scholarly journals Exploring Heat-Response Mechanisms of MicroRNAs Based on Microarray Data of Rice Post-meiosis Panicle

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Yan Peng ◽  
Xianwen Zhang ◽  
Yuewu Liu ◽  
Xinbo Chen
Keyword(s):  
Heat Stress ◽  
Target Genes ◽  
Expression Profiles ◽  
Time Lag ◽  
Interaction Network ◽  
Biological Processes ◽  
Functional Identification ◽  
Protein Protein Interaction ◽  
Heat Response ◽  
Protein Protein Interaction Network

To explore heat response mechanisms of mircoRNAs (miRNAs) in rice post-meiosis panicle, microarray analysis was performed on RNA isolated from rice post-meiosis panicles which were treated at 40°C for 0 min, 10 min, 20 min, 60 min, and 2 h. By integrating paired differentially expressed (DE) miRNAs and mRNA expression profiles, we found that the expression levels of 29 DE-miRNA families were negatively correlated to their 178 DE-target genes. Further analysis showed that the majority of miRNAs in 29 DE-miRNA families resisted the heat stress by downregulating their target genes and a time lag existed between expression of miRNAs and their target genes. Then, GO-Slim classification and functional identification of these 178 target genes showed that (1) miRNAs were mainly involved in a series of basic biological processes even under heat conditions; (2) some miRNAs might play important roles in the heat resistance (such as osa-miR164, osa-miR166, osa-miR169, osa-miR319, osa-miR390, osa-miR395, and osa-miR399); (3) osa-miR172 might play important roles in protecting the rice panicle under the heat stress, but osa-miR437, osa-miR418, osa-miR164, miR156, and miR529 might negatively affect rice fertility and panicle flower; and (4) osa-miR414 might inhibit the flowering gene expression by downregulation of LOC_Os 05g51830 to delay the heading of rice. Finally, a heat-induced miRNA-PPI (protein-protein interaction) network was constructed, and three miRNA coregulatory modules were discovered.

scholarly journals Similarity of regulatory network between leukemia stem cells and normal hemopoietic stem cells

2018 ◽  
Vol 6 (4) ◽  
pp. 129-140
Author(s):  
Zhi-Jian Li ◽  
Xing-Ling Sui ◽  
Xue-Bo Yang ◽  
Wen Sun
Keyword(s):  
Stem Cells ◽  
Gene Ontology ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Interaction Network ◽  
Impact Factors ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

AbstractTo reveal the biology of AML, we compared gene-expression profiles between normal hematopoietic cells from 38 healthy donors and leukemic blasts (LBs) from 26 AML patients. We defined the comparison of LB and unselected BM as experiment 1, LB and CD34+ isolated from BM as experiment 2, LB and unselected PB as experiment 3, and LB and CD34+ isolated from PB as experiment 4. Then, protein–protein interaction network of DEGs was constructed to identify critical genes. Regulatory impact factors were used to identify critical transcription factors from the differential co-expression network constructed via reanalyzing the microarray profile from the perspective of differential co-expression. Gene ontology enrichment was performed to extract biological meaning. The comparison among the number of DEGs obtained in four experiments showed that cells did not tend to differentiation and CD34+ was more similar to cancer stem cells. Based on the results of protein–protein interaction network,CREBBP,F2RL1,MCM2, andTP53were respectively the key genes in experiments 1, 2, 3, and 4. From gene ontology analysis, we found that immune response was the most common one in four stages. Our results might provide a platform for determining the pathology and therapy of AML.

scholarly journals Supervised machine learning models and protein-protein interaction network analysis of gene expression profiles induced by omega-3 polyunsaturated fatty acids

2022 ◽  
Vol 02 ◽  
Author(s):  
Sergey Shityakov ◽  
Jane Pei-Chen Chang ◽  
Ching-Fang Sun ◽  
David Ta-Wei Guu ◽  
Thomas Dandekar ◽  
...  
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Protein Interaction ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Interaction Network ◽  
Supervised Machine Learning ◽  
Omega 3 ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

