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1Academic Journal
Συγγραφείς: D. O. Kuzmina, E. A. Zorin, A. S. Sulima, D. A. Romanyuk, M. L. Gordon, A. I. Zhernako, O. A. Kulaeva, G. A. Akhtemova, O. . Shtark, I. A. Tikhonovich, V. A. Zhukov, Д. О. Кузьмина, Е. А. Зорин, А. С. Сулима, Д. А. Романюк, М. Л. Гордон, А. И. Жернаков, О. А. Кулаева, Г. А. Ахтемова, О. Ю, Штарк, И. А. Тихонович, В. А. Жуков
Συνεισφορές: The study was supported by the Russian Science Foundation (grant # 22-16-00109, https://rscf.ru/project/ 22-16-00109/).
Πηγή: Vavilov Journal of Genetics and Breeding; Том 29, № 2 (2025); 248-258 ; Вавиловский журнал генетики и селекции; Том 29, № 2 (2025); 248-258 ; 2500-3259 ; 10.18699/vjgb-25-20
Θεματικοί όροι: транскриптомика, legume-rhizobial symbiosis, arbuscular mycorrhiza, symbiotic responsivity, transcriptomics, бобово-ризобиальный симбиоз, арбускулярная микориза, симбиотическая отзывчивость
Περιγραφή αρχείου: application/pdf
Relation: https://vavilov.elpub.ru/jour/article/view/4543/1932; Afonin A.M., Gribchenko E.S., Zorin E.A., Sulima A.S., Romanyuk D.A., Zhernakov A.I., Shtark O.Y., Akhtemova G.A., Zhukov V.A. Unique transcriptome features of pea (Pisum sativum L.) lines with differing responses to beneficial soil microorganisms. Ecol Genet. 2021;19(2):131-141. doi 10.17816/ECOGEN54703; Alexa A., Rahnenfuhrer J. topGO: enrichment analysis for gene ontology. R Package Version 2.58.0. 2024. doi 10.18129/B9.bioc.topGO. https://bioconductor.org/packages/topGO; Andrews S. FastQC: a quality control tool for high throughput sequence data. 2010. Available at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc; Bolger A.M., Lohse M., Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30(15):2114-2120. doi 10.1093/bioinformatics/btu170; Camacho C., Coulouris G., Avagyan V., Ma N., Papadopoulos J., Bealer K., Madden T. L. BLAST+: architecture and applications. BMC Bioinformatics. 2009;10:421. doi 10.1186/1471-2105-10-421; Castillejo M.Á., Bani M., Rubiales D. Understanding pea resistance mechanisms in response to Fusarium oxysporum through proteomic analysis. Phytochemistry. 2015;115(1):44-58. doi 10.1016/j.phytochem.2015.01.009; Catford J., Staehelin C., Lerat S., Piché Y., Vierheilig H. Suppression of arbuscular mycorrhizal colonization and nodulation in split‐root systems of alfalfa after pre‐inoculation and treatment with Nod factors. J Exp Bot. 2003;54(386):1481-1487. doi 10.1093/jxb/erg156; Cerna H., Černý M., Habánová H., Šafářová D., Abushamsiya K., Navrátil M., Brzobohatý B. Proteomics offers insight to the mechanism behind Pisum sativum L. response to pea seed-borne mosaic virus (PSbMV). J Proteomics. 2017;153:78-88. doi 10.1016/j.jprot.2016.05.018; Chen H., Boutros P.C. VennDiagram: a package for the generation of highly-customizable Venn and Euler diagrams in R. BMC Bioinformatics. 2011;12:35. doi 10.1186/1471-2105-12-35; Danecek P., Bonfield J.K., Liddle J., Marshall J., Ohan V., Pollard M.O., Whitwham A., Keane T., McCarthy S.A., Davies R.M. Twelve years of SAMtools and BCFtools. GigaScience. 2021;10(2):giab008. doi 10.1093/gigascience/giab008; Dobin A., Davis C.A., Schlesinger F., Drenkow J., Zaleski C., Jha S., Batut P., Chaisson M., Gingeras T.R. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29(1):15-21. doi 10.1093/bioinformatics/bts635; Ewels P., Magnusson M., Lundin S., Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics. 2016;32(19):3047-3048. doi 10.1093/bioinformatics/btw354; Ferguson B.J., Mens C., Hastwell A.H., Zhang M., Su H., Jones C.H., Chu X., Gresshoff P.M. Legume nodulation: the host controls the party. Plant Cell Environ. 2019;42(1):41-51. doi 10.1111/pce.13348; Firmin J.L., Wilson K.E., Carlson R.W., Davies A.E., Downie J.A. Resistance to nodulation of cv. Afghanistan peas is overcome by nodX, which mediates an O‐acetylation of the Rhizobium leguminosarum lipo‐oligosaccharide nodulation factor. Mol Microbiol. 1993; 10(2):351-360. doi 10.1111/j.1365-2958.1993.tb01961.x; Fonseca-García C., López-García C.M., Pacheco R., Armada E., Nava N., Pérez-Aguilar R., Solis-Miranda J., Quinto C. Metallothionein1A regulates rhizobial infection and nodulation in Phaseolus vulgaris. Int J Mol Sci. 2022;23(3):1491. doi 10.3390/ijms23031491; Gautrat P., Mortier V., Laffont C., De Keyser A., Fromentin J., Frugier F., Goormachtig S. Unraveling new molecular players involved in the autoregulation of nodulation in Medicago truncatula. J Exp Bot. 2019;70(4):1407-1417. doi 10.1093/jxb/ery465; Goyal R.K., Mattoo A.K., Schmidt M.A. Rhizobial–host interactions and symbiotic nitrogen fixation in legume crops toward agriculture sustainability. Front Microbiol. 2021;12:669404. doi 10.3389/fmicb. 2021.669404; Harrison M.J. Cellular programs for arbuscular mycorrhizal symbiosis. Curr Opin Plant Biol. 2012;15(6):691-698. doi 10.1016/j.pbi.2012.08.010; Jeyasri R., Muthuramalingam P., Karthick K., Shin H., Choi S.H., Ramesh M. Methyl jasmonate and salicylic acid as powerful elicitors for enhancing the production of secondary metabolites in medicinal plants: an updated review. Plant Cell Tissue Organ Cult. 2023; 153(3):447-458. doi 10.1007/s11240-023-02485-8; Kälin C., Piombo E., Bourras S., Brantestam A.K., Dubey M., Elfstrand M., Karlsson M. Transcriptomic analysis identifies candidate genes for Aphanomyces root rot disease resistance in pea. BMC Plant Biol. 2024;24(1):144. doi 10.1186/s12870-024-04817-y; Kolde R. Package ‘pheatmap.’ R Package. 2015;1(7):790 Kotera Y., Komori H., Tasaki K., Takagi K., Imano S., Katou S. The peroxisomal β-oxidative pathway and benzyl alcohol O-benzoyltransferase HSR201 cooperatively contribute to the biosynthesis of salicylic acid. Plant Cell Physiol. 2023;64(7):758-770. doi 10.1093/pcp/pcad034; Lackman P., González-Guzmán M., Tilleman S., Carqueijeiro I., Pérez A.C., Moses T., Seo M., Kanno Y., Häkkinen S.T., Van Montagu M.C.E. Jasmonate signaling involves the abscisic acid receptor PYL4 to regulate metabolic reprogramming in Arabidopsis and tobacco. Proc Natl Acad Sci USA. 2011;108(14):5891-5896. doi 10.1073/pnas.1103010108; Liao Y., Smyth G.K., Shi W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2014;30(7):923-930. doi 10.1093/bioinformatics/btt656; Lie T.A. Host genes in Pisum sativum L. conferring resistance to European Rhizobium leguminosarum strains. Plant Soil. 1984;82(3): 415-425. doi 10.1007/BF02184279; Liu C., Han X., Steenwyk J.L., Shen X.-X. Temporal transcriptomics provides insights into host-pathogen interactions: a case study of Didymella pinodella and disease-resistant and disease-susceptible pea varieties. Crop Health. 2023;1(1):5. doi 10.1007/s44297-023-00005-w; Love M.I., Huber W., Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014; 15(12):550. doi 10.1186/s13059-014-0550-8; Mamontova T., Afonin A.M., Ihling C., Soboleva A., Lukasheva E., Sulima A.S., Shtark O.Y., Akhtemova G.A., Povydysh M.N., Sinz A., Frolov A., Zhukov V.A., Tikhonovich I.A. Profiling of seed proteome in pea (Pisum sativum L.) lines characterized with high and low responsivity to combined inoculation with nodule bacteria and arbuscular mycorrhizal fungi. Molecules. 2019;24(8):1603. doi 10.3390/molecules24081603; Martin D.N., Proebsting W.M., Hedden P. Mendel’s dwarfing gene: cDNAs from the Le alleles and function of the expressed proteins. Proc Natl Acad Sci USA. 1997;94(16):8907-8911. doi 10.1073/pnas.94.16.8907; Mohanty S.K., Arthikala M.-K., Nanjareddy K., Lara M. Plant-symbiont interactions: the functional role of expansins. Symbiosis. 2018; 74:1-10. doi 10.1007/s13199-017-0501-8; Monte I. Jasmonates and salicylic acid: evolution of defense hormones in land plants. Curr Opin Plant Biol. 2023;76:102470. doi 10.1016/j.pbi.2023.102470; Müller L.M., Harrison M.J. Phytohormones, miRNAs, and peptide signals integrate plant phosphorus status with arbuscular mycorrhizal symbiosis. Curr Opin Plant Biol. 2019;50:132-139. doi 10.1016/j.pbi.2019.05.004; Okamoto S., Tabata R., Matsubayashi Y. Long-distance peptide signaling essential for nutrient homeostasis in plants. Curr Opin Plant Biol. 2016;34:35-40. doi 10.1016/j.pbi.2016.07.009; Oldroyd G.E.D. Speak, friend, and enter: signalling systems that promote beneficial symbiotic associations in plants. Nat Rev Microbiol. 2013;11(4):252-263. doi 10.1038/nrmicro2990; Parihar A.K., Kumar J., Gupta D.S., Lamichaney A., Naik S.J.S., Singh A.K., Dixit G.P., Gupta S., Toklu F. Genomics enabled breeding strategies for major biotic stresses in pea (Pisum sativum L.). Front Plant Sci. 2022;13:861191. doi 10.3389/fpls.2022.861191; Parniske M. Arbuscular mycorrhiza: the mother of plant root endosymbioses. Nat Rev Microbiol. 2008;6(10):763-775. doi 10.1038/nrmicro1987; Pii Y., Astegno A., Peroni E., Zaccardelli M., Pandolfini T., Crimi M. The Medicago truncatula N5 gene encoding a root-specific lipid transfer protein is required for the symbiotic interaction with Sinorhizobium meliloti. Mol Plant Microbe Interact. 2009;22(12): 1577-1587. doi 10.1094/MPMI-22-12-1577; Reid D.E., Ferguson B.J., Hayashi S., Lin Y.H., Gresshoff P.M. Molecular mechanisms controlling legume autoregulation of nodulation. Ann Botany. 2011;108(5):789-795. doi 10.1093/aob/mcr205; Roy S., Liu W., Nandety R.S., Crook A., Mysore K.S., Pislariu C.I., Frugoli J., Dickstein R., Udvardi M.K. Celebrating 20 years of genetic discoveries in legume nodulation and symbiotic nitrogen fixation. Plant Cell. 2020;32(1):15-41. doi 10.1105/tpc19.00279; Rubiales D., Barilli E., Rispail N. Breeding for biotic stress resistance in pea. Agriculture. 2023;13(9):1825. doi 10.3390/agriculture13091825; Rubiales D., Mikic A. Introduction: legumes in sustainable agriculture. Crit Rev Plant Sci. 2015;34(1-3):2-3. doi 10.1080/07352689.2014.897896; Shtark O.Y., Danilova T.N., Naumkina T.S., Vasilchikov A.G., Chebotar V.K., Kazakov A.E., Zhernakov A.I., Nemankin T.A., Prilepskaya N.A., Borisov A.U. Analysis of pea (Pisum sativum L.) source material for breeding of cultivars with high symbiotic potential and choice of criteria for its evaluation. Ecol Genet. 2006;4(2):22-28. doi 10.17816/ecogen4222-28; Shtark O.Y., Borisov A.Y., Zhukov V.A., Tikhonovich I.A. Mutually beneficial legume symbioses with soil microbes and their potential for plant production. Symbiosis. 2012;58(1-3):51-62. doi 10.1007/s13199-013-0226-2; Shtark O.Y., Zhukov V.A., Sulima A.S., Singh R., Naumkina T.S., Borisov A.Y. Prospects for the use of multi-component symbiotic systems of the Legumes. Ecol Genet. 2015;13(1):33-46. doi 10.17816/ecogen13133-46; Shtark O.Y., Puzanskiy R.K., Avdeeva G.S., Yurkov A.P., Smolikova G.N., Yemelyanov V.V., Kliukova M.S., Shavarda A.L., Kirpichnikova A.A., Zhernakov A.I. Metabolic alterations in pea leaves during arbuscular mycorrhiza development. PeerJ. 2019;7:e7495. doi 10.7717/peerj.7495; Smith S.E., Read D. The symbionts forming arbuscular mycorrhizas. In: Smith S.E., Read D. Mycorrhizal Symbiosis. Academic Press, 2008;13-41. doi 10.1016/b978-012370526-6.50003-9; Sulima A.S., Zhukov V.A., Afonin A.A., Zhernakov A.I., Tikhonovich I.A., Lutova L.A. Selection signatures in the first exon of paralogous receptor kinase genes from the Sym2 region of the Pisum sativum L. genome. Front Plant Sci. 2017;8:1957. doi 10.3389/fpls.2017.01957; Sulima A.S., Zhukov V.A., Kulaeva O.A., Vasileva E.N., Borisov A.Y., Tikhonovich I.A. New sources of Sym2A allele in the pea (Pisum sativum L.) carry the unique variant of candidate LysM-RLK gene LykX. PeerJ. 2019;7:e8070. doi 10.7717/peerj.8070; Tsyganov V.E., Tsyganova A.V. Symbiotic regulatory genes controlling nodule development in Pisum sativum L. Plants. 