Journal articles: 'Tissue specificity'

1

Vinogradov, A. E. "Isochores and tissue-specificity." Nucleic Acids Research 31, no. 17 (September 1, 2003): 5212–20. http://dx.doi.org/10.1093/nar/gkg699.

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Pei, Guangsheng, Yulin Dai, Zhongming Zhao, and Peilin Jia. "deTS: tissue-specific enrichment analysis to decode tissue specificity." Bioinformatics 35, no. 19 (March 1, 2019): 3842–45. http://dx.doi.org/10.1093/bioinformatics/btz138.

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Abstract Motivation Diseases and traits are under dynamic tissue-specific regulation. However, heterogeneous tissues are often collected in biomedical studies, which reduce the power in the identification of disease-associated variants and gene expression profiles. Results We present deTS, an R package, to conduct tissue-specific enrichment analysis with two built-in reference panels. Statistical methods are developed and implemented for detecting tissue-specific genes and for enrichment test of different forms of query data. Our applications using multi-trait genome-wide association studies data and cancer expression data showed that deTS could effectively identify the most relevant tissues for each query trait or sample, providing insights for future studies. Availability and implementation https://github.com/bsml320/deTS and CRAN https://cran.r-project.org/web/packages/deTS/ Supplementary information Supplementary data are available at Bioinformatics online.

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Lundborg, Göran, Lars B. Dahlin, Nils Danielsen, and Ann K. Nachemson. "Tissue Specificity in Nerve Regeneration." Scandinavian Journal of Plastic and Reconstructive Surgery 20, no. 3 (January 1986): 279–83. http://dx.doi.org/10.3109/02844318609004486.

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Milgrom, Felix. "Tissue Specificity and Autoimmune Responses." Immunological Investigations 18, no. 1-4 (January 1989): xxxi—xliv. http://dx.doi.org/10.3109/08820138909112222.

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Rossignol, Rodrigue, Monique Malgat, Jean-Pierre Mazat, and Thierry Letellier. "Threshold Effect and Tissue Specificity." Journal of Biological Chemistry 274, no. 47 (November 19, 1999): 33426–32. http://dx.doi.org/10.1074/jbc.274.47.33426.

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Alderton, Gemma. "Tissue specificity in cancer drivers." Science 363, no. 6432 (March 14, 2019): 1187.14–1189. http://dx.doi.org/10.1126/science.363.6432.1187-n.

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Aguet, François, and Kristin G. Ardlie. "Tissue Specificity of Gene Expression." Current Genetic Medicine Reports 4, no. 4 (September 29, 2016): 163–69. http://dx.doi.org/10.1007/s40142-016-0105-2.

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Funder, John W. "Target tissue specificity of mineralocorticoids." Trends in Endocrinology & Metabolism 1, no. 3 (January 1990): 145–48. http://dx.doi.org/10.1016/1043-2760(90)90026-y.

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9

de Graaf, D. C., and F. J. Jacobs. "Tissue specificity of Nosema apis." Journal of Invertebrate Pathology 58, no. 2 (September 1991): 277–78. http://dx.doi.org/10.1016/0022-2011(91)90073-y.

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Miyazaki, Jun-Ichi, Toshiki Makioka, Yoshihiro Fujiwara, and Tamio Hirabayashi. "Tissue specificity of crustacean tropomyosin." Journal of Experimental Zoology 263, no. 3 (September 1, 1992): 235–44. http://dx.doi.org/10.1002/jez.1402630303.

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Miyazaki, Jun-Ichi, Kensuke Yahata, Toshiki Makioka, and Tamio Hirabayashi. "Tissue specificity of arthropod tropomyosin." Journal of Experimental Zoology 267, no. 5 (December 1, 1993): 501–9. http://dx.doi.org/10.1002/jez.1402670505.

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12

Gale, RE, H. Wheadon, P. Boulos, and DC Linch. "Tissue specificity of X-chromosome inactivation patterns." Blood 83, no. 10 (May 15, 1994): 2899–905. http://dx.doi.org/10.1182/blood.v83.10.2899.2899.

