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Journal articles on the topic 'Differential gene expression'

To see the other types of publications on this topic, follow the link: Differential gene expression.

Author: Grafiati

Published: 4 June 2021

Last updated: 20 February 2023

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1

Gammelgård, E., M. L. Mohan, R. A. Andersson, and J. P. T. Valkonen. "Host gene expression at an early stage of virus resistance induction." Plant Protection Science 38, SI 2 - 6th Conf EFPP 2002 (December 31, 2017): 502–3. http://dx.doi.org/10.17221/10535-pps.

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Suppression subtractive hybridization (SSH) was carried out to detect genes differentially expressed in plants expressing resistance to systemic infection with Potato virus A (PVA), genus Potyvirus. Differential screening has up to now revealed 19 putative differentially expressed genes. Nothern blot hybridization has confirmed the differential expression of seven genes. Three of them were only induced by the virus, but four genes were also wound-induced.

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2

Meade, Jonathan. "‘Differential Gene Expression 2002’." Pharmacogenomics 4, no. 2 (March 2003): 117–18. http://dx.doi.org/10.1517/phgs.4.2.117.22635.

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3

Chatterjee-Kishore, Moitreyee, and Maryann Z. Whitley. "From differential gene expression to differential gene function and back." Drug Discovery Today: Technologies 1, no. 2 (October 2004): 149–56. http://dx.doi.org/10.1016/j.ddtec.2004.09.005.

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4

Jakobs, T. C. "Differential Gene Expression in Glaucoma." Cold Spring Harbor Perspectives in Medicine 4, no. 7 (July 1, 2014): a020636. http://dx.doi.org/10.1101/cshperspect.a020636.

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5

Rihn, B. H., S. Mohr, S. A. McDowell, S. Binet, J. Loubinoux, F. Galateau, G. Keith, and G. D. Leikauf. "Differential gene expression in mesothelioma." FEBS Letters 480, no. 2-3 (August 30, 2000): 95–100. http://dx.doi.org/10.1016/s0014-5793(00)01913-x.

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6

Seroude, Laurent. "Differential Gene Expression and Aging." Scientific World JOURNAL 2 (2002): 618–31. http://dx.doi.org/10.1100/tsw.2002.135.

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It has been established that an intricate program of gene expression controls progression through the different stages in development. The equally complex biological phenomenon known as aging is genetically determined and environmentally modulated. This review focuses on the genetic component of aging, with a special emphasis on differential gene expression. At least two genetic pathways regulating organism longevity act by modifying gene expression. Many genes are also subjected to age-dependent transcriptional regulation. Some age-related gene expression changes are prevented by caloric restriction, the most robust intervention that slows down the aging process. Manipulating the expression of some age-regulated genes can extend an organism's life span. Remarkably, the activity of many transcription regulatory elements is linked to physiological age as opposed to chronological age, indicating that orderly and tightly controlled regulatory pathways are active during aging.

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7

Skubitz, Keith M., and Amy P. N. Skubitz. "Differential gene expression in leiomyosarcoma." Cancer 98, no. 5 (August 20, 2003): 1029–38. http://dx.doi.org/10.1002/cncr.11586.

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8

Campbell, W. G., S. E. Gordon, C. J. Carlson, J. S. Pattison, M. T. Hamilton, and F. W. Booth. "Differential global gene expression in red and white skeletal muscle." American Journal of Physiology-Cell Physiology 280, no. 4 (April 1, 2001): C763—C768. http://dx.doi.org/10.1152/ajpcell.2001.280.4.c763.

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The differences in gene expression among the fiber types of skeletal muscle have long fascinated scientists, but for the most part, previous experiments have only reported differences of one or two genes at a time. The evolving technology of global mRNA expression analysis was employed to determine the potential differential expression of ∼3,000 mRNAs between the white quad (white muscle) and the red soleus muscle (mixed red muscle) of female ICR mice (30–35 g). Microarray analysis identified 49 mRNA sequences that were differentially expressed between white and mixed red skeletal muscle, including newly identified differential expressions between muscle types. For example, the current findings increase the number of known, differentially expressed mRNAs for transcription factors/coregulators by nine and signaling proteins by three. The expanding knowledge of the diversity of mRNA expression between white and mixed red muscle suggests that there could be quite a complex regulation of phenotype between muscles of different fiber types.

