scholarly journals Transcriptome Analysis Reveals Differential Gene Expression between the Closing Ductus Arteriosus and the Patent Ductus Arteriosus in Humans

2021 ◽  
Vol 8 (4) ◽  
pp. 45
Author(s):  
Junichi Saito ◽  
Tomoyuki Kojima ◽  
Shota Tanifuji ◽  
Yuko Kato ◽  
Sayuki Oka ◽  
...  
Keyword(s):  
Gene Expression ◽  
Smooth Muscle ◽  
Transcriptome Analysis ◽  
Expression Profiles ◽  
Ductus Arteriosus ◽  
Gene Expression Profiles ◽  
Elastic Fibers ◽  
Specific Gene ◽  
Gene Expressions ◽  
Tunica Media

The ductus arteriosus (DA) immediately starts closing after birth. This dynamic process involves DA-specific properties, including highly differentiated smooth muscle, sparse elastic fibers, and intimal thickening (IT). Although several studies have demonstrated DA-specific gene expressions using animal tissues and human fetuses, the transcriptional profiles of the closing DA and the patent DA remain largely unknown. We performed transcriptome analysis using four human DA samples. The three closing DA samples exhibited typical DA morphology, but the patent DA exhibited aorta-like elastic lamellae and poorly formed IT. A cluster analysis revealed that samples were clearly divided into two major clusters, the closing DA and patent DA clusters, and showed distinct gene expression profiles in IT and the tunica media of the closing DA samples. Cardiac neural crest-related genes such as JAG1 were highly expressed in the tunica media and IT of the closing DA samples compared to the patent DA sample. Abundant protein expressions of jagged 1 and the differentiated smooth muscle marker calponin were observed in the closing DA samples but not in the patent DA sample. Second heart field-related genes such as ISL1 were enriched in the patent DA sample. These data indicate that the patent DA may have different cell lineages compared to the closing DA.

scholarly journals Transcriptome analysis of gravitational effects on mouse skeletal muscles under microgravity and artificial 1 g onboard environment

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Risa Okada ◽  
Shin-ichiro Fujita ◽  
Riku Suzuki ◽  
Takuto Hayashi ◽  
Hirona Tsubouchi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Muscle Mass ◽  
Muscle Atrophy ◽  
Transcriptome Analysis ◽  
Fiber Type ◽  
Expression Profiles ◽  
Space Station ◽  
Gene Expression Profiles

AbstractSpaceflight causes a decrease in skeletal muscle mass and strength. We set two murine experimental groups in orbit for 35 days aboard the International Space Station, under artificial earth-gravity (artificial 1 g; AG) and microgravity (μg; MG), to investigate whether artificial 1 g exposure prevents muscle atrophy at the molecular level. Our main findings indicated that AG onboard environment prevented changes under microgravity in soleus muscle not only in muscle mass and fiber type composition but also in the alteration of gene expression profiles. In particular, transcriptome analysis suggested that AG condition could prevent the alterations of some atrophy-related genes. We further screened novel candidate genes to reveal the muscle atrophy mechanism from these gene expression profiles. We suggest the potential role of Cacng1 in the atrophy of myotubes using in vitro and in vivo gene transductions. This critical project may accelerate the elucidation of muscle atrophy mechanisms.

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.

scholarly journals High resolution spatial transcriptome analysis by photo-isolation chemistry

2020 ◽  
Author(s):  
Mizuki Honda ◽  
Shinya Oki ◽  
Akihito Harada ◽  
Kazumitsu Maehara ◽  
Kaori Tanaka ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcriptome Analysis ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Transcriptome Profiling ◽  
Tissue Sections ◽  
Complex Functions ◽  
Multicellular Organisms

ABSTRACTIn multicellular organisms, individual cells are characterized by their gene expression profiles and the spatial interactions among cells enable the elaboration of complex functions. Expression profiling in spatially defined regions is crucial to elucidate cell interactions and functions. Here, we established a transcriptome profiling method coupled with photo-isolation chemistry (PIC) that allows the determination of expression profiles specifically from photo-irradiated regions of whole tissues. PIC uses photo-caged oligodeoxynucleotides for in situ reverse transcription. After photo-irradiation of limited areas, gene expression was detected from at least 10 cells in the tissue sections. PIC transcriptome analysis detected genes specifically expressed in small distinct areas of the mouse embryo. Thus, PIC enables transcriptome profiles to be determined from limited regions at a spatial resolution up to the diffraction limit.

scholarly journals Convergent evolution of venom gland transcriptomes across Metazoa

2021 ◽  
Author(s):  
Giulia Zancolli ◽  
Maarten Reijnders ◽  
Robert Waterhouse ◽  
Marc Robinson-Rechavi
Keyword(s):  
Gene Expression ◽  
Regulatory Networks ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Venom Gland ◽  
Bioactive Molecules ◽  
Specific Gene ◽  
Animal Kingdom ◽  
Venom Glands

Animals have repeatedly evolved specialized organs and anatomical structures to produce and deliver a cocktail of potent bioactive molecules to subdue prey or predators: venom. This makes it one of the most widespread convergent functions in the animal kingdom. Whether animals have adopted the same genetic toolkit to evolved venom systems is a fascinating question that still eludes us. Here, we performed the first comparative analysis of venom gland transcriptomes from 20 venomous species spanning the main Metazoan lineages, to test whether different animals have independently adopted similar molecular mechanisms to perform the same function. We found a strong convergence in gene expression profiles, with venom glands being more similar to each other than to any other tissue from the same species, and their differences closely mirroring the species phylogeny. Although venom glands secrete some of the fastest evolving molecules (toxins), their gene expression does not evolve faster than evolutionarily older tissues. We found 15 venom gland specific gene modules enriched in endoplasmic reticulum stress and unfolded protein response pathways, indicating that animals have independently adopted stress response mechanisms to cope with mass production of toxins. This, in turns, activates regulatory networks for epithelial development, cell turnover and maintenance which seem composed of both convergent and lineage-specific factors, possibly reflecting the different developmental origins of venom glands. This study represents the first step towards an understanding of the molecular mechanisms underlying the repeated evolution of one of the most successful adaptive traits in the animal kingdom.