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Abstract The analysis of X-chromosome inactivation patterns has been used in a number of clinical situations such as the identification of carrier status in X-linked genetic disorders and the establishment of the monoclonal origin of tumors. Interpretation of the result obtained requires comparison with the constitutive pattern for the individual, and for hematopoietic malignancies, skin biopsies or cultured fibroblasts have often been used as the control tissue because normal cells of the same lineage as the malignancy are not generally available. However, this assumes that patterns in the different tissues are constitutionally the same. We have therefore compared X-chromosome inactivation patterns from peripheral blood (granulocytes, E- cells, and T cells), skin, and muscle from 20 hematologically normal females, and colonic mucosa from 9 individuals. In 11 patients (55%), the results obtained were similar for all tissues of an individual, but in 9 patients, significant differences were observed between tissues. The most consistent feature was a skewing in peripheral blood (> 75% expression of one allele) but not skin and/or muscle. These studies suggest that skin cannot be used as a control tissue for the interpretation of X-chromosome inactivation patterns in hematopoietic cells.

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McComb, Ellen A., A. Raymond Miller, and Joseph C. Scheerens. "Tissue Specificity of `Chandler' Strawberry Peroxidase Isozymes." HortScience 32, no. 3 (June 1997): 439B—439. http://dx.doi.org/10.21273/hortsci.32.3.439b.

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Peroxidase activity in extracts from freeze-dried tissue of Fragaria × ananassa Duch. cv. Chandler was highest in tissue-cultured (TC) plants, followed by field-grown (FG) and lowest in greenhouse (GH) plants. Among tissue types, activity was highest in petioles, with leaves second highest. Fruit, root, and crown tissue all exhibited low or no activity. When subjected to isoelectric focusing (IEF), petiole tissue extracts exhibited more isozymes than extracts from other organs regardless of staining substrate. Using 4-chloro-1-naphthol and H2O2 as substrates, anionic and cationic isozymes were observed in TC petiole extract with nine isozyme bands ranging in pI from 3.9 to 9.5. In TC leaf extract an isozyme at pI 7.4 was observed that was not present in other organ extracts when H2O2 and benzidine, p-phenylenediamine or 3-amino-9-ethylcarbazole were used as substrates. Specific isozymes and number of isozymes varied according to plant organ and developmental stage. Mature leaves and over-ripe fruit appeared to exhibit more activity and a larger number of isozymes than developing tissues of those plant organs.

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14

Au, Binh, Tim Seabrook, William Andrade, Christopher A. G. McCulloch, and Jack B. Hay. "Tissue specificity of lymphocyte migration into sheep gingival tissue." Archives of Oral Biology 46, no. 9 (September 2001): 835–45. http://dx.doi.org/10.1016/s0003-9969(01)00038-3.

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15

Frost, H. Robert. "Analyzing cancer gene expression data through the lens of normal tissue-specificity." PLOS Computational Biology 17, no. 6 (June 18, 2021): e1009085. http://dx.doi.org/10.1371/journal.pcbi.1009085.

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The genetic alterations that underlie cancer development are highly tissue-specific with the majority of driving alterations occurring in only a few cancer types and with alterations common to multiple cancer types often showing a tissue-specific functional impact. This tissue-specificity means that the biology of normal tissues carries important information regarding the pathophysiology of the associated cancers, information that can be leveraged to improve the power and accuracy of cancer genomic analyses. Research exploring the use of normal tissue data for the analysis of cancer genomics has primarily focused on the paired analysis of tumor and adjacent normal samples. Efforts to leverage the general characteristics of normal tissue for cancer analysis has received less attention with most investigations focusing on understanding the tissue-specific factors that lead to individual genomic alterations or dysregulated pathways within a single cancer type. To address this gap and support scenarios where adjacent normal tissue samples are not available, we explored the genome-wide association between the transcriptomes of 21 solid human cancers and their associated normal tissues as profiled in healthy individuals. While the average gene expression profiles of normal and cancerous tissue may appear distinct, with normal tissues more similar to other normal tissues than to the associated cancer types, when transformed into relative expression values, i.e., the ratio of expression in one tissue or cancer relative to the mean in other tissues or cancers, the close association between gene activity in normal tissues and related cancers is revealed. As we demonstrate through an analysis of tumor data from The Cancer Genome Atlas and normal tissue data from the Human Protein Atlas, this association between tissue-specific and cancer-specific expression values can be leveraged to improve the prognostic modeling of cancer, the comparative analysis of different cancer types, and the analysis of cancer and normal tissue pairs.

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Myhan, Ryszard, and Marek Markowski. "The compression specificity of plant tissue." Journal of Texture Studies 51, no. 4 (February 19, 2020): 593–600. http://dx.doi.org/10.1111/jtxs.12512.

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17

LESLIE, J. B. "Haemodynamics and tissue specificity with isradipine." Acta Anaesthesiologica Scandinavica 37 (September 1993): 33–37. http://dx.doi.org/10.1111/j.1399-6576.1993.tb03822.x.

