scholarly journals Using LASSO in gene co-expression network for genome-wide identification of gene interactions responding to salt stress in rice

2018 ◽  
Author(s):  
Qian Du ◽  
Malachy Campbell ◽  
Huihui Yu ◽  
Kan Liu ◽  
Harkamal Walia ◽  
...  
Keyword(s):  
Gene Expression ◽  
Salt Stress ◽  
Variable Selection ◽  
Expression Profiles ◽  
Physiological Responses ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Gene Interactions ◽  
Lasso Regression ◽  
Network Analyses

AbstractIn many applications, such as gene co-expression network analyses, data arises with a huge number of covariates while the size of sample is comparatively small. To improve the accuracy of prediction, variable selection is often used to get a sparse solution by forcing coefficients of variables contributing less to the observed response variable to zero. Various algorithms were developed for variable selection, but LASSO is well known for its statistical accuracy, computational feasibility and broad applicability to adaptation. In this project, we applied LASSO to the gene co-expression network of rice with salt stress to discover key gene interactions for salt-tolerance related phenotypes. The dataset we have is a high-dimensional one, having 50K genes from 100 samples, with the issue of multicollinearity for fitting linear regression - the expression level of genes in the same pathway tends to be highly correlated. The property of LASSO with sparse parameters is naturally suitable to identify gene interactions of interest in this dataset. After biologically functional modules in the co-expression network was identified, the major changed expression patterns were further selected by LASSO regression to establish a linear relationship between gene expression profiles and physiological responses, such as sodium/potassium condenses, with salt stress. Five modules of intensively co-expressed genes, from 45 to 291 genes, were identified by our method with significant P-values, which indicate these modules are significantly associated with physiological responses to stress. Genes in these modules have functions related to ion transport, osmotic adjustment, and oxidative tolerance. For example, LOC_Os7g47350 and LOC_Os07g37320 are co-expressed gene in the same module 15. Both are ion transporter genes and have higher gene expression levels for rice with low sodium levels with salt stress.

scholarly journals Characterization of Interactions between the Soybean Salt-Stress Responsive Membrane-Intrinsic Proteins GmPIP1 and GmPIP2

Agronomy ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1312
Author(s):  
Jia Liu ◽  
Weicong Qi ◽  
Haiying Lu ◽  
Hongbo Shao ◽  
Dayong Zhang
Keyword(s):  
Plasma Membrane ◽  
Salt Stress ◽  
Salt Tolerance ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Early Gene ◽  
Plant Responses ◽  
Constitutive Overexpression ◽  
Important Trait

Salt tolerance is an important trait in soybean cultivation and breeding. Plant responses to salt stress include physiological and biochemical changes that affect the movement of water across the plasma membrane. Plasma membrane intrinsic proteins (PIPs) localize to the plasma membrane and regulate the water and solutes flow. In this study, quantitative real-time PCR and yeast two-hybridization were engaged to analyze the early gene expression profiles and interactions of a set of soybean PIPs (GmPIPs) in response to salt stress. A total of 20 GmPIPs-encoding genes had varied expression profiles after salt stress. Among them, 13 genes exhibited a downregulated expression pattern, including GmPIP1;6, the constitutive overexpression of which could improve soybean salt tolerance, and its close homologs GmPIP1;7 and 1;5. Three genes showed upregulated patterns, including the GmPIP1;6 close homolog GmPIP1;4, when four genes with earlier increased and then decreased expression patterns. GmPIP1;5 and GmPIP1;6 could both physically interact strongly with GmPIP2;2, GmPIP2;4, GmPIP2;6, GmPIP2;8, GmPIP2;9, GmPIP2;11, and GmPIP2;13. Definite interactions between GmPIP1;6 and GmPIP1;7 were detected and GmPIP2;9 performed homo-interaction. The interactions of GmPIP1;5 with GmPIP2;11 and 2;13, GmPIP1;6 with GmPIP2;9, 2;11 and GmPIP2;13, and GmPIP2;9 with itself were strengthened upon salt stress rather than osmotic stress. Taken together, we inferred that GmPIP1 type and GmPIP2 type could associate with each other to synergistically function in the plant cell; a salt-stress environment could promote part of their interactions. This result provided new clues to further understand the soybean PIP–isoform interactions, which lead to potentially functional homo- and heterotetramers for salt tolerance.

scholarly journals Discovering Distinct Patterns in Gene Expression Profiles

2008 ◽  
Vol 5 (2) ◽  
Author(s):  
Li Teng ◽  
Laiwan Chan
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Clustering Methods ◽  
Gene Expressions ◽  
Real Gene ◽  
Large Scale Dataset ◽  
Coexpressed Genes