2020;9(12):1741. doi 10.3390/plants9121741; Wang D., Dong W., Murray J., Wang E. Innovation and appropriation in mycorrhizal and rhizobial Symbioses. Plant Cell. 2022;34(5): 1573-1599. doi 10.1093/plcell/koac039; Wang L., Sun Z., Su C., Wang Y., Yan Q., Chen J., Ott T., Li X. A GmNINa-miR172c-NNC1 regulatory network coordinates the nodulation and autoregulation of nodulation pathways in soybean. Mol Plant. 2019;12(9):1211-1226. doi 10.1016/j.molp.2019.06.002; Wickham H. Getting Started with ggplot2. In: ggplot2. Use R! Springer, 2016;11-31. doi 10.1007/978-3-319-24277-4_2; Yang J., Lan L., Jin Y., Yu N., Wang D., Wang E. Mechanisms underlying legume–rhizobium symbioses. J Int Plant Biol. 2022;64(2): 244-267. doi 10.1111/jipb.13207; Zhukov V.A., Akhtemova G.A., Zhernakov A.I., Sulima A.S., Shtark O.Y., Tikhonovich I.A. Evaluation of the symbiotic effectiveness of pea (Pisum sativum L.) genotypes in pot experiment. Agric Biol. 2017;52(3):607-614. doi 10.15389/agrobiology.2017.3.607eng; Zhukov V.A., Zhernakov A.I., Sulima A.S., Kulaeva O.A., Kliukova M.S., Afonin A.M., Shtark O.Y., Tikhonovich I.A. Association study of symbiotic genes in pea (Pisum sativum L.) cultivars grown in symbiotic conditions. Agronomy. 2021a;11(11):2368. doi 10.3390/agronomy11112368; Zhukov V., Zorin E., Zhernakov A., Afonin A., Akhtemova G., Bovin A., Dolgikh A., Gorshkov A., Gribchenko E., Ivanova K., Kirienko A., Kitaeva A., Kliukova M., Kulaeva O., Kusakin P., Leppyanen I., Pavlova O., Romanyuk D., Rudaya E., Serova T., Shtark O., Sulima A., Tsyganova A., Vasileva E., Dolgikh E., Tsyganov V., Tikhonovich I. Transcriptomic analysis of sym28 and sym29 supernodulating mutants of pea (Pisum sativum L.) under complex inoculation with beneficial microorganisms. Biol Commun. 2021b; 66(3):181-197. doi 10.21638/spbu03.2021.301; Zorin E.A., Kliukova M.S., AfoninA.M., Gribchenko E.S., Gordon M.L., Sulima A.S., Zhernakov A.I., Kulaeva O.A., Romanyuk D.A., Kusakin P.G., Tsyganova A.V., Tsyganov V.E., Tikhonovich I.A., Zhukov V.A. A variable gene family encoding nodule-specific cysteinerich peptides in pea (Pisum sativum L.). Front Plant Sci. 2022;13: 884726. doi 10.3389/fpls.2022.884726; Zorin E.A., Sulima A.S., Zhernakov A.I., Kuzmina D.O., Rakova V.A., Kliukova M.S., Romanyuk D.A., Kulaeva O.A., Akhtemova G.A., Shtark O.Y., Tikhonovich I.A., Zhukov V.A. Genomic and transcriptomic analysis of pea (Pisum sativum L.) breeding line ‘Triumph’ with high symbiotic responsivity. Plants. 2023;13(1):78. doi 10.3390/plants13010078; https://vavilov.elpub.ru/jour/article/view/4543
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2Academic Journal
Συγγραφείς: А. N. Rubashkina, I. Yu. Torshin, O. A. Gromova, A. N. Galustyan, А. Н. Рубашкина, И. Ю. Торшин, О. А. Громова, А. Н. Галустян
Συνεισφορές: Thе work was supported by the grant of the Russian Science Foundation (Project No. 23-21-00154)., Работа выполнена при поддержке гранта Российского научного фонда (проект № 23-21-00154).
Πηγή: FARMAKOEKONOMIKA. Modern Pharmacoeconomics and Pharmacoepidemiology; Vol 17, No 2 (2024); 191-199 ; ФАРМАКОЭКОНОМИКА. Современная фармакоэкономика и фармакоэпидемиология; Vol 17, No 2 (2024); 191-199 ; 2070-4933 ; 2070-4909
Θεματικοί όροι: транскриптомика, lignans, pathogenetic therapy, pharmacoinformatics, human proteome, transcriptomics, лигнаны, патогенетическая терапия, фармакоинформатика, протеом человека
Περιγραφή αρχείου: application/pdf
Relation: https://www.pharmacoeconomics.ru/jour/article/view/1012/552; Gerstenmeyer E., Reimer S., Berghofer E., et al. Effect of thermal heating on some lignans in flax seeds, sesame seeds and rye. Food Chem. 2013; 138 (2-3): 1847–55. https://doi.org/10.1016/j.foodchem.2012.11.117.; Громова О.А., Торшин И.Ю., Рубашкина А.Н. и др. Систематический анализ фундаментальных и клинических исследований лигнана 7-гидроксиматаирезинола. Эффективная фармакотерапия. 2019; 15 (13): 34–41.; Udani J.K., Brown D.J., Tan M.O., Hardy M. Pharmacokinetics and bioavailability of plant lignan 7-hydroxymatairesinol and effects on serum enterolactone and clinical symptoms in postmenopausal women: a single-blinded, parallel, dose-comparison study. J Am Coll Nutr. 2013; 32 (6): 428–35. https://doi.org/10.1080/07315724.2013.849578.; Lina B., Korte H., Nyman L., Unkila M. A thirteen week dietary toxicity study with 7-hydroxymatairesinol potassium acetate (HMRlignan) in rats. Regul Toxicol Pharmacol. 2005; 41 (1): 28–38. https://doi.org/10.1016/j.yrtph.2004.09.001.; Wolterbeek A.P., Roberts A., Korte H., et al. Prenatal developmental toxicity study with 7-hydroxymatairesinol potassium acetate (HMRlignan) in rats. Regul Toxicol Pharmacol. 2004; 40 (1): 1–8. https://doi.org/10.1016/j.yrtph.2004.04.001.; Saarinen N.M., Wärri A., Mäkelä S.I., et al. Hydroxymatairesinol, a novel enterolactone precursor with antitumor properties from coniferous tree (Picea abies). Nutr Cancer. 2000; 36 (2): 207–16. https://doi.org/10.1207/S15327914NC3602_10.; Журавлёв Ю.И., Рудаков К.В., Торшин И.Ю. Алгебраические критерии локальной разрешимости и регулярности как инструмент исследования морфологии аминокислотных последовательностей. Труды МФТИ. 2011; 3 (4): 45–54.; Torshin I.Yu., Rudakov K.V. On the application of the combinatorial theory of solvability to the analysis of chemographs. Part 1: Fundamentals of modern chemical bonding theory and the concept of the chemograph. Pattern Recognit Image Anal. 2014; 24 (1): 11–23. https://doi.org/10.1134/S1054661814010209.; Torshin I.Yu. On solvability, regularity, and locality of the problem of genome annotation. Pattern Recognit Image Anal. 2010; 20 (3): 386–95. https://doi.org/10.1134/S1054661810030156.; Торшин И.Ю., Громова О.А. Экспертный анализ данных в молекулярной фармакологии. М.: МЦНМО; 2012: 687 с.; Торшин И.Ю., Рубашкина А.Н., Лапочкина Н.П., Громова О.А. Хемореактомный анализ 7-гидроксиматаирезинола, 17-эстрадиола, фитоэстрогена, β-ситостерола и эпигаллокатехин-3-галлата. Акушерство, гинекология и репродукция. 2020; 14 (3): 347–60. https://doi.org/10.17749/2313-7347/ob.gyn.rep.2020.152.; Торшин И.Ю., Громова О.А., Фролова Д.Е. и др. Дозозависимый хемотранскриптомный анализ дифференциального действия витамина D на экспрессию генов в клетках-предшественниках нейронов NPC и в опухолевых клетках MCF7 человека. Фармакокинетика и фармакодинамика. 2018; 2: 35–51. https://doi.org/110.24411/2587-7836-2018-10013.; Torshin I.Yu. Sensing the change: from molecular genetics to personalized medicine. 1st ed. Nova Science Publishers; 2009: 366 pp.; Торшин И. Ю., Рубашкина А. Н., Громова О. А. Хемотранскриптомный анализ молекулы 7-гидроксиматаирезинола на опухолевые клетки молочной железы человека линии MCF7. Акушерство, гинекология и репродукция. 2023; 17 (5): 584–96. https://doi.org/10.17749/2313-7347/ob.gyn.rep.2023.409.; Громова О.А., Фролова Д.Е., Торшин И.Ю., Филимонова М.В., Сорокина М.А., Рейер И.А., Лиманова O.А., Федотова Л.Э., Майорова Л.А. Противоопухолевые эффекты витамина В12 in vitro, in vivo, in silico. ФАРМАКОЭКОНОМИКА. Современная фармакоэкономика и фармакоэпидемиология. 2024: [принятая рукопись]. https://doi.org/10.17749/2070-4909/farmakoekonomika.2024.231.; Torshin I.Yu., Rudakov K.V. On metric spaces arising during formalization of problems of recognition and classification. Part 2: Density properties. Pattern Recognit Image Anal. 2016; 26 (3): 483–96. https://doi.org/10.1134/S1054661816030202.; Torshin I.Yu., Rudakov K.V. Combinatorial analysis of the solvability properties of the problems of recognition and completeness of algorithmic models. Part 2: Metric approach within the framework of the theory of classification of feature values. Pattern Recognit Image Anal. 2017; 27 (2): 184–99. https://doi.org/10.1134/S1054661817020110.; Cosentino M., Marino F., Maio R.C., et al. Immunomodulatory activity of the lignan 7-hydroxymatairesinol potassium acetate (HMR/lignan) extracted from the heartwood of Norway spruce (Picea abies). Int Immunopharmacol. 2010; 10 (3): 339–43. https://doi.org/10.1016/j.intimp.2009.12.005.; Spilioti E., Holmbom B., Papavassiliou A.G., Moutsatsou P. Lignans 7-hydroxymatairesinol and 7-hydroxymatairesinol 2 exhibit anti-inflammatory activity in human aortic endothelial cells. Mol Nutr Food Res. 2014; 58 (4): 749–59. https://doi.org/10.1002/mnfr.201300318.; Yang D., Xiao C.X., Su Z.H., et al. (-)-7(S)-hydroxymatairesinol protects against tumor necrosis factor-alpha-mediated inflammation response in endothelial cells by blocking the MAPK/NF- κB and activating Nrf2/HO-1. Phytomedicine. 2017; 32: 15–23. https://doi.org/10.1016/j.phymed.2017.04.005.; Samson K. LEE011 CDK inhibitor showing early promise in drug-resistant cancers. Oncol Times. 2014; 36 (3): 39–40. https://doi.org/10.1097/01.COT.0000444043.33304.c1.; Liang W., Wu X., Fang W., et al. Network meta-analysis of erlotinib, gefitinib, afatinib and icotinib in patients with advanced non-small-cell lung cancer harboring EGFR mutations. PLoS One. 2014; 9 (2): e85245. https://doi.org/10.1371/journal.pone.0085245.; Lamming D.W., Ye L., Sabatini D.M, Baur J.A. Rapalogs and mTOR inhibitors as anti-aging therapeutics. J Clin Invest. 2013; 123 (3): 980–9. https://doi.org/10.1172/JCI64099.; Rogowski M., Gollahon L., Chellini G., Assadi-Porter F.M. Uptake of 3-iodothyronamine hormone analogs inhibits the growth and viability of cancer cells. FEBS Open Bio. 2017; 7 (4): 587–601. https://doi.org/10.1002/2211-5463.12205.; Aouad P., Saikali M., Abdel-Samad R., et al. Antitumor activities of the synthetic retinoid ST1926 in two-dimensional and three-dimensional human breast cancer models. Anticancer Drugs. 2017; 28 (7): 757–70. https://doi.org/10.1097/CAD.0000000000000511.; Saarinen N.M., Huovinen R., Wärri A., et al. Uptake and metabolism of hydroxymatairesinol in relation to its anticarcinogenicity in DMBA-induced rat mammary carcinoma model. Nutr Cancer. 2001; 41 (1-2): 82–90. https://doi.org/10.1080/01635581.2001.9680616.; Miura D., Saarinen N.M., Miura Y., et al. Hydroxymatairesinol and its mammalian metabolite enterolactone reduce the growth and metastasis of subcutaneous AH109A hepatomas in rats. Nutr Cancer. 2007; 58 (1): 49–59. https://doi.org/10.1080/01635580701308133.; Bylund A., Saarinen N., Zhang J.X., et al. Anticancer effects of a plant lignan 7-hydroxymatairesinol on a prostate cancer model in vivo. Exp Biol Med. 2005; 230 (3): 217–23. https://doi.org/10.1177/153537020523000308.; Katsuda S., Yoshida M., Saarinen N., et al. Chemopreventive effects of hydroxymatairesinol on uterine carcinogenesis in Donryu rats. Exp Biol Med. 2004; 229 (5): 417–24. https://doi.org/10.1177/153537020422900510.; Oikarinen S.I., Pajari A., Mutanen M. Chemopreventive activity of crude hydroxsymatairesinol (HMR) extract in Apc(Min) mice. Cancer Lett. 2000; 161 (2): 253–8. https://doi.org/10.1016/s0304-3835(00)00662-5.; Громова О.А., Рубашкина А.Н., Филимонова М.В. и др. Адъювантная терапия лигнаном 7-гидроксиматаирезинолом как метод повышения онкологической безопасности приема эстрогенов. Эффективная фармакотерапия. 2018; 14 (13): 14–9.; Рубашкина А.Н., Лапочкина Н.П., Торшин И.Ю., Громова О.А. Роль 7-гидроксиматоирезинола в модуляции метаболизма эстрогенов и терапии мастопатии. Гинекология. 2020; 22 (4): 43–8.; https://www.pharmacoeconomics.ru/jour/article/view/1012
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3Academic Journal
Συγγραφείς: А. N. Rubashkina, I. Yu. Torshin, O. A. Gromova, А. Н. Рубашкина, И. Ю. Торшин, О. А. Громова
Συνεισφορές: This work was supported by a grant from the Russian Science Foundation (project № 23-21-00154)., Работа выполнена по гранту Российского научного фонда (проект № 23-21-00154).