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9

Lykhenko, O. "СONSECUTIVE INTEGRATION OF AVAILABLE MICROARRAY DATA FOR ANALYSIS OF DIFFERENTIAL GENE EXPRESSION IN HUMAN PLACENTA." Biotechnologia Acta 14, no. 1 (February 2021): 38–45. http://dx.doi.org/10.15407/biotech14.01.38.

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The purpose of the study was to provide the pipeline for processing of publicly available unprocessed data on gene expression via integration and differential gene expression analysis. Data collection from open gene expression databases, normalization and integration into a single expression matrix in accordance with metadata and determination of differentially expressed genes were fulfilled. To demonstrate all stages of data processing and integrative analysis, there were used the data from gene expression in the human placenta from the first and second trimesters of normal pregnancy. The source code for the integrative analysis was written in the R programming language and publicly available as a repository on GitHub. Four clusters of functionally enriched differentially expressed genes were identified for the human placenta in the interval between the first and second trimester of pregnancy. Immune processes, developmental processes, vasculogenesis and angiogenesis, signaling and the processes associated with zinc ions varied in the considered interval between the first and second trimester of placental development. The proposed sequence of actions for integrative analysis could be applied to any data obtained by microarray technology.

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10

Jiang, Xue, Han Zhang, and Xiongwen Quan. "Differentially Coexpressed Disease Gene Identification Based on Gene Coexpression Network." BioMed Research International 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/3962761.

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Screening disease-related genes by analyzing gene expression data has become a popular theme. Traditional disease-related gene selection methods always focus on identifying differentially expressed gene between case samples and a control group. These traditional methods may not fully consider the changes of interactions between genes at different cell states and the dynamic processes of gene expression levels during the disease progression. However, in order to understand the mechanism of disease, it is important to explore the dynamic changes of interactions between genes in biological networks at different cell states. In this study, we designed a novel framework to identify disease-related genes and developed a differentially coexpressed disease-related gene identification method based on gene coexpression network (DCGN) to screen differentially coexpressed genes. We firstly constructed phase-specific gene coexpression network using time-series gene expression data and defined the conception of differential coexpression of genes in coexpression network. Then, we designed two metrics to measure the value of gene differential coexpression according to the change of local topological structures between different phase-specific networks. Finally, we conducted meta-analysis of gene differential coexpression based on the rank-product method. Experimental results demonstrated the feasibility and effectiveness of DCGN and the superior performance of DCGN over other popular disease-related gene selection methods through real-world gene expression data sets.

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11

Mountza, John D., J. Frederic Mushinski, and Alfred D. Steinberga. "Differential gene expression in autoimmune mice." Survey of Immunologic Research 4, no. 1 (March 1985): 48–64. http://dx.doi.org/10.1007/bf02918586.

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12

Crow, Megan, Nathaniel Lim, Sara Ballouz, Paul Pavlidis, and Jesse Gillis. "Predictability of human differential gene expression." Proceedings of the National Academy of Sciences 116, no. 13 (March 7, 2019): 6491–500. http://dx.doi.org/10.1073/pnas.1802973116.

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Differential expression (DE) is commonly used to explore molecular mechanisms of biological conditions. While many studies report significant results between their groups of interest, the degree to which results are specific to the question at hand is not generally assessed, potentially leading to inaccurate interpretation. This could be particularly problematic for metaanalysis where replicability across datasets is taken as strong evidence for the existence of a specific, biologically relevant signal, but which instead may arise from recurrence of generic processes. To address this, we developed an approach to predict DE based on an analysis of over 600 studies. A predictor based on empirical prior probability of DE performs very well at this task (mean area under the receiver operating characteristic curve, ∼0.8), indicating that a large fraction of DE hit lists are nonspecific. In contrast, predictors based on attributes such as gene function, mutation rates, or network features perform poorly. Genes associated with sex, the extracellular matrix, the immune system, and stress responses are prominent within the “DE prior.” In a series of control studies, we show that these patterns reflect shared biology rather than technical artifacts or ascertainment biases. Finally, we demonstrate the application of the DE prior to data interpretation in three use cases: (i) breast cancer subtyping, (ii) single-cell genomics of pancreatic islet cells, and (iii) metaanalysis of lung adenocarcinoma and renal transplant rejection transcriptomics. In all cases, we find hallmarks of generic DE, highlighting the need for nuanced interpretation of gene phenotypic associations.