SummaryTraditional analysis of gene expression profiles use clustering to find groups of coexpressed genes which have similar expression patterns. However clustering is time consuming and could be diffcult for very large scale dataset. We proposed the idea of Discovering Distinct Patterns (DDP) in gene expression profiles. Since patterns showing by the gene expressions reveal their regulate mechanisms. It is significant to find all different patterns existing in the dataset when there is little prior knowledge. It is also a helpful start before taking on further analysis. We propose an algorithm for DDP by iteratively picking out pairs of gene expression patterns which have the largest dissimilarities. This method can also be used as preprocessing to initialize centers for clustering methods, like K-means. Experiments on both synthetic dataset and real gene expression datasets show our method is very effective in finding distinct patterns which have gene functional significance and is also effcient.

CFTR ΔF508 mutation has minimal effect on the gene expression profile of differentiated human airway epithelia

2005 ◽  
Vol 289 (4) ◽  
pp. L545-L553 ◽  
Author(s):  
Joseph Zabner ◽  
Todd E. Scheetz ◽  
Hakeem G. Almabrazi ◽  
Thomas L. Casavant ◽  
Jian Huang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cystic Fibrosis ◽  
Large Scale ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Primary Cultures ◽  
Gene Expression Profiles ◽  
Filter Method ◽  
Tissue Destruction ◽  
Airway Epithelia

Cystic fibrosis (CF) is caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR), an epithelial chloride channel regulated by phosphorylation. Most of the disease-associated morbidity is the consequence of chronic lung infection with progressive tissue destruction. As an approach to investigate the cellular effects of CFTR mutations, we used large-scale microarray hybridization to contrast the gene expression profiles of well-differentiated primary cultures of human CF and non-CF airway epithelia grown under resting culture conditions. We surveyed the expression profiles for 10 non-CF and 10 ΔF508 homozygote samples. Of the 22,283 genes represented on the Affymetrix U133A GeneChip, we found evidence of significant changes in expression in 24 genes by two-sample t-test ( P < 0.00001). A second, three-filter method of comparative analysis found no significant differences between the groups. The levels of CFTR mRNA were comparable in both groups. There were no significant differences in the gene expression patterns between male and female CF specimens. There were 18 genes with significant increases and 6 genes with decreases in CF relative to non-CF samples. Although the function of many of the differentially expressed genes is unknown, one transcript that was elevated in CF, the KCl cotransporter (KCC4), is a candidate for further study. Overall, the results indicate that CFTR dysfunction has little direct impact on airway epithelial gene expression in samples grown under these conditions.

scholarly journals Comparative transcriptomics identifies differences in the regulation of the floral transition between Arabidopsis and Brassica rapa cultivars

2020 ◽  
Author(s):  
Alexander Calderwood ◽  
Jo Hepworth ◽  
Shannon Woodhouse ◽  
Lorelei Bilham ◽  
D. Marc Jones ◽  
...  
Keyword(s):  
Gene Expression ◽  
Brassica Rapa ◽  
Regulatory Networks ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Detailed Comparison ◽  
Developmental Time ◽  
Floral Transition ◽  
Gene Regulatory

AbstractThe timing of the floral transition affects reproduction and yield, however its regulation in crops remains poorly understood. Here, we use RNA-Seq to determine and compare gene expression dynamics through the floral transition in the model species Arabidopsis thaliana and the closely related crop Brassica rapa. A direct comparison of gene expression over time between species shows little similarity, which could lead to the inference that different gene regulatory networks are at play. However, these differences can be largely resolved by synchronisation, through curve registration, of gene expression profiles. We find that different registration functions are required for different genes, indicating that there is no common ‘developmental time’ to which Arabidopsis and B. rapa can be mapped through gene expression. Instead, the expression patterns of different genes progress at different rates. We find that co-regulated genes show similar changes in synchronisation between species, suggesting that similar gene regulatory sub-network structures may be active with different wiring between them. A detailed comparison of the regulation of the floral transition between Arabidopsis and B. rapa, and between two B. rapa accessions reveals different modes of regulation of the key floral integrator SOC1, and that the floral transition in the B. rapa accessions is triggered by different pathways, even when grown under the same environmental conditions. Our study adds to the mechanistic understanding of the regulatory network of flowering time in rapid cycling B. rapa under long days and highlights the importance of registration methods for the comparison of developmental gene expression data.