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18

Bolger, Gordon T., Francine Liard, Richard Krogsrud, Diane Thibeault, and Jorge Jaramillo. "Tissue Specificity of Endothelin Binding Sites." Journal of Cardiovascular Pharmacology 16, no. 3 (September 1990): 367–75. http://dx.doi.org/10.1097/00005344-199009000-00004.

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19

Mijatovic, V. "Receptor selectivity, enzymes and tissue specificity." Maturitas 37, no. 3 (January 2001): 147–49. http://dx.doi.org/10.1016/s0378-5122(00)00185-7.

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20

De Loof, Arnold. "Tissue specificity of steroid hormone action." Insect Biochemistry 16, no. 1 (January 1986): 169–73. http://dx.doi.org/10.1016/0020-1790(86)90092-2.

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21

Ahmad, Sami, Dawn L. Duval, Leanne C. Weinhold, and Ronald S. Pardini. "Cabbage looper antioxidant enzymes: Tissue specificity." Insect Biochemistry 21, no. 5 (January 1991): 563–72. http://dx.doi.org/10.1016/0020-1790(91)90111-q.

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22

Sun, Siman, Michael D. Osterman, and Mo Li. "Tissue specificity of DNA damage response and tumorigenesis." Cancer Biology & Medicine 16, no. 3 (August 1, 2019): 396–414. http://dx.doi.org/10.20892/j.issn.2095-3941.2019.0097.

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The genome of cells is constantly challenged by DNA damages from endogenous metabolism and environmental agents. These damages could potentially lead to genomic instability and thus to tumorigenesis. To cope with the threats, cells have evolved an intricate network, namely DNA damage response (DDR) system that senses and deals with the lesions of DNA. Although the DDR operates by relatively uniform principles, different tissues give rise to distinct types of DNA damages combined with high diversity of microenvironments across tissues. In this review, we discuss recent findings on specific DNA damage among different tissues as well as the main DNA repair way in corresponding microenvironments, highlighting tissue specificity of DDR and tumorigenesis. We hope the current review will provide further insights into molecular process of tumorigenesis and generate new strategies for cancer treatment.

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23

Godfraind, T., N. Morel, and M. Wibo. "Tissue specificity of dihydropyridine-type calcium antagonists in human isolated tissues." Trends in Pharmacological Sciences 9, no. 1 (January 1988): 37–39. http://dx.doi.org/10.1016/0165-6147(88)90241-6.

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24

Shi, Chun-Sheng, Na Shu, Li-Li Jiang, and Bo Jiang. "Expression and role of specificity protein 1 and collagen I in recurrent pterygial tissues." International Journal of Ophthalmology 14, no. 2 (February 18, 2021): 223–27. http://dx.doi.org/10.18240/ijo.2021.02.07.

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AIM: To investigate the expression profiles of the transcription factor specificity protein 1 (Sp1) and collagen I in recurrent pterygial tissues. What is more, to compare the changes of Sp1 and collagen I among primary pterygial tissue, recurrent pterygial tissue and conjunctival tissue. METHODS: In the prospective study, we collected the pterygial tissues of 40 patients who underwent resection of primary pterygial tissue and recurrent pterygial tissue, and the conjunctival tissues of 10 patients with enucleation due to trauma. The relative expression levels of Sp1 and collagen I were analyzed by reverse transcription quantitative-polymerase chain reaction and Western blot. Paired t-test was performed to compare the Sp1 and collagen I of recurrent pterygial tissues, as well as the primary pterygial tissues and conjunctival tissues. In further, Pearson’s hypothesis testing of correlation coefficients was used to compare the correlations of Sp1 and Collagen I. RESULTS: The content of Sp1 and collagen I mRNA and protein was significantly greater in recurrent pterygial tissue than that was in primary and conjunctival tissue (P<0.05). There was a positive correlation between the mRNA and protein levels of Sp1 and collagen I in recurrent pterygial tissues (protein: r=0.913, P<0.05; mRNA: r=0.945, P<0.05). CONCLUSION: Sp1 and collagen I are expressed in normal conjunctival, primary, and recurrent pterygial tissues, but expression is significantly greater in the latter. Sp1 and collagen I may be involved in the regulation of the development of recurrent pterygium.

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Yu, Mubin, Xiaoyuan Zhang, Jiamao Yan, Jianhua Guo, Fali Zhang, Kexin Zhu, Shuqin Liu, Yujiang Sun, Wei Shen, and Junjie Wang. "Transcriptional Specificity Analysis of Testis and Epididymis Tissues in Donkey." Genes 13, no. 12 (December 11, 2022): 2339. http://dx.doi.org/10.3390/genes13122339.

