scholarly journals Reference gene selection for qRT-PCR analysis of season- and tissue-specific gene expression profiles in the honey bee Apis mellifera

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Ji Hyang Jeon ◽  
KyungHwan Moon ◽  
YeongHo Kim ◽  
Young Ho Kim
Keyword(s):  
Gene Expression ◽  
Apis Mellifera ◽  
Honey Bee ◽  
Gene Selection ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Pcr Analysis ◽  
Specific Gene ◽  
Qrt Pcr ◽  
Selection For

scholarly journals Comparison of Stemness and Gene Expression between Gingiva and Dental Follicles in Children

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Chung-Min Kang ◽  
Seong-Oh Kim ◽  
Mijeong Jeon ◽  
Hyung-Jun Choi ◽  
Han-Sung Jung ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stem Cells ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Pcr Analysis ◽  
Expression Levels ◽  
Qrt Pcr ◽  
Stem Cell Source ◽  
Regenerative Dentistry ◽  
Induced Pluripotent

The aim of this study was to compare the differential gene expression and stemness in the human gingiva and dental follicles (DFs) according to their biological characteristics. Gingiva (n=9) and DFs (n=9) were collected from 18 children. Comparative gene expression profiles were collected using cDNA microarray. The expression of development, chemotaxis, mesenchymal stem cells (MSCs), and induced pluripotent stem cells (iPSs) related genes was assessed by quantitative reverse transcription-polymerase chain reaction (qRT-PCR). Histological analysis was performed using hematoxylin-eosin and immunohistochemical staining. Gingiva had greater expression of genes related to keratinization, ectodermal development, and chemotaxis whereas DFs exhibited higher expression levels of genes related to tooth and embryo development. qRT-PCR analysis showed that the expression levels of iPSc factors includingSOX2,KLF4, andC-MYCwere58.5±26.3,12.4±3.5, and12.2±1.9times higher in gingiva andVCAM1(CD146) andALCAM(CD166) were33.5±6.9and4.3±0.8times higher in DFs. Genes related to MSCs markers includingCD13,CD34,CD73,CD90, andCD105were expressed at higher levels in DFs. The results of qRT-PCR and IHC staining supported the microarray analysis results. Interestingly, this study demonstrated transcription factors of iPS cells were expressed at higher levels in the gingiva. Given the minimal surgical discomfort and simple accessibility, gingiva is a good candidate stem cell source in regenerative dentistry.

scholarly journals Differential gene expression profiles of invasive and non-invasive non-functioning pituitary adenomas based on microarray analysis

2010 ◽  
Vol 17 (2) ◽  
pp. 361-371 ◽  
Author(s):  
Françoise Galland ◽  
Ludovic Lacroix ◽  
Patrick Saulnier ◽  
Philippe Dessen ◽  
Geri Meduri ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pituitary Adenomas ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Molecular Signature ◽  
Specific Gene ◽  
Tumor Invasiveness ◽  
Non Invasive ◽  
Qrt Pcr ◽  
Non Functioning Pituitary Adenomas

Non-functioning pituitary adenomas (NFPAs) may be locally invasive. Markers of invasiveness are needed to guide patient management and particularly the use of adjuvant radiotherapy. To examine whether invasive NFPAs display a specific gene expression profile relative to non-invasive tumors, we selected 40 NFPAs (38 of the gonadotroph type) and classified them as invasive (n=22) or non-invasive (n=18) on the basis of magnetic resonance imaging and surgical findings. We then performed pangenomic analysis with the 44k Agilent human whole genome expression oligonucleotide microarray in order to identify genes with differential expression between invasive and non-invasive NFPAs. Candidate genes were then tested in qRT-PCR. Prediction class analysis showed that the expression of 346 genes differed between invasive and non-invasive NFPAs (P<0.001), of which 233 genes were up-regulated and 113 genes were down-regulated in invasive tumors. On the basis of Ingenuity networks and the degree of up- or down-regulation in invasive versus non-invasive tumors, 35 genes were selected for expression quantification by qRT-PCR. Overexpression of only four genes was confirmed, namely IGFBP5 (P=0.02), MYO5A (P=0.04), FLT3 (P=0.01), and NFE2L1 (P=0.02). At the protein level, only myosin 5A (MYO5A) immunostaining was stronger in invasive than in non-invasive NFPAs. Molecular signature allows to differentiate ‘grossly’ invasive from non-invasive NFPAs. The product of one of these genes, MYO5A, may be a useful marker of tumor invasiveness.

scholarly journals A Robust Gene selection Method for Microarray-based Cancer Classification

2010 ◽  
Vol 9 ◽  
pp. CIN.S3794 ◽  
Author(s):  
Xiaosheng Wang ◽  
Osamu Gotoh
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Gene Selection ◽  
Information Gain ◽  
Expression Profiles ◽  
Feature Selection Method ◽  
Gene Expression Profiles ◽  
Molecular Classification ◽  
Selection Method ◽  
Chi Square

Gene selection is of vital importance in molecular classification of cancer using high-dimensional gene expression data. Because of the distinct characteristics inherent to specific cancerous gene expression profiles, developing flexible and robust feature selection methods is extremely crucial. We investigated the properties of one feature selection approach proposed in our previous work, which was the generalization of the feature selection method based on the depended degree of attribute in rough sets. We compared the feature selection method with the established methods: the depended degree, chi-square, information gain, Relief-F and symmetric uncertainty, and analyzed its properties through a series of classification experiments. The results revealed that our method was superior to the canonical depended degree of attribute based method in robustness and applicability. Moreover, the method was comparable to the other four commonly used methods. More importantly, the method can exhibit the inherent classification difficulty with respect to different gene expression datasets, indicating the inherent biology of specific cancers.

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.

scholarly journals PCA-based unsupervised feature extraction for gene expression analysis of COVID-19 patients

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kota Fujisawa ◽  
Mamoru Shimo ◽  
Y.-H. Taguchi ◽  
Shinya Ikematsu ◽  
Ryota Miyata
Keyword(s):  
Gene Expression ◽  
Feature Extraction ◽  
Target Genes ◽  
Gene Selection ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Principal Component ◽  
Data Set ◽  
Immune Related Genes ◽  
Unsupervised Feature Extraction

AbstractCoronavirus disease 2019 (COVID-19) is raging worldwide. This potentially fatal infectious disease is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, the complete mechanism of COVID-19 is not well understood. Therefore, we analyzed gene expression profiles of COVID-19 patients to identify disease-related genes through an innovative machine learning method that enables a data-driven strategy for gene selection from a data set with a small number of samples and many candidates. Principal-component-analysis-based unsupervised feature extraction (PCAUFE) was applied to the RNA expression profiles of 16 COVID-19 patients and 18 healthy control subjects. The results identified 123 genes as critical for COVID-19 progression from 60,683 candidate probes, including immune-related genes. The 123 genes were enriched in binding sites for transcription factors NFKB1 and RELA, which are involved in various biological phenomena such as immune response and cell survival: the primary mediator of canonical nuclear factor-kappa B (NF-κB) activity is the heterodimer RelA-p50. The genes were also enriched in histone modification H3K36me3, and they largely overlapped the target genes of NFKB1 and RELA. We found that the overlapping genes were downregulated in COVID-19 patients. These results suggest that canonical NF-κB activity was suppressed by H3K36me3 in COVID-19 patient blood.

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