Spatial Transcriptomics analysis of uterine gene expression in enhancer of Zeste homolog 2 (Ezh2) conditional knockout mice

2021 ◽  
Author(s):  
Ana M Mesa ◽  
Jiude Mao ◽  
Theresa I Medrano ◽  
Nathan J Bivens ◽  
Alexander Jurkevich ◽  
...  
Keyword(s):  
Gene Expression ◽  
Epithelial Cells ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Reproductive Organs ◽  
Conditional Knockout ◽  
Estrogen Signaling ◽  
Uterine Epithelial Cell ◽  
Stromal Tissue

Abstract Histone proteins undergo various modifications that alter chromatin structure, including addition of methyl groups. Enhancer of homolog 2 (EZH2), is a histone methyltransferase that methylates lysine residue 27, and thereby, suppresses gene expression. EZH2 plays integral role in the uterus and other reproductive organs. We have previously shown that conditional deletion of uterine EZH2 results in increased proliferation of luminal and glandular epithelial cells, and RNAseq analyses reveal several uterine transcriptomic changes in Ezh2 conditional (c) knockout (KO) mice that can affect estrogen signaling pathways. To pinpoint the origin of such gene expression changes, we used the recently developed spatial transcriptomics (ST) method with the hypotheses that Ezh2cKO mice would predominantly demonstrate changes in epithelial cells and/or ablation of this gene would disrupt normal epithelial/stromal gene expression patterns. Uteri were collected from ovariectomized adult WT and Ezh2cKO mice and analyzed by ST. Asb4, Cxcl14, Dio2, and Igfbp5 were increased, Sult1d1, Mt3, and Lcn2 were reduced in Ezh2cKO uterine epithelium vs. WT epithelium. For Ezh2cKO uterine stroma, differentially expressed key hub genes included Cald1, Fbln1, Myh11, Acta2, and Tagln. Conditional loss of uterine Ezh2 also appears to shift the balance of gene expression profiles in epithelial vs. stromal tissue toward uterine epithelial cell and gland development and proliferation, consistent with uterine gland hyperplasia in these mice. Current findings provide further insight into how EZH2 may selectively affect uterine epithelial and stromal compartments. Additionally, these transcriptome data might provide the mechanistic understanding and valuable biomarkers for human endometrial disorders with epigenetic underpinnings.

Building Gene Networks by Analyzing Gene Expression Profiles

2019 ◽  
pp. 27-44
Author(s):  
Crescenzio Gallo
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Gene Networks ◽  
Dna Microarrays ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Expression Data ◽  
Gene Expressions ◽  
Over Time

The possible applications of modeling and simulation in the field of bioinformatics are very extensive, ranging from understanding basic metabolic paths to exploring genetic variability. Experimental results carried out with DNA microarrays allow researchers to measure expression levels for thousands of genes simultaneously, across different conditions and over time. A key step in the analysis of gene expression data is the detection of groups of genes that manifest similar expression patterns. In this chapter, the authors examine various methods for analyzing gene expression data, addressing the important topics of (1) selecting the most differentially expressed genes, (2) grouping them by means of their relationships, and (3) classifying samples based on gene expressions.

scholarly journals Genome-Wide Gene Expression Profiles Analysis Reveal Novel Insights into Drought Stress in Foxtail Millet (Setaria italica L.)

2020 ◽  
Vol 21 (22) ◽  
pp. 8520
Author(s):  
Ling Qin ◽  
Erying Chen ◽  
Feifei Li ◽  
Xiao Yu ◽  
Zhenyu Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Signal Transduction ◽  
Drought Stress ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Foxtail Millet ◽  
Gene Expression Profiles ◽  
Sugar Metabolism ◽  
Setaria Italica ◽  
Phenylpropanoid Biosynthesis

Foxtail millet (Setaria italica (L.) P. Beauv) is an important food and forage crop because of its health benefits and adaptation to drought stress; however, reports of transcriptomic analysis of genes responding to re-watering after drought stress in foxtail millet are rare. The present study evaluated physiological parameters, such as proline content, p5cs enzyme activity, anti-oxidation enzyme activities, and investigated gene expression patterns using RNA sequencing of the drought-tolerant foxtail millet variety (Jigu 16) treated with drought stress and rehydration. The results indicated that drought stress-responsive genes were related to many multiple metabolic processes, such as photosynthesis, signal transduction, phenylpropanoid biosynthesis, starch and sucrose metabolism, and osmotic adjustment. Furthermore, the Δ1-pyrroline-5-carboxylate synthetase genes, SiP5CS1 and SiP5CS2, were remarkably upregulated in foxtail millet under drought stress conditions. Foxtail millet can also recover well on rehydration after drought stress through gene regulation. Our data demonstrate that recovery on rehydration primarily involves proline metabolism, sugar metabolism, hormone signal transduction, water transport, and detoxification, plus reversal of the expression direction of most drought-responsive genes. Our results provided a detailed description of the comparative transcriptome response of foxtail millet variety Jigu 16 under drought and rehydration environments. Furthermore, we identify SiP5CS2 as an important gene likely involved in the drought tolerance of foxtail millet.