Πηγή: Pharmacokinetics and Pharmacodynamics; № 1 (2024); 66-76 ; Фармакокинетика и Фармакодинамика; № 1 (2024); 66-76 ; 2686-8830 ; 2587-7836
Θεματικοί όροι: транскриптомика, lignans, pathogenetic therapy, pharmacoinformatics, human proteome, transcriptomics, лигнаны, патогенетическая терапия, фармакоинформатика, протеом человека
Περιγραφή αρχείου: application/pdf
Relation: https://www.pharmacokinetica.ru/jour/article/view/411/369; Gerstenmeyer E, Reimer S, Berghofer E, et al. Effect of thermal heating on some lignans in flax seeds, sesame seeds and rye. Food Chem. 2013 Jun 1;138(2-3):1847-55. doi:10.1016/j.foodchem.2012.11.117.; Громова О.А., Торшин И.Ю., Рубашкина А.Н., и др. Систематический анализ фундаментальных и клинических исследований лигнана 7-гидроксиматаирезинола. Эффективная фармакотерапия. 2019;15(13):34-41.; Udani JK, Brown DJ, Tan MO, Hardy M. Pharmacokinetics and bioavailability of plant lignan 7-hydroxymatairesinol and effects on serum enterolactone and clinical symptoms in postmenopausal women: a singleblinded, parallel, dose-comparison study. J Am Coll Nutr. 2013;32(6):428-35. doi:10.1080/07315724.2013.849578.; Lina B, Korte H, Nyman L, Unkila M. A thirteen week dietary toxicity study with 7-hydroxymatairesinol potassium acetate (HMRlignan) in rats. Regul Toxicol Pharmacol. 2005 Feb;41(1):28-38. doi:10.1016/j.yrtph.2004.09.001.; Wolterbeek AP, Roberts A, Korte H, et al. Prenatal developmental toxicity study with 7-hydroxymatairesinol potassium acetate (HMRlignan) in rats. Regul Toxicol Pharmacol. 2004 Aug;40(1):1-8. doi:10.1016/j.yrtph.2004.04.001.; Saarinen NM, Wärri A, Mäkelä SI, et al. Hydroxymatairesinol, a novel enterolactone precursor with antitumor properties from coniferous tree (Picea abies). Nutr Cancer. 2000;36(2):207-16. doi:10.1207/S15327914NC3602_10.; Журавлёв Ю.И., Рудаков К.В., Торшин И.Ю. Алгебраические критерии локальной разрешимости и регулярности как инструментисследования морфологии аминокислотных последовательностей. Труды МФТИ. 2011;3(4):67-76.; Torshin IYu, Rudakov KV. On the application of the combinatorial theory of solvability to the analysis of chemographs. Part 1: fundamentals of modern chemical bonding theory and the concept of the chemograph. Pattern Recognition and Image Analysis (Advances in Mathematical Theory and Applications). 2014;24(1):11-23. doi:10.1134/S1054661814010209.; Torshin IYu. On solvability, regularity, and locality of the problem of genome annotation. Pattern Recognition and Image Analysis. 2010;20(3):386-395. doi:10.1134/S1054661810030156.; Торшин И.Ю., Громова О.А. Экспертный анализ данных в молекулярной фармакологии. М.: МЦНМО, 2012. 747 с.; Торшин И.Ю., Рубашкина А.Н., Лапочкина Н.П., Громова О.А. Хемореактомный анализ 7-гидроксиматаирезинола, 17-эстрадиола, фитоэстрогена β-ситостирола и эпигаллокатехин-3-галлата. Акушерство, Гинекология и Репродукция. 2020;14(3):347-360.; Торшин И.Ю., Громова О.А., Фролова Д.Е., Гришина Т.Р., Лапочкина Н.П. Дозозависимый хемотранскриптомный анализ дифференциального действия витамина D на экспрессию генов в клетках-предшественниках нейронов NPC и в опухолевых клетках MCF7 человека. Фармакокинетика и Фармакодинамика. 2018;(2):35-51.; Torshin IYu. Bioinformatics in the post-genomic era: sensing the change from molecular genetics to personalized medicine. New York : Nova biomedical books, 2009, ISBN: 978-1-60692-217.; Торшин И.Ю., Рубашкина А.Н., Громова О.А. Хемотранскриптомный анализ эффектов молекулы 7-гидроксиматаирезинола на опухолевые клетки молочной железы человека линии MCF7. Акушерство, Гинекология и Репродукция. 2023;17(5):584-596.; Громова О.А., Фролова Д.Е., Торшин И.Ю., и др. Противоопухолевые эффекты витамина В 12 in vitro, in vivo, in silico. ФАРМАКОЭКО-НОМИКА. Современная фармакоэкономика и фармакоэпидемиология. 2024: [принятая рукопись].; Torshin IYu, Rudakov KV. On metric spaces arising during formalization of problems of recognition and classification. part 2: density properties. Pattern Recognition and Image Analysis (Advances in Mathematical Theory and Applications). 2016;26(3):483-496. doi:10.1134/S1054661816030202.; Torshin IYu, Rudakov KV. Combinatorial analysis of the solvability properties of the problems of recognition and completeness of algorithmic models. part 2: metric approach within the framework of the theory of classification of feature values. Pattern Recognition and Image Analysis (Advances in Mathematical Theory and Applications). 2017;27(2):184-199. doi:10.1134/S1054661817020110.; Cosentino M, Marino F, Maio RC, et al. Immunomodulatory activity of the lignan 7-hydroxymatairesinol potassium acetate (HMR/lignan) extracted from the heartwood of Norway spruce (Picea abies). Int Immunopharmacol. 2010 Mar;10(3):339-43. doi:10.1016/j.intimp.2009.12.005.; Spilioti E, Holmbom B, Papavassiliou AG, Moutsatsou P. Lignans 7-hydroxymatairesinol and 7-hydroxymatairesinol 2 exhibit anti-inflammatory activity in human aortic endothelial cells. Mol Nutr Food Res. 2014 Apr;58(4):749-59. doi:10.1002/mnfr.201300318.; Yang D, Xiao CX, Su ZH, Huang MW, Qin M, Wu WJ, Jia WW, Zhu YZ, Hu JF, Liu XH. (-)-7(S)-hydroxymatairesinol protects against tumor necrosis factor-α-mediated inflammation response in endothelial cells by blocking the MAPK/NF-κB and activating Nrf2/HO-1. Phytomedicine. 2017 Aug 15;32:15-23. doi:10.1016/j.phymed.2017.04.005.; Samson K. LEE011 CDK Inhibitor Showing Early Promise in Drug-Resistant Cancers. Oncology Times. 2014;36(3):39-40. doi:10.1097/01.COT.0000444043.33304.c1.; Liang W, Wu X, Fang W, et al. Network meta-analysis of erlotinib, gefitinib, afatinib and icotinib in patients with advanced non-small-cell lung cancer harboring EGFR mutations. PLoS One. 2014 Feb 12;9(2):e85245. doi:10.1371/journal.pone.0085245.; Lamming DW, Ye L, Sabatini DM, Baur JA. Rapalogs and mTOR inhibitors as anti-aging therapeutics. J Clin Invest. 2013 Mar;123(3):980-9. doi:10.1172/JCI64099. PMID: 23454761.; Rogowski M, Gollahon L, Chellini G, Assadi-Porter FM. Uptake of 3-iodothyronamine hormone analogs inhibits the growth and viability of cancer cells. FEBS Open Bio. 2017 Mar 6;7(4):587-601. doi:10.1002/2211-5463.12205. Erratum in: FEBS Open Bio. 2017 May 22;7(6):887.; Aouad P, Saikali M, Abdel-Samad R, et al. Antitumor activities of the synthetic retinoid ST1926 in two-dimensional and three-dimensional human breast cancer models. Anticancer Drugs. 2017 Aug;28(7):757-770. doi:10.1097/CAD.0000000000000511.; Saarinen NM, Huovinen R, Wärri A, et al. Uptake and metabolism of hydroxymatairesinol in relation to its anticarcinogenicity in DMBA-induced rat mammary carcinoma model. Nutr Cancer. 2001;41(1-2):82-90. doi:10.1080/01635581.2001.9680616.; Miura D, Saarinen NM, Miura Y, et al. Hydroxymatairesinol and its mammalian metabolite enterolactone reduce the growth and metastasis of subcutaneous AH109A hepatomas in rats. Nutr Cancer. 2007;58(1):49-59. doi:10.1080/01635580701308133.; Bylund A, Saarinen N, Zhang JX, et al. Anticancer effects of a plant lignan 7-hydroxymatairesinol on a prostate cancer model in vivo. Exp Biol Med (Maywood). 2005 Mar;230(3):217-23. doi:10.1177/153537020523000308.; Katsuda S, Yoshida M, Saarinen N, et al. Chemopreventive effects of hydroxymatairesinol on uterine carcinogenesis in Donryu rats. Exp Biol Med (Maywood). 2004 May;229(5):417-24. doi:10.1177/153537020422900510.; Oikarinen SI, Pajari A, Mutanen M. Chemopreventive activity of crude hydroxsymatairesinol (HMR) extract in Apc(Min) mice. Cancer Lett. 2000 Dec 20;161(2):253-8. doi:10.1016/s0304-3835(00)00662-5.; Рубашкина А.Н., Лапочкина Н.П., Торшин И.Ю., Громова О.А. Роль 7-гидроксиматаирезинола в модуляции метаболизма эстрогенов и терапии мастопатии. Гинекология. 2020;22(4):43-48.; Kangas L, Saarinen N, Mutanen M, et al. Antioxidant and antitumor effects of hydroxymatairesinol (HM-3000, HMR), a lignan isolated from the knots of spruce. Eur J Cancer Prev. 2002 Aug;11 Suppl 2:S48-57.; Britschgi A, Duss S, Kim S, et al. The Hippo kinases LATS1 and 2 control human breast cell fate via crosstalk with ERα. Nature. 2017 Jan 26;541(7638):541-545. doi:10.1038/nature20829.; Lee YF, Young WJ, Lin WJ, Shyr CR, Chang C. Differential regulation of direct repeat 3 vitamin D3 and direct repeat 4 thyroid hormone signaling pathways by the human TR4 orphan receptor. J Biol Chem. 1999 Jun 4;274(23):16198-205. doi:10.1074/jbc.274.23.16198.; van Agthoven T, van Agthoven TL, Dekker A, et al. Identification of BCAR3 by a random search for genes involved in antiestrogen resistance of human breast cancer cells. EMBO J. 1998 May 15;17(10):2799-808. doi:10.1093/emboj/17.10.2799.; https://www.pharmacokinetica.ru/jour/article/view/411
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4
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5Academic Journal
Συγγραφείς: Shevchenko V.E., Arnotskaya N.E., Kushnir T.I., Bryukhovetskiy A.S.