Background: Omega-3 polyunsaturated fatty acids (PUFAs), such as eicosapentaenoic (EPA) and docosahexaenoic (DHA) acids, have beneficial effects on human health, but their effect on gene expression in elderly individuals (age ≥ 65) is largely unknown. In order to examine this, the gene expression profiles were analyzed in the healthy subjects (n = 96) at baseline and after 26 weeks of supplementation with EPA+DHA to determine up-regulated and down-regulated dif-ferentially expressed genes (DEGs) triggered by PUFAs. The protein-protein interaction (PPI) networks were constructed by mapping these DEGs to a human interactome and linking them to the specific pathways. Objective: This study aimed to implement supervised machine learning models and protein-protein interaction network analysis of gene expression profiles induced by PUFAs. Methods: The transcriptional profile of GSE12375 was obtained from the Gene Expression Om-nibus database, which is based on the Affymetrix NuGO array. The probe cell intensity data were converted into the gene expression values, and the background correction was performed by the multi-array average algorithm. The LIMMA (Linear Models for Microarray Data) algo-rithm was implemented to identify relevant DEGs at baseline and after 26 weeks of supplemen-tation with a p-value < 0.05. The DAVID web server was used to identify and construct the en-riched KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways. Finally, the construction of machine learning (ML) models, including logistic regression, naïve Bayes, and deep neural networks, were implemented for the analyzed DEGs associated with the specific pathways. Results: The results revealed that up-regulated DEGs were associated with neurotrophin/MAPK signaling, whereas the down-regulated DEGs were linked to cancer, acute myeloid leukemia, and long-term depression pathways. Additionally, ML approaches were able to cluster the EPA/DHA-treated and control groups by the logistic regression performing the best. Conclusion: Overall, this study highlights the pivotal changes in DEGs induced by PUFAs and provides the rationale for the implementation of ML algorithms as predictive models for this type of biomedical data.

scholarly journals The Significance of Heat Responsive Long Noncoding RNAs in Rice Discovered by Integrating Expression, Network and Genetic Analyses

2021 ◽  
Author(s):  
zhengfeng zhang ◽  
Huahua Zhong ◽  
Bo Nan ◽  
Benze Xiao
Keyword(s):  
Heat Stress ◽  
Steady State ◽  
Target Genes ◽  
Expression Profiles ◽  
Noncoding Rnas ◽  
Interaction Network ◽  
Long Noncoding Rnas ◽  
Plant Responses ◽  
Sequence Motif ◽  
Motif Analysis

Abstract Long noncoding RNAs (lncRNAs) play vital roles in plant responses to environments. However, the systematic identification of lncRNAs in rice under heat stress remains to be fully performed. We performed steady-state strand-specific RNA-seq for rice seedlings under heat stress to predict lncRNAs. In total, 2743 lncRNAs were predicted, and their expression profiles in response to heat treatments were provided. We identified 231 differentially expressed lncRNAs (DELs) in their steady-state level under heat stress, including 31 DELs common to both varieties and 103 and 97 specific to ZS97B and SYD2, respectively, all defined as heat-responsive lncRNAs (HRLs). The target-coding genes of HRLs were predicted through cis-action, ceRNAs, precursors of miRNA, antisense modes and other trans-action targets. GO and KEGG enrichment analyses of HRL targets revealed functions in which HRLs were involved. The interaction network between HRLs, target genes and relevant miRNAs was constructed. The HRLs and their targets were integrated with publicly available QTLs for rice seedling growth under heat stimulus, and 10 HRLs and 12 target genes were co-localized with 5 heat-relevant QTLs. Sequence motif analysis found a few significant motifs enriched in 231 HRL sequences, and the differential significance of enriched motifs between HRLs and background sequences was tested. Our findings provide new insights into the characterization of lncRNAs in terms of the heat response and resources for further investigations of plant heat tolerance improvement.

Construction and analysis of mRNA and lncRNA regulatory networks reveal the key genes associated with prostate cancer related fatigue during localized radiation therapy

2020 ◽  
Vol 15 ◽  
Author(s):  
Yechen Wu ◽  
Yaping Gui ◽  
Denglong Wu ◽  
Qiang Wu
Keyword(s):  
Prostate Cancer ◽  
Radiation Therapy ◽  
Regulatory Networks ◽  
Interaction Network ◽  
Biological Processes ◽  
Protein Protein Interaction ◽  
Cancer Related Fatigue ◽  
Non Coding Rnas ◽  
New Biomarkers ◽  
Protein Protein Interaction Network

Background: Localized radiation therapy is the first line option for the treatment of non-metastatic prostate cancer (PCa). Previous studies revealed long non-coding RNAs (lncRNAs) had crucial roles in diseases progression. However, the mechanisms of lncRNAs underlying prostate cancer related fatigue remained largely unclear. Objective: The present study aimed to uncover the hub genes related to PCa related fatigue during localized radiation therapy by constructing mRNA and lncRNA regulatory networks. Methods: We analyzed GSE30174, which included 10 control samples and 40 PCa related fatigue samples, to identify differently expressed lncRNAs and mRNAs in PCa related fatigue. Protein-protein interaction network was constructed to uncover the interactions among mRNAs. Co-expression network analysis was appled to identify the key lncRNAs and reveal the functions of these lncRNAs in PCa related fatigue. Results and Discussion: This research found 1271 dysregulated mRNAs and 205 dysregulated lncRNAs in PCa related fatigue using GSE30174. Bioinformatics analysis showed PCa related fatigue related mRNAs and lncRNAs were associated with inflammatory response and immune response related biological processes. Furthermore, we constructed PPI network and lncRNA co-expression network related to fatigue in PCa. Of note, we observed the dysregulated lncRNAs and mRNAs, such as SEC61A2, ADCY6, LPAR5, COL7A1, ALB, COL1A1, SNHG1, LINC01215, LINC00926, GNG4, LMO7, and COL4A6, in PCa related fatigue could predict the outcome of PCa patients. Conclusions: This research could provide novel mechanisms underlying fatigue and identify new biomarkers for the prognosis of PCa.