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13

Bowden, G. T., and P. Krieg. "Differential gene expression during multistage carcinogenesis." Environmental Health Perspectives 93 (June 1991): 51–56. http://dx.doi.org/10.1289/ehp.919351.

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14

Thomas, Kelwyn H., Thomas M. Wilkie, Philip Tomashefsky, Anthony R. Bellvé, and Melvin I. Simon. "Differential Gene Expression during Mouse Spermatogenesis1." Biology of Reproduction 41, no. 4 (October 1, 1989): 729–39. http://dx.doi.org/10.1095/biolreprod41.4.729.

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15

Braun, Felix, Seyed Mehdi Hosseini, Sven Laabs, Peggy Bothur, and Burckhardt Ringe. "DIFFERENTIAL GENE EXPRESSION DURING ISCHEMIA/ REPERFUSION." Transplantation 69, Supplement (April 2000): S205. http://dx.doi.org/10.1097/00007890-200004271-00354.

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16

Liang, Peng, and Arthur B. Pardee. "Analysing differential gene expression in cancer." Nature Reviews Cancer 3, no. 11 (November 2003): 869–76. http://dx.doi.org/10.1038/nrc1214.

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17

Hayashi, Masaaki, Tomoaki Nakamura, Jun Mukai, and Toshio Tanaka. "Differential gene expression in cardiac development." Japanese Journal of Pharmacology 71 (1996): 331. http://dx.doi.org/10.1016/s0021-5198(19)37563-8.

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18

Smith, Farin, Ambrosio Hernandez, Xiang Ying Xue, and Glenn C. Hunter. "Differential gene expression in carotid disease." Journal of the American College of Surgeons 191, no. 4 (October 2000): S73. http://dx.doi.org/10.1016/s1072-7515(00)00601-3.

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19

Carty, David, Christine Akehurst, Rachel Savage, Liliya Sungatullina, Scott Robinson, Martin McBride, John McClure, Dilys Freeman, and Christian Delles. "Differential gene expression in obese pregnancy." Pregnancy Hypertension: An International Journal of Women's Cardiovascular Health 4, no. 3 (July 2014): 232–33. http://dx.doi.org/10.1016/j.preghy.2014.03.011.

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20

Skubitz, Keith M., and Amy P. N. Skubitz. "Differential gene expression in uterine leiomyoma." Journal of Laboratory and Clinical Medicine 141, no. 5 (May 2003): 297–308. http://dx.doi.org/10.1016/s0022-2143(03)00007-6.

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21

Hibbs, Kathleen, Keith M. Skubitz, Stefan E. Pambuccian, Rachael C. Casey, Kathryn M. Burleson, Theodore R. Oegema, Jeannine J. Thiele, Suzanne M. Grindle, Robin L. Bliss, and Amy P. N. Skubitz. "Differential Gene Expression in Ovarian Carcinoma." American Journal of Pathology 165, no. 2 (August 2004): 397–414. http://dx.doi.org/10.1016/s0002-9440(10)63306-8.

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22

Dunsmuir, Pamela. "Differential gene expression and plant development." Trends in Genetics 3 (1987): 231. http://dx.doi.org/10.1016/0168-9525(87)90242-3.

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23

Lenman, M., A. Falk, J. Rodin, A. S. Hoglund, B. Ek, and L. Rask. "Differential Expression of Myrosinase Gene Families." Plant Physiology 103, no. 3 (November 1, 1993): 703–11. http://dx.doi.org/10.1104/pp.103.3.703.

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24

Xia, Chun Lin, Ming Hui Wan, Ye Zhang, Jiang Hong He, Mei Juan Yan, and Mao Min Sun. "Differential gene expression profile of astrocytes." Cell Biology International 32, no. 3 (March 2008): S6—S7. http://dx.doi.org/10.1016/j.cellbi.2008.01.034.