Συνεισφορές: It is funded under the state budget theme (No. 2021-76)., Финансируется в рамках госбюджетной темы (№ 2021-76).
Πηγή: Advances in Molecular Oncology; Vol 10, No 4 (2023); 137-148 ; Успехи молекулярной онкологии; Vol 10, No 4 (2023); 137-148 ; 2413-3787 ; 2313-805X
Θεματικοί όροι: glioblastoma, glioblastoma stem cells, neural stem cells, progenitor cells, transcriptomics, глиобластома, стволовые клетки глиобластомы, нейральные стволовые клетки, прогениторные клетки, транскриптомика
Περιγραφή αρχείου: application/pdf
Relation: https://umo.abvpress.ru/jour/article/view/615/332; https://umo.abvpress.ru/jour/article/view/615
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6Academic Journal
Συγγραφείς: I. A. Mazerkina, И. А. Мазеркина
Συνεισφορές: The study reported in this publication was carried out as part of publicly funded research project No. 056-00052-23-00 and was supported by the Scientific Centre for Expert Evaluation of Medicinal Products (R&D public accounting No. 121022400082-4)., Работа выполнена в рамках государственного задания ФГБУ «НЦЭСМП» Минздрава России № 056-00052-23-00 на проведение прикладных научных исследований (номер государственного учета НИР 121022400082-4).
Πηγή: Safety and Risk of Pharmacotherapy; Том 11, № 2 (2023); 131-144 ; Безопасность и риск фармакотерапии; Том 11, № 2 (2023); 131-144 ; 2619-1164 ; 2312-7821
Θεματικοί όροι: доклинические исследования, drug-induced liver injury, in vitro studies, cell cultures, cell models, organ-on-a-chip, omics, transcriptomics, proteomics, metabolomics, non-clinical studies, лекарственное повреждение печени, исследования in vitro, клеточные культуры, клеточные модели, орган-на-чипе, омик-технологии, транскриптомика, протеомика, метаболомика
Περιγραφή αρχείου: application/pdf
Relation: https://www.risksafety.ru/jour/article/view/351/722; https://www.risksafety.ru/jour/article/view/351/723; https://www.risksafety.ru/jour/article/view/351/724; https://www.risksafety.ru/jour/article/view/351/725; https://www.risksafety.ru/jour/article/view/351/726; https://www.risksafety.ru/jour/article/view/351/727; https://www.risksafety.ru/jour/article/view/351/728; https://www.risksafety.ru/jour/article/view/351/729; https://www.risksafety.ru/jour/article/view/351/730; https://www.risksafety.ru/jour/article/view/351/780; https://www.risksafety.ru/jour/article/downloadSuppFile/351/341; Onakpoya IJ, Heneghan CJ, Aronson JK. Post-marketing withdrawal of 462 medicinal products because of adverse drug reactions: a systematic review of the world literature. BMC Med. 2016;4(14):10. https://doi.org/10.1186/s12916-016-0553-2; Wouters OJ, McKee M, Luyten J. Estimated research and development investment needed to bring a new medicine to market, 2009–2018. JAMA. 2020;323(9):844–53. https://doi.org/10.1001/jama.2020.1166; DiMasi JA, Grabowski HG, Hansen RW. Innovation in the pharmaceutical industry: new estimates of R&D costs. J Health Econ. 2016;47:20–33. https://doi.org/10.1016/j.jhealeco.2016.01.012; Sakai C, Iwano S, Yamazaki Y, Ando A, Nakane F, Kouno M, et al. Species differences in the pharmacokinetic parameters of cytochrome P450 probe substrates between experimental animals, such as mice, rats, dogs, monkeys, and microminipigs, and humans. Drug Metab Toxicol. 2014;5:6. https://doi.org/10.4172/2157-7609.1000173; Spanhaak S, Cook D, Barnes J, Reynolds J. Species concordance for liver injury. In: Safety Intelligence Program Board. Cambridge, UK: BioWisdom, Ltd; 2008.; Hewitt NJ, Gómez Lechón MJ, Houston JB, Hallifax D, Brown HS, Maurel P, et al. Primary hepatocytes: current understanding of the regulation of metabolic enzymes and transporter proteins, and pharmaceutical practice for the use of hepatocytes in metabolism, enzyme induction, transporter, clearance, and hepatotoxicity studies. Drug Metab Rev. 2007;39(1):159–234. https://doi.org/10.1080/03602530601093489; Vickers AE, Bentley P, Fisher RL. Consequences of mitochondrial injury induced by pharmaceutical fatty acid oxidation inhibitors is characterized in human and rat liver slices. Toxicol In Vitro. 2006;20(7):1173–82. https://doi.org/10.1016/j.tiv.2006.01.021; Edwards RJ, Price RJ, Watts PS, Renwick AB, Tredger JM, Boobis AR, Lake BG. Induction of cytochrome P450 enzymes in cultured precision-cut human liver slices. Drug Metab Dispos. 2003;31(3):282–8. https://doi.org/10.1124/dmd.31.3.282; Starokozhko V, Vatakuti S, Schievink B, Merema MT, Asplund A, Synnergren J, et al. Maintenance of drug metabolism and transport functions in human precision-cut liver slices during prolonged incubation for 5 days. Arch Toxicol. 2017;91(5):2079–92. https://doi.org/10.1007/s00204-016-1865-x; Hadi M, Westra IM, Starokozhko V, Dragovic S, Merema MT, Groothuis GM. Human precision-cut liver slices as an ex vivo model to study idiosyncratic drug-induced liver injury. Chem Res Toxicol. 2013;26(5):710–20. https://doi.org/10.1021/tx300519p; Khetani SR, Kanchagar C, Ukairo O, Krzyzewski S, Moore A, Shi J, et al. Use of micropatterned cocultures to detect compounds that cause drug-induced liver injury in humans. Toxicol Sci. 2013;132(1):107–17. https://doi.org/10.1093/toxsci/kfs326; Burkard A, Dähn C, Heinz S, Zutavern A, Sonn tag-Buck V, Maltman D, et al. Generation of proliferating human hepatocytes using Upcyte® technology: characterisation and applications in induction and cytotoxicity assays. Xenobiotica. 2012;42(10):939–56. https://doi.org/10.3109/00498254.2012.675093; Segovia-Zafra A, Di Zeo-Sánchez DE, López-Gómez C, Pérez-Valdés Z, García-Fuentes E, Andrade RJ, et al. Preclinical models of idiosyncratic drug-induced liver injury (iDILI): moving towards prediction. Acta Pharm Sin B. 2021;11(12):3685–726. https://doi.org/10.1016/j.apsb.2021.11.013; Sison-Young RL, Mitsa D, Jenkins RE, Mottram D, Alexandre E, Richert L, et al. Comparative proteomic characterization of 4 human liver-derived single cell culture models reveals significant variation in the capacity for drug disposition, bioactivation, and detoxication. Toxicol Sci. 2015;147(2):412–24. https://doi.org/10.1093/toxsci/kfv136; Saran C, Fu D, Ho H, Klein A, Fallon JK, Honkakoski P, et al. A novel differentiated HuH-7 cell model to examine bile acid metabolism, transport and cholestatic hepatotoxicity. Sci Rep. 2022;12(1):14333. https://doi.org/10.1038/s41598-022-18174-z; Schwartz RE, Fleming HE, Khetani SR, Bhatia SN. Pluripotent stem cell-derived hepatocyte-like cells. Biotechnol Adv. 2014;32(2):504–13. https://doi.org/10.1016/j.biotechadv.2014.01.003; Imagawa K, Takayama K, Isoyama S, Tanikawa K, Shinkai M, Harada K, et al. Generation of a bile salt export pump deficiency model using patient-specific induced pluripotent stem cell-derived hepatocyte-like cells. Sci Rep. 2017;7:41806. https://doi.org/10.1038/srep41806; Nguyen TV, Ukairo O, Khetani SR, McVay M, Kanchagar C, Seghezzi W, et al. Establishment of a hepatocyte-Kupffer cell coculture model for assessment of proinflammatory cytokine effects on metabolizing enzymes and drug transporters. Drug Metab Dispos. 2015;43(5):774–85. https://doi.org/10.1124/dmd.114.061317; Baze A, Parmentier C, Hendriks DFG, Hurrell T, Heyd B, Bachellier P, et al. Three-dimensional spheroid primary human hepatocytes in monoculture and coculture with nonparenchymal cells. Tissue Eng Part C Methods. 2018;24(9):534–45. https://doi.org/10.1089/ten.TEC.2018.0134; Olsen AL, Bloomer SA, Chan EP, Gaça MD, Georges PC, Sackey B, et al. Hepatic stellate cells require a stiff environment for myofibroblastic differentiation. Am J Physiol Gastrointest Liver Physiol. 2011;301(1):110–8. https://doi.org/10.1152/ajpgi.00412.2010; Khetani SR, Berger DR, Ballinger KR, Davidson MD, Lin C, Ware BR. Microengineered liver tissues for drug testing. J Lab Autom. 2015;20(3):216–50. https://doi.org/10.1177/2211068214566939; Yokoyama Y, Sasaki Y, Terasaki N, Kawataki T, Takekawa K, Iwase Y, et al. Comparison of drug metabolism and its related hepatotoxic effects in HepaRG, cryopreserved human hepatocytes, and HepG2 cell cultures. Biol Pharm Bull. 2018;41(5):722–32. https://doi.org/10.1248/bpb.b17-00913; Wu X, Wang S, Li M, Li J, Shen J, Zhao Y, et al. Conditional reprogramming: next generation cell culture. Acta Pharm Sin B. 2020;10(8):1360–81. https://doi.org/10.1016/j.apsb.2020.01.011; Su S, Di Poto C, Roy R, Liu X, Cui W, Kroemer A, Ressom HW. Long-term culture and characterization of patient-derived primary hepatocytes using conditional reprogramming. Exp Biol Med (Maywood). 2019;244(11):857–64. https://doi.org/10.1177/1535370219855398; De Bruyn T, Chatterjee S, Fattah S, Keemink J, Nicolaï J, Augustijns P, et al. Sandwich-cultured hepatocytes: utility for in vitro exploration of hepatobiliary drug disposition and drug-induced hepatotoxicity. Expert Opin Drug Metab Toxicol. 2013;9(5):589–616. https://doi.org/10.1517/17425255.2013.773973; Yanni SB, Augustijns PF, Benjamin DK Jr, Brouwer KL, Thakker DR, Annaert PP. In vitro investigation of the hepatobiliary disposition mechanisms of the antifungal agent micafungin in humans and rats. Drug Metab Dispos. 2010;38(10):1848–56. https://doi.org/10.1124/dmd.110.033811; Matsunaga N, Suzuki K, Nakanishi T, Ogawa M, Imawaka H, Tamai I. Modeling approach for multiple transporters-mediated drug-drug interactions in sandwich-cultured human hepatocytes: effect of cyclosporin A on hepatic disposition of mycophenolic acid phenyl-glucuronide. Drug Metab Pharmacokinet. 2015;30(2):142–8. https://doi.org/10.1016/j.dmpk.2014.10.006; Fu D, Cardona P, Ho H, Watkins PB, Brouwer KLR. Novel mechanisms of valproate hepatotoxicity: impaired Mrp2 trafficking and hepatocyte depolarization. Toxicol Sci. 2019;171(2):431–42. https://doi.org/10.1093/toxsci/kfz154; Berger DR, Ware BR, Davidson MD, Allsup SR, Khetani SR. Enhancing the functional maturity of induced pluripotent stem cell-derived human hepatocytes by controlled presentation of cell-cell interactions in vitro. Hepatology. 2015;61(4):1370–81. https://doi.org/10.1002/hep.27621; Takahashi Y, Hori Y, Yamamoto T, Urashima T, Ohara Y, Tanaka H. 3D spheroid cultures improve the metabolic gene expression profiles of HepaRG cells. Biosci Rep. 2015;35(3):e00208. https://doi.org/10.1042/BSR20150034; Bell CC, Dankers ACA, Lauschke VM, Sison-Young R, Jenkins R, Rowe C, et al. Comparison of hepatic 2D sandwich cultures and 3D spheroids for long-term toxicity applications: a multicenter study. Toxicol Sci. 2018;162(2):655–66. https://doi.org/10.1093/toxsci/kfx289; Kostadinova R, Boess F, Applegate D, Suter L, Weiser T, Singer T, et al. A long-term three dimensional liver co-culture system for improved prediction of clinically relevant drug-induced hepatotoxicity. Toxicol Appl Pharmacol. 2013;268(1):1–16. https://doi.org/10.1016/j.taap.2013.01.012; Vorrink SU, Zhou Y, Ingelman-Sundberg M, Lauschke VM. Prediction of drug-induced hepatotoxicity using long-term stable primary hepatic 3D spheroid cultures in chemically defined conditions. Toxicol Sci. 2018;163(2):655–65. https://doi.org/10.1093/toxsci/kfy058; Ide I, Nagao E, Kajiyama S, Mizoguchi N. A novel evaluation method for determining drug-induced hepatotoxicity using 3D bio-printed human liver tissue. Toxicol Mech Methods. 2020;30(3):189–96. https://doi.org/10.1080/15376516.2019.1686795; Schmidt K, Berg J, Roehrs V, Kurreck J, Al-Zeer MA. 3D-bioprinted HepaRG cultures as a model for testing long term aflatoxin B1 toxicity in vitro. Toxicol Rep. 2020;7:1578–87. https://doi.org/10.1016/j.toxrep.2020.11.003; Deng J, Wei W, Chen Z, Lin B, Zhao W, Luo Y, et al. Engineered liver-on-a-chip platform to mimic liver functions and its biomedical applications: a review. Micromachines (Basel). 2019;10(10):676. https://doi.org/10.3390/mi10100676; Rubiano A, Indapurkar A, Yokosawa R, Miedzik A, Rosenzweig B, Arefin A, et al. Characterizing the reproducibility in using a liver microphysiological system for assaying drug toxicity, metabolism, and accumulation. Clin Transl Sci. 2021;14(3):1049–61. https://doi.org/10.1111/cts.12969; Picollet-D’hahan N, Zuchowska A, ILemeunier I, Le Gac S. Multiorgan-on-a-chip: a systemic approach to model and decipher inter-organ communication. Trends in Biotechnology. 