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.

scholarly journals Gene Expression Analysis of Glioma Neural Stem Cells Shows Disturbed Amino Acids Metabolism and Axonal Growth Cone Dynamics in Glioblastoma Multiforme.

2021 ◽  
Author(s):  
Rutvi D Vaja
Keyword(s):  
Gene Expression ◽  
Stem Cells ◽  
Glioblastoma Multiforme ◽  
Neural Stem Cells ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
High Throughput Sequencing ◽  
Poor Quality ◽  
Sequencing Analysis ◽  
The Central Nervous System

Glioblastoma multiforme(GBM) is a group of fatal and aggressive tumors of the central nervous system. Despite advancements in the treatment of GBM, patients diagnosed with these tumors typically have a poor prognosis and poor quality of life as the disease develops. The single-cell RNA high-throughput sequencing processed data for Glioma cancer stem cells were taken from GEO and analyzed to find out the underlying expression differences at the gene level between glioma neural stem cells(GSCs) and Normal neural stem cells(NSCs). In the current study, we have performed an RNA-sequencing analysis between GSCs and NSCs to better understand the origin of GBM. We have performed bioinformatics analysis on the transcriptional profile of 134 samples which consisted of 75 GSCs and 59 NSCs obtained from the NCBI bio project(PRJNA546254). First, an exploratory analysis was performed which showed significant variation patterns between GSCs and NSCs. Subsequently, Deseq2 differential gene expression analysis identified 1436 differentially expressed genes between GSCs and NSCs[(padj. value <0.05, log2 fold change (>=+/-1.5)]. This study reveals genes like MAOA, MAOB, GATM, GLDC, AMT, and SHMT1 as the key features contributing to the disturbed processes of Glycine, threonine, and serine amino acid metabolism, axonal cone growth curve, and cell migration in Glioma. Conclusively, our study also depicts gene expression changes in amyloid beta-binding protein in between GSCs and NSCs which plays an important role in tumor microenvironment formation. Besides, the results presented here reveal new insight into the progression of GBM and the identification of novel genes involved in gliomagenesis.

Screening and Identification of Key Genes for the Survival and Multiplication of Salmonella typhimurium in the Host

2019 ◽  
Author(s):  
Na Sun ◽  
Yanying Song ◽  
Cong Liu ◽  
Yu Dai ◽  
Peng Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Salmonella Typhimurium ◽  
Mammalian Cells ◽  
High Throughput Sequencing ◽  
Bacterial Load ◽  
Sequencing Analysis ◽  
Rna Seq ◽  
Virulence Attenuation ◽  
Key Genes

Abstract BackgroundSalmonella typhimurium is an important intracellular pathogen that poses a health threat to humans. The key to studying the pathogenesis of Salmonella is to clarify the mechanisms responsible for its survival and reproduction in macrophages. In this study, RNA was extracted from S. typhimurium reference strain (ATCC 14028) and S. typhimurium isolated from the spleen of infected mice for RNA high-throughput sequencing analysis, based on the BGISEQ-500 platform.ResultsA total of 1,340 significant differentially expressed genes (DEGs) were screened through quantitative gene analysis and various analyses based on gene expression. Of these, 16 genes were randomly selected for fluorescence quantitative PCR verification. Two pairs of primers, 16S and pyridoxol 5ʹ-phosphate synthase (pdxJ), were used as internal references. The coincidence rate was determined to be 93.75%, which was consistent with the RNA-seq data, proving the reliability of the RNA-seq data. Functional annotation revealed DEGs associated with regulation, metabolism, transport and binding, pathogenesis, and motility. Through data mining and literature retrieval, 26 of the 58 upregulated DEGs (FPKM >10) were not reported to be related to the adaptation to intracellular survival, and were classified as candidate key genes (CKGs) for survival and proliferation in vivo. Among the CKGs, five were biotin synthetic bio family proteins. BioF is one of the first enzymes in the protein synthesis pathway. To evaluate the potential role of bioF in survival and proliferation, bioF mutants of Salmonella were constructed, and the virulence/attenuation was evaluated in vivo. Through the infection of BALB/c mice, bioF deficiency was found to significantly reduce the bacterial load and the fatality rate of mice. ConclusionsOur results indicated that the bioF gene plays an important role in the survival and proliferation of S. typhimurium in vivo. These data contribute to our understanding of the mechanisms used by Salmonella to regulate virulence gene expression whilst replicating inside mammalian cells.