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25

Dawid, Igor B. "Differential Gene Expression in Vertebrate Embryos." Journal of Biological Chemistry 284, no. 20 (January 21, 2009): 13277–83. http://dx.doi.org/10.1074/jbc.x800018200.

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26

Lewin, Alex, Sylvia Richardson, Clare Marshall, Anne Glazier, and Tim Aitman. "Bayesian Modeling of Differential Gene Expression." Biometrics 62, no. 1 (July 19, 2005): 10–18. http://dx.doi.org/10.1111/j.1541-0420.2005.00394.x.

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27

Zenkel, Matthias, Ernst Po¨schl, Klaus von der Mark, Carmen Hofmann-Rummelt, Gottfried O. H. Naumann, Friedrich E. Kruse, and Ursula Schlo¨tzer-Schrehardt. "Differential Gene Expression in Pseudoexfoliation Syndrome." Investigative Opthalmology & Visual Science 46, no. 10 (October 1, 2005): 3742. http://dx.doi.org/10.1167/iovs.05-0249.

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28

Wu, B. "Cancer outlier differential gene expression detection." Biostatistics 8, no. 3 (October 4, 2006): 566–75. http://dx.doi.org/10.1093/biostatistics/kxl029.

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29

Lian, Heng. "MOST: detecting cancer differential gene expression." Biostatistics 9, no. 3 (November 29, 2007): 411–18. http://dx.doi.org/10.1093/biostatistics/kxm042.

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30

Samadani, Uzma, Alexander R. Judkins, Albert Akpalu, Eleonora Aronica, and Peter B. Crino. "Differential Cellular Gene Expression in Ganglioglioma." Epilepsia 48, no. 4 (April 2007): 646–53. http://dx.doi.org/10.1111/j.1528-1167.2007.00925.x.

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31

Enquobahrie, Daniel A., Margaret Meller, Kenneth Rice, Bruce M. Psaty, David S. Siscovick, and Michelle A. Williams. "Differential placental gene expression in preeclampsia." American Journal of Obstetrics and Gynecology 199, no. 5 (November 2008): 566.e1–566.e11. http://dx.doi.org/10.1016/j.ajog.2008.04.020.

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32

Ma, Wen-Juan, Fantin Carpentier, Tatiana Giraud, and Michael E. Hood. "Differential Gene Expression between Fungal Mating Types Is Associated with Sequence Degeneration." Genome Biology and Evolution 12, no. 4 (February 14, 2020): 243–58. http://dx.doi.org/10.1093/gbe/evaa028.

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Abstract Degenerative mutations in non-recombining regions, such as in sex chromosomes, may lead to differential expression between alleles if mutations occur stochastically in one or the other allele. Reduced allelic expression due to degeneration has indeed been suggested to occur in various sex-chromosome systems. However, whether an association occurs between specific signatures of degeneration and differential expression between alleles has not been extensively tested, and sexual antagonism can also cause differential expression on sex chromosomes. The anther-smut fungus Microbotryum lychnidis-dioicae is ideal for testing associations between specific degenerative signatures and differential expression because 1) there are multiple evolutionary strata on the mating-type chromosomes, reflecting successive recombination suppression linked to mating-type loci; 2) separate haploid cultures of opposite mating types help identify differential expression between alleles; and 3) there is no sexual antagonism as a confounding factor accounting for differential expression. We found that differentially expressed genes were enriched in the four oldest evolutionary strata compared with other genomic compartments, and that, within compartments, several signatures of sequence degeneration were greater for differentially expressed than non-differentially expressed genes. Two particular degenerative signatures were significantly associated with lower expression levels within differentially expressed allele pairs: upstream insertion of transposable elements and mutations truncating the protein length. Other degenerative mutations associated with differential expression included nonsynonymous substitutions and altered intron or GC content. The association between differential expression and allele degeneration is relevant for a broad range of taxa where mating compatibility or sex is determined by genes located in large regions where recombination is suppressed.

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33

ODIBAT, OMAR, and CHANDAN K. REDDY. "RANKING DIFFERENTIAL HUBS IN GENE CO-EXPRESSION NETWORKS." Journal of Bioinformatics and Computational Biology 10, no. 01 (February 2012): 1240002. http://dx.doi.org/10.1142/s0219720012400021.