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Donkeys, with high economic value for meat, skin and milk production, are important livestock. However, the current insights into reproduction of donkeys are far from enough. To obtain a deeper understanding, the differential expression analysis and weighted gene co-expression network analysis (WGCNA) of transcriptomic data of testicular and epididymis tissues in donkeys were performed. In the result, there were 4313 differentially expressed genes (DEGs) in the two tissues, including 2047 enriched in testicular tissue and 2266 in epididymis tissue. WGCNA identified 1081 hub genes associated with testis development and 6110 genes with epididymal development. Next, the tissue-specific genes were identified with the above two methods, and the gene ontology (GO) analysis revealed that the epididymal-specific genes were associated with gonad development. On the other hand, the testis-specific genes were involved in the formation of sperm flagella, meiosis period, ciliary assembly, ciliary movement, etc. In addition, we found that eca-Mir-711 and eca-Mir-143 likely participated in regulating the development of epididymal tissue. Meanwhile, eca-Mir-429, eca-Mir-761, eca-Mir-200a, eca-Mir-191 and eca-Mir-200b potentially played an important role in regulating the development of testicular tissue. In short, these results will contribute to functional studies of the male reproductive trait in donkeys.

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Zhuang, Jimmy J., and Craig P. Hunter. "Tissue Specificity ofCaenorhabditis elegansEnhanced RNA Interference Mutants." Genetics 188, no. 1 (March 8, 2011): 235–37. http://dx.doi.org/10.1534/genetics.111.127209.

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27

Ruiz-Bravo, Norka. "Tissue and Cell Specificity of Immobilin Biosynthesis1." Biology of Reproduction 39, no. 4 (November 1, 1988): 901–11. http://dx.doi.org/10.1095/biolreprod39.4.901.

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28

Petretto, Enrico, Jonathan M. Mangion, Nicholas Dickens, Stuart Cook, Kumaran K. Mandek, Han Lu, Judith Fischer, et al. "Heritability and Tissue-Specificity of Expression QTLs." PLoS Genetics preprint, no. 2006 (2005): e172. http://dx.doi.org/10.1371/journal.pgen.0020172.eor.

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Masaki, Tomoh. "Tissue Specificity of the Endothelin-Induced Responses." Journal of Cardiovascular Pharmacology 17 (1991): s1–4. http://dx.doi.org/10.1097/00005344-199100177-00002.

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MINEGISHI, YOSHIHIKO. "Tissue-specificity of cholesterol biosynthesis control mechanism." Kagaku To Seibutsu 41, no. 6 (2003): 375–77. http://dx.doi.org/10.1271/kagakutoseibutsu1962.41.375.

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31

Hattori, Tasuku, Shiro Mitsuya, Takashi Fujiwara, Andre T. Jagendorf, and Tetsuko Takabe. "Tissue specificity of glycinebetaine synthesis in barley." Plant Science 176, no. 1 (January 2009): 112–18. http://dx.doi.org/10.1016/j.plantsci.2008.10.003.

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Ishimoda-Takagi, Tadashi, Mitsuaki Kobayashi, and Masako Yaguchi. "Polymorphism and tissue specificity of scallop tropomyosin." Comparative Biochemistry and Physiology Part B: Comparative Biochemistry 83, no. 3 (January 1986): 515–21. http://dx.doi.org/10.1016/0305-0491(86)90289-0.

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Seliskar, Matej, and Damjana Rozman. "Mammalian cytochromes P450—Importance of tissue specificity." Biochimica et Biophysica Acta (BBA) - General Subjects 1770, no. 3 (March 2007): 458–66. http://dx.doi.org/10.1016/j.bbagen.2006.09.016.

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Perera, Bambarendage P. U., and Joomyeong Kim. "Sex and Tissue Specificity of Peg3 Promoters." PLOS ONE 11, no. 10 (October 6, 2016): e0164158. http://dx.doi.org/10.1371/journal.pone.0164158.

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35

Benito, Manuel. "Tissue-specificity of insulin action and resistance*." Archives of Physiology and Biochemistry 117, no. 3 (April 20, 2011): 96–104. http://dx.doi.org/10.3109/13813455.2011.563748.

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36

Crooks, Daniel R., Suh Young Jeong, Wing-Hang Tong, Manik C. Ghosh, Hayden Olivierre, Ronald G. Haller, and Tracey A. Rouault. "Tissue Specificity of a Human Mitochondrial Disease." Journal of Biological Chemistry 287, no. 48 (October 3, 2012): 40119–30. http://dx.doi.org/10.1074/jbc.m112.418889.