scholarly journals Analyzing the Whole Transcriptome Profiles of ncRNAs and Predicting the Competing Endogenous RNA Networks in Cervical Cancer Cell Lines With Cisplatin Resistance

2021 ◽  
Author(s):  
Huimin Lv ◽  
Shanshan Jin ◽  
Binbin Zou ◽  
Yuxiang Liang ◽  
Jun Xie ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cervical Cancer ◽  
Reporter Gene ◽  
High Throughput Sequencing ◽  
Biological Function ◽  
Luciferase Reporter ◽  
Sequencing Analysis ◽  
Luciferase Reporter Gene ◽  
Siha Cells ◽  
Whole Transcriptome

Abstract Objective:: Cervical cancer (CC) is one of the most common malignant tumors in women. In order to identify the function between mRNA and non-coding RNA (ncRNA, including lncRNA,circRNA, miRNA) in CC DDP resistance, we analyzed its expression related to transcription profile and the RNA regulatory network between ncRNA and mRNA.Methods: In this study, whole transcriptome high-throughput sequencing (RNA-sequencing) analysis was used to study the ncRNA profiles of parental SiHA cells and DDP-resistant SiHA/DDP cells. Conducted gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and analyzed mRNAs (DE) with significant differences in expression. Then, based on the authoritative cytoscape software v.3.7.2, drug resistance-related genes and signal transduction pathways, a ceRNA network combining lncRNA and mRNA was predicted and constructed. In addition, a constructed ceRNA regulatory pathway was randomly selected, namely lnc-AC010198.2 / hsa-miR-34b-3p / STC2, and verified by real-time qPCR, dual luciferase reporter gene system, and RNA pull-down assay. After transfection with si-lnc-AC010198.2 and DDP resistance, the changes in gene expression and biological function in SiHA and SiHA/DDP cells were further analyzed.Results: Through bioinformatics and dual-luciferase reporter gene analysis, we found that lnc-AC010198.2 / miR-34b-3p / STC2 may be the mechanism by which SiHA / DDP cells are resistant to DDP compared to parent SiHA cells. After si-lnc-AC010198.2 is resistant to DDP after transfection, there are significant differences between SiHA/DDP and SiHA cells' downstream gene expression, the biological function of colony formation, invasion efficiency, and cell apoptosis.Conclusions: Our study may provide new markers and potential mechanisms for CC DDP resistance, and discover some novel targets for reversing it.

scholarly journals A Sparse and Low-Rank Regression Model for Identifying the Relationships Between DNA Methylation and Gene Expression Levels in Gastric Cancer and the Prediction of Prognosis

Genes ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 854
Author(s):  
Yishu Wang ◽  
Lingyun Xu ◽  
Dongmei Ai
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Dna Methylation ◽  
Regression Model ◽  
The Cancer Genome Atlas ◽  
Low Rank ◽  
Expression Levels ◽  
Rank Regression ◽  
Prediction Of Prognosis ◽  
Gene Expression Levels

DNA methylation is an important regulator of gene expression that can influence tumor heterogeneity and shows weak and varying expression levels among different genes. Gastric cancer (GC) is a highly heterogeneous cancer of the digestive system with a high mortality rate worldwide. The heterogeneous subtypes of GC lead to different prognoses. In this study, we explored the relationships between DNA methylation and gene expression levels by introducing a sparse low-rank regression model based on a GC dataset with 375 tumor samples and 32 normal samples from The Cancer Genome Atlas database. Differences in the DNA methylation levels and sites were found to be associated with differences in the expressed genes related to GC development. Overall, 29 methylation-driven genes were found to be related to the GC subtypes, and in the prognostic model, we explored five prognoses related to the methylation sites. Finally, based on a low-rank matrix, seven subgroups were identified with different methylation statuses. These specific classifications based on DNA methylation levels may help to account for heterogeneity and aid in personalized treatments.

scholarly journals Predicting Host Immune Cell Dynamics and Key Disease-Associated Genes Using Tissue Transcriptional Profiles