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Identifying the genes that change their expressions between two conditions (such as normal versus cancer) is a crucial task that can help in understanding the causes of diseases. Differential networking has emerged as a powerful approach to detect the changes in network structures and to identify the differentially connected genes among two networks. However, existing differential network-based methods primarily depend on pairwise comparisons of the genes based on their connectivity. Therefore, these methods cannot capture the essential topological changes in the network structures. In this paper, we propose a novel algorithm, DiffRank, which ranks the genes based on their contribution to the differences between the two networks. To achieve this goal, we define two novel structural scoring measures: a local structure measure (differential connectivity) and a global structure measure (differential betweenness centrality). These measures are optimized by propagating the scores through the network structure and then ranking the genes based on these propagated scores. We demonstrate the effectiveness of DiffRank on synthetic and real datasets. For the synthetic datasets, we developed a simulator for generating synthetic differential scale-free networks, and we compared our method with existing methods. The comparisons show that our algorithm outperforms these existing methods. For the real datasets, we apply the proposed algorithm on several gene expression datasets and demonstrate that the proposed method provides biologically interesting results.

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34

Bystrykh, Leonid. "Python for gene expression." F1000Research 10 (June 23, 2022): 870. http://dx.doi.org/10.12688/f1000research.53842.2.

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Genome biology shows substantial progress in its analytical and computational part in the last decades. Differential gene expression is one of many computationally intense areas; it is largely developed under R programming language. Here we explain possible reasons for such dominance of R in gene expression data. Next, we discuss the prospects for Python to become competitive in this area of research in coming years. We indicate that Python can be used already in a field of a single cell differential gene expression. We pinpoint still missing parts in Python and possibilities for improvement.

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35

Bystrykh, Leonid. "Python for gene expression." F1000Research 10 (August 31, 2021): 870. http://dx.doi.org/10.12688/f1000research.53842.1.

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Genome biology shows substantial progress in its analytical and computational part in the last decades. Differential gene expression is one of many computationally intense areas; it is largely developed under R programming language. Here we explain possible reasons for such dominance of R in gene expression data. Next, we discuss the prospects for Python to become competitive in this area of research in coming years. We indicate that Python can be used already in a field of a single cell differential gene expression. We pinpoint still missing parts in Python and possibilities for improvement.

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36

Monajemi, Houshang, E. Karin Arkenbout, and Hans Pannekoek. "Gene Expression in Atherogenesis." Thrombosis and Haemostasis 86, no. 07 (2001): 404–12. http://dx.doi.org/10.1055/s-0037-1616238.

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SummaryIt is conceivable that the extent and spatio-temperal expression of dozens or even a few hundred genes are significantly altered during the development and progression of atherosclerosis as compared to normal circumstances. Differential gene expression in vascular cells and in blood cells, due to gene-gene and gene-environment interactions can be considered the molecular basis for this disease. To comprehend the coherence of the complex genetic response to systemic and local atherosclerotic challenges, one needs accessible high through-put technologies to analyze a panel of differentially expressed genes and to describe the interactions between and among their gene products. Fortunately, new technologies have been developed which allow a complete inventory of differential gene expression, i.e. DD/RT-PCR, SAGE and DNA micro-array. The initial data on the application of these technologies in cardiovascular research are now being reported. This review summarizes a number of key observations. Special attention is paid to a few central transcription factors which are differentially expressed in endothelial cells, smooth muscle cells or monocytes/ macrophages. Recent data on the role of nuclear factor- B (NF-κB) and peroxisome proliferation-activating receptors (PPARs) are discussed. Like the PPARs, the NGFI-B subfamily of orphan receptors (TR3, MINOR and NOT) also belongs to the steroid/thryroid hormone receptor superfamily of transcription factors. We report that this subfamily is specifically induced in a sub-population of neointimal smooth muscle cells. Furthermore, intriguing new data implicating the Sp/XKLF family of transcription factors in cell-cell communication and maintenance of the atherogenic phenotype are mentioned. A member of the Sp/XKLF family, the shear stress-regulated lung Krüppel-like factor (LKLF) is speculated to be instrumental for the communication between endothelial cells and smooth muscle cells. Taken together, the expectation is that the fundamental knowledge obtained on atherogenesis and the data that will be acquired during the coming decade with the new, powerful high through-put methodologies will lead to novel modalities to treat patients suffering from cardiovascular disease. In view of the phenotypic changes of vascular and blood-borne cells during atherogenesis, therapeutic interventions likely will focus on reversal of an acquired phenotype by gene therapy approach or by using specific drugs which interfere with aberrant gene expression.