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37

Yao, Fupan, Seyed Ali Madani Tonekaboni, Zhaleh Safikhani, Petr Smirnov, Nehme El-Hachem, Mark Freeman, Venkata Satya Kumar Manem, and Benjamin Haibe-Kains. "Tissue specificity of in vitro drug sensitivity." Journal of the American Medical Informatics Association 25, no. 2 (July 7, 2017): 158–66. http://dx.doi.org/10.1093/jamia/ocx062.

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Abstract Objectives We sought to investigate the tissue specificity of drug sensitivities in large-scale pharmacological studies and compare these associations to those found in drug clinical indications. Materials and Methods We leveraged the curated cell line response data from PharmacoGx and applied an enrichment algorithm on drug sensitivity values’ area under the drug dose-response curves (AUCs) with and without adjustment for general level of drug sensitivity. Results We observed tissue specificity in 63% of tested drugs, with 8% of total interactions deemed significant (false discovery rate <0.05). By restricting the drug-tissue interactions to those with AUC > 0.2, we found that in 52% of interactions, the tissue was predictive of drug sensitivity (concordance index > 0.65). When compared with clinical indications, the observed overlap was weak (Matthew correlation coefficient, MCC = 0.0003, P > .10). Discussion While drugs exhibit significant tissue specificity in vitro, there is little overlap with clinical indications. This can be attributed to factors such as underlying biological differences between in vitro models and patient tumors, or the inability of tissue-specific drugs to bring additional benefits beyond gold standard treatments during clinical trials. Conclusion Our meta-analysis of pan-cancer drug screening datasets indicates that most tested drugs exhibit tissue-specific sensitivities in a large panel of cancer cell lines. However, the observed preclinical results do not translate to the clinical setting. Our results suggest that additional research into showing parallels between preclinical and clinical data is required to increase the translational potential of in vitro drug screening.

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Madison, Edwin L., Gary S. Coombs, and David R. Corey. "Substrate Specificity of Tissue Type Plasminogen Activator." Journal of Biological Chemistry 270, no. 13 (March 31, 1995): 7558–62. http://dx.doi.org/10.1074/jbc.270.13.7558.

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39

KOBAYASHI, SHINYA, YAN GAO, and CONSTANCE S. PITTMAN. "The substrate specificity, tissue specificity and regulation of the 5' deiodination systems in rat liver and kidney tissues." Endocrinologia Japonica 32, no. 6 (1985): 781–92. http://dx.doi.org/10.1507/endocrj1954.32.781.

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40

Wu, Hua, Guolong Zhang, Christopher R. Ross, and Frank Blecha. "Cathelicidin Gene Expression in Porcine Tissues: Roles in Ontogeny and Tissue Specificity." Infection and Immunity 67, no. 1 (January 1, 1999): 439–42. http://dx.doi.org/10.1128/iai.67.1.439-442.1999.

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ABSTRACT Cathelicidins constitute a family of mammalian antimicrobial peptides that are synthesized in the bone marrow as prepropeptides, stored in neutrophil granules as propeptides, and released as active, mature peptides upon neutrophil degranulation. We investigated the developmental expression of two porcine cathelicidins, PR-39 and protegrin. Both cathelicidins were expressed constitutively in the bone marrow of all pigs at all of the ages tested. Peripheral blood neutrophils from young pigs expressed PR-39 and protegrin mRNA, which were not detectable at 42 days of age. At earlier ages, expression of PR-39 mRNA was detected in the kidney and liver and several lymphoid organs, including the thymus, spleen, and mesenteric lymph nodes, but disappeared at 4 weeks of age. These data provide the first evidence of cathelicidin gene expression in peripheral leukocytes and may indicate a role for these antimicrobial peptides in the development of host defense mechanisms.

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Ji, Shaoquan, R. L. Losinski, S. G. Cornelius, G. R. Frank, G. M. Willis, D. E. Gerrard, F. F. S. Depreux, and M. E. Spurlock. "Myostatin expression in porcine tissues: tissue specificity and developmental and postnatal regulation." American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 275, no. 4 (October 1, 1998): R1265—R1273. http://dx.doi.org/10.1152/ajpregu.1998.275.4.r1265.

