scholarly journals Study on the Anti-Aging Physiological Characteristics and Molecular Mechanism of Camellia Oleifera

2021 ◽  
Author(s):  
Yongzhong Chen ◽  
Jianjun Chen ◽  
Zhen Zhang ◽  
Yanming Xu ◽  
Zhilong He ◽  
...  
Keyword(s):  
High Throughput Sequencing ◽  
Protein Gene ◽  
Tree Age ◽  
Sequencing Analysis ◽  
Oxygen Species ◽  
Camellia Oleifera ◽  
Transcription Factor Gene ◽  
Analysis Technique ◽  
Binding Protein Gene ◽  
Regulated Gene Expression

Abstract To study the physiological and molecular regulating mechanism of ancient Camellia oleifera which kept a exuberant vitality for more than one hundred years, leaves of 30a year old and > 100 year old Camellia oleifera were selected as targets. On the basis of the study of the generation and the clearance of reactive oxygen species, sequencing analysis of the transcriptome and expression profiling by high throughput sequencing analysis technique was conducted to study differentially expressed functional genes related to the tree age. It showed that the chlorophyll content and enzyme activities increased in ancient Camellia oleifera leaves. Expression of chlorophyll a/b binding protein gene, auxin related gene, the signal transduction factor and the transcription factor gene in ancient trees were all higher than mature tree. The down regulated gene expression of inductive genes related to protein degradation in ancient tree. Under the comprehensive function of those factors, ancient Camellia oleifera leaves still kept an exuberant vitality which was very useful for studies of stress resistance molecular biology and genetic improvement of Camellia oleifera.

A New Approach for Predicting the Value of Gene Expression: Two-way Collaborative Filtering

2019 ◽  
Vol 14 (6) ◽  
pp. 480-490 ◽  
Author(s):  
Tuncay Bayrak ◽  
Hasan Oğul
Keyword(s):  
Gene Expression ◽  
Regression Model ◽  
Collaborative Filtering ◽  
High Throughput Sequencing ◽  
Mean Squared Error ◽  
Experimental Studies ◽  
Kernel Functions ◽  
Feature Representation ◽  
Sequencing Analysis ◽  
Computational Systems Biology

Background: Predicting the value of gene expression in a given condition is a challenging topic in computational systems biology. Only a limited number of studies in this area have provided solutions to predict the expression in a particular pattern, whether or not it can be done effectively. However, the value of expression for the measurement is usually needed for further meta-data analysis. Methods: Because the problem is considered as a regression task where a feature representation of the gene under consideration is fed into a trained model to predict a continuous variable that refers to its exact expression level, we introduced a novel feature representation scheme to support work on such a task based on two-way collaborative filtering. At this point, our main argument is that the expressions of other genes in the current condition are as important as the expression of the current gene in other conditions. For regression analysis, linear regression and a recently popularized method, called Relevance Vector Machine (RVM), are used. Pearson and Spearman correlation coefficients and Root Mean Squared Error are used for evaluation. The effects of regression model type, RVM kernel functions, and parameters have been analysed in our study in a gene expression profiling data comprising a set of prostate cancer samples. Results: According to the findings of this study, in addition to promising results from the experimental studies, integrating data from another disease type, such as colon cancer in our case, can significantly improve the prediction performance of the regression model. Conclusion: The results also showed that the performed new feature representation approach and RVM regression model are promising for many machine learning problems in microarray and high throughput sequencing analysis.

The species evenness of “prey” bacteria correlated with Bdellovibrio-and-like-organisms (BALOs) in the microbial network supports the biomass of BALOs in a paddy soil

2020 ◽  
Vol 96 (12) ◽  
Author(s):  
Hang Qian ◽  
Chunli Hou ◽  
Hao Liao ◽  
Li Wang ◽  
Shun Han ◽  
...  
Keyword(s):  
Soil Properties ◽  
Paddy Soil ◽  
High Throughput Sequencing ◽  
Abiotic Factors ◽  
Negative Relationship ◽  
Sequencing Analysis ◽  
Nitrogen Use ◽  
Microbial Network ◽  
Α Diversity ◽  
Positive Effect

ABSTRACT To seek how soil biotic and abiotic factors which might shape the Bdellovibrio-and-like-organisms community, we sampled paddy soils under different fertilization treatments including fertilization without nitrogen (Control), the nitrogen use treatment (N) and the nitrogen overuse one (HNK) at three rice growing stages. The abundances of BALOs were impacted by the rice-growing stages but not the fertilization treatments. The abundances of Bdellovibrionaceae-like were positively associated with soil moisture, which showed a negative relationship with Bacteriovoracaceae-like bacteria. High-throughput sequencing analysis of the whole bacterial community revealed that the α-diversity of BALOs was not correlated with any soil properties data. Network analysis detected eight families directly linked to BALOs, namely, Pseudomonadaceae, Peptostreptococcaceae, Flavobacteriaceae, Sediment-4, Verrucomicrobiaceae, OM27, Solirubrobacteraceae and Roseiflexaceae. The richness and composition of OTUs in the eight families were correlated with different soil properties, while the evenness of them had a positive effect on the predicted BALO biomass. These results highlighted that the bottom-up control of BALOs in paddy soil at least partially relied on the changes of soil water content and the diversity of bacteria directly linked to BALOs in the microbial network.

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