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

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.

2020 ◽  
Author(s):  
Weicong Qi ◽  
Jia Liu ◽  
Dayong Zhang ◽  
Haiying Lu ◽  
Hongbo Shao ◽  
...  

Abstract Background: Salt tolerance is a key trait in soybean breeding and plant responses to salt stress include physiological and biochemical changes that affect the movement of water across the plasma membrane. In this study, we report the interactions of a set of aquaporins, soybean (Glycine max) plasma membrane-intrinsic proteins (GmPIPs), in response to salt stress. Results: GmPIP1;5 and GmPIP1;6 formed hetero-tetramers with GmPIP2;4, GmPIP2;6, GmPIP2;8, GmPIP2;9, GmPIP2;11, and GmPIP2;13. We detected interactions between GmPIP1;6 and GmPIP1;7, but not between GmPIP1;6 and GmPIP1;5. Furthermore, GmPIP2;9 formed homo-tetramers, and this interaction was strengthened under salt and osmotic stress. Expression analysis indicated complex and unique responses to salt stress depending on the duration of the stress. For example, GmPIP2;8, encoding one of the heteromer-forming PIP proteins, was highly up-regulated under early salt stress.Conclusions: Our study highlights the vital role of hetero- and homo-tetramers, in salt tolerance; and improves understanding of the mechanisms by which soybean aquaporin isoforms respond to abiotic stress.


2018 ◽  
Author(s):  
Qian Du ◽  
Malachy Campbell ◽  
Huihui Yu ◽  
Kan Liu ◽  
Harkamal Walia ◽  
...  

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.


2008 ◽  
Vol 5 (2) ◽  
Author(s):  
Li Teng ◽  
Laiwan Chan

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.


2018 ◽  
Vol 19 (11) ◽  
pp. 3412 ◽  
Author(s):  
Fenjuan Shao ◽  
Lisha Zhang ◽  
Iain Wilson ◽  
Deyou Qiu

Soil salinization is a matter of concern worldwide. It can eventually lead to the desertification of land and severely damage local agricultural production and the ecological environment. Betula halophila is a tree with high salt tolerance, so it is of importance to understand and discover the salt responsive genes of B. halophila for breeding salinity resistant varieties of trees. However, there is no report on the transcriptome in response to salt stress in B. halophila. Using Illumina sequencing platform, approximately 460 M raw reads were generated and assembled into 117,091 unigenes. Among these unigenes, 64,551 unigenes (55.12%) were annotated with gene descriptions, while the other 44.88% were unknown. 168 up-regulated genes and 351 down-regulated genes were identified, respectively. These Differentially Expressed Genes (DEGs) involved in multiple pathways including the Salt Overly Sensitive (SOS) pathway, ion transport and uptake, antioxidant enzyme, ABA signal pathway and so on. The gene ontology (GO) enrichments suggested that the DEGs were mainly involved in a plant-type cell wall organization biological process, cell wall cellular component, and structural constituent of cell wall molecular function. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment showed that the top-four enriched pathways were ‘Fatty acid elongation’, ‘Ribosome’, ‘Sphingolipid metabolism’ and ‘Flavonoid biosynthesis’. The expression patterns of sixteen DEGs were analyzed by qRT-PCR to verify the RNA-seq data. Among them, the transcription factor AT-Hook Motif Nuclear Localized gene and dehydrins might play an important role in response to salt stress in B. halophila. Our results provide an important gene resource to breed salt tolerant plants and useful information for further elucidation of the molecular mechanism of salt tolerance in B. halophila.


2005 ◽  
Vol 289 (4) ◽  
pp. L545-L553 ◽  
Author(s):  
Joseph Zabner ◽  
Todd E. Scheetz ◽  
Hakeem G. Almabrazi ◽  
Thomas L. Casavant ◽  
Jian Huang ◽  
...  

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.


2020 ◽  
Author(s):  
Alexander Calderwood ◽  
Jo Hepworth ◽  
Shannon Woodhouse ◽  
Lorelei Bilham ◽  
D. Marc Jones ◽  
...  

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.


2022 ◽  
Vol 12 ◽  
Author(s):  
Maria Cristina Della Lucia ◽  
Ali Baghdadi ◽  
Francesca Mangione ◽  
Matteo Borella ◽  
Walter Zegada-Lizarazu ◽  
...  

This work aimed to study the effects in tomato (Solanum lycopersicum L.) of foliar applications of a novel calcium-based biostimulant (SOB01) using an omics approach involving transcriptomics and physiological profiling. A calcium-chloride fertilizer (SOB02) was used as a product reference standard. Plants were grown under well-watered (WW) and water stress (WS) conditions in a growth chamber. We firstly compared the transcriptome profile of treated and untreated tomato plants using the software RStudio. Totally, 968 and 1,657 differentially expressed genes (DEGs) (adj-p-value &lt; 0.1 and |log2(fold change)| ≥ 1) were identified after SOB01 and SOB02 leaf treatments, respectively. Expression patterns of 9 DEGs involved in nutrient metabolism and osmotic stress tolerance were validated by real-time quantitative reverse transcription PCR (RT-qPCR) analysis. Principal component analysis (PCA) on RT-qPCR results highlighted that the gene expression profiles after SOB01 treatment in different water regimes were clustering together, suggesting that the expression pattern of the analyzed genes in well water and water stress plants was similar in the presence of SOB01 treatment. Physiological analyses demonstrated that the biostimulant application increased the photosynthetic rate and the chlorophyll content under water deficiency compared to the standard fertilizer and led to a higher yield in terms of fruit dry matter and a reduction in the number of cracked fruits. In conclusion, transcriptome and physiological profiling provided comprehensive information on the biostimulant effects highlighting that SOB01 applications improved the ability of the tomato plants to mitigate the negative effects of water stress.


Author(s):  
Ana M Mesa ◽  
Jiude Mao ◽  
Theresa I Medrano ◽  
Nathan J Bivens ◽  
Alexander Jurkevich ◽  
...  

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.


Author(s):  
Crescenzio Gallo

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.


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