxidase, NBS, glucanase and chitinase genes that were grouped into Class I and II might contribute to production of the resistance to PM in resistant cultivated variety. Furthermore, several members of Pathogen Response-2 family, such as glucanases and chitinases, were identified as DEGs, suggesting that cucumber might enhance the resistance to PM by accelerating the degradation of the pathogen cell walls. Our data allowed us to identify and analyze more potential genes related to PM resistance.

First demonstration of giant panda's immune response to canine distemper vaccine.

The Canine Distemper Virus (CDV) is a high fatal virus to the giant panda (Ailuropoda melanoleuca), where CDV vaccination is a key preventative measure in captive giant pandas. However, the immune response of giant pandas to CDV vaccination has been little studied. In this study, we investigated the blood transcriptome expression profiles of five giant panda cubs after three inoculations, 21 days apart. Blood samples were collected before vaccination (0 Day), and 24 h after each of the three inoculations; defined here as 1 Day, 21  Day, and 42  Day. Compared to 0 Day, we obtained 1262 differentially expressed genes (DEGs) during inoculations. GO and KEGG pathways enrichment analysis of these DEGs found 222 GO terms and 40 pathways. The maximum immune-related terms were enriched by DEGs from comparisons of 21  Day and 0 Day. In the PPI analysis, we identified RSAD2, IL18, ISG15 immune-related hub genes from 1 Day and 21 Day comparison. Compared to 0 Day, innate immune-related genes, TLR4 and TLR8, were up-regulated at 1 Day, and the expressions of IRF1, RSAD2, MX1, and OAS2 were highest at 21  Day. Of the adaptive immune-related genes, IL15, promoting T cell differentiation into CD8+T cells, was up-regulated after the first two inoculations, IL12ß, promoting T cell differentiation into memory cells, and IL10, promoting B cell proliferation and differentiation, were down-regulated during three inoculations. Our results indicated that the immune response of five giant panda cubs was strongest after the second inoculation, most likely protected against CDV infection through innate immunity and T cells, but did not produce enough memory cells to maintain long-term immunity after CDV vaccination.

Molecule mechanisms of Ganoderma lucidum treated hepatocellular carcinoma based on the transcriptional profiles and miRNA-target network.

Ganoderma lucidum has salutary effects on tumor treatment, including pancreatic cancer and hepatocellular carcinoma. However, the molecular mechanisms underlying Ganoderma lucidum therapy is obscure. In this study, the Hepa1-6-bearing C57 BL/6 mouse model was utilized to explore the therapeutic efficacy of Ganoderma lucidum extract (GLE), documenting that it could effectively inhibit tumor growth. The microRNA (miRNA) profiles of GLE-treated and untreated mice were detected, and 25 differentially expressed (DE) miRNAs were determined, including 24 up-expressed and one down-expressed miRNAs. Using the ClusterOne algorithm, 8 hub miRNAs were isolated from the established miRNA-target network. The qRT-PCR assay demonstrated that these 8 miRNAs were up-expressed in the GLE treated tumor mice. Furthermore, the mRNA profiles showed that there are 76 DE mRNAs between GLE treated and model groups. The protein-protein interaction (PPI) network shows that Cntn1, Irs1, Nfkbia, Rybp and Ywhaz playing important roles, and qRT-PCR further revealed they were down-expressed in GLE treated Hepa1-6-bearing C57 BL/6 mice. The rebuilt miRNA-target network was shown that these 5 mRNAs were regulated by mmu-mir-23a-5p, -3102-3p, -337-3p, and -467a-3p, respectively. This study suggested that these 4 interesting miRNAs were potential biomarkers for evaluation of GLE efficacy, which may down-regulate the expression of Cntn1, Irs1, Nfkbia, Rybp and Ywhaz, and mediate many signaling pathways occurring in tumor treatment.

A Novel Method to Efficiently Highlight Nonlinearly Expressed Genes.