2021;39(8):788–810. https://doi.org/10.1016/j.tibtech.2020.11.014; Bricks T, Paullier P, Legendre A, Fleury MJ, Zeller P, Merlier F, et al. Development of a new microfluidic platform integrating co-cultures of intestinal and liver cell lines. Toxicol Vitro. 2014;28(5):885–95. https://doi.org/10.1016/j.tiv.2014.02.005; Oleaga C, Bernabini C, Smith AS, Srinivasan B, Jackson M, McLamb W, et al. Multi-organ toxicity demonstration in a functional human in vitro system composed of four organs. Sci Rep. 2016;6(1):20030. https://doi.org/10.1038/srep20030; Weaver RJ, Betts C, Blomme EAG, Gerets HHJ, Gjervig Jensen K, Hewitt PG, et al. Test systems in drug discovery for hazard identification and risk assessment of human drug-induced liver injury. Expert Opin Drug Metab Toxicol. 2017;13(7):767–82. https://doi.org/10.1080/17425255.2017.1341489; Benbow JW, Aubrecht J, Banker MJ, Nettleton D, Aleo MD. Predicting safety toleration of pharmaceutical chemical leads: cytotoxicity correlations to exploratory toxicity studies. Toxicol Lett. 2010;197(3):175–82. https://doi.org/10.1016/j.toxlet.2010.05.016; Roth AD, Lee MY. Idiosyncratic drug-induced liver injury (IDILI): potential mechanisms and predictive assays. Biomed Res Int. 2017;2017:9176937. https://doi.org/10.1155/2017/9176937; Jee A, Sernoskie SC, Uetrecht J. Idiosyncratic drug-induced liver injury: mechanistic and clinical challenges. Int J Mol Sci. 2021;22(6):2954. https://doi.org/10.3390/ijms22062954; Devi SS, Palkar PS, Mehendale HM. Measuring covalent binding in hepatotoxicity. Curr Protoc Toxicol. 2007;14:Unit14.6. https://doi.org/10.1002/0471140856.tx1406s32; Wang Q, Liu H, Slavsky M, Fitzgerald M, Lu C, O’Shea T. A high-throughput glutathione trapping assay with combined high sensitivity and specificity in high-resolution mass spectrometry by applying product ion extraction and data-dependent neutral loss. J Mass Spectrom. 2019;54(2):158–66. https://doi.org/10.1002/jms.4320; Ramachandran A, Jaeschke H. Oxidative stress and acute hepatic injury. Curr Opin Toxicol. 2018;7:17–21. https://doi.org/10.1016/j.cotox.2017.10.011; Chen S, Zhang Z, Qing T, Ren Z, Yu D, Couch L, Ning B, Mei N, Shi L, Tolleson WH, Guo L. Activation of the Nrf2 signaling pathway in usnic acid-induced toxicity in HepG2 cells. Arch Toxicol. 2017;91(3):1293–307. https://doi.org/10.1007/s00204-016-1775-y; Mihajlovic M, Vinken M. Mitochondria as the target of hepatotoxicity and drug-induced liver injury: molecular mechanisms and detection methods. Int J Mol Sci. 2022;23:3315. https://doi.org/10.3390/ijms23063315; Nadanaciva S, Will Y. The role of mitochondrial dysfunction and drug safety. IDrugs. 2009;12(11):706–10. PMID: 19844857; Begriche K, Massart J, Robin M, Borgne-Sanchez A, Fromenty B. Drug-induced toxicity on mitochondria and lipid metabolism: mechanistic diversity and deleterious consequences for the liver. J Hepatol. 2011;54(14):773–94. https://doi.org/10.1016/j.jhep.2010.11.006; Fromenty B. Alteration of mitochondrial DNA homeostasis in drug-induced liver injury. Food Chem Toxicol. 2020;135:110916. https://doi.org/10.1016/j.fct.2019.110916; McKee E, Ferguson M, Bentley A, Marks T. Inhibition of mammalian mitocondrial protein synthesis by oxazolidinones. Antimicrobial Agents Chemother. 2006;50(16):2042–9. https://doi.org/10.1128/AAC.01411-05; Kamalian L, Douglas O, Jolly CE, Snoeys J, Simic D, Monshouwer M, et al. Acute metabolic switch assay using glucose/galactose medium in HepaRG cells to detect mitochondrial toxicity. Curr Protoc Toxicol. 2019;80(1):76. https://doi.org/10.1002/cptx.76; Nadanaciva S, Willis J, Barker M, Gharaibeh D, Capaldi R, Marusich M, et al. Assessment of drug-induced mitochondrial dysfunction via altered cellular respiration and acidification measured in a 96-well platform. J Bioenerg Biomembr. 2012;44(14):421–37. https://doi.org/10.1007/s10863-012-9446-z; Marchetti P, Fovez Q, Germain N, Khamari R, Kluza J. Mitochondrial spare respiratory capacity: mechanisms, regulation, and significance in non-transformed and cancer cells. FASEB J. 2020;34(10):13106–24. https://doi.org/10.1096/fj.202000767R; Nadanaciva S, Willis JH, Barker ML, Gharaibeh D, Capaldi RA, Marusich MF, et al. Lateral-flow immunoassay for detecting drug-induced inhibition of mitochondrial DNA replication and mtDNA-encoded protein synthesis. J Immunol Methods. 2009;343(1):1–12. https://doi.org/10.1016/j.jim.2008.12.002; Porceddu M, Buron N, Rustin P, Fromenty B, Borgne-Sanchez A. In vitro assessment of mitochondrial toxicity to predict drug-induced liver injury. In: Chen M, Will Y, eds. Drug-induced liver toxicity. New York: Springer New York; 2018. P. 283–300. https://doi.org/10.1007/978-1-4939-7677-5_14; Perez MJ, Briz O. Bile-acid-induced cell injury and protection. World J Gastroenterol. 2009;15(14):1677–89. https://doi.org/10.3748/wjg.15.1677; Garzel B, Yang H, Zhang L, Huang SM, Polli JE, Wang H. The role of bile salt export pump gene repression in drug-induced cholestatic liver toxicity. Drug Metab Dispos. 2014;42(3):318–22. https://doi.org/10.1124/dmd.113.054189; Stieger B, Mahdi ZM. Model systems for studying the role of canalicular efflux transporters in drug-induced cholestatic liver disease. J Pharm Sci. 2017;106(9):2295–301. https://doi.org/10.1016/j.xphs.2017.03.023; Schaefer M, Morinaga G, Matsui A, Schänzle G, Bischoff D, Süssmuth RD. Quantitative expression of hepatobiliary transporters and functional uptake of substrates in hepatic two-dimensional sandwich cultures: a comparative evaluation of upcyte and primary human hepatocytes. Drug Metab Dispos. 2018;46(2):166–77. https://doi.org/10.1124/dmd.117.078238; Funk C, Pantze M, Jehle L, Ponelle C, Scheuermann G, Lazendic M, et al. Troglitazone-induced intrahepatic cholestasis by an interference with the hepatobiliary export of bile acids in male and female rats. Correlation with the gender difference in troglitazone sulfate formation and the inhibition of the canalicular bile salt export pump (Bsep) by troglitazone and troglitazone sulfate. Toxicology. 2001;167(1):83–98. https://doi.org/10.1016/s0300-483x(01)00460-7; Chan R, Benet LZ. Measures of BSEP inhibition in vitro are not useful predictors of DILI. Toxicol Sci. 2018;162(2):499–508. https://doi.org/10.1093/toxsci/kfx284; LeCluyse EL, Witek RP, Andersen ME, Powers MJ. Organotypic liver culture models: meeting current challenges in toxicity testing. Crit Rev Toxicol. 2012;42(6):501–48. https://doi.org/10.3109/10408444.2012.682115; Ogese MO, Faulkner L, Jenkins RE, French NS, Copple IM, Antoine DJ, et al. Characterization of drug-specific signaling between primary human hepatocytes and immune cells. Toxicol Sci. 2017;158(1):76–89. https://doi.org/10.1093/toxsci/kfx069; Persson M. High content screening for prediction of human drug-induced liver injury. In: Chen M, Will Y, eds. Drug-induced liver toxicity. New York: Springer New York; 2018. P. 331–43. https://doi.org/10.1007/978-1-4939-7677-5_16; O’Brien PJ, Irwin W, Diaz D, Howard-Cofield E, Krejsa CM, Slaughter MR, et al. High concordance of drug-induced human hepatotoxicity with in vitro cytotoxicity measured in a novel cell-based model using high content screening. Arch Toxicol. 2006;80(9):580–604. https://doi.org/10.1007/s00204-006-0091-3; Hong S, Song JM. A 3D cell printing-fabricated HepG2 liver spheroid model for high-content in situ quantification of drug-induced liver toxicity. Biomater Sci. 2021;9(17):5939–50. https://doi.org/10.1039/d1bm00749a; Donato M, Tolosa L. High-content screening for the detection of drug-induced oxidative stress in liver cells. Antioxidants (Basel). 2021;10(1):106. https://doi.org/10.3390/antiox10010106; Kozak K, Rinn B, Leven O, Emmenlauer M. Strategies and solutions to maintain and retain data from high content imaging, analysis, and screening assays. Methods Mol Biol. 2018;1683:131–48. https://doi.org/10.1007/978-1-4939-7357-6_9; Lowe R, Shirley N, Bleackley M, Dolan S, Shafee T. Transcriptomics technologies. PLoS Comput Biol. 2017;13(5):1005457. https://doi.org/10.1371/journal.pcbi.1005457; Gupta R, Schrooders Y, Hauser D, van Herwijnen M, Albrecht W, Ter Braak B, et al. Comparing in vitro human liver models to in vivo human liver using RNA-Seq. Arch Toxicol. 2021;95(2):573–89. https://doi.org/10.1007/s00204-020-02937-6; Russo MW, Steuerwald N, Norton HJ, Anderson WE, Foureau D, Chalasani N, et al. Profiles of miRNAs in serum in severe acute drug induced liver injury and their prognostic significance. Liver Int. 2017;37(5):757–64. https://doi.org/10.1111/liv.13312; De Abrew KN, Overmann GJ, Adams RL, Tiesman JP, Dunavent J, Shan YK, Carr GJ, et al. A novel transcriptomics based in vitro method to compare and predict hepatotoxicity based on mode of action. Toxicology. 2015;328:29–39. https://doi.org/10.1016/j.tox.2014.11.008; Ware BR, McVay M, Sunada WY, Khetani SR. Exploring chronic drug effects on microengineered human liver cultures using global gene expression profiling. Toxicol Sci. 2017;157(2):387–98. https://doi.org/10.1093/toxsci/kfx059; Cha HJ, Ko MJ, Ahn SM, Ahn JI, Shin HJ, Jeong HS, et al. Identification of classifier genes for hepatotoxicity prediction in non-steroidal anti-inflammatory drugs. Mol Cell Toxicol. 2010;6:247–53. https://doi.org/10.1007/s13273-010-0034-1; Ölander M, Wiśniewski JR, Artursson P. Cell-type-resolved proteomic analysis of the human liver. Liver Int. 2020;40(7):1770–80. https://doi.org/10.1111/liv.14452; Aebersold R, Mann M. Mass-spectrometric exploration of proteome structure and function. Nature. 2016;537(7620):347–55. https://doi.org/10.1038/nature19949; Alvergnas M, Rouleau A, Lucchi G, Heyd B, Ducoroy P, Richert L, et al. Proteomic mapping of bezafibrate-treated human hepatocytes in primary culture using two-dimensional liquid chromatography. Toxicol Lett. 2011;201(2):123–9. https://doi.org/10.1016/j.toxlet.2010.12.015; Ramirez T, Daneshian M, Kamp H, Bois FY, Clench MR, Coen M et al. Metabolomics in toxicology and preclinical research. ALTEX. 2013;30(2):209–25. https://doi.org/10.14573/altex.2013.2.209; Cuykx M, Rodrigues RM, Laukens K, Vanhaecke T, Covaci A. In vitro assessment of hepatotoxicity by metabolomics: a review. Arch Toxicol. 2018;92(10):3007–29. https://doi.org/10.1007/s00204-018-2286-9; Ruiz-Aracama A, Peijnenburg A, Kleinjans J, Jennen D, van Delft J, Hellfrisch C, et al. An untargeted multi-technique metabolomics approach to studying intracellular metabolites of HepG2 cells exposed to 2,3,7,8-tetrachlorodibenzo-p-dioxin. BMC Genomics. 2011;12:251. https://doi.org/10.1186/1471-2164-12-251; Krajnc E, Visentin M, Gai Z, Stieger B, Samodelov SL, Häusler S, Kullak-Ublick GA. Untargeted metabolomics reveals anaerobic glycolysis as a novel target of the hepatotoxic antidepressant nefazodone. J Pharmacol Exp Ther. 2020;375(2):239–46. https://doi.org/10.1124/jpet.120.000120; Rodrigues RM, Kollipara L, Chaudhari U, Sachinidis A, Zahedi RP, Sickmann A, et al. Omics-based responses induced by bosentan in human hepatoma HepaRG cell cultures. Arch Toxicol. 2018;92(6):1939–52. https://doi.org/10.1007/s00204-018-2214-z; https://www.risksafety.ru/jour/article/view/351
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7Academic Journal
Συγγραφείς: M. А. Sorokina, T. R. Grishina, М. А. Сорокина, Т. Р. Гришина
Συνεισφορές: The work was supported by a grant of the Russian Science Foundation (project No. 23-21-00154 “Development of methods for predicting the properties of pharmacological preparations based on their molecular structure using the theory of topological analysis of chemographs”), FRC IU RAS., Работа выполнена за счет гранта Российского научного фонда (проект № 23-21-00154 «Разработка методов прогноза свойств фармакологических препаратов по их молекулярной структуре с помощью теории топологического анализа хемографов»), ФИЦ ИУ РАН.