scholarly journals RNAseq Studies Reveal Distinct Transcriptional Response to Vitamin a (VA) Deficiency in Small Intestine (SI) versus Colon, Discovering Novel VA-regulated Genes (P15-002-19)

2019 ◽  
Vol 3 (Supplement_1) ◽  
Author(s):  
Zhi Chai ◽  
Yafei Lyu ◽  
Qiuyan Chen ◽  
Cheng-Hsin Wei ◽  
Lindsay Snyder ◽  
...  
Keyword(s):  
Gene Expression ◽  
Small Intestine ◽  
Vitamin A ◽  
Target Genes ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Differentially Expressed Gene ◽  
Gene Expression Profiles ◽  
Illumina Hiseq ◽  
The Impact

Abstract Objectives To characterize and compare the impact of vitamin A (VA) deficiency on gene expression patterns in the small intestine (SI) and the colon, and to discover novel target genes in VA-related biological pathways. Methods vitamin A deficient (VAD) mice were generated by feeding VAD diet to pregnant C57/BL6 dams and their post-weaning offspring. Total mRNA extracted from SI and colon were sequenced using Illumina HiSeq 2500 platform. Differentially Expressed Gene (DEG), Gene Ontology (GO) enrichment, and Weighted Gene Co-expression Network Analysis (WGCNA) were performed to characterize expression patterns and co-expression patterns. Results The comparison between vitamin A sufficient (VAS) and VAD groups detected 49 and 94 DEGs in SI and colon, respectively. According to GO information, DEGs in the SI demonstrated significant enrichment in categories relevant to retinoid metabolic process, molecule binding, and immune function. Immunity related pathways, such as “humoral immune response” and “complement activation,” were positively associated with VA in SI. On the contrary, in colon, “cell division” was the only enriched category and was negatively associated with VA. WGCNA identified modules significantly correlated with VA status in SI and in colon. One of those modules contained five known retinoic acid targets. Therefore we have prioritized the other module members (e.g., Mbl2, Mmp9, Mmp13, Cxcl14 and Pkd1l2) to be investigated as candidate genes regulated by VA. Comparison of co-expression modules between SI and colon indicated distinct VA effects on these two organs. Conclusions The results show that VA deficiency alters the gene expression profiles in SI and colon quite differently. Some immune-related genes (Mbl2, Mmp9, Mmp13, Cxcl14 and Pkd1l2) may be novel targets under the control of VA in SI. Funding Sources NIH training grant and NIH research grant. Supporting Tables, Images and/or Graphs

scholarly journals RankerGUI: A Computational Framework to Compare Differential Gene Expression Profiles Using Rank Based Statistics

2019 ◽  
Vol 20 (23) ◽  
pp. 6098 ◽  
Author(s):  
Amarinder Singh Thind ◽  
Kumar Parijat Tripathi ◽  
Mario Rosario Guarracino
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Differential Gene Expression ◽  
Web Application ◽  
Cellular Response ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Experimental Conditions ◽  
Differential Gene

The comparison of high throughput gene expression datasets obtained from different experimental conditions is a challenging task. It provides an opportunity to explore the cellular response to various biological events such as disease, environmental conditions, and drugs. There is a need for tools that allow the integration and analysis of such data. We developed the “RankerGUI pipeline”, a user-friendly web application for the biological community. It allows users to use various rank based statistical approaches for the comparison of full differential gene expression profiles between the same or different biological states obtained from different sources. The pipeline modules are an integration of various open-source packages, a few of which are modified for extended functionality. The main modules include rank rank hypergeometric overlap, enriched rank rank hypergeometric overlap and distance calculations. Additionally, preprocessing steps such as merging differential expression profiles of multiple independent studies can be added before running the main modules. Output plots show the strength, pattern, and trends among complete differential expression profiles. In this paper, we describe the various modules and functionalities of the developed pipeline. We also present a case study that demonstrates how the pipeline can be used for the comparison of differential expression profiles obtained from multiple platforms’ data of the Gene Expression Omnibus. Using these comparisons, we investigate gene expression patterns in kidney and lung cancers.

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