Geometric control of Myosin-II orientation during axis elongation

The actomyosin cytoskeleton is a crucial driver of morphogenesis. Yet how the behavior of large scale cytoskeletal patterns in deforming tissues emerges from the interplay of geometry, genetics, and mechanics remains incompletely understood. Convergent extension flow in D. melanogaster embryos provides the opportunity to establish a quantitative understanding of the dynamics of anisotropic non-muscle myosin II. Cell-scale analysis of protein localization in fixed embryos suggests that there are complex rules governing how the control of myosin anisotropy is regulated by gene expression patterns. However, technical limitations have impeded quantitative and dynamic studies of this process at the whole embryo level, leaving the role of geometry open. Here we combine in toto live imaging with quantitative analysis of molecular dynamics to characterize the distribution of myosin anisotropy and corresponding genetic patterning. We found pair rule gene expression continuously deformed, flowing with the tissue frame. In contrast, myosin anisotropy orientation remained nearly static, aligned with the stationary dorsal-ventral axis of the embryo. We propose myosin recruitment by a geometrically defined static source, potentially related to the embryo-scale epithelial tension, and account for transient deflections by the interplay of cytoskeletal turnover with junction reorientation by flow. With only one parameter, this model quantitatively accounts for the time course of myosin anisotropy orientation in wild-type, twist, and even-skipped embryos as well as embryos with perturbed egg geometry. Geometric patterning of the cytoskeleton suggests a simple physical strategy to ensure a robust flow and formation of shape.

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Microarray analysis of global changes in gene expression during cardiac myocyte differentiation

Physiological Genomics ◽

10.1152/physiolgenomics.00027.2002 ◽

2002 ◽

Vol 9 (3) ◽

pp. 145-155 ◽

Cited By ~ 37

Author(s):

Chang-Fu Peng ◽

Yi Wei ◽

Jeffrey M. Levsky ◽

Thomas V. McDonald ◽

Geoffrey Childs ◽

...

Keyword(s):

Gene Expression ◽

Cardiac Myocytes ◽

Large Scale ◽

Time Course ◽

Cardiac Myocyte ◽

Expression Patterns ◽

Global Gene Expression ◽

Global Changes ◽

Novel Genes ◽

Myocyte Differentiation

Significant progress has been made in defining pathways that mediate the formation of the mammalian heart. Little is known, however, about the genetic program that directs the differentiation of cardiac myocytes from their precursor cells. A major hindrance to this kind of investigation has been the absence of an appropriate cell culture model of cardiac myocyte differentiation. Recently, a subline of P19 cells (P19CL6) was derived that, following dimethyl sulfoxide (DMSO) treatment, differentiate efficiently over 10 days into spontaneously beating cardiac myocytes. We demonstrate that these cells are indeed cardiac myocytes as they express cell type-specific markers and exhibit electrophysiological properties indicative of cardiac myocytes. The requirement for DMSO stimulation in this paradigm was shown to be limited to the first 4 days, suggesting that critical events in the differentiation process occur over this interval. To uncover relationships among known genes and identify novel genes that mediate cardiac myocyte differentiation, a detailed time course of changes in global gene expression was carried out using cDNA microarrays. In addition to the activation of genes encoding cardiac transcription factors and structural proteins, increases were noted in the expression of multiple known genes and expressed sequence tags (ESTs). Analysis of the former suggested the involvement of a variety of signaling pathways in cardiac myocyte differentiation. The 16 ESTs whose expression was increased during the early, stimulus-dependent phase of cardiac myocyte differentiation may be novel regulators of this process. Thus this first report of large-scale changes in gene expression during cardiac myocyte differentiation has delineated relationships among the expression patterns of known genes and identified a number of novel genes that merit further study.

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Discovering Distinct Patterns in Gene Expression Profiles

Journal of Integrative Bioinformatics ◽

10.1515/jib-2008-105 ◽

2008 ◽

Vol 5 (2) ◽

Cited By ~ 1

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.