Processes ◽  
2019 ◽  
Vol 7 (5) ◽  
pp. 301
Author(s):  
Muying Wang ◽  
Satoshi Fukuyama ◽  
Yoshihiro Kawaoka ◽  
Jason E. Shoemaker
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Immune Cell ◽  
Mean Squared Error ◽  
Expression Profiles ◽  
Statistical Tests ◽  
Critical Factor ◽  
Expression Data ◽  
Cell Dynamics ◽  
Cell Counts

Motivation: Immune cell dynamics is a critical factor of disease-associated pathology (immunopathology) that also impacts the levels of mRNAs in diseased tissue. Deconvolution algorithms attempt to infer cell quantities in a tissue/organ sample based on gene expression profiles and are often evaluated using artificial, non-complex samples. Their accuracy on estimating cell counts given temporal tissue gene expression data remains not well characterized and has never been characterized when using diseased lung. Further, how to remove the effects of cell migration on transcript counts to improve discovery of disease factors is an open question. Results: Four cell count inference (i.e., deconvolution) tools are evaluated using microarray data from influenza-infected lung sampled at several time points post-infection. The analysis finds that inferred cell quantities are accurate only for select cell types and there is a tendency for algorithms to have a good relative fit (R 2 ) but a poor absolute fit (normalized mean squared error; NMSE), which suggests systemic biases exist. Nonetheless, using cell fraction estimates to adjust gene expression data, we show that genes associated with influenza virus replication and increased infection pathology are more likely to be identified as significant than when applying traditional statistical tests.

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.

scholarly journals High-Throughput Sequencing Analysis of the Actinobacterial Spatial Diversity in Moonmilk Deposits

Antibiotics ◽  
2018 ◽  
Vol 7 (2) ◽  
pp. 27 ◽  
Author(s):  
Marta Maciejewska ◽  
Magdalena Całusińska ◽  
Luc Cornet ◽  
Delphine Adam ◽  
Igor Pessi ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Spatial Diversity ◽  
Sequencing Analysis

Macrophage-derived CXCL9 and CXCL11, T-cell skin homing and disease control in mogamulizumab-treated CTCL patients

Blood ◽  
2021 ◽  
Author(s):  
Adèle de Masson ◽  
Delphine Darbord ◽  
Gabor Dobos ◽  
Marie Boisson ◽  
Marie Roelens ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
Disease Control ◽  
High Throughput Sequencing ◽  
Sequencing Analysis ◽  
Cell Clones ◽  
Skin Homing ◽  
T Cell Homing ◽  
T Cell Immune Responses

Cutaneous T-cell lymphoma (CTCL) is a malignancy of skin-homing T-cells. Long-term remissions are rare in CTCL, and the pathophysiology of long-lasting disease control is unknown. Mogamulizumab is a defucosylated anti-human CCR4 antibody that depletes CCR4-expressing CTCL tumor cells and peripheral blood memory regulatory T cells. Prolonged remissions and immune side effects have been observed in mogamulizumab-treated CTCL patients. We report that mogamulizumab induced skin rashes in 32% of 44 CTCL patients. These rashes were associated with long-term CTCL remission, even in the absence of specific CTCL treatment. CTCL patients with mogamulizumab-induced rash had significantly higher overall survival (hazard ratio, 0.16 (0.04-0.73, p=0.01)). Histopathology and immunohistochemistry of the rashes revealed granulomatous and lichenoid patterns with CD163 macrophagic and CD8 T-cell infiltrates. Depletion of skin CTCL cells was confirmed by high-throughput sequencing analysis of TCRβ genes and in blood by flow cytometry. New reactive T-cell clones were recruited in skin. Gene expression analysis showed overexpression of CXCL9 and CXCL11, two chemokines involved in CXCR3-expressing T-cell homing to skin. Single-cell RNA sequencing analysis in skin of CTCL patients confirmed that CXCL9 and CXCL11 were primarily macrophage-derived and that skin T-cells expressed CXCR3. Finally, patients with rashes had a significantly higher proportion of exhausted reactive blood T-cells expressing TIGIT and PD1 at baseline compared to patients without rash, which decreased under mogamulizumab treatment, consistent with an activation of the antitumor immunity. Together, these data suggest that mogamulizumab may induce long-term immune control in CTCL patients by activation of the macrophagic and T-cell immune responses.

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