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The objective of this study was to establish the developmental pattern and tissue specificity of porcine myostatin expression and to evaluate expression in skeletal muscle during circumstances in which muscle growth was altered. Northern blot analysis revealed two transcripts (1.5 and 0.8 kb). Myostatin mRNA was detected in whole fetuses at 21 and 35 days and was markedly increased ( P < 0.05) by 49 days. At birth, mRNA abundance in longissimus muscle had declined significantly ( P < 0.05) from that at day 105 of gestation and continued to decrease ( P < 0.05) to its lowest level 2 wk postnatally (4 kg body wt). Myostatin expression was higher ( P < 0.05) at 55, 107, and 162 kg body wt than at 4 kg body wt. Postnatally, myostatin mRNA was detected in skeletal muscle and mammary gland. Expression at birth was 65% higher ( P < 0.04) in longissimus muscle of low-birth-weight piglets (0.57 ± 0.052 kg body wt) vs. normal (1.37 ± 0.077 kg body wt) littermates, irrespective of gender. However, suppression of longissimus muscle growth by food deprivation (3 days) did not alter ( P > 0.15) myostatin expression in either 4- or 7-wk-old piglets. Additionally, myostatin mRNA abundance was not changed by porcine growth hormone administration in growing animals. These data indicate that myostatin expression in skeletal muscle peaks prenatally and that greater expression is associated with low birth weight. Expression in mammary gland indicates a possible role for myostatin in mammary gland development and/or lactation.

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Biegniewska, Anna, Edward F. Skorkowski, and Kenneth B. Storey. "Tissue specificity of the mitochondrial forms of malic enzyme in herring tissues." Comparative Biochemistry and Physiology Part B: Comparative Biochemistry 95, no. 4 (January 1990): 817–20. http://dx.doi.org/10.1016/0305-0491(90)90322-k.

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Symonds, Erin L., Beibei Yao, Susanne Kartin Pedersen, David Murray, and Graeme P. Young. "Specificity of methylated BCAT1 and IKZF1 for colorectal cancer." Journal of Clinical Oncology 36, no. 4_suppl (February 1, 2018): 580. http://dx.doi.org/10.1200/jco.2018.36.4_suppl.580.

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580 Background: Methylated BCAT1 and IKZF1 are useful circulating tumor DNA (ctDNA) biomarkers for detection and following the course of colorectal cancer (CRC). This study aimed to determine the specificity of methylated BCAT1/ IKZF1 for CRC detection by assaying specimens from patients with other adenocarcinomas. Methods: Blood was collected from patients with invasive adenocarcinoma of the prostate (n = 32), breast (16), oesophagus (15) or colon/rectum (212), prior to any treatment or resection, and from 245 clinically assessed controls with no known prior or current adenocarcinoma. Biopsies were collected from cancer tissue and adjacent non-neoplastic tissue either prior to treatment or at surgery from 9 prostate, 26 breast, 6 oesophagus, 15 CRC cases. All specimens were assayed for methylated BCAT1 and IKZF1 DNA. Calculation of positivity rates: tissue, the proportion of tissue cases with ≥ 5% methylation; blood, the proportion of cases with any detectable signal of either marker. Results: ctDNA positivity rates were significantly higher in CRC (126/212, 59.4%, 95% CI: 52.5 - 66.1) and oesophageal cancer (6/16, 33.3%, 11.0 - 58.7) cases only compared to controls (16/245, 6.5%, 3.8 - 10.4; p < 0.01). ctDNA was more likely to be positive in late stage cancers, although only significant for CRC, Table. Cancer tissue positivity rates were: CRC, 15/15, 100% (96.4 - 100); oesophageal, 5/6, 83.3% (35.9 - 99.6); prostate, 4/9, 44.4% (13.7 - 78.8); breast, 5/26, 19.2% (6.6 - 39.4). All cancer tissues had significantly higher methylation levels than the adjacent tissue (Chi2 test, p >0.05). Conclusions: Only colorectal and oesophageal cancer patients had significantly higher ctDNA positivity rates (using methylated BCAT1/IKZF1) compared to controls. This was also reflected in a higher proportion of cases showing methylation in the cancer tissue. The methylated BCAT1/IKZF1 blood test should be investigated further as a screening and surveillance tool for oesophageal cancer. [Table: see text]

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Goroshinskaya, Irina A., Oleg I. Kit, Elena M. Frantsiyants, Valeria A. Bandovkina, Viktoria V. Pozdnyakova, Alla I. Shikhlyarova, Amira A. Akhmedova, Olga V. Khokhlova, Irina V. Neskubina, and Natalia D. Cheryarina. "Specificity of markers CD44 and S100 for skin melanoma and nevi tissues." Journal of Clinical Oncology 37, no. 15_suppl (May 20, 2019): e21036-e21036. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.e21036.