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37

Chen, Z. J. "Gene expression profiling using a novel method: amplified differential gene expression (ADGE)." Nucleic Acids Research 29, no. 10 (May 15, 2001): 46e—46. http://dx.doi.org/10.1093/nar/29.10.e46.

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38

Pan, Wei. "Incorporating gene functional annotations in detecting differential gene expression." Journal of the Royal Statistical Society: Series C (Applied Statistics) 55, no. 3 (February 28, 2006): 301–16. http://dx.doi.org/10.1111/1467-9876.00066-i1.

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39

Stegmaier, Philip. "Identifying the Causes of Differential Gene Expression." Genetic Engineering & Biotechnology News 32, no. 8 (April 15, 2012): 22–24. http://dx.doi.org/10.1089/gen.32.8.09.

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40

Kotlyarov, Stanislav, and Anna Kotlyarova. "Analysis of ABC Transporter Gene Expression in Atherosclerosis." Cardiogenetics 11, no. 4 (November 4, 2021): 204–20. http://dx.doi.org/10.3390/cardiogenetics11040021.

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ABC transporters are a large family of membrane proteins that transport chemically diverse substrates across the cell membrane. Disruption of transport mechanisms mediated by ABC transporters causes the development of various diseases, including atherosclerosis. Methods: A bioinformatic analysis of a dataset from Gene Expression Omnibus (GEO) was performed. A GEO dataset containing data on gene expression levels in samples of atherosclerotic lesions and control arteries without atherosclerotic lesions from carotid, femoral, and infrapopliteal arteries was used for analysis. To evaluate differentially expressed genes, a bioinformatic analysis was performed in comparison groups using the limma package in R (v. 4.0.2) and the GEO2R and Phantasus tools (v. 1.11.0). Results: The obtained data indicate the differential expression of many ABC transporters belonging to different subfamilies. The differential expressions of ABC transporter genes involved in lipid transport, mechanisms of multidrug resistance, and mechanisms of ion exchange are shown. Differences in the expression of transporters in tissue samples from different arteries are established. Conclusions: The expression of ABC transporter genes demonstrates differences in atherosclerotic samples and normal arteries, which may indicate the involvement of transporters in the pathogenesis of atherosclerosis.

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41

Shaw, Elisabeth J., Brian Haylock, David Husband, Daniel du Plessis, D. Ross Sibson, Peter C. Warnke, and Carol Walker. "Gene Expression in Oligodendroglial Tumors." Analytical Cellular Pathology 33, no. 2 (2010): 81–94. http://dx.doi.org/10.1155/2010/304806.

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Background: Oligodendroglial tumors with 1p/19q loss are more likely to be chemosensitive and have longer survival than those with intact 1p/19q, but not all respond to chemotherapy, warranting investigation of the biological basis of chemosensitivity.Methods: Gene expression profiling was performed using amplified antisense RNA from 28 oligodendroglial tumors treated with chemotherapy (26 serial stereotactic biopsy, 2 resection). Expression of differentially expressed genes was validated by real-time PCR.Results: Unsupervised hierarchical clustering showed clustering of multiple samples from the same case in 14/17 cases and identified subgroups associated with tumor grade and 1p/19q status. 176 genes were differentially expressed, 164 being associated with 1p/19q loss (86% not on 1p or 19q). 94 genes differed between responders and non-responders to chemotherapy; 12 were not associated with 1p/19q loss. Significant differential expression was confirmed in 11/13 selected genes. Novel genes associated with response to therapy includedSSBP2,GFRA1,FAPandRASD1.IQGAP1,INA,TGIF1,NR2F2andMYCBPwere differentially expressed in oligodendroglial tumors with 1p/19q loss.Conclusion: Gene expression profiling using serial stereotactic biopsies indicated greater homogeneity within tumors than between tumors. Genes associated with 1p/19q status or response were identified warranting further elucidation of their role in oligodendroglial tumors.

