scholarly journals GEsture: an online hand-drawing tool for gene expression pattern search

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e4927 ◽  
Author(s):  
Chunyan Wang ◽  
Yiqing Xu ◽  
Xuelin Wang ◽  
Li Zhang ◽  
Suyun Wei ◽  
...  
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Expression Pattern ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Pattern Search ◽  
Clustering Methods ◽  
Specific Expression ◽  
Time Series Gene Expression ◽  
Specific Expression Pattern

Gene expression profiling data provide useful information for the investigation of biological function and process. However, identifying a specific expression pattern from extensive time series gene expression data is not an easy task. Clustering, a popular method, is often used to classify similar expression genes, however, genes with a ‘desirable’ or ‘user-defined’ pattern cannot be efficiently detected by clustering methods. To address these limitations, we developed an online tool called GEsture. Users can draw, or graph a curve using a mouse instead of inputting abstract parameters of clustering methods. GEsture explores genes showing similar, opposite and time-delay expression patterns with a gene expression curve as input from time series datasets. We presented three examples that illustrate the capacity of GEsture in gene hunting while following users’ requirements. GEsture also provides visualization tools (such as expression pattern figure, heat map and correlation network) to display the searching results. The result outputs may provide useful information for researchers to understand the targets, function and biological processes of the involved genes.

scholarly journals RNA-Seq Analyses Identify Additivity as the Predominant Gene Expression Pattern in F1 Chicken Embryonic Brain and Liver

Genes ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 27 ◽  
Author(s):  
Zhu Zhuo ◽  
Susan J. Lamont ◽  
Behnam Abasht
Keyword(s):  
Gene Expression ◽  
Expression Pattern ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Pairwise Comparison ◽  
Intermediate Phenotype ◽  
Superior Performance ◽  
Rna Seq ◽  
Specific Expression ◽  
Parental Lines

The superior performance of hybrids to parents, termed heterosis, has been widely utilized in animal and plant breeding programs, but the molecular mechanism underlying heterosis remains an enigma. RNA-Seq provides a novel way to investigate heterosis at the transcriptome-wide level, because gene expression functions as an intermediate phenotype that contributes to observable traits. Here we compared embryonic gene expression between chicken hybrids and their inbred parental lines to identify inheritance patterns of gene expression. Inbred Fayoumi and Leghorn were crossed reciprocally to obtain F1 fertile eggs. RNA-Seq was carried out using 24 brain and liver samples taken from day 12 embryos, and the differentially expressed (DE) genes were identified by pairwise comparison among the hybrids, parental lines, and mid-parent expression values. Our results indicated the expression levels of the majority of the genes in the F1 cross are not significantly different from the mid-parental values, suggesting additivity as the predominant gene expression pattern in the F1. The second and third prevalent gene expression patterns are dominance and over-dominance. Additionally, we found only 7–20% of the DE genes exhibit allele-specific expression in the F1, suggesting that trans regulation is the main driver for differential gene expression and thus contributes to heterosis effect in the F1 crosses.

scholarly journals Verticillium wilt resistant and susceptible olive cultivars express a very different basal set of genes in roots

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Jorge A. Ramírez-Tejero ◽  
Jaime Jiménez-Ruiz ◽  
Alicia Serrano ◽  
Angjelina Belaj ◽  
Lorenzo León ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Pattern ◽  
Growth And Development ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Expression Level ◽  
Resistant Cultivars ◽  
Pathogen Invasion ◽  
Wide Range ◽  
Olive Cultivars

