scholarly journals Identification of Genes Associated with Nitrogen Stress Responses in Apple Leaves

Plants ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 2649
Author(s):  
Youngsuk Lee ◽  
Van Giap Do ◽  
Seonae Kim ◽  
Hunjoong Kweon
Keyword(s):  
Stress Responses ◽  
Interaction Network ◽  
Stress Conditions ◽  
Future Research ◽  
Protein Protein Interaction ◽  
Physiological Processes ◽  
Apple Leaves ◽  
Survival And Development ◽  
Molecular Signals ◽  
Functional Analyses

Nitrogen (N) is an essential macronutrient that regulates diverse physiological processes for plant survival and development. In apple orchards, inappropriate N conditions can cause imbalanced growth and subsequent physiological disorders in trees. In order to investigate the molecular basis underlying the physiological signals for N stress responses, we examined the metabolic signals responsive to contrasting N stress conditions (deficient/excessive) in apple leaves using transcriptome approaches. The clustering of differentially expressed genes (DEGs) showed the expression dynamics of genes associated with each N stress group. Functional analyses of gene ontology and pathway enrichments revealed the potential candidates of metabolic signals responsible for N-deficient/excessive stress responses. The functional interactions of DEGs in each cluster were further explored by protein–protein interaction network analysis. Our results provided a comprehensive insight into molecular signals responsive to N stress conditions, and will be useful in future research to enhance the nutrition tolerance of tree crops.

scholarly journals Systematic Analysis of FASTK Gene Family Alterations in Cancer

2021 ◽  
Vol 22 (21) ◽  
pp. 11337
Author(s):  
Lorena Magraner-Pardo ◽  
Dino Gobelli ◽  
Miguel A. de la Fuente ◽  
Tirso Pons ◽  
María Simarro
Keyword(s):  
Mitochondrial Gene ◽  
Interaction Network ◽  
Genetic Alterations ◽  
Future Research ◽  
Mrna Levels ◽  
Systematic Analysis ◽  
Lung Cancers ◽  
Protein Protein Interaction ◽  
Starting Point ◽  
Post Transcriptional Regulation

The FASTK family of proteins have been recently reported to play a key role in the post-transcriptional regulation of mitochondrial gene expression, including mRNA stability and translation. Accumulated studies have provided evidence that the expression of some FASTK genes is altered in certain types of cancer, in agreement with the central role of mitochondria in cancer development. Here, we obtained a pan-cancer overview of the genomic and transcriptomic alterations of FASTK genes. FASTK, FASTKD1, FASTKD3 and FASTKD5 showed the highest rates of genetic alterations. FASTK and FASTKD3 alterations consisted mainly of amplifications that were seen in more than 8% of ovarian and lung cancers, respectively. FASTKD1 and FASTKD5 were the most frequently mutated FASTK genes, and the mutations were identified in 5–7% of uterine cancers, as well as in 4% of melanomas. Our results also showed that the mRNA levels of all FASTK members were strongly upregulated in esophageal, stomach, liver and lung cancers. Finally, the protein-protein interaction network for FASTK proteins uncovers the interaction of FASTK, FASTKD2, FASTKD4 and FASTKD5 with cancer signaling pathways. These results serve as a starting point for future research into the potential of the FASTK family members as diagnostic and therapeutic targets for certain types of cancer.

scholarly journals Proteomic Profile of Glyphosate-Resistant Soybean under Combined Herbicide and Drought Stress Conditions

Plants ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 2381
Author(s):  
Rafael Fonseca Benevenuto ◽  
Caroline Bedin Zanatta ◽  
Miguel Pedro Guerra ◽  
Rubens Onofre Nodari ◽  
Sarah Z. Agapito-Tenfen
Keyword(s):  
Adverse Effects ◽  
Stress Responses ◽  
Energy Demand ◽  
Interaction Network ◽  
Metabolic Cost ◽  
Stress Conditions ◽  
Stress Factors ◽  
Gm Plants ◽  
Abiotic Stressors ◽  
Gm Soybean