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Jonckheere–Terpstra–Kendall-based non-parametric analysis of temporal differential gene expression

NAR Genomics and Bioinformatics ◽

10.1093/nargab/lqab021 ◽

2021 ◽

Vol 3 (1) ◽

Author(s):

Hitoshi Iuchi ◽

Michiaki Hamada

Keyword(s):

Gene Expression ◽

Time Series ◽

Time Course ◽

Time Series Data ◽

Expression Patterns ◽

Detection Methods ◽

Series Data ◽

Expression Levels ◽

Over Time ◽

Non Parametric

Abstract Time-course experiments using parallel sequencers have the potential to uncover gradual changes in cells over time that cannot be observed in a two-point comparison. An essential step in time-series data analysis is the identification of temporal differentially expressed genes (TEGs) under two conditions (e.g. control versus case). Model-based approaches, which are typical TEG detection methods, often set one parameter (e.g. degree or degree of freedom) for one dataset. This approach risks modeling of linearly increasing genes with higher-order functions, or fitting of cyclic gene expression with linear functions, thereby leading to false positives/negatives. Here, we present a Jonckheere–Terpstra–Kendall (JTK)-based non-parametric algorithm for TEG detection. Benchmarks, using simulation data, show that the JTK-based approach outperforms existing methods, especially in long time-series experiments. Additionally, application of JTK in the analysis of time-series RNA-seq data from seven tissue types, across developmental stages in mouse and rat, suggested that the wave pattern contributes to the TEG identification of JTK, not the difference in expression levels. This result suggests that JTK is a suitable algorithm when focusing on expression patterns over time rather than expression levels, such as comparisons between different species. These results show that JTK is an excellent candidate for TEG detection.

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Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data

Journal of Integrative Bioinformatics ◽

10.1515/jib-2017-0011 ◽

2017 ◽

Vol 14 (2) ◽

Author(s):

Qihua Tan ◽

Mads Thomassen ◽

Mark Burton ◽

Kristian Fredløv Mose ◽

Klaus Ejner Andersen ◽

...

Keyword(s):

Gene Expression ◽

Correlation Coefficient ◽

Parametric Analysis ◽

Time Course ◽

Expression Patterns ◽

Gene Expression Patterns ◽

Microarray Time Course ◽

Time Course Data ◽

Generalized Correlation ◽

Non Parametric

AbstractModeling complex time-course patterns is a challenging issue in microarray study due to complex gene expression patterns in response to the time-course experiment. We introduce the generalized correlation coefficient and propose a combinatory approach for detecting, testing and clustering the heterogeneous time-course gene expression patterns. Application of the method identified nonlinear time-course patterns in high agreement with parametric analysis. We conclude that the non-parametric nature in the generalized correlation analysis could be an useful and efficient tool for analyzing microarray time-course data and for exploring the complex relationships in the omics data for studying their association with disease and health.

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

AJP Lung Cellular and Molecular Physiology ◽

10.1152/ajplung.00065.2005 ◽

2005 ◽

Vol 289 (4) ◽

pp. L545-L553 ◽

Cited By ~ 29

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.

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SOMDE: A scalable method for identifying spatially variable genes with self-organizing map

10.1101/2020.12.10.419549 ◽

2020 ◽

Author(s):

Minsheng Hao ◽

Kui Hua ◽

Xuegong Zhang

Keyword(s):

Gene Expression ◽

Large Scale ◽

Expression Patterns ◽

Self Organizing Map ◽

Expression Data ◽

Spatial Expression ◽

Variable Expression ◽

Sequencing Technologies ◽

Physical Context ◽

Variable Genes

AbstractRecent developments of spatial transcriptomic sequencing technologies provide powerful tools for understanding cells in the physical context of tissue micro-environments. A fundamental task in spatial gene expression analysis is to identify genes with spatially variable expression patterns, or spatially variable genes (SVgenes). Several computational methods have been developed for this task. Their high computational complexity limited their scalability to the latest and future large-scale spatial expression data.We present SOMDE, an efficient method for identifying SVgenes in large-scale spatial expression data. SOMDE uses selforganizing map (SOM) to cluster neighboring cells into nodes, and then uses a Gaussian Process to fit the node-level spatial gene expression to identify SVgenes. Experiments show that SOMDE is about 5-50 times faster than existing methods with comparable results. The adjustable resolution of SOMDE makes it the only method that can give results in ~5 minutes in large datasets of more than 20,000 sequencing sites. SOMDE is available as a python package on PyPI at https://pypi.org/project/somde.