Abstract Background Olive orchards are threatened by a wide range of pathogens. Of these, Verticillium dahliae has been in the spotlight for its high incidence, the difficulty to control it and the few cultivars that has increased tolerance to the pathogen. Disease resistance not only depends on detection of pathogen invasion and induction of responses by the plant, but also on barriers to avoid the invasion and active resistance mechanisms constitutively expressed in the absence of the pathogen. In a previous work we found that two healthy non-infected plants from cultivars that differ in V. dahliae resistance such as ‘Frantoio’ (resistant) and ‘Picual’ (susceptible) had a different root morphology and gene expression pattern. In this work, we have addressed the issue of basal differences in the roots between Resistant and Susceptible cultivars. Results The gene expression pattern of roots from 29 olive cultivars with different degree of resistance/susceptibility to V. dahliae was analyzed by RNA-Seq. However, only the Highly Resistant and Extremely Susceptible cultivars showed significant differences in gene expression among various groups of cultivars. A set of 421 genes showing an inverse differential expression level between the Highly Resistant to Extremely Susceptible cultivars was found and analyzed. The main differences involved higher expression of a series of transcription factors and genes involved in processes of molecules importation to nucleus, plant defense genes and lower expression of root growth and development genes in Highly Resistant cultivars, while a reverse pattern in Moderately Susceptible and more pronounced in Extremely Susceptible cultivars were observed. Conclusion According to the different gene expression patterns, it seems that the roots of the Extremely Susceptible cultivars focus more on growth and development, while some other functions, such as defense against pathogens, have a higher expression level in roots of Highly Resistant cultivars. Therefore, it seems that there are constitutive differences in the roots between Resistant and Susceptible cultivars, and that susceptible roots seem to provide a more suitable environment for the pathogen than the resistant ones.

scholarly journals Tissue Distribution of ACE2 Protein in Syrian Golden Hamster (Mesocricetus auratus) and Its Possible Implications in SARS-CoV-2 Related Studies

2021 ◽  
Vol 11 ◽  
Author(s):  
Voddu Suresh ◽  
Deepti Parida ◽  
Aliva P. Minz ◽  
Manisha Sethi ◽  
Bhabani S. Sahoo ◽  
...  
Keyword(s):  
Expression Pattern ◽  
Golden Hamster ◽  
Expression Patterns ◽  
Mesocricetus Auratus ◽  
Syrian Golden Hamster ◽  
Surface Epithelium ◽  
Specific Expression ◽  
Tissue Specific ◽  
Tissue Specific Expression ◽  
Specific Expression Pattern

The Syrian golden hamster (Mesocricetus auratus) has recently been demonstrated as a clinically relevant animal model for SARS-CoV-2 infection. However, lack of knowledge about the tissue-specific expression pattern of various proteins in these animals and the unavailability of reagents like antibodies against this species hampers these models’ optimal use. The major objective of our current study was to analyze the tissue-specific expression pattern of angiotensin-converting enzyme 2, a proven functional receptor for SARS-CoV-2 in different organs of the hamster. Using two different antibodies (MA5-32307 and AF933), we have conducted immunoblotting, immunohistochemistry, and immunofluorescence analysis to evaluate the ACE2 expression in different tissues of the hamster. Further, at the mRNA level, the expression of Ace2 in tissues was evaluated through RT-qPCR analysis. Both the antibodies detected expression of ACE2 in kidney, small intestine, tongue, and liver. Epithelium of proximal tubules of kidney and surface epithelium of ileum expresses a very high amount of this protein. Surprisingly, analysis of stained tissue sections showed no detectable expression of ACE2 in the lung or tracheal epithelial cells. Similarly, all parts of the large intestine were negative for ACE2 expression. Analysis of tissues from different age groups and sex didn’t show any obvious difference in ACE2 expression pattern or level. Together, our findings corroborate some of the earlier reports related to ACE2 expression patterns in human tissues and contradict others. We believe that this study’s findings have provided evidence that demands further investigation to understand the predominant respiratory pathology of SARS-CoV-2 infection and disease.

Biological Image Analysis via Matrix Approximation

2011 ◽  
pp. 166-170
Author(s):  
Jieping Ye ◽  
Ravi Janardan ◽  
Sudhir Kumar
Keyword(s):  
Gene Expression ◽  
Expression Pattern ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Controlled Vocabulary ◽  
Gene Expression Patterns ◽  
Image Content ◽  
Gene Expressions ◽  
Microarray Gene Expression ◽  
Individual Gene