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42

Alto, Sarah I., Chih-Ning Chang, Kevin Brown, Chrissa Kioussi, and Theresa M. Filtz. "Gene Expression Profiling of Skeletal Muscles." Genes 12, no. 11 (October 28, 2021): 1718. http://dx.doi.org/10.3390/genes12111718.

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Next-generation sequencing provides an opportunity for an in-depth biocomputational analysis to identify gene expression patterns between soleus and tibialis anterior, two well-characterized skeletal muscles, and analyze their gene expression profiling. RNA read counts were analyzed for differential gene expression using the R package edgeR. Differentially expressed genes were filtered using a false discovery rate of less than 0.05 c, a fold-change value of more than twenty, and an association with overrepresented pathways based on the Reactome pathway over-representation analysis tool. Most of the differentially expressed genes associated with soleus are coded for components of lipid metabolism and unique contractile elements. Differentially expressed genes associated with tibialis anterior encoded mostly for glucose and glycogen metabolic pathway regulatory enzymes and calcium-sensitive contractile components. These gene expression distinctions partly explain the genetic basis for skeletal muscle specialization, and they may help to explain skeletal muscle susceptibility to disease and drugs and further refine tissue engineering approaches.

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43

Mazzarella, Richard, Gina Pengue, Jaeyoung Yoon, Jonathan Jones, and David Schlessinger. "Differential Expression of XAP5, a Candidate Disease Gene." Genomics 45, no. 1 (October 1997): 216–19. http://dx.doi.org/10.1006/geno.1997.4912.

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44

Fann, Monchou, Jason M. Godlove, Marta Catalfamo, William H. Wood, Francis J. Chrest, Nicholas Chun, Larry Granger, et al. "Histone acetylation is associated with differential gene expression in the rapid and robust memory CD8+ T-cell response." Blood 108, no. 10 (November 15, 2006): 3363–70. http://dx.doi.org/10.1182/blood-2006-02-005520.

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Abstract To understand the molecular basis for the rapid and robust memory T-cell responses, we examined gene expression and chromatin modification by histone H3 lysine 9 (H3K9) acetylation in resting and activated human naive and memory CD8+ T cells. We found that, although overall gene expression patterns were similar, a number of genes are differentially expressed in either memory or naive cells in their resting and activated states. To further elucidate the basis for differential gene expression, we assessed the role of histone H3K9 acetylation in differential gene expression. Strikingly, higher H3K9 acetylation levels were detected in resting memory cells, prior to their activation, for those genes that were differentially expressed following activation, indicating that hyperacetylation of histone H3K9 may play a role in selective and rapid gene expression of memory CD8+ T cells. Consistent with this model, we showed that inducing high levels of H3K9 acetylation resulted in an increased expression in naive cells of those genes that are normally expressed differentially in memory cells. Together, these findings suggest that differential gene expression mediated at least in part by histone H3K9 hyperacetylation may be responsible for the rapid and robust memory CD8+ T-cell response.

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Zhang, H., R. Zhang, and P. Liang. "Differential Screening of Gene Expression Difference Enriched by Differential Display." Nucleic Acids Research 24, no. 12 (June 1, 1996): 2454–55. http://dx.doi.org/10.1093/nar/24.12.2454.

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Ma, Guowei, Mingyan Liu, Ke Du, Xin Zhong, Shiqiang Gong, Linchi Jiao, and Minjie Wei. "Differential Expression of mRNAs in the Brain Tissues of Patients with Alzheimer’s Disease Based on GEO Expression Profile and Its Clinical Significance." BioMed Research International 2019 (February 26, 2019): 1–9. http://dx.doi.org/10.1155/2019/8179145.