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Large-Scale Metadata Analysis of Ovary Based Multi-Omics Datasets for Understanding the Genes Regulating Litter Size

10.21203/rs.3.rs-22487/v1 ◽

2020 ◽

Author(s):

Ayyappa Kumar Sista Kameshwar ◽

Julang Li

Keyword(s):

Gene Expression ◽

Differentially Expressed Genes ◽

Litter Size ◽

Large Scale ◽

Expression Patterns ◽

Genetic Selection ◽

Follicular Development ◽

Oocyte Quality ◽

Differentially Expressed ◽

Metadata Analysis

Abstract Background : Litter size is a very important production index in the livestock industry, which is controlled by various complex physiological processes. To understand and reveal the common gene expression patterns involved in controlling prolificacy, we have performed a large-scale metadata analysis of five genome-wide transcriptome datasets of pig and sheep ovary samples obtained from high and low litter groups, respectively. We analyzed separately each transcriptome dataset using GeneSpring v14.8 software by implementing standard, generic analysis pipelines and further compared the list of most significant and differentially expressed genes obtained from each dataset to identify genes that are found to be common and significant across all the studies. Results : We have observed a total of 62 differentially expressed genes common among more than two gene expression datasets. The KEGG pathway analysis of most significant genes has shown that they are involved in metabolism, the biosynthesis of lipids, cholesterol and steroid hormones, immune system, cell growth and death, cancer-related pathways and signal transduction pathways. Of these 62 genes, we further narrowed the list to the 25 most significant genes by focusing on the ones with fold change >1.5 and p<0.05. These genes are CYP11A1, HSD17B2, STAR, SCARB1, IGSF8, MSMB, SERPINA1 , FAM46C, HEXA, PTTG1, TIMP1, FAM167B, CCNG1, FAXDC2, HMGCS1, L2HGDH, Lipin1, MME, MSMO1, PARM1, PTGFR, SLC22A4, SLC35F5, CCNA2, CENPU, CEP55, RASSF2, and SLC16A3 . Conclusions : Interestingly, comparing the list of genes with the list of genes obtained from our literature search analysis, we found only three genes in common. These genes are HEXA, PTTG1, and TIMP1. Our finding points to the potential of a few genes that may be important for ovarian follicular development and oocyte quality. Future studies revealing the function of these genes will further our understanding of how litter size is controlled in the ovary while also providing insight on genetic selection of high litter gilts.

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A comprehensive annotation and differential expression analysis of short and long non-coding RNAs in 16 bat genomes

NAR Genomics and Bioinformatics ◽

10.1093/nargab/lqz006 ◽

2019 ◽

Vol 2 (1) ◽

Author(s):

Nelly F Mostajo ◽

Marie Lataretu ◽

Sebastian Krautwurst ◽

Florian Mock ◽

Daniel Desirò ◽

...

Keyword(s):

Gene Expression ◽

Expression Analysis ◽

Large Scale ◽

Viral Infections ◽

Gene Expression Regulation ◽

Expression Patterns ◽

Differential Expression Analysis ◽

Regulatory Processes ◽

Genome Annotations ◽

Host Virus Interactions

Abstract Although bats are increasingly becoming the focus of scientific studies due to their unique properties, these exceptional animals are still among the least studied mammals. Assembly quality and completeness of bat genomes vary a lot and especially non-coding RNA (ncRNA) annotations are incomplete or simply missing. Accordingly, standard bioinformatics pipelines for gene expression analysis often ignore ncRNAs such as microRNAs or long antisense RNAs. The main cause of this problem is the use of incomplete genome annotations. We present a complete screening for ncRNAs within 16 bat genomes. NcRNAs affect a remarkable variety of vital biological functions, including gene expression regulation, RNA processing, RNA interference and, as recently described, regulatory processes in viral infections. Within all investigated bat assemblies, we annotated 667 ncRNA families including 162 snoRNAs and 193 miRNAs as well as rRNAs, tRNAs, several snRNAs and lncRNAs, and other structural ncRNA elements. We validated our ncRNA candidates by six RNA-Seq data sets and show significant expression patterns that have never been described before in a bat species on such a large scale. Our annotations will be usable as a resource (rna.uni-jena.de/supplements/bats) for deeper studying of bat evolution, ncRNAs repertoire, gene expression and regulation, ecology and important host–virus interactions.