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e21036 Background: The high metastatic potential of melanoma and the need for long-term patient monitoring causes the search for tumor markers of this malignant neoplasm. Our aim was a comparative analysis of levels of tumor-specific proteins CD44 and S100 and protein composition in melanocytic lesions of the skin. Methods: We studied 86 samples of cutaneous melanoma and nevus tissues, their perifocal tissues and resection line tissues obtained during tumor excision from 23 patients with cutaneous melanoma pT1-4N0-1M0 and 14 patients with nevi. Intact skin samples obtained from non-cancer patients during reconstructive plastic surgery were used as the comparison group. All patients gave their written informed consent. Levels of CD44 (BenderMedSystems, USA) and S100 (Fujirebio, Sweden) were determined by ELISA in 10% homogenates of all tissues; total protein levels were determined by standard spectrometry and fractional composition of proteins were studied by turbidimetric method. Statistical processing of results was performed using the Statistika 6.0 program with Student’s t-test for two independent groups. Results: Melanoma was characterized by a sharp increase in S100B levels, 28 and 7 times exceeding the levels in intact tissues and nevi. The level of CD44 in melanoma tissue was increased only by 2 times, in nevus tissue - by 48%. The ratio of albumin and gamma globulins in the tissue of melanoma and nevi was 79% and 29% lower than in healthy skin. A more than twofold increase in the gamma globulin fraction in the melanoma tumor tissue against a decrease in albumin and the absence of changes in other globulins, as well as a moderate but statistically significant increase in the gamma globulin fraction in nevi tissue, indicates that S100B and CD44 proteins belong to the gamma globulin fraction. Conclusions: A highly specific increase of S100 levels and a less specific increase of CD44 levels in supernatant liquid of melanoma tissue homogenates, together with the predominance of the gamma globulin fraction, allow considering such factors as a prognostically unfavorable sign of tumor progression, which can be important when choosing a personalized treatment strategy.

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Li, Binglan, Yogasudha Veturi, Anurag Verma, Yuki Bradford, Eric S. Daar, Roy M. Gulick, Sharon A. Riddler, et al. "Tissue specificity-aware TWAS (TSA-TWAS) framework identifies novel associations with metabolic, immunologic, and virologic traits in HIV-positive adults." PLOS Genetics 17, no. 4 (April 26, 2021): e1009464. http://dx.doi.org/10.1371/journal.pgen.1009464.

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As a type of relatively new methodology, the transcriptome-wide association study (TWAS) has gained interest due to capacity for gene-level association testing. However, the development of TWAS has outpaced statistical evaluation of TWAS gene prioritization performance. Current TWAS methods vary in underlying biological assumptions about tissue specificity of transcriptional regulatory mechanisms. In a previous study from our group, this may have affected whether TWAS methods better identified associations in single tissues versus multiple tissues. We therefore designed simulation analyses to examine how the interplay between particular TWAS methods and tissue specificity of gene expression affects power and type I error rates for gene prioritization. We found that cross-tissue identification of expression quantitative trait loci (eQTLs) improved TWAS power. Single-tissue TWAS (i.e., PrediXcan) had robust power to identify genes expressed in single tissues, but, often found significant associations in the wrong tissues as well (therefore had high false positive rates). Cross-tissue TWAS (i.e., UTMOST) had overall equal or greater power and controlled type I error rates for genes expressed in multiple tissues. Based on these simulation results, we applied a tissue specificity-aware TWAS (TSA-TWAS) analytic framework to look for gene-based associations with pre-treatment laboratory values from AIDS Clinical Trial Group (ACTG) studies. We replicated several proof-of-concept transcriptionally regulated gene-trait associations, including UGT1A1 (encoding bilirubin uridine diphosphate glucuronosyltransferase enzyme) and total bilirubin levels (p = 3.59×10−12), and CETP (cholesteryl ester transfer protein) with high-density lipoprotein cholesterol (p = 4.49×10−12). We also identified several novel genes associated with metabolic and virologic traits, as well as pleiotropic genes that linked plasma viral load, absolute basophil count, and/or triglyceride levels. By highlighting the advantages of different TWAS methods, our simulation study promotes a tissue specificity-aware TWAS analytic framework that revealed novel aspects of HIV-related traits.

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Paul, M., D. W. Burt, J. E. Krieger, N. Nakamura, and V. J. Dzau. "Tissue specificity of renin promoter activity and regulation in mice." American Journal of Physiology-Endocrinology and Metabolism 262, no. 5 (May 1, 1992): E644—E650. http://dx.doi.org/10.1152/ajpendo.1992.262.5.e644.