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Background. Early diagnosis of Alzheimer’s disease (AD) is an urgent point for AD prevention and treatment. The biomarkers of AD still remain indefinite. Based on the bioinformatics analysis of mRNA differential expressions in the brain tissues and the peripheral blood samples of Alzheimer’s disease (AD) patients, we investigated the target mRNAs that could be used as an AD biomarker and developed a new effective, practical clinical examination program. Methods. We compared the AD peripheral blood mononuclear cells (PBMCs) expression dataset (GEO accession GSE4226 and GSE18309) with AD brain tissue expression datasets (GEO accessions GSE1297 and GSE5281) from GEO in the present study. The GEO gene database was used to download the appropriate gene expression profiles to analyze the differential mRNA expressions between brain tissue and blood of AD patients and normal elderly. The Venn diagram was used to screen out the differential expression of mRNAs between the brain tissue and blood. The protein-protein interaction network map (PPI) was used to view the correlation between the possible genes. GO (gene ontology) and KEGG (Kyoto Gene and Genomic Encyclopedia) were used for gene enrichment analysis to determine the major affected genes and the function or pathway. Results. Bioinformatics analysis revealed that there were differentially expressed genes in peripheral blood and hippocampus of AD patients. There were 4958 differential mRNAs in GSE18309, 577 differential mRNAs in GSE4226 in AD PBMCs sample, 7464 differential mRNAs in GSE5281, and 317 differential mRNAs in GSE129 in AD brain tissues, when comparing between AD patients and healthy elderly. Two mRNAs of RAB7A and ITGB1 coexpressed in hippocampus and peripheral blood were screened. Furthermore, functions of differential genes were enriched by the PPI network map, GO, and KEGG analysis, and finally the chemotaxis, adhesion, and inflammatory reactions were found out, respectively. Conclusions. ITGB1 and RAB7A mRNA expressions were both changed in hippocampus and PBMCs, highly suggested being used as an AD biomarker with AD. Also, according to the results of this analysis, it is indicated that we can test the blood routine of the elderly for 2-3 years at a frequency of 6 months or one year. When a patient continuously detects the inflammatory manifestations, it is indicated as a potentially high-risk AD patient for AD prevention.

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Yoon, Man Soo, Ki Hyung Kim, Dong Hyung Lee, Jee Yeon Kim, and Kyung Un Choi. "Differential Gene Expression in Uterine Cervical Cancer." Korean Journal of Gynecologic Oncology and Colposcopy 15, no. 4 (2004): 301. http://dx.doi.org/10.3802/kjgoc.2004.15.4.301.

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Krieger, Marco Aurelio, Andrea Rodrigues Ávila, Sueli Fumie Yamada Ogatta, Claire Plazanet-Menut, and Samuel Goldenberg. "Differential gene expression during Trypanosoma cruzi metacyclogenesis." Memórias do Instituto Oswaldo Cruz 94, suppl 1 (September 1999): 165–68. http://dx.doi.org/10.1590/s0074-02761999000700021.

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Meister, R., and U. Mansmann. "Testing Differential Gene Expression in Functional Groups." Methods of Information in Medicine 44, no. 03 (2005): 449–53. http://dx.doi.org/10.1055/s-0038-1633992.

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Summary Objectives: Single genes are not, in general, the primary focus of gene expression experiments. The researcher might be more interested in relevant pathways, functional sets, or genomic regions consisting of several genes. Efficient statistical tools to handle this task are of interest to research of biology and medicine. Methods: A simultaneous test on phenotype main effect and gene-phenotype interaction in a two-way layout linear model is introduced as a global test on differential expression for gene groups. Its statistical properties are compared with those of the global test for groups of genes by Goeman et al. [5] in a preliminary simulation study. The procedure presented also allows adjusting for covariates. Results: The proposed ANCOVA global test is equivalent to Goeman’s global test in a setting of independent genes. In our simulation setting for correlated genes, both tests lose power, however with a stronger loss for Goeman’s test. Especially in cases where the asymptotic distribution cannot be used, the stratified use of the ANCOVA global test shows a better performance than Goeman’s test. Conclusions: Our ANCOVA-based approach is a competitive alternative to Goeman’s global test in assessing differential gene expression between groups. It can be extended and generalized in several ways by a modification of the projection matrix.

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Skubitz, Keith M., and Amy P. N. Skubitz. "Differential gene expression in renal-cell cancer." Journal of Laboratory and Clinical Medicine 140, no. 1 (July 2002): 52–64. http://dx.doi.org/10.1067/mlc.2002.125213.

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