FYN and CD247: key Genes for Septic Shock Based on Bioinformatics and Meta-Analysis

2021 ◽  
Vol 24 ◽  
Author(s):  
Yue Jiang ◽  
Qian Miao ◽  
Lin Hu ◽  
Tingyan Zhou ◽  
Yingchun Hu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Septic Shock ◽  
Survival Analysis ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Interaction Network ◽  
Protein Protein Interaction ◽  
Key Genes ◽  
Protein Protein Interaction Network ◽  
Geo Database

Background: Septic shock is sepsis accompanied by hemodynamic instability and high clinical mortality. Material and Methods: GSE95233, GSE57065, GSE131761 gene-expression profiles of healthy control subjects and septic shock patients were downloaded from the Gene-Expression Omnibus (GEO) database, and differences of expression profiles and their intersection were analysed using GEO2R. Function and pathway enrichment analysis was performed on common differentially expressed genes (DEG), and key genes for septic shock were screened using a protein-protein interaction network created with STRING. Also, data from the GEO database were used for survival analysis for key genes, and a meta-analysis was used to explore expression trends of core genes. Finally, high-throughput sequencing using the blood of a murine sepsis model was performed to analyse the expression of CD247 and FYN in mice. Results: A total of 539 DEGs were obtained (p < 0.05). Gene ontology analysis showed that key genes were enriched in functions, such as immune response and T cell activity, and DEGs were enriched in signal pathways, such as T cell receptors. FYN and CD247 are in the centre of the protein-protein interaction network, and survival analysis found that they are positively correlated with survival from sepsis. Further, meta-analysis results showed that FYN could be useful for the prognosis of patients, and CD247 might distinguish between sepsis and systemic inflammatory response syndrome patients. Finally, RNA sequencing using a mouse septic shock model showed low expression of CD247 and FYN in this model. Conclusion: FYN and CD247 are expected to become new biomarkers of septic shock.

scholarly journals Genome-Wide Identification and Expression Analysis of the Aux/IAA and Auxin Response Factor Gene Family in Medicago truncatula

2021 ◽  
Vol 22 (19) ◽  
pp. 10494
Author(s):  
Rui Liu ◽  
Zhenfei Guo ◽  
Shaoyun Lu
Keyword(s):  
Medicago Truncatula ◽  
Reference Genome ◽  
Expression Profiles ◽  
Interaction Network ◽  
Auxin Response ◽  
Cis Acting ◽  
Protein Protein Interaction ◽  
Genome Wide ◽  
Auxin Response Factor Gene ◽  
Protein Protein Interaction Network

Aux/IAA and auxin response transcription factor (ARF) genes are key regulators of auxin responses in plants. A total of 25 MtIAA and 40 MtARF genes were identified based on the latest updated Medicago truncatula reference genome sequence. They were clustered into 10 and 8 major groups, respectively. The homologs among M. truncatula, soybean, and Arabidopsis thaliana shared close relationships based on phylogenetic analysis. Gene structure analysis revealed that MtIAA and MtARF genes contained one to four concern motifs and they are localized to eight chromosomes, except chromosome 6 without MtARFs. In addition, some MtIAA and MtARF genes were expressed in all tissues, while others were specifically expressed in specific tissues. Analysis of cis-acting elements in promoter region and expression profiles revealed the potential response of MtIAA and MtARF genes to hormones and abiotic stresses. The prediction protein–protein interaction network showed that some ARF proteins could interact with multiple Aux/IAA proteins, and the reverse is also true. The investigation provides valuable, basic information for further studies on the biological functions of MtIAA and MtARF genes in the regulation of auxin-related pathways in M. truncatula.

scholarly journals Protein-Protein Interaction Network Analysis and Identification of Key Players in nor-NOHA and NOHA Mediated Pathways for Treatment of Cancer through Arginase Inhibition: Insights from Systems Biology

2018 ◽  
Author(s):  
Ishtiaque Ahammad
Keyword(s):  
Systems Biology ◽  
Cancer Progression ◽  
Molecular Mechanisms ◽  
Interaction Network ◽  
Cancer Cell Proliferation ◽  
Biological Processes ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Key Players ◽  
Protein Protein Interaction Network