Understanding the roles of genes and their interactions is one of the central challenges in genome research. One popular approach is based on the analysis of microarray gene expression data (Golub et al., 1999; White, et al., 1999; Oshlack et al., 2007). By their very nature, these data often do not capture spatial patterns of individual gene expressions, which is accomplished by direct visualization of the presence or absence of gene products (mRNA or protein) (e.g., Tomancak et al., 2002; Christiansen et al., 2006). For instance, the gene expression pattern images of a Drosophila melanogaster embryo capture the spatial and temporal distribution of gene expression patterns at a given developmental stage (Bownes, 1975; Tsai et al., 1998; Myasnikova et al., 2002; Harmon et al., 2007). The identification of genes showing spatial overlaps in their expression patterns is fundamentally important to formulating and testing gene interaction hypotheses (Kumar et al., 2002; Tomancak et al., 2002; Gurunathan et al., 2004; Peng & Myers, 2004; Pan et al., 2006). Recent high-throughput experiments of Drosophila have produced over fifty thousand images (http://www. fruitfly.org/cgi-bin/ex/insitu.pl). It is thus desirable to design efficient computational approaches that can automatically retrieve images with overlapping expression patterns. There are two primary ways of accomplishing this task. In one approach, gene expression patterns are described using a controlled vocabulary, and images containing overlapping patterns are found based on the similarity of textual annotations. In the second approach, the most similar expression patterns are identified by a direct comparison of image content, emulating the visual inspection carried out by biologists [(Kumar et al., 2002); see also www.flyexpress.net]. The direct comparison of image content is expected to be complementary to, and more powerful than, the controlled vocabulary approach, because it is unlikely that all attributes of an expression pattern can be completely captured via textual descriptions. Hence, to facilitate the efficient and widespread use of such datasets, there is a significant need for sophisticated, high-performance, informatics-based solutions for the analysis of large collections of biological images.

Acute Myeloid Leukemia with Translocation t(8;16) Demonstrates Specific Cytomorphological, Cytogenetic, and Gene Expression Characteristics and Can Clearly Be Discriminated from Other AML with Balanced Translocations.

Blood ◽  
2004 ◽  
Vol 104 (11) ◽  
pp. 2897-2897
Author(s):  
Torsten Haferlach ◽  
Helmut Loeffler ◽  
Alexander Kohlmann ◽  
Martin Dugas ◽  
Wolfgang Hiddemann ◽  
...  
Keyword(s):  
Gene Expression ◽  
Who Classification ◽  
De Novo ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Support Vector ◽  
Gene Expression Patterns ◽  
Down Regulation ◽  
Specific Expression ◽  
Balanced Translocations

Abstract Balanced chromosomal rearrangements leading to fusion genes on the molecular level define distinct biological subsets in AML. The four balanced rearrangements (t(15;17), t(8;21), inv(16), and 11q23/MLL) show a close correlation to cytomorphology and gene expression patterns. We here focused on seven AML with t(8;16)(p11;p13). This translocation is rare (7/3515 cases in own cohort). It is more frequently found in therapy-related AML than in de novo AML (3/258 t-AML, and 4/3287 de novo, p=0.0003). Cytomorphologically, AML with t(8;16) is characterized by striking features: In all 7 cases the positivity for myeloperoxidase on bone marrow smears was >70% and intriguingly, in parallel >80% of blast cells stained strongly positive for non-specific esterase (NSE) in all cases. Thus, these cases can not be classified according to FAB categories. These data suggest that AML-t(8;16) arise from a very early stem cell with both myeloid and monoblastic potential. Furthermore, we detected erythrophagocytosis in 6/7 cases that was described as specific feature in AML with t(8;16). Four pts. had chromosomal aberrations in addition to t(8;16), 3 of these were t-AML all showing aberrations of 7q. Survival was poor with 0, 1, 1, 2, 20 and 18+ (after alloBMT) mo., one lost to follow-up, respectively. We then analyzed gene expression patterns in 4 cases (Affymetrix U133A+B). First we compared t(8;16) AML with 46 AML FAB M1, 41 M4, 9 M5a, and 16 M5b, all with normal karyotype. Hierachical clustering and principal component analyses (PCA) revealed that t(8;16) AML were intercalating with FAB M4 and M5b and did not cluster near to M1. Thus, monocytic characteristics influence the gene expression pattern stronger than myeloid. Next we compared the t(8;16) AML with the 4 other balanced subtypes according to the WHO classification (t(15;17): 43; t(8;21): 40; inv(16): 49; 11q23/MLL-rearrangements: 50). Using support vector machines the overall accuracy for correct subgroup assignment was 97.3% (10-fold CV), and 96.8% (2/3 training and 1/3 test set, 100 runs). In PCA and hierarchical cluster analysis the t(8;16) were grouped in the vicinity of the 11q23 cases. However, in a pairwise comparison these two subgroups could be discriminated with an accuracy of 94.4% (10-fold CV). Genes with a specific expression in AML-t(8;16) were further investigated in pathway analyses (Ingenuity). 15 of the top 100 genes associated with AML-t(8;16) were involved in the CMYC-pathway with up regulation of BCOR, COXB5, CDK10, FLI1, HNRPA2B1, NSEP1, PDIP38, RAD50, SUPT5H, TLR2 and USP33, and down regulation of ERG, GATA2, NCOR2 and RPS20. CEBP beta, known to play a role in myelomonocytic differentiation, was also up-regulated in t(8;16)-AML. Ten additional genes out of the 100 top differentially expressed genes were also involved in this pathway with up-regulation of DDB2, HIST1H3D, NSAP1, PTPNS1, RAN, USP4, TRIM8, ZNF278 and down regulation of KIT and MBD2. In conclusion, AML with t(8;16) is a specific subtype of AML with unique characteristics in morphology and gene expression patterns. It is more frequently found in t-AML, outcome is inferior in comparison to other AML with balanced translocations. Due to its unique features, it is a candidate for inclusion into the WHO classification as a specific entity.