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Transcriptome Profiling of Adipose Tissue Reveals Depot-Specific Metabolic Alterations Among Patients with Colorectal Cancer

The Journal of Clinical Endocrinology & Metabolism ◽

10.1210/jc.2019-00461 ◽

2019 ◽

Vol 104 (11) ◽

pp. 5225-5237 ◽

Cited By ~ 3

Author(s):

Mariam Haffa ◽

Andreana N Holowatyj ◽

Mario Kratz ◽

Reka Toth ◽

Axel Benner ◽

...

Keyword(s):

Gene Expression ◽

Colorectal Cancer ◽

Adipose Tissue ◽

Large Scale ◽

Expression Profiles ◽

Expression Patterns ◽

Human Study ◽

Subcutaneous Fat ◽

Specific Gene ◽

Adipose Tissue Depots

Abstract Context Adipose tissue inflammation and dysregulated energy homeostasis are key mechanisms linking obesity and cancer. Distinct adipose tissue depots strongly differ in their metabolic profiles; however, comprehensive studies of depot-specific perturbations among patients with cancer are lacking. Objective We compared transcriptome profiles of visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) from patients with colorectal cancer and assessed the associations of different anthropometric measures with depot-specific gene expression. Design Whole transcriptomes of VAT and SAT were measured in 233 patients from the ColoCare Study, and visceral and subcutaneous fat area were quantified via CT. Results VAT compared with SAT showed elevated gene expression of cytokines, cell adhesion molecules, and key regulators of metabolic homeostasis. Increased fat area was associated with downregulated lipid and small molecule metabolism and upregulated inflammatory pathways in both compartments. Comparing these patterns between depots proved specific and more pronounced gene expression alterations in SAT and identified unique associations of integrins and lipid metabolism–related enzymes. VAT gene expression patterns that were associated with visceral fat area poorly overlapped with patterns associated with self-reported body mass index (BMI). However, subcutaneous fat area and BMI showed similar associations with SAT gene expression. Conclusions This large-scale human study demonstrates pronounced disparities between distinct adipose tissue depots and reveals that BMI poorly correlates with fat mass–associated changes in VAT. Taken together, these results provide crucial evidence for the necessity to differentiate between distinct adipose tissue depots for a correct characterization of gene expression profiles that may affect metabolic health of patients with colorectal cancer.

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Gene Expression Patterns Underlying the Reinstatement of Plasticity in the Adult Visual System

Neural Plasticity ◽

10.1155/2013/605079 ◽

2013 ◽

Vol 2013 ◽

pp. 1-8 ◽

Cited By ~ 11

Author(s):

Ettore Tiraboschi ◽

Ramon Guirado ◽

Dario Greco ◽

Petri Auvinen ◽

Jose Fernando Maya-Vetencourt ◽

...

Keyword(s):

Gene Expression ◽

Visual Cortex ◽

Large Scale ◽

Molecular Mechanisms ◽

Expression Patterns ◽

Monocular Deprivation ◽

Adult Brain ◽

Gene Expression Patterns ◽

Postnatal Life ◽

Critical Periods

The nervous system is highly sensitive to experience during early postnatal life, but this phase of heightened plasticity decreases with age. Recent studies have demonstrated that developmental-like plasticity can be reactivated in the visual cortex of adult animals through environmental or pharmacological manipulations. These findings provide a unique opportunity to study the cellular and molecular mechanisms of adult plasticity. Here we used the monocular deprivation paradigm to investigate large-scale gene expression patterns underlying the reinstatement of plasticity produced by fluoxetine in the adult rat visual cortex. We found changes, confirmed with RT-PCRs, in gene expression in different biological themes, such as chromatin structure remodelling, transcription factors, molecules involved in synaptic plasticity, extracellular matrix, and excitatory and inhibitory neurotransmission. Our findings reveal a key role for several molecules such as the metalloproteases Mmp2 and Mmp9 or the glycoprotein Reelin and open up new insights into the mechanisms underlying the reopening of the critical periods in the adult brain.

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