Πηγή: FARMAKOEKONOMIKA. Modern Pharmacoeconomics and Pharmacoepidemiology; Vol 16, No 1 (2023); 126–133 ; ФАРМАКОЭКОНОМИКА. Современная фармакоэкономика и фармакоэпидемиология; Vol 16, No 1 (2023); 126–133 ; 2070-4933 ; 2070-4909
Θεματικοί όροι: иммуногистохимия, tumor microenvironment, genomics, transcriptomics, immunohistochemistry, микроокружение опухоли, геномика, транскриптомика
Περιγραφή αρχείου: application/pdf
Relation: https://www.pharmacoeconomics.ru/jour/article/view/777/453; https://www.pharmacoeconomics.ru/jour/article/view/777/455; Han Y., Liu D., Li L. PD-1/PD-L1 pathway: current researches in cancer. Am J Cancer Res. 2020; 10 (3): 727–42.; Naimi A., Mohammed R.N., Raji A., et al. Tumor immunotherapies by immune checkpoint inhibitors (ICIs); the pros and cons. Cell Commun Signal. 2022; 20 (1): 44. https://doi.org/10.1186/s12964-022-00854-y.; Bagaev A., Kotlov N., Nomie K., et al. Conserved pan-cancer microenvironment subtypes predict response to immunotherapy. Cancer Cell. 2021; 39 (6): 845–65.e7. https://doi.org/10.1016/j.ccell.2021.04.014.; Jia M., Yao L., Yang Q., Chi T. Association of MSH2 expression with tumor mutational burden and the immune microenvironment in lung adenocarcinoma. Front Oncol. 2020; 10: 168. https://doi.org/10.3389/fonc.2020.00168.; Del Re M., Cucchiara F., Rofi E., et al. A multiparametric approach to improve the prediction of response to immunotherapy in patients with metastatic NSCLC. Cancer Immunol Immunother. 2021; 70 (6): 1667–78. https://doi.org/10.1007/s00262-020-02810-6.; Ye Y., Zhang Y., Yang N., et al. Profiling of immune features to predict immunotherapy efficacy. Innovation (Camb). 2021; 3 (1): 100194. https://doi.org/10.1016/j.xinn.2021.100194.; Casarrubios M., Provencio M., Nadal E., et al. Tumor microenvironment gene expression profiles associated to complete pathological response and disease progression in resectable NSCLC patients treated with neoadjuvant chemoimmunotherapy. J Immunother Cancer. 2022; 10 (9): e005320. https://doi.org/10.1136/jitc-2022-005320.; Feng C., Li T., Xiao J., et al. Tumor microenvironment profiling identifies prognostic signatures and suggests immunotherapeutic benefits in neuroblastoma. Front Cell Dev Biol. 2022; 10: 814836. https://doi.org/10.3389/fcell.2022.814836.; Zhu X., Tian X., Ji L., et al. A tumor microenvironment-specific gene expression signature predicts chemotherapy resistance in colorectal cancer patients. NPJ Precis Oncol. 2021; 5 (1): 7. https://doi.org/10.1038/s41698-021-00142-x.; Sarhadi V.K., Armengol G. Molecular biomarkers in cancer biomolecules. 2022; 12 (8): 1021. https://doi.org/10.3390/biom12081021.; National Human Genome Research Institute. The cost of sequencing a human genome. URL: https://www.genome.gov/about-genomics/fact-sheets/Sequencing-Human-Genome-cost (дата обращения 28.01.2023).; Paul S.M., Mytelka D.S., Dunwiddie C.T., et al. How to improve R&D productivity: the pharmaceutical industry’s grand challenge. Nat Rev Drug Discov. 2010; 9 (3): 203–14. https://doi.org/10.1038/nrd3078.; Goossens N., Nakagawa S., Sun X., Hoshida Y. Cancer biomarker discovery and validation. Transl Cancer Res. 2015; 4 (3): 256–69. https://doi.org/10.3978/j.issn.2218-676X.2015.06.04.; Hayes D.F. Biomarker validation and testing. Mol Oncol. 2015; 9 (5): 960–6. https://doi.org/10.1016/j.molonc.2014.10.004.; Gion M., Trevisiol C., Fabricio A.S.C. State of the art and trends of circulating cancer biomarkers. Int J Biol Markers. 2020; 35 (1 Suppl.): 12–5. https://doi.org/10.1177/1724600819900512.; Mocan L.P., Ilieș M., Melincovici C.S., et al. Novel approaches in search for biomarkers of cholangiocarcinoma. World J Gastroenterol. 2022; 28 (15): 1508–25. https://doi.org/10.3748/wjg.v28.i15.1508.; Sahin U., Derhovanessian E., Miller M., et al. Personalized RNA mutanome vaccines mobilize poly-specific therapeutic immunity against cancer. Nature. 2017; 547 (7662): 222–6. https://doi.org/10.1038/nature23003.; Melief C.J.M. Cancer: Precision T-cell therapy targets tumours. Nature. 2017; 547 (7662): 165–7. https://doi.org/10.1038/nature23093.; Kharchenko P.V., Silberstein L., Scadden D.T. Bayesian approach to single-cell differential expression analysis. Nat Methods. 2014; 11 (7): 740–2. https://doi.org/10.1038/nmeth.2967.; Seiler Vellame D., Castanho I., Dahir A., et al. Characterizing the properties of bisulfite sequencing data: maximizing power and sensitivity to identify between-group differences in DNA methylation. BMC Genomics. 2021; 22 (1): 446. https://doi.org/10.1186/s12864-021-07721-z.; Finotello F., Eduati F. Multi-omics profiling of the tumor microenvironment: paving the way to precision immuno-oncology. Front Oncol. 2018; 8: 430. https://doi.org/10.3389/fonc.2018.00430.; Nielsen T.O., Leung S.C.Y., Rimm D.L., et al. Assessment of Ki67 in breast cancer: updated recommendations from the International Ki67 in Breast Cancer Working Group. J Natl Cancer Inst. 2021; 113 (7): 808–19. https://doi.org/10.1093/jnci/djaa201.; Johnston S.R.D., Harbeck N., Hegg R., et al. Abemaciclib combined with endocrine therapy for the adjuvant treatment of HR+, HER2-, node-positive, high-risk, early breast cancer (monarchE). J Clin Oncol. 2020; 38 (34): 3987–98. https://doi.org/10.1200/JCO.20.02514.; Allison K.H., Hammond M.E.H., Dowsett M., et al. Estrogen and progesterone receptor testing in breast cancer: ASCO/CAP guideline update. J Clin Oncol. 2020; 38 (12): 1346–66. https://doi.org/10.1200/JCO.19.02309.; Wolff A.C., Hammond M.E.H., Allison K.H., et al. Human epidermal growth factor receptor 2 testing in breast cancer: American Society of Clinical Oncology/College of American Pathologists clinical practice guideline focused update. Arch Pathol Lab Med. 2018; 142 (11): 1364–82. https://doi.org/10.5858/arpa.2018-0902-SA.; Prince E.A., Sanzari J.K., Pandya D., et al. Analytical concordance of PD-L1 assays utilizing antibodies from FDA-approved diagnostics in advanced cancers: a systematic literature review. JCO Precis Oncol. 2021; 5: 953–73. https://doi.org/10.1200/PO.20.00412.; Noske A. Reproducibility and concordance of 4 clinically developed programmed death-ligand 1 (PD-L1) immunohistochemistry (IHC) assays in triple negative breast cancer (TNBC). Ann Oncol. 2019; 30 (Suppl. 5): v130–1. https://doi.org/10.1093/annonc/mdz242.; Laenkholm A.V., Grabau D., Møller Talman M.L. et al. An inter-observer Ki67 reproducibility study applying two different assessment methods: on behalf of the Danish Scientific Committee of Pathology, Danish breast cancer cooperative group (DBCG). Acta Oncol. 2018; 57 (1): 83–9. https://doi.org/10.1080/0284186X.2017.1404127.; Barnes M., Srinivas C., Bai I., et al. Whole tumor section quantitative image analysis maximizes between-pathologists’ reproducibility for clinical immunohistochemistry-based biomarkers. Lab Invest. 2017; 97 (12): 1508–15. https://doi.org/10.1038/labinvest.2017.82.; Pu T., Shui R., Shi J., et al. External quality assessment (EQA) program for the immunohistochemical detection of ER, PR and Ki-67 in breast cancer: results of an interlaboratory reproducibility ring study in China. BMC Cancer. 2019; 19 (1): 978. https://doi.org/10.1186/s12885-019-6210-3.; Griguolo G., Brasó-Maristany F., González-Farré B., et al. ERBB2 mRNA expression and response to ado-trastuzumab emtansine (T-DM1) in HER2-positive breast cancer. Cancers (Basel). 2020; 12 (7): 1902. https://doi.org/10.3390/cancers12071902.; Li A., Keck J.M., Parmar S., et al. Characterizing advanced breast cancer heterogeneity and treatment resistance through serial biopsies and comprehensive analytics. NPJ Precis Oncol. 2021; 5 (1): 28. https://doi.org/10.1038/s41698-021-00165-4.; Akhtar M., Rashid S., Al-Bozom I.A. PD-L1 immunostaining: what pathologists need to know. Diagn Pathol. 2021; 16 (1): 94. https://doi.org/10.1186/s13000-021-01151-x.; Tarantino P., Curigliano G., Tolaney S.M. Navigating the HER2-low paradigm in breast oncology: new standards, future horizons. Cancer Discov. 2022; 12 (9): 2026–30. https://doi.org/10.1158/2159-8290.CD-22-0703.; Metzger Filho O., Viale G., Trippa L., et al. HER2 heterogeneity as a predictor of response to neoadjuvant T-DM1 plus pertuzumab: results from a prospective clinical trial. J Clin Oncol. 2019; 37 (15 Suppl.): 502. https://doi.org/10.1200/JCO.2019.37.15_suppl.502.; Lee H.J., Seo A.N., Kim E.J., et al. HER2 heterogeneity affects trastuzumab responses and survival in patients with HER2-positive metastatic breast cancer. Am J Clin Pathol. 2014; 142 (6): 755–66. https://doi.org/10.1309/AJCPIRL4GUVGK3YX.; Kammerer-Jacquet S.F., Deleuze A., Saout J., et al. Targeting the PD-1/PD-L1 pathway in renal cell carcinoma. Int J Mol Sci. 2019; 20 (7): 1692. https://doi.org/10.3390/ijms20071692.; Zhu J., Armstrong A.J., Friedlander T.W., et al. Biomarkers of immunotherapy in urothelial and renal cell carcinoma: PD-L1, tumor mutational burden, and beyond. J Immunother Cancer. 2018; 6 (1): 4. https://doi.org/10.1186/s40425-018-0314-1.; Brueffer C., Vallon-Christersson J., Grabau D., et al Clinical value of RNA sequencing-based classifiers for prediction of the five conventional breast cancer biomarkers: a report from the population-based multicenter Sweden Cancerome Analysis Network-Breast Initiative. JCO Precis Oncol. 2018; 2: PO.17.00.135. https://doi.org/10.1200/PO.17.00135.; Darmon-Novello M., Adam J., Lamant L., et al. Harmonization of programmed death-ligand 1 immunohistochemistry and mRNA expression scoring in metastatic melanoma: a multicentre analysis. Histopathology. 2022; 80 (7): 1091–101. https://doi.org/10.1111/his.14651.; Duncan D.J., Scott M., Scorer P., Barker C., et al. Assessment of PD-L1 mRNA and protein expression in non-small cell lung cancer, head and neck squamous cell carcinoma and urothelial carcinoma tissue specimens using RNAScope and immunohistochemistry. PLoS One. 2019; 14 (4): e0215393. https://doi.org/10.1371/journal.pone.0215393.; Tsimafeyeu I., Imyanitov E., Zavalishina L., et al. Agreement between PDL1 immunohistochemistry assays and polymerase chain reaction in non-small cell lung cancer: CLOVER comparison study. Sci Rep. 2020; 10 (1): 3928. https://doi.org/10.1038/s41598-020-60950-2.; Byron S.A., Van Keuren-Jensen K.R., Engelthaler D.M., et al. Translating RNA sequencing into clinical diagnostics: opportunities and challenges. Nat Rev Genet. 2016; 17 (5): 257–71. https://doi.org/10.1038/nrg.2016.10.; https://www.pharmacoeconomics.ru/jour/article/view/777
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8Academic Journal
Συγγραφείς: N. A. Shmakov, Н. А. Шмаков
Συνεισφορές: The work was supported by Russian Science Foundation project No. 18-14-00293 (problem statement, algorithm creation, data analysis). Computational resources of Core Facility ‘Bioinformatics’ supported by budget project No. 0259-2021-0009 were implemented in this work.