Follistatin alters myostatin gene expression in C2C12 muscle cells

2004 ◽  
Vol 52 (2) ◽  
pp. 135-141 ◽  
Author(s):  
H. Kocams¸ ◽  
N. Gulmez ◽  
S. Aslan ◽  
M. Nazlı
Keyword(s):  
Gene Expression ◽  
Expression Pattern ◽  
Time Course ◽  
Expression Patterns ◽  
Mrna Level ◽  
Gene Expression Pattern ◽  
Muscle Cells ◽  
Treated Group ◽  
Myostatin Gene ◽  
Significant Difference

The objective of the present study was to determine the effects of follistatin addition on myostatin and follistatin gene expression patterns in C2C12 muscle cells. C2C12 cells were administered with 100 ng/ml recombinant human (rh) follistatin in Dulbecco's modified Eagle medium (DMEM) containing 10% fetal bovine serum (FBS), 4 mM glutamine and antibiotics daily for three days. Rh follistatin was not added in the control wells. Follistatin and myostatin gene cDNAs were synthesised by reverse transcriptase polymerase chain reaction (RT-PCR).The time course of follistatin gene expression pattern was similar in both the control and the follistatin-treated group. Myostatin mRNA level significantly increased in the follistatin-treated group after 24 h of culture (Fig. 3, P < 0.01). Amounts then sharply decreased (Fig. 3, P < 0.01) at 48 h of culture, whereas there was no significant difference between the control and the follistatin-treated group at 72 h of culture. Our results demonstrated that myostatin and follistatin mRNA were expressed in C2C12 cells and rh follistatin changed the myostatin expression pattern.

scholarly journals Selection and Validation of Reference Genes for RT-qPCR Analysis in Spinacia oleracea under Abiotic Stress

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Hao Xie ◽  
Bo Li ◽  
Yu Chang ◽  
Xiaoyan Hou ◽  
Yue Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Pattern ◽  
Reference Genes ◽  
18S Rrna ◽  
Spinacia Oleracea ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Housekeeping Genes ◽  
Qpcr Analysis ◽  
The Stability

Reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) is an accurate and convenient method for mRNA quantification. Selection of optimal reference gene(s) is an important step in RT-qPCR experiments. However, the stability of housekeeping genes in spinach (Spinacia oleracea) under various abiotic stresses is unclear. Evaluating the stability of candidate genes and determining the optimal gene(s) for normalization of gene expression in spinach are necessary to investigate the gene expression patterns during development and stress response. In this study, ten housekeeping genes, 18S ribosomal RNA (18S rRNA), actin, ADP ribosylation factor (ARF), cytochrome c oxidase subunit 5C (COX), cyclophilin (CYP), elongation factor 1-alpha (EF1α), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), histone H3 (H3), 50S ribosomal protein L2 (RPL2), and tubulin alpha chain (TUBα) from spinach, were selected as candidates in roots, stems, leaves, flowers, and seedlings in response to high temperature, CdCl2, NaCl, NaHCO3, and Na2CO3 stresses. The expression of these genes was quantified by RT-qPCR and evaluated by NormFinder, BestKeeper, and geNorm. 18S rRNA, actin, ARF, COX, CYP, EF1α, GAPDH, H3, and RPL2 were detected as optimal reference genes for gene expression analysis of different organs and stress responses. The results were further confirmed by the expression pattern normalized with different reference genes of two heat-responsive genes. Here, we optimized the detection method of the gene expression pattern in spinach. Our results provide the optimal candidate reference genes which were crucial for RT-qPCR analysis.

scholarly journals Transcriptomic Analysis Identifies Gene Networks Regulated by Estrogen Receptor α (ERα) and ERβ that Control Distinct Effects of Different Botanical Estrogens

2014 ◽  
Vol 12 (1) ◽  
pp. nrs.12001 ◽  
Author(s):  
Ping Gong ◽  
Zeynep Madak-Erdogan ◽  
Jilong Li ◽  
Jianlin Cheng ◽  
C Michael Greenlief ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Pattern ◽  
Gene Networks ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Licorice Root ◽  
Target Cells ◽  
Human Breast ◽  
Mcf7 Cells ◽  
Gene Regulations

The estrogen receptors (ERs) ERα and ERβ mediate the actions of endogenous estrogens as well as those of botanical estrogens (BEs) present in plants. BEs are ingested in the diet and also widely consumed by postmenopausal women as dietary supplements, often as a substitute for the loss of endogenous estrogens at menopause. However, their activities and efficacies, and similarities and differences in gene expression programs with respect to endogenous estrogens such as estradiol (E2) are not fully understood. Because gene expression patterns underlie and control the broad physiological effects of estrogens, we have investigated and compared the gene networks that are regulated by different BEs and by E2. Our aim was to determine if the soy and licorice BEs control similar or different gene expression programs and to compare their gene regulations with that of E2. Gene expression was examined by RNA-Seq in human breast cancer (MCF7) cells treated with control vehicle, BE or E2. These cells contained three different complements of ERs, ERα only, ERα+ERβ, or ERβ only, reflecting the different ratios of these two receptors in different human breast cancers and in different estrogen target cells. Using principal component, hierarchical clustering, and gene ontology and interactome analyses, we found that BEs regulated many of the same genes as did E2. The genes regulated by each BE, however, were somewhat different from one another, with some genes being regulated uniquely by each compound. The overlap with E2 in regulated genes was greatest for the soy isoflavones genistein and S-equol, while the greatest difference from E2 in gene expression pattern was observed for the licorice root BE liquiritigenin. The gene expression pattern of each ligand depended greatly on the cell background of ERs present. Despite similarities in gene expression pattern with E2, the BEs were generally less stimulatory of genes promoting proliferation and were more pro-apoptotic in their gene regulations than E2. The distinctive patterns of gene regulation by the individual BEs and E2 may underlie differences in the activities of these soy and licorice-derived BEs in estrogen target cells containing different levels of the two ERs.

scholarly journals Identification of Gene Expression Pattern Related to Breast Cancer Survival Using Integrated TCGA Datasets and Genomic Tools

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Zhenzhen Huang ◽  
Huilong Duan ◽  
Haomin Li
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cancer Patients ◽  
Expression Pattern ◽  
Clinical Data ◽  
Cancer Genomics ◽  
Expression Patterns ◽  
Gene List ◽  
Gene Expression Pattern ◽  
Cancer Development

Several large-scale human cancer genomics projects such as TCGA offered huge genomic and clinical data for researchers to obtain meaningful genomics alterations which intervene in the development and metastasis of the tumor. A web-based TCGA data analysis platform called TCGA4U was developed in this study. TCGA4U provides a visualization solution for this study to illustrate the relationship of these genomics alternations with clinical data. A whole genome screening of the survival related gene expression patterns in breast cancer was studied. The gene list that impacts the breast cancer patient survival was divided into two patterns. Gene list of each of these patterns was separately analyzed on DAVID. The result showed that mitochondrial ribosomes play a more crucial role in the cancer development. We also reported that breast cancer patients with low HSPA2 expression level had shorter overall survival time. This is widely different to findings of HSPA2 expression pattern in other cancer types. TCGA4U provided a new perspective for the TCGA datasets. We believe it can inspire more biomedical researchers to study and explain the genomic alterations in cancer development and discover more targeted therapies to help more cancer patients.

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