<p>L-arginine is involved in a number of biological processes in our bodies. Metabolism of L-arginine by the enzyme arginase has been found to be associated with cancer cell proliferation. Arginase inhibition has been proposed as a potential therapeutic means to inhibit this process. N-hydroxy-nor-L-Arg (nor-NOHA) and N (omega)-hydroxy-L-arginine (NOHA) has shown promise in inhibiting cancer progression through arginase inhibition. In this study, nor-NOHA and NOHA-associated genes and proteins were analyzed with several Bioinformatics and Systems Biology tools to identify the associated pathways and the key players involved so that a more comprehensive view of the molecular mechanisms including the regulatory mechanisms can be achieved and more potential targets for treatment of cancer can be discovered. Based on the analyses carried out, 3 significant modules have been identified from the PPI network. Five pathways/processes have been found to be significantly associated with nor-NOHA and NOHA associated genes. Out of the 1996 proteins in the PPI network, 4 have been identified as hub proteins- SOD, SOD1, AMD1, and NOS2. These 4 proteins have been implicated in cancer by other studies. Thus, this study provided further validation into the claim of these 4 proteins being potential targets for cancer treatment.</p>

scholarly journals Insights Into Heat Response Mechanisms in Clematis Species: Physiological Analysis, Expression Profiles and Function Verification

2021 ◽  
Author(s):  
Hao Zhang ◽  
Changhua Jiang ◽  
Rui Wang ◽  
Long Zhang ◽  
Ruonan Gai ◽  
...  
Keyword(s):  
Heat Stress ◽  
Heat Shock ◽  
Expression Profiles ◽  
Interaction Network ◽  
Differentially Expressed ◽  
Genes Encoding ◽  
Heat Response ◽  
Physiological Analysis ◽  
Japanese Gardens ◽  
Shock Proteins

Abstract Clematis species are commonly grown in western and Japanese gardens. Heat stress can inhibit many physiological processes mediating plant growth and development. The mechanism regulating responses to heat has been well characterized in Arabidopsis thaliana and some crops, but not in horticultural plants, including Clematis species. In this study, we found that Clematis alpina ‘Stolwijk Gold’ was heat-sensitive whereas Clematis vitalba and Clematis viticella ‘Polish Spirit’ were heat-tolerant based on the physiological analyses in heat stress. Transcriptomic profiling identified a set of heat tolerance-related genes (HTGs). Consistent with the observed phenotype in heat stress, 41.43% of the differentially expressed HTGs between heat treatment and control were down-regulated in heat-sensitive cultivar Stolwijk Gold, but only 9.80% and 20.79% of the differentially expressed HTGs in heat resistant C. vitalba and Polish Spirit, respectively. Co-expression network, protein–protein interaction network and phylogenetic analysis revealed that the genes encoding heat shock transcription factors (HSFs) and heat shock proteins (HSPs) played an essential role in Clematis resistance to heat stress. Ultimately, we proposed that two clades of HSFs may have diverse functions in regulating heat resistance from C. vitalba and CvHSFA2-2 could endow different host with high temperature resistance. This study provides first insights into the diversity of the heat response mechanisms among Clematis species.

scholarly journals In silico investigation of the mechanism of action of kojic acid effects via protein-protein interaction network

2021 ◽  
Vol 20 (10) ◽  
pp. 2063-2069
Author(s):  
Awais Wahab ◽  
Ghulam Murtaza ◽  
Hafsa Anam ◽  
Chuanhong Wu
Keyword(s):  
Protein Interaction ◽  
Protein Interaction Network ◽  
Mode Of Action ◽  
Transcription Initiation ◽  
Kojic Acid ◽  
Interaction Network ◽  
Biological Processes ◽  
Go Analysis ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

Purpose: To evaluate the molecular mechanism of kojic acid by network pharmacology.Methods: This study was conducted by designing a protein-protein interaction network through the STITCH database and analyzing biological processes via Cytoscape plugin ClueGO.Results: A total of 19 protein targets of kojic acid including TYR, NOS3, NOS2, and NOS1 were found. The PPI network helped to understand the mode of action of kojic acid at a molecular level. Gene Ontology (GO) analysis resulted in the retrieval of 104 GO terms which were related to variousphysiological processes. GO analysis revealed that kojic acid might be involved in the regulation of several biological processes such as circadian gene expression and transcription initiation of RNA polymerase 2.Conclusion: The findings from this study reveal that the retrieved GO pathways are known to be involved in several diseases such as inflammation, cancer, aging, pigmentation, and melisma. Furthermore, these pathways are directly or indirectly related to kojic acid. Thus, this study has contributed to a better understanding of the mode of action of kojic acid.

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