Πηγή: Vavilov Journal of Genetics and Breeding; Том 25, № 1 (2021); 30-38 ; Вавиловский журнал генетики и селекции; Том 25, № 1 (2021); 30-38 ; 2500-3259 ; 10.18699/VJ20.677
Θεματικοί όροι: IonTorrent, transcriptomics, de novo transcriptome reconstruction, транскриптомика, de novo реконструкция транскриптома
Περιγραφή αρχείου: application/pdf
Relation: https://vavilov.elpub.ru/jour/article/view/2913/1479; Bürckert J.P., Dubois A.R.S.X., Faison W.J., Farinelle S., Charpentier E., Sinner R., Wienecke-Baldacchino A., Muller C.P. Functionally convergent B cell receptor sequences in transgenic rats expressing a human B cell repertoire in response to tetanus toxoid and measles antigens. Front. Immunol. 2017. DOI 10.3389/fimmu.2017.01834.; Bushmanova E., Antipov D., Lapidus A., Przhibelskiy A.D. rnaSPAdes: a de novo transcriptome assembler and its application to RNA-Seq data. BioRxiv. 2018. DOI 10.1101/420208.; Bushmanova E., Antipov D., Lapidus A., Suvorov V., Prjibelski A.D. rnaQUAST: a quality assessment tool for de novo transcriptome assemblies. Bioinformatics. 2016;32(14):2210-2212. DOI 10.1093/bioinformatics/btw218.; Cerveau N., Jackson D.J. Combining independent de novo assemblies optimizes the coding transcriptome for nonconventional model eukaryotic organisms. BMC Bioinform. 2016;17:525. PMid: 27938328. DOI 10.1186/s12859-016-1406-x.; Chang Z., Wang Z., Li G. The impacts of read length and transcriptome complexity for de novo assembly: a simulation study. PLoS One. 2014;9(4):e94825. PMid: 24736633. DOI 10.1371/journal.pone.0094825.; Cui J., Shen N., Lu Z., Xu G., Wang Y., Jin B. Analysis and comprehensive comparison of PacBio and nanopore-based RNA sequencing of the Arabidopsis transcriptome. Plant Methods. 2020;16:85. DOI 10.1186/s13007-020-00629-x.; vEngström P.G., Steijger T., Sipos B., Grant G.R., Kahles A., Rätsch G., Goldman N., Hubbard T.J., Harrow J., Guigó R., Bertone P., Alioto T., Behr J., Bohnert R., Campagna D., Davis C.A., Dobin A., Gingeras T.R., Jean G., Kosarev P., Li S., Liu J., Mason C.E., Molodtsov V., Ning Z., Ponstingl H., Prins J.F., Ribeca P., Seledtsov I., Solovyev V., Valle G., Vitulo N., Wang K., Wu T.D., Zeller G. Systematic evaluation of spliced alignment programs for RNA-seq data. Nat. Methods. 2013;10:1185-1191. PMid: 24185836. DOI 10.1038/nmeth.2722.; Evangelistella C., Valentini A., Ludovisi R., Firrincieli A., Fabbrini F., Scalabrin S., Cattonaro F., Morgante M., Mugnozza G.S., Keurentjes J.J.B., Harfouche A. De novo assembly, functional annotation, and analysis of the giant reed (Arundo donax L.) leaf transcriptome provide tools for the development of a biofuel feedstock. Biotechnol. Biofuels. 2017;10:138. DOI 10.1186/s13068-017-0828-7.; Fu S., Ma Y., Yao H., Xu Z., Chen S., Song J., Au K.F. IDP-denovo: de novo transcriptome assembly and isoform annotation by hybrid sequencing. Bioinformatics. 2018;34(13):2168-2176. PMid: 28407034. DOI 10.1093/bioinformatics/bty098.; Gilbert D.G. Genes of the pig, Sus scrofa, reconstructed with EvidentialGene. PeerJ. 2019;7:e6374. DOI 10.7717/peerj.6374.; Glagoleva A.Y., Shmakov N.A., Shoeva O.Y., Vasiliev G.V., Shatskaya N.V., Börner A., Afonnikov D.A., Khlestkina E.K. Metabolic pathways and genes identified by RNA-seq analysis of barley near-isogenic lines differing by allelic state of the Black lemma and pericarp (Blp) gene. BMC Plant Biol. 2017;17:182. DOI 10.1186/s12870-017-1124-1.; Grabherr M.G., Haas B.J., Yassour M., Levin J.Z., Thompson D.A., Amit I., Adiconis X., Fan L., Raychowdhury R., Zeng Q., Chen Z., Mauceli E., Hacohen N., Gnirke A., Rhind N., di Palma F., Birren B.W., Nusbaum C., Lindblad-Toh K., Friedman N., Regev A. Trinity: reconstructing a full-length transcriptome without a genome from RNA-Seq data. Nat. Biotechnol. 2013;29:644-652. PMid: 21572440. DOI 10.1038/nbt.1883.Trinity.; Hölzer M., Marz M. De novo transcriptome assembly: a comprehensive cross-species comparison of short-read RNA-Seq assemblers. GigaScience. 2019;8(5):giz039. PMid: 31077315. DOI 10.1093/gigascience/giz039.; Honaas L.A., Wafula E.K., Wickett N.J., Der J.P., Zhang Y., Edger P.P., Altman N.S., Chris Pires J., Leebens-Mack J.H., DePamphilis C.W. Selecting superior de novo transcriptome assemblies: lessons learned by leveraging the best plant genome. PLoS One. 2016;11(1):e0146062. PMid: 26731733. DOI 10.1371/journal.pone.0146062.; Hrdlickova R., Toloue M., Tian B. RNA-Seq methods for transcriptome analysis. Wiley Interdiscip. Rev. RNA. 2017;8:e1364. PMid: 27198714. DOI 10.1002/wrna.1364.; Jain P., Krishnan N.M., Panda B. Augmenting transcriptome assembly by combining de novo and genome-guided tools. PeerJ. 2013;1: e133. PMid: 24024083. DOI 10.7717/peerj.133.; Lafond-Lapalme J., Duceppe M.O., Wang S., Moffett P., Mimee B. A new method for decontamination of de novo transcriptomes using a hierarchical clustering algorithm. Bioinformatics. 2017;33(9): 1293-1300. PMid: 28011783. DOI 10.1093/bioinformatics/btw793.; Lahens N.F., Ricciotti E., Smirnova O., Toorens E., Kim E.J., Baruzzo G., Hayer K.E., Ganguly T., Schug J., Grant G.R. A comparison of Illumina and Ion Torrent sequencing platforms in the context of differential gene expression. BMC Genom. 2017;18:602. PMid: 28797240. DOI 10.1186/s12864-017-4011-0.; Lee S., La T.M., Lee H.J., Choi I.S., Song C.S., Park S.Y., Lee J.B., Lee S.W. Characterization of microbial communities in the chicken oviduct and the origin of chicken embryo gut microbiota. Sci. Rep. 2019;9:6838. PMid: 31048728. DOI 10.1038/s41598-019-43280-w.; Li Z., Chen Y., Mu D., Yuan J., Shi Y., Zhang H., Gan J., Li N., Hu X., Liu B., Yang B., Fan W. Comparison of the two major classes of assembly algorithms: overlap–layout–consensus and de-bruijn-graph. Brief Funct. Genomics. 2012;11(1):25-37. PMid: 22184334. DOI 10.1093/bfgp/elr035.; Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.Journal. 2011;17(1):10-12. PMid: 1000006697. DOI 10.14806/ej.17.1.200.; Payá-Milans M., Olmstead J.W., Nunez G., Rinehart T.A., Staton M. Comprehensive evaluation of RNA-Seq analysis pipelines in diploid and polyploid species. GigaScience. 2018;7(12):giy132. PMid: 30418578. DOI 10.1093/gigascience/giy132.; Robertson G., Schein J., Chiu R., Corbett R., Field M., Jackman S.D., Mungall K., Lee S., Okada H.M., Qian J.Q., Griffith M., Raymond A., Thiessen N., Cezard T., Butterfield Y.S., Newsome R., Chan S.K., She R., Varhol R., Kamoh B., Prabhu A.L., Tam A., Zhao Y., Moore R.A., Hirst M., Marra M.A., Jones S.J.M., Hoodless P.A., Birol I. De novo assembly and analysis of RNA-seq data. Nat. Methods. 2010;7(11):909-912. DOI 10.1038/nmeth.1517.; Salina E.A., Nesterov M.A., Frenkel Z., Kiseleva A.A., Timonova E.M., Magni F., Vrána J., Šafár J., Šimková H., Doležel J., Korol A., Sergeeva E.M. Features of the organization of bread wheat chromosome 5BS based on physical mapping. BMC Genom. 2018; 19:80. PMid: 29504906. DOI 10.1186/s12864-018-4470-y.; Schliesky S., Gowik U., Weber A.P.M., Bräutigam A. RNA-seq assembly – are we there yet? Front. Plant Sci. 2012;3:220. DOI 10.3389/fpls.2012.00220.; Schmieder R., Edwards R. Quality control and preprocessing of metagenomic datasets. Bioinformatics. 2011;27:863-864. PMid: 21278185. DOI 10.1093/bioinformatics/btr026.; Schulz M.H., Zerbino D.R., Vingron M., Birney E. Oases: robust de novo RNA-seq assembly across the dynamic range of expression levels. Bioinformatics. 2012;28(8):1086-1092. PMid: 22368243. DOI 10.1093/bioinformatics/bts094.; Shekhovtsov S.V., Ershov N.I., Vasiliev G.V., Peltek S.E. Transcriptomic analysis confirms differences among nuclear genomes of cryptic earthworm lineages living in sympatry. BMC Evol. Biol. 2019; 19:50. PMid: 30813890. DOI 10.1186/s12862-019-1370-y.; Shmakov N.A., Vasiliev G.V., Shatskaya N.V., Doroshkov A.V., Gordeeva E.I., Afonnikov D.A., Khlestkina E.K. Identification of nuclear genes controlling chlorophyll synthesis in barley by RNA-seq. BMC Plant Biol. 2016;16. DOI 10.1186/s12870-016-0926-x.; Simão F.A., Waterhouse R.M., Ioannidis P., Kriventseva E.V., Zdobnov E.M. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics. 2015;31: 3210-3212. PMid: 26059717. DOI 10.1093/bioinformatics/btv351.; Smith-Unna R., Boursnell C., Patro R., Hibberd J.M., Kelly S. TransRate: reference-free quality assessment of de novo transcriptome assemblies. Genome Res. 2016;26:1134-1144. PMid: 27252236. DOI 10.1101/gr.196469.115.; Venturini L., Caim S., Kaithakottil G.G., Mapleson D.L., Swarbreck D. Leveraging multiple transcriptome assembly methods for improved gene structure annotation. GigaScience. 2018;7(8):giy093. PMid: 30052957. DOI 10.1093/gigascience/giy093.; Wang S., Gribskov M. Comprehensive evaluation of de novo transcriptome assembly programs and their effects on differential gene expression analysis. Bioinformatics. 2017;33(3):327-333. PMid: 27694201. DOI 10.1093/bioinformatics/btw625.; Xie Y., Wu G., Tang J., Luo R., Patterson J., Liu S., Huang W., He G., Gu S., Li S., Zhou X., Lam T.W., Li Y., Xu X., Wong G.K.S., Wang J. SOAPdenovo-Trans: de novo transcriptome assembly with short RNA-Seq reads. Bioinformatics. 2014;30(12):1660-1666. DOI 10.1093/bioinformatics/btu077.; https://vavilov.elpub.ru/jour/article/view/2913