While some genetically modified (GM) plants have been targeted to confer tolerance to abiotic stressors, transgenes are impacted by abiotic stressors, causing adverse effects on plant physiology and yield. However, routine safety analyses do not assess the response of GM plants under different environmental stress conditions. In the context of climate change, the combination of abiotic stressors is a reality in agroecosystems. Therefore, the aim of this study was to analyze the metabolic cost by assessing the proteomic profiles of GM soybean varieties under glyphosate spraying and water deficit conditions compared to their non-transgenic conventional counterparts. We found evidence of cumulative adverse effects that resulted in the reduction of enzymes involved in carbohydrate metabolism, along with the expression of amino acids and nitrogen metabolic enzymes. Ribosomal metabolism was significantly enriched, particularly the protein families associated with ribosomal complexes L5 and L18. The interaction network map showed that the affected module representing the ribosome pathway interacts strongly with other important proteins, such as the chloro-plastic gamma ATP synthase subunit. Combined, these findings provide clear evidence for increasing the metabolic costs of GM soybean plants in response to the accumulation of stress factors. First, alterations in the ribosome pathway indicate that the GM plant itself carries a metabolic burden associated with the biosynthesis of proteins as effects of genetic transformation. GM plants also showed an imbalance in energy demand and production under controlled conditions, which was increased under drought conditions. Identifying the consequences of altered metabolism related to the interaction between plant transgene stress responses allows us to understand the possible effects on the ecology and evolution of plants in the medium and long term and the potential interactions with other organisms when these organisms are released in the environment.

scholarly journals Functions of silicon in plant drought stress responses

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Min Wang ◽  
Ruirui Wang ◽  
Luis Alejandro Jose Mur ◽  
Jianyun Ruan ◽  
Qirong Shen ◽  
...  
Keyword(s):  
Drought Stress ◽  
Water Balance ◽  
Plant Species ◽  
Stress Responses ◽  
Environmental Conditions ◽  
Water Status ◽  
Stress Conditions ◽  
Plant Water Status ◽  
Future Research ◽  
Plant Water

AbstractSilicon (Si), the second most abundant element in Earth’s crust, exerts beneficial effects on the growth and productivity of a variety of plant species under various environmental conditions. However, the benefits of Si and its importance to plants are controversial due to differences among the species, genotypes, and the environmental conditions. Although Si has been widely reported to alleviate plant drought stress in both the Si-accumulating and nonaccumulating plants, the underlying mechanisms through which Si improves plant water status and maintains water balance remain unclear. The aim of this review is to summarize the morphoanatomical, physiological, biochemical, and molecular processes that are involved in plant water status that are regulated by Si in response to drought stress, especially the integrated modulation of Si-triggered drought stress responses in Si accumulators and intermediate- and excluder-type plants. The key mechanisms influencing the ability of Si to mitigate the effects of drought stress include enhancing water uptake and transport, regulating stomatal behavior and transpirational water loss, accumulating solutes and osmoregulatory substances, and inducing plant defense- associated with signaling events, consequently maintaining whole-plant water balance. This study evaluates the ability of Si to maintain water balance under drought stress conditions and suggests future research that is needed to implement the use of Si in agriculture. Considering the complex relationships between Si and different plant species, genotypes, and the environment, detailed studies are needed to understand the interactions between Si and plant responses under stress conditions.

CLUSTERING ALGORITHMS FOR DETECTING FUNCTIONAL MODULES IN PROTEIN INTERACTION NETWORKS

2009 ◽  
Vol 07 (01) ◽  
pp. 217-242 ◽  
Author(s):  
LIN GAO ◽  
PENG-GANG SUN ◽  
JIA SONG
Keyword(s):  
Protein Interaction ◽  
Protein Complexes ◽  
Clustering Algorithms ◽  
Interaction Network ◽  
Future Research ◽  
Functional Modules ◽  
Sources Of Information ◽  
Protein Protein Interaction ◽  
Metabolic Functions ◽  
Ppi Networks