For precision medicine, there is a need to identify genes that accurately distinguish the physiological state or response to a particular therapy, but this can be challenging. Many methods of analyzing differential expression have been established and applied to this problem, such as t-test, edgeR, and DEseq2. A common feature of these methods is their focus on a linear relationship (differential expression) between gene expression and phenotype. However, they may overlook nonlinear relationships due to various factors, such as the degree of disease progression, sex, age, ethnicity, and environmental factors. Maximal information coefficient (MIC) was proposed to capture a wide range of associations of two variables in both linear and nonlinear relationships. However, with MIC it is difficult to highlight genes with nonlinear expression patterns as the genes giving the most strongly supported hits are linearly expressed, especially for noisy data. It is thus important to also efficiently identify nonlinearly expressed genes in order to unravel the molecular basis of disease and to reveal new therapeutic targets. We propose a novel nonlinearity measure called normalized differential correlation (NDC) to efficiently highlight nonlinearly expressed genes in transcriptome datasets. Validation using six real-world cancer datasets revealed that the NDC method could highlight nonlinearly expressed genes that could not be highlighted by t-test, MIC, edgeR, and DEseq2, although MIC could capture nonlinear correlations. The classification accuracy indicated that analysis of these genes could adequately distinguish cancer and paracarcinoma tissue samples. Furthermore, the results of biological interpretation of the identified genes suggested that some of them were involved in key functional pathways associated with cancer progression and metastasis. All of this evidence suggests that these nonlinearly expressed genes may play a central role in regulating cancer progression.

Influence of PCOS in Obese vs. Non-Obese women from Mesenchymal Progenitors Stem Cells and Other Endometrial Cells: An in silico biomarker discovery.

Polycystic ovary syndrome (PCOS) is endocrine system disease which affect women ages 18 to 44 where the women's hormones are imbalance. Recently it has been reported to occur in early age. Alteration of normal gene expression in PCOS has shown negative effects on long-term health issues. PCOS has been the responsible factor for the infertility in women of reproductive age group. Early diagnosis and treatment can improve the women's health suffering from PCOS. Earlier Studies shows correlation of PCOS upon insulin resistance with significant outcome, Current study shows the linkage between PCOS with obesity and non-obese patients. Gene expression datasets has been downloaded from GEO (control and PCOS affected patients). Normalization of the datasets were performed using R based on RMA and differentially expressed gene (DEG) were selected on the basis of p-value 0.05 followed by functional annotation of selected gene using Enrich R and DAVID. The DEGs were significantly related to PCOS with obesity and other risk factors involved in disease. The Gene Enrichment Analysis suggests alteration of genes and associated pathway in case of obesity. Current study provides a productive groundwork for specific biomarkers identification for the accurate diagnosis and efficient target for the treatment of PCOS.

Differential gene expression analysis in glioblastoma cells and normal human brain cells based on GEO database.

The differentially expressed genes between glioblastoma (GBM) cells and normal human brain cells were investigated to performed pathway analysis and protein interaction network analysis for the differentially expressed genes. GSE12657 and GSE42656 gene chips, which contain gene expression profile of GBM were obtained from Gene Expression Omniub (GEO) database of National Center for Biotechnology Information (NCBI). The 'limma' data packet in 'R' software was used to analyze the differentially expressed genes in the two gene chips, and gene integration was performed using 'RobustRankAggreg' package. Finally, pheatmap software was used for heatmap analysis and Cytoscape, DAVID, STRING and KOBAS were used for protein-protein interaction, Gene Ontology (GO) and KEGG analyses. As results: i) 702 differentially expressed genes were identified in GSE12657, among those genes, 548 were significantly upregulated and 154 were significantly downregulated (p<0.01, fold-change >1), and 1,854 differentially expressed genes were identified in GSE42656, among the genes, 1,068 were significantly upregulated and 786 were significantly downregulated (p<0.01, fold-change >1). A total of 167 differentially expressed genes including 100 upregulated genes and 67 downregulated genes were identified after gene integration, and the genes showed significantly different expression levels in GBM compared with normal human brain cells (p<0.05). ii) Interactions between the protein products of 101 differentially expressed genes were identified using STRING and expression network was established. A key gene, called CALM3, was identified by Cytoscape software. iii) GO enrichment analysis showed that differentially expressed genes were mainly enriched in 'neurotransmitter:sodium symporter activity' and 'neurotransmitter transporter activity', which can affect the activity of neurotransmitter transportation. KEGG pathway analysis showed that the differentially expressed genes were mainly enriched in 'protein processing in endoplasmic reticulum', which can affect protein processing in endoplasmic reticulum. The results showed that: i) 167 differentially expressed genes were identified from two gene chips after integration; and ii) protein interaction network was established, and GO and KEGG pathway analyses were successfully performed to identify and annotate the key gene, which provide new insights for the studies on GBN at gene level.