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Certain mouse strains (e.g., DBA/2) contain two renin genes (termed Ren-1 and Ren-2) and express higher renin levels in nonkidney tissues than strains with a single renin gene. The 5'-flanking regions of the Ren-1 and Ren-2 genes contain several TATA boxes preceding putative transcriptional start sites. These initiators are termed P1a, P1, P2 (from 5' to 3'), and their function (with the exception of P2) is largely unknown. In this study, we mapped the renin transcriptional start sites in renal and extrarenal tissues [adrenal, brain, testis, heart, and submandibular gland (SMG)] and examined the effect of adenosine 3',5'-cyclic monophosphate (cAMP) on tissue specific promoter usage. Our results showed that, in the unstimulated state, P2 (the predicted initiator) is active in all DBA/2 mouse tissues. Additional transcriptional start sites were detected in the adrenal and testis (originated by P1a and P2) and the SMG (originated by P1a, P1, and P2). The administration of 8-bromoadenosine 3',5'-cyclic monophosphate led to selective stimulation of P1a in the adrenal but did not affect the selective usage of initiation sites in other organs. A locus-specific ddNTP primer extension assay was used to verify which renin gene is induced by cAMP. Results indicated that both Ren-1 and Ren-2 responded to cAMP treatment in identical fashion. Taken together, these data indicate that more than one form of renin transcript is present in several mouse tissues. There is tissue specificity in promoter usage in the unstimulated state and in response to cAMP.

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Lang, H., K. Minaian, N. Freudenberg, R. Hoffmann, and R. Brandsch. "Tissue specificity of rat mitochondrial dimethylglycine dehydrogenase expression." Biochemical Journal 299, no. 2 (April 15, 1994): 393–98. http://dx.doi.org/10.1042/bj2990393.

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Expression of mitochondrial dimethylglycine dehydrogenase (Me2GlyDH) was analysed in various tissues, liver cell types and developmental stages of the rat. Total RNA extracted from liver, spleen, brain, kidney, lung and heart was reverse-transcribed into cDNA and amplified with Me2GlyDH cDNA-specific oligonucleotides by PCR. Expression of the enzyme was observed mainly in liver and kidney. In addition, Me2GlyDH mRNA could be demonstrated in total RNA samples of lung, heart and brain but was barely detectable in spleen total RNA. In RNA prepared from 14-day rat embryos, Me2GlyDH-specific mRNA was clearly present. Among various liver cell types, besides hepatocytes, endothelial cells showed a high level of Me2GlyDH mRNA expression. There was no amplification product detectable in liver macrophages (Kupffer cells) and only a very faint one in fat-storing cells (Ito cells). Western blots confirmed at the protein level the predominant expression of the enzyme in liver and kidney, but Me2GlyDH protein was also present in the protein extract of lung, heart, spleen and brain. Immunohistochemical staining of liver slices with Me2GlyDH-specific antiserum revealed that expression of this enzyme is evenly distributed throughout the liver tissue. In the kidney, expression of the enzyme was located in the proximal tubule cells. Our results demonstrate that, contrary to the previously assumed liver-restricted expression, this enzyme is specifically expressed predominantly in the liver and kidney, but, in addition, it is detectable in many other tissues of the rat.

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Burry, Richard W. "Specificity Controls for Immunocytochemical Methods." Journal of Histochemistry & Cytochemistry 48, no. 2 (February 2000): 163–65. http://dx.doi.org/10.1177/002215540004800201.

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Immunocytochemistry is used for antibody localization of proteins in cells and tissues. The specificity of the results depends on two independent criteria: the specificity of the antibody and of the method used. The antibody specificity is best determined by immunoblot and or immunoprecipitation. Absorption of the antibody with a protein does not determine that the antibody would have bound to the same protein in the tissue, and therefore is not a good control for antibody specificity. The specificity of the method is best determined by both a negative control, replacing the primary antibody with serum, and a positive control, using the antibody with cells known to contain the protein. With the increasing use of immunocytochemistry, it is important to be aware of the appropriate controls needed to show specificity of the labeling.

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Morra, Marc R., and Ian T. D. Petty. "Tissue Specificity of Geminivirus Infection Is Genetically Determined." Plant Cell 12, no. 11 (November 2000): 2259. http://dx.doi.org/10.2307/3871119.

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Suhovskih, A. V., and E. V. Grigor’eva. "Tissue-specificity of proteoglycans expression in different cancers." Advances in molecular oncology 3, no. 1 (April 25, 2016): 53–60. http://dx.doi.org/10.17650/2313-805x.2016.3.1.53-60.

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Journal articles on the topic 'Tissue specificity'

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