Protein–Protein Interaction (PPI) networks are believed to be important sources of information related to biological processes and complex metabolic functions of the cell. When studying the workings of a biological cell, it is useful to be able to detect known and predict still undiscovered protein complexes within the cell's PPI networks. Such predictions may be used as an inexpensive tool to direct biological experiments. The increasing amount of available PPI data necessitate a fast, accurate approach to biological complex identification. Because of its importance in the studies of protein interaction network, there are different models and algorithms in identifying functional modules in PPI networks. In this paper, we review some representative algorithms, focusing on the algorithms underlying the approaches and how the algorithms relate to each other. In particular, a comparison is given based on the property of the algorithms. Since the PPI network is noisy and still incomplete, some methods which consider other additional properties for preprocessing and purifying of PPI data are presented. We also give a discussion about the functional annotation and validation of protein complexes. Finally, new progress and future research directions are discussed from the computational viewpoint.

scholarly journals A Novel Model for Identifying Essential Proteins Based on Key Target Convergence Sets

2021 ◽  
Vol 12 ◽  
Author(s):  
Jiaxin Peng ◽  
Linai Kuang ◽  
Zhen Zhang ◽  
Yihong Tan ◽  
Zhiping Chen ◽  
...  
Keyword(s):  
Prediction Accuracy ◽  
Computational Models ◽  
Interaction Network ◽  
Predictive Performance ◽  
Future Research ◽  
Domain Interaction ◽  
Essential Proteins ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Novel Model

In recent years, many computational models have been designed to detect essential proteins based on protein-protein interaction (PPI) networks. However, due to the incompleteness of PPI networks, the prediction accuracy of these models is still not satisfactory. In this manuscript, a novel key target convergence sets based prediction model (KTCSPM) is proposed to identify essential proteins. In KTCSPM, a weighted PPI network and a weighted (Domain-Domain Interaction) network are constructed first based on known PPIs and PDIs downloaded from benchmark databases. And then, by integrating these two kinds of networks, a novel weighted PDI network is built. Next, through assigning a unique key target convergence set (KTCS) for each node in the weighted PDI network, an improved method based on the random walk with restart is designed to identify essential proteins. Finally, in order to evaluate the predictive effects of KTCSPM, it is compared with 12 competitive state-of-the-art models, and experimental results show that KTCSPM can achieve better prediction accuracy. Considering the satisfactory predictive performance achieved by KTCSPM, it indicates that KTCSPM might be a good supplement to the future research on prediction of essential proteins.

scholarly journals Identification of genes that influence poor prognosis in osteosarcoma

2021 ◽  
Author(s):  
Yao Lu ◽  
Yanli Li ◽  
Xueliang Zeng ◽  
Bei Li ◽  
Qiongjun Xie ◽  
...  
Keyword(s):  
Gene Expression ◽  
Poor Prognosis ◽  
Bone Cancer ◽  
Interaction Network ◽  
Venn Diagram ◽  
Future Research ◽  
Protein Protein Interaction ◽  
Normal Tissues ◽  
Kaplan Meier ◽  
Long Term Prognosis

Abstract Objectives Osteosarcoma (OS) is the most common primary bone cancer in children and adolescents. At present, the 5-year overall survival rate of OS patients is about 65%, and the long-term prognosis is still not ideal. The study was designed to screen genes that could contribute to the poor prognosis of OS and explore their potential pathogenic mechanisms. Methods The gene expression profile of the GSE94805 dataset from the GEO database, containing data from 12 U2OS cell samples, including four control, four quiescent, and four senescent samples was obtained. Co-expressed differentially expressed genes (DEGs) in OS U2OS cells were selected using the GEO2R tool and Venn diagram analysis. Next, using the STRING, Cytoscap, and Molecular Complex Detection (MCODE) plug-in, the related protein-protein interaction network among upregulated genes was analyzed. Moreover, Kaplan-Meier plots were used to analyze the relationship between the identified genes and OS prognosis. Genes significantly associated with worse prognosis were evaluated using the Gene Expression Profiling Interactive Analysis. Results Thirteen genes were confirmed to be significantly more expressed in OS than in normal tissues. Five genes (AURKB, EXO1, KIF4A, KIF15, and MCM4) were found to influence OS prognosis. Conclusion We identified five core genes related to the prognosis of OS and constructed a clinical prediction model for OS. Our data may provide a reference for future research on mechanisms, clinical diagnosis, and treatment of OS.