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 Technique of Gene Expression Profiles Extraction Based on the Complex Use of Clustering and Classification Methods

Diagnostics ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 584
Author(s):  
Sergii Babichev ◽  
Jiří Škvor
Keyword(s):  
Gene Expression ◽  
Regulatory Networks ◽  
Fuzzy Inference ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Quality Criterion ◽  
Gene Expressions ◽  
Inference System ◽  
Stepwise Procedure ◽  
Binary Classifiers

In this paper, we present the results of the research concerning extraction of informative gene expression profiles from high-dimensional array of gene expressions considering the state of patients’ health using clustering method, ML-based binary classifiers and fuzzy inference system. Applying of the proposed stepwise procedure can allow us to extract the most informative genes taking into account both the subtypes of disease or state of the patient’s health for further reconstruction of gene regulatory networks based on the allocated genes and following simulation of the reconstructed models. We used the publicly available gene expressions data as the experimental ones which were obtained using DNA microarray experiments and contained two types of patients’ gene expression profiles—the patients with lung cancer tumor and healthy patients. The stepwise procedure of the data processing assumes the following steps—in the beginning, we reduce the number of genes by removing non-informative genes in terms of statistical criteria and Shannon entropy; then, we perform the stepwise hierarchical clustering of gene expression profiles at hierarchical levels from 1 to 10 using the SOTA (Self-Organizing Tree Algorithm) clustering algorithm with correlation distance metric. The quality of the obtained clustering was evaluated using the complex clustering quality criterion which is considered both the gene expression profiles distribution relative to center of the clusters where these gene expression profiles are allocated and the centers of the clusters distribution. The result of this stage execution was a selection of the optimal cluster at each of the hierarchical levels which corresponded to the minimum value of the quality criterion. At the next step, we have implemented a classification procedure of the examined objects using four well known binary classifiers—logistic regression, support-vector machine, decision trees and random forest classifier. The effectiveness of the appropriate technique was evaluated based on the use of ROC (Receiver Operating Characteristic) analysis using criteria, included as the components, the errors of both the first and the second kinds. The final decision concerning the extraction of the most informative subset of gene expression profiles was taken based on the use of the fuzzy inference system, the inputs of which are the results of the appropriate single classifiers operation and the output is the final solution concerning state of the patient’s health. To our mind, the implementation of the proposed stepwise procedure of the informative gene expression profiles extraction create the conditions for the increasing effectiveness of the further procedure of gene regulatory networks reconstruction and the following simulation of the reconstructed models considering the subtypes of the disease and/or state of the patient’s health.

scholarly journals In Vivo mRNA Profiling of Uropathogenic Escherichia coli from Diverse Phylogroups Reveals Common and Group-Specific Gene Expression Profiles

mBio ◽  
2014 ◽  
Vol 5 (4) ◽  
Author(s):  
Piotr Bielecki ◽  
Uthayakumar Muthukumarasamy ◽  
Denitsa Eckweiler ◽  
Agata Bielecka ◽  
Sarah Pohl ◽  
...  
Keyword(s):  
Gene Expression ◽  
Escherichia Coli ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Specific Gene ◽  
Content Type ◽  
E Coli ◽  
Human Host ◽  
Tract Infections

ABSTRACTmRNA profiling of pathogens during the course of human infections gives detailed information on the expression levels of relevant genes that drive pathogenicity and adaptation and at the same time allows for the delineation of phylogenetic relatedness of pathogens that cause specific diseases. In this study, we used mRNA sequencing to acquire information on the expression ofEscherichia colipathogenicity genes during urinary tract infections (UTI) in humans and to assign the UTI-associatedE. coliisolates to different phylogenetic groups. Whereas thein vivogene expression profiles of the majority of genes were conserved among 21E. colistrains in the urine of elderly patients suffering from an acute UTI, the specific gene expression profiles of the flexible genomes was diverse and reflected phylogenetic relationships. Furthermore, genes transcribedin vivorelative to laboratory media included well-described virulence factors, small regulatory RNAs, as well as genes not previously linked to bacterial virulence. Knowledge on relevant transcriptional responses that drive pathogenicity and adaptation of isolates to the human host might lead to the introduction of a virulence typing strategy into clinical microbiology, potentially facilitating management and prevention of the disease.IMPORTANCEUrinary tract infections (UTI) are very common; at least half of all women experience UTI, most of which are caused by pathogenicEscherichia colistrains. In this study, we applied massive parallel cDNA sequencing (RNA-seq) to provide unbiased, deep, and accurate insight into the nature and the dimension of the uropathogenicE. coligene expression profile during an acute UTI within the human host. This work was undertaken to identify key players in physiological adaptation processes and, hence, potential targets for new infection prevention and therapy interventions specifically aimed at sabotaging bacterial adaptation to the human host.

Sign in / Sign up

Export Citation Format

Share Document