Pathway analysis based on Monte Carlo Cross-Validation in polyarticular juvenile idiopathic arthritis.

INTRODUCTION: Juvenile idiopathic arthritis (JIA) is a common chronic disease with onset before the 16 years old in a child. Polyarticular JIA has been reported as the main form of JIA in several locations. Until now, understanding of the genetic basis of JIA is incomplete. The purpose of this study was to identify pathway pairs of great potential functional relevance in the progression of polyarticular JIA. MATERIALS AND METHODS: Microarray data of 59 peripheral blood samples from healthy children and 61 samples from polyarticular JIA were transformed to gene expression data. Differential expressed genes (DEG) between patients and normal controls were identified using Linear Models for Microarray Analysis. After performed enrichment of DEG, differential pathways were identified with Fisher's test and false discovery rate. Differential pathway pairs were constructed with random two differential pathways, and were evaluated by Random Forest classification. Monte Carlo Cross-Validation was introduced to screen the best pathway pair. RESULTS: 42 DEG with P-values<0.01 were identified. 19 differential pathways with P-values<0.01 were identified. Area under the curve (AUC) of pathway pairs was generated with RF classification. After 50 bootstraps of Monte Carlo Cross-Validation, the best pathway pair with the highest AUC value was identified, and it was the pair of tumoricidal function of hepatic natural killer cells pathway and erythropoietin signaling pathway. CONCLUSION: These identified pathway pairs may play pivotal roles in the progress of polyarticular JIA and can be applied for diagnosis. Particular attention can be focused on them for further research.

Oligo-dT anchored cDNA-SCoT: a novel differential display method for analyzing differential gene expression in response to several stress treatments in mango (Mangifera indica L.).

Differential display is a powerful technique for analyzing differences in gene expression. Oligo-dT cDNAstart codon targeted marker (cDNA-SCoT) technique is a novel, simple, cheap, rapid, and efficient method for differential gene expression research. In the present study, the oligo-dT anchored cDNA-SCoT technique was exploited to identify differentially expressed genes during several stress treatments in mango. A total of 37 primers combined with oligo-dT anchor primers 3side amplified approximately 150 fragments of 150 bp to 1500 bp in length. Up to 100 fragments were differentially expressed among the stress treatments and control samples, among which 92 were obtained and sequenced. Out of the 92 transcript derived fragments (TDFs), 70% were highly homologous to known genes, and 30% encoded unclassified proteins with unknown functions. The expression pattern of nine genes with known functions involved in several abiotic stresses in other species was confirmed by quantitative reverse transcription polymerase chain reaction (qRT-PCR) under cold (4 °C), salinity (NaCl), polyethylene glycol (PEG, MW 6000), and heavy metal treatments in leaves and stems at different time points (0, 24, 48, and 72 h). The expression patterns of the genes (TDF4, TDF7, TDF23, TDF45, TDF49, TDF50, TDF57, TDF91 and TDF92) that had direct or indirect relationships with cold, salinity, drought and heavy metal stress response were analyzed through qRT-PCR. The possible roles of these genes are discussed. This study suggests that the oligo-dT anchored cDNA-SCoT differential display method is a useful tool to serve as an initial step for characterizing transcriptional changes induced by abiotic stresses and provide gene information for further study and application in genetic improvement and breeding in mango.