Imaging the “social brain” in schizophrenia: A systematic review of neuroimaging studies of social reward and punishment

2020 ◽  
Author(s):  
Jessica Mow ◽  
Arti Gandhi ◽  
Daniel Fulford
Keyword(s):  
Brain Activity ◽  
Interaction Network ◽  
Social Motivation ◽  
Social Impairment ◽  
Future Research ◽  
Social Reward ◽  
Schizophrenia Spectrum Disorders ◽  
Social Stimuli ◽  
Schizophrenia Spectrum ◽  
Reward And Punishment

Decreased social functioning and high levels of loneliness and social isolation are common in schizophrenia spectrum disorders (SSD), contributing to reduced quality of life. One key contributor to social impairment is low social motivation, which may stem from aberrant neural processing of socially rewarding or punishing stimuli. To summarize research on the neurobiology of social motivation in SSD, we performed a systematic literature review of neuroimaging studies involving the presentation of social stimuli intended to elicit feelings of reward and/or punishment. Across 11 studies meeting criteria, people with SSD demonstrated weaker modulation of brain activity in regions within a proposed social interaction network, including prefrontal, cingulate, and striatal regions, as well as the amygdala and insula. Firm conclusions regarding neural differences in SSD in these regions, as well as connections within networks, are limited due to conceptual and methodological inconsistencies across the available studies. We conclude by making recommendations for the study of social reward and punishment processing in SSD in future research.

Bioinformatics Analysis Reveals Functions of MicroRNAs in Rice Under the Drought Stress

2021 ◽  
Vol 15 (8) ◽  
pp. 927-936 ◽  
Author(s):  
Yan Peng ◽  
Yuewu Liu ◽  
Xinbo Chen
Keyword(s):  
Drought Stress ◽  
Target Genes ◽  
Hot Spot ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Differentially Expressed ◽  
Protein Protein Interaction ◽  
Promising Tool ◽  
Differentially Expressed Mirnas ◽  
Aba Biosynthesis

Background: Drought is one of the most damaging and widespread abiotic stresses that can severely limit the rice production. MicroRNAs (miRNAs) act as a promising tool for improving the drought tolerance of rice and have become a hot spot in recent years. Objective: In order to further extend the understanding of miRNAs, the functions of miRNAs in rice under drought stress are analyzed by bioinformatics. Method: In this study, we integrated miRNAs and genes transcriptome data of rice under the drought stress. Some bioinformatics methods were used to reveal the functions of miRNAs in rice under drought stress. These methods included target genes identification, differentially expressed miRNAs screening, enrichment analysis of DEGs, network constructions for miRNA-target and target-target proteins interaction. Results: (1) A total of 229 miRNAs with differential expression in rice under the drought stress, corresponding to 73 rice miRNAs families, were identified. (2) 1035 differentially expressed genes (DEGs) were identified, which included 357 up-regulated genes, 542 down-regulated genes and 136 up/down-regulated genes. (3) The network of regulatory relationships between 73 rice miRNAs families and 1035 DEGs was constructed. (4) 25 UP_KEYWORDS terms of DEGs, 125 GO terms and 7 pathways were obtained. (5) The protein-protein interaction network of 1035 DEGs was constructed. Conclusion: (1) MiRNA-regulated targets in rice might mainly involve in a series of basic biological processes and pathways under drought conditions. (2) MiRNAs in rice might play critical roles in Lignin degradation and ABA biosynthesis. (3) MiRNAs in rice might play an important role in drought signal perceiving and transduction.

Sign in / Sign up

Export Citation Format

Share Document