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9Academic Journal
Συγγραφείς: A. P. Seryakov, R. M. Akhmaev, A. A. Guryanova, A. A. Prokofieva, А. П. Серяков, Р. М. Ахмаев, А. А. Гурьянова, А. А. Прокофьева
Πηγή: Pharmacogenetics and Pharmacogenomics; № 1 (2021); 33-37 ; Фармакогенетика и фармакогеномика; № 1 (2021); 33-37 ; 2686-8849 ; 2588-0527
Θεματικοί όροι: молекулярная диагностика, recurrent malignant tumors, molecular profiling, transcriptomics, RNA sequencing, Oncobox, experimental therapy, molecular diagnostics, рецидивирующие злокачественные опухоли, молекулярное профилирование, транскриптомика, РНК-секвенирование, Онкобокс, экспериментальная терапия
Περιγραφή αρχείου: application/pdf
Relation: https://www.pharmacogenetics-pharmacogenomics.ru/jour/article/view/213/213; Ferlay J, Soerjomataram I, Dikshit R, Eser S, Mathers C, Rebelo M, Parkin DM, Forman D, Bray F. Cancer incidence and mortality worldwide: Sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer. 2015 Mar 1;136(5):E359–386. DOI:10.1002/ijc.29210; Lacey JV, Chia VM, Rush BB, Carreon DJ, Richesson DA, Ioffe OB, Ronnett BM, Chatterjee N, Langholz B, Sherman ME, Glass AG. Incidence rates of endometrial hyperplasia, endometrial cancer and hysterectomy from 1980 to 2003 within a large prepaid health plan. Int J Cancer. 2012 Oct 15;131(8):1921–1929. DOI:10.1002/ijc.27457; Key TJ, Pike MC. The dose-effect relationship between ‘unopposed’ostrogens and endometrial mitotic rate: its central role in explaining and predicting endometrial cancer risk. Br J Cancer. 1988 Feb;57(2):205–212. DOI:10.1038/bjc.1988.44.; Pike MC, Peters RK, Cozen W, Probst-Hensch NM, Felix JC, Wan PC, Mack TM. Estrogen-progestin replacement therapy and endometrial cancer. J Natl Cancer Inst. 1997 Aug 6;89(15):1110–1116. DOI:10.1093/jnci/89.15.1110.; Kaaks R, Lukanova A, Kurzer MS (2002). Obesity, endogenous hormones, and endometrial cancer risk: A synthetic review. Cancer Epidemiol Biomarkers Prev. 2002 Dec;11(12):1531–1543.; Renehan AG, Tyson M, Egger M, Heller RF, Zwahlen M. Body-mass index and incidence of cancer: a systematic review and meta-analysis of prospective observational studies. Lancet. 2008 Feb 16;371(9612):569–578. DOI:10.1016/S0140-6736(08)60269-X.; Grady D, Gebretsadik T, Kerlikowske K, Ernster V, Petitti D. Hormone replacement therapy and endometrial cancer risk: a meta-analysis. Obstet Gynecol. 1995 Feb;85(2):304–313. DOI:10.1016/0029-7844(94)00383-O.; Purdie DM, Green AC. Epidemiology of endometrial cancer. Best Pract Res Clin Obstet Gynaecol. 2001 Jun;15(3):341–354. DOI:10.1053/beog.2000.0180.; Duska LR, Garrett A, Rueda BR, Haas J, Chang Y, Fuller AF. Endometrial cancer in women 40 years old or younger. Gynecol Oncol. 2001 Nov;83(2):388–393. DOI:10.1006/gyno.2001.6434.; Tarone RE, Chu KC. Age-period-cohort analyses of breast-, ovarian-, endometrial- and cervical-cancer mortality rates for Caucasian women in the USA. J Epidemiol Biostat. 2000;5(4):221–231.; Fung-Kee-Fung M, Dodge J, Elit L, Lukka H, Chambers A, Oliver T. Follow-up after primary therapy for endometrial cancer: A systematic review. Gynecol Oncol. 2006 Jun;101(3):520–529. DOI:10.1016/j.ygyno.2006.02.011.; Huijgens ANJ, Mertens HJMM. Factors predicting recurrent endometrial cancer. Facts Views Vis Obgyn. 2013;5(3):179-86.; Jeppesen MM, Jensen P T, Gilså Hansen D, Iachina M, Mogensen O. The nature of early-stage endometrial cancer recurrence—A national cohort study. Eur J Cancer. 2016 Dec;69:51–60. DOI:10.1016/j.ejca.2016.09.033.; Getz G, Gabriel SB, Cibulskis K, Lander E, Sivachenko A, Sougnez C, Lawrence M, Kandoth C, Dooling D, Fulton R, Fulton L, Kalicki-Veizer, J; McLellan MD, O’Laughlin M, Schmidt H, Wilson RK, Ye K, Li D, Ally A, Levine DA. Integrated genomic characterization of endometrial carcinoma. Nature. 2013 May 2;497(7447):67–73. DOI:10.1038/nature12113.; Bokhman JV. Two pathogenetic types of endometrial carcinoma. Gynecol Oncol. 1983 Feb;15(1):10–17. DOI:10.1016/0090-8258(83)90111-7.; Byron SA, Pollock PM. FGFR2 as a molecular target in endometrial cancer. Future Oncol. 2009 Feb;5(1):27–32. DOI:10.2217/14796694.5.1.27.; Sasada S, Yunokawa M, Takehara Y, Ishikawa M, Ikeda S, Kato T, Tamura K. Baseline risk of recurrence in stage I–II endometrial carcinoma. J Gynecol Oncol. 2018 Jan;29(1):e9. DOI:10.3802/jgo.2018.29.e9.; Morrow CP, Bundy BN, Kurman RJ, Creasman W T, Heller P, Homesley HD, Graham JE. Relationship between surgical-pathological risk factors and outcome in clinical stage I and II carcinoma of the endometrium: A gynecologic oncology group study. Gynecol Oncol. 1991 Jan;40(1):55–65. DOI:10.1016/0090-8258(91)90086-k.; Carey MS, O’Connell GJ, Johanson CR, Goodyear MD, Murphy KJ, Daya DM, Schepansky A, Peloquin A, Lumsden BJ. Good outcome associated with a standardized treatment protocol using selective postoperative radiation in patients with clinical stage i adenocarcinoma of the endometrium. Gynecol Oncol. 1995 May;57(2):138–144. DOI:10.1006/gyno.1995.1115.; Mariani A, Webb MJ, Keeney GL, Haddock MG, Calori G, Podratz KC. Low-risk corpus cancer: Is lymphadenectomy or radiotherapy necessary? Am J Obstet Gynecol. 2000 Jun;182(6):1506–1519. DOI:10.1067/mob.2000.107335.; Güngördük K, Cuylan ZF, Kahramanoglu I, Oge T, Akbayir O, Dede M, Taşkln S, Ozgul N, Simsek T, Turan H, Gülseren V, Ozdemir A, Meydanll MM, Ayhan A. Risk Factors for Recurrence in Low-Risk Endometrial Cancer: A Case-Control Study. Oncol Res Treat. 2018;41(7-8):466–470. DOI:10.1159/000488112.; Versluis MA, De Jong RA, Plat A, Bosse T, Smit VT, Mackay H, Powell M, Leary A, Mileshkin L, Kitchener HC, Crosbie EJ, Edmondson RJ, Creutzberg CL, Hollema H, Daemen T, De Bock GH, Nijman HW. Prediction model for regional or distant recurrence in endometrial cancer based on classical pathological and immunological parameters. Br J Cancer. 2015 Sep 1;113(5):786–793. DOI:10.1038/bjc.2015.268.; Zeimet AG, Reimer D, Huszar M, Winterhoff B, Puistola U., Azim SA, Müller-Holzner E, Ben-Arie A, Van Kempen LC, Petru E, Jahn S, Geels YP, Massuger LF, Amant F, Polterauer S, Lappi-Blanco E, Bulten J, Meuter A, Tanouye S, Fogel M. L1CAM in early-stage type i endometrial cancer: Results of a large multicenter evaluation. J Natl Cancer Inst. 2013 Aug 7;105(15):1142–1150. DOI:10.1093/jnci/djt144.; Bosse T, Nout RA, Stelloo E, Dreef E, Nijman HW, Jürgenliemk- Schulz IM, Jobsen JJ, Creutzberg CL, Smit VTHBM. L1 cell adhesion molecule is a strong predictor for distant recurrence and overall survival in early stage endometrial cancer: Pooled PORTEC trial results. Eur J Cancer. 2014 Oct;50(15):2602–2610. DOI:10.1016/j.ejca.2014.07.014.; Werner HMJ, Trovik J, Halle MK, Wik E, Akslen LA, Birkeland E, Bredholt T, Tangen IL, Krakstad C, Salvesen HB. Stathmin protein level, a potential predictive marker for taxane treatment response in endometrial cancer. PLoS One. 2014 Feb 25;9(2):e90141. DOI:10.1371/journal.pone.0090141.; Odagiri T, Watari H, Hosaka M, Mitamura T, Konno Y, Kato T, Kobayashi N, Sudo S, Takeda M, Kaneuchi M, Sakuragi N. Multivariate survival analysis of the patients with recurrent endometrial cancer. J Gynecol Oncol. 2011 Mar 31;22(1):3–8. DOI:10.3802/jgo.2011.22.1.3.; Shim SH, Kim DY, Kim HJ, Lee SW, Park JY, Suh DS, Kim JH, Kim YM, Kim YT, Nam JH. Stratification of risk groups according to survival after recurrence in endometrial cancer patients. Medicine (Baltimore). 2017 May;96(21):e6920. DOI:10.1097/MD.0000000000006920.; Francis SR, Ager BJ, Do OA, Huang YHJ, Soisson AP, Dodson MK, Werner TL, Sause WT, Grant JD, Gaffney DK. Recurrent early stage endometrial cancer: Patterns of recurrence and results of salvage therapy. Gynecol Oncol. 2019 Jul;154(1):38–44. DOI:10.1016/j.ygyno.2019.04.676.; DeVita VT, Lawrence, TS, Rosenberg SA. DeVita, Hellman, and Rosenberg’s cancer: Principles & practice of oncology. In DeVita, Hellman, and Rosenberg’s Cancer: Principles & Practice of Oncology, Wolters Kluwer. 2018. p. 2170–2101.; Tkachev V, Sorokin M, Garazha A, Borisov N, Buzdin A. Oncobox Method for Scoring Efficiencies of Anticancer Drugs Based on Gene Expression Data. Methods Mol Biol. 2020;2063:235–255. DOI:10.1007/978-1-0716-0138-9_17; Sorokin M, Poddubskaya E, Baranova M, et al. RNA sequencing profiles and diagnostic signatures linked with response to ramucirumab in gastric cancer. Cold Spring Harb Mol Case Stud. 2020;6(2):a004945. Published 2020 Apr 1. DOI:10.1101/mcs.a004945; Poddubskaya E, Bondarenko A, Boroda A, et al. Transcriptomics- Guided Personalized Prescription of Targeted Therapeutics for Metastatic ALK-Positive Lung Cancer Case Following Recurrence on ALK Inhibitors. Front Oncol. 2019;9:1026. Published 2019 Oct 15. DOI:10.3389/fonc.2019.01026; Poddubskaya EV, Baranova MP, Allina DO, et al. Personalized prescription of imatinib in recurrent granulosa cell tumor of the ovