scholarly journals Scaling dictates the decoder structure

2021 ◽  
Author(s):  
Jingxiang Shen ◽  
Feng Liu ◽  
Chao Tang
Keyword(s):  
Gene Expression ◽  
Expression Pattern ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Fruit Fly ◽  
The Body ◽  
Scale Invariant ◽  
Embryo Size ◽  
Gene System ◽  
Gap Gene

AbstractDespite variability in embryo size, the tissue, organ and body plan developin proportionwith embryo size, known as the scaling phenomenon. Scale-invariant patterning of gene expression is a common feature in development and regeneration, and can be generated by mechanisms such as scaling morphogen gradient and dynamic oscillation. However, whether and how static non-scaling morphogens (input) can induce a scaling gene expression (output) across the entire embryo is not clear. Here we show that scaling requirement sets severe constraints on the geometric structure of the input-output relation (the decoder), from which information about the regulation and mutants’ behavior can be deduced without going into any molecular details. We demonstrate that theDrosophilagap gene system achieves scaling in the way that is entirely consistent with our theory. Remarkably, following the geometry dictated by scaling, a parameter-free decoder correctly and quantitatively accounts for the gap gene expression patterns in nearly all morphogen mutants. Furthermore, the regulation logic and the coding/decoding strategy of the gap gene system can also be revealed from the decoder geometry. Our work provides a general theoretical framework on a large class of problems where scaling output is induced by non-scaling input, as well as a unified understanding of scaling, mutants’ behavior and regulation in theDrosophilagap gene and related systems.Significance StatementWithin a given species, fluctuation in egg or embryo size is unavoidable. Despite this, the gene expression pattern and hence the embryonic structure often scale in proportion with the body length. Thisscalingphenomenon is very common in development and regeneration, and has long fascinated scientists. In this paper, the authors address the question of whether and how a scaling gene expression pattern can originate from non-scaling signals (morphogens). They found that scaling has profound implications in the developmental programming -- properties and behaviors of the underlying gene network can be deduced from the scaling requirement. They demonstrated that the scaling in fruit fly embryogenesis indeed works in this way. Thus, although biological regulatory systems are very complex in general, it can be forced to exhibit simple macroscopic behaviors due to selection pressure, as demonstrated in this study.

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.

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.

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.

scholarly journals BEST: A Novel Computational Approach for Comparing Gene Expression Patterns From Early Stages of Drosophila melanogaster Development

Genetics ◽  
2002 ◽  
Vol 162 (4) ◽  
pp. 2037-2047
Author(s):  
Sudhir Kumar ◽  
Karthik Jayaraman ◽  
Sethuraman Panchanathan ◽  
Rajalakshmi Gurunathan ◽  
Ana Marti-Subirana ◽  
...  
Keyword(s):  
Gene Expression ◽  
Developmental Biology ◽  
Drosophila Melanogaster ◽  
Expression Pattern ◽  
Early Stage ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Computational Approach ◽  
Gene Expression Patterns ◽  
Early Stages

Abstract Embryonic gene expression patterns are an indispensable part of modern developmental biology. Currently, investigators must visually inspect numerous images containing embryonic expression patterns to identify spatially similar patterns for inferring potential genetic interactions. The lack of a computational approach to identify pattern similarities is an impediment to advancement in developmental biology research because of the rapidly increasing amount of available embryonic gene expression data. Therefore, we have developed computational approaches to automate the comparison of gene expression patterns contained in images of early stage Drosophila melanogaster embryos (prior to the beginning of germ-band elongation); similarities and differences in gene expression patterns in these early stages have extensive developmental effects. Here we describe a basic expression search tool (BEST) to retrieve best matching expression patterns for a given query expression pattern and a computational device for gene interaction inference using gene expression pattern images and information on the associated genotypes and probes. Analysis of a prototype collection of Drosophila gene expression pattern images is presented to demonstrate the utility of these methods in identifying biologically meaningful matches and inferring gene interactions by direct image content analysis. In particular, the use of BEST searches for gene expression patterns is akin to that of BLAST searches for finding similar sequences. These computational developmental biology methodologies are likely to make the great wealth of embryonic gene expression pattern data easily accessible and to accelerate the discovery of developmental networks.

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 Transcriptome Sequencing of Trichoderma Koningiopsis Strain Tk1 to Identify and Analyze Differential Gene Expression Patterns

2020 ◽  
Author(s):  
Mei Luo ◽  
Zhangyong Dong ◽  
Yongxin Shu ◽  
Mobing Chen
Keyword(s):  
Gene Expression ◽  
Expression Pattern ◽  
Differential Gene Expression ◽  
De Novo ◽  
Transcriptome Assembly ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Antimicrobial Effect ◽  
Carbohydrate Active Enzymes ◽  
Differential Gene

Abstract Background: Trichoderma koningiopsis strain Tk1 shows good biocontrol potential. However, its biocontrol function may differ under different conditions. The objective of this study is to elucidate the biological and transcriptome differences of T. koningiopsis Tk1 under different media. Results: In this study, the mycelium weight and sporulation of T. koningiopsis Tk1 was found to differ in various media. Further, the Tk1 strain inhibited the growth of the pathogen Fusarium oxysporum in the three media tested. Fries3, PD, and PS were collected for RNA sequencing of Tk1 mycelia to identify the genes that are differentially expressed genes (DEGs) between Tk1 grown on different media. De novo transcriptome assembly resulted in identification of 14,208 unigenes. The differential gene expression pattern was more similar between the Fries3 and PS samples, whereas PD samples showed a different expression pattern. The DEGs were enriched in some metabolic and biosynthetic pathways. Additional analysis of the DEGs identified a set of carbohydrate-active enzymes that are upregulated or downregulated under different conditions.Conclusions: These results indicate that the Tk1 strain cultured in Fires3 and PS mediums can produce specific metabolic and carbohydrate-active enzymes to enhance their antimicrobial effect, providing a foundation for the subsequent mining of specific genes.

scholarly journals In Silico Analysis of the Gene Expression Patterns between Aldosterone-Producing Adenoma and Nonfunctional Adrenocortical Adenoma

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Yongfa Dai ◽  
Jing Li ◽  
Hong Wen ◽  
Jie Liu ◽  
Jianling Li
Keyword(s):  
Gene Expression ◽  
Expression Pattern ◽  
Primary Aldosteronism ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Gene Expression Patterns ◽  
Pathophysiological Mechanism ◽  
Adrenocortical Adenoma ◽  
Hub Genes ◽  
Aldosterone Producing Adenoma

Primary aldosteronism is the most common form of secondary hypertension, and aldosteronoma makes up a significant proportion of primary aldosteronism cases. Aldosteronoma is also called aldosterone-producing adenoma (APA). Although there have been many studies about APA, the pathogenesis of this disease is not yet fully understood. In this study, we aimed to find out the difference of gene expression patterns between APA and nonfunctional adrenocortical adenoma (NFAA) using a weighted gene coexpression network (WGCNA) and differentially expressed gene (DEG) analysis; only the genes that meet the corresponding standards of both methods were defined as real hub genes and then used for further analysis. Twenty-nine real hub genes were found out, most of which were enriched in the phospholipid metabolic process. WISP2, S100A10, SSTR5-AS1, SLC29A1, APOC1, and SLITRK4 are six real hub genes with the same gene expression pattern between the combined and validation datasets, three of which indirectly or directly participate in lipid metabolism including WISP2, S100A10, and APOC1. According to the gene expression pattern of DEGs, we speculated five candidate drugs with potential therapeutic value for APA, one of which is cycloheximide, an inhibitor for phospholipid biosynthesis. All the evidence suggests that phospholipid metabolism may be an important pathophysiological mechanism for APA. Our study provides a new perspective regarding the pathophysiological mechanism of APA and offers some small molecules that may possibly be effective drugs against APA.

Sign in / Sign up

Export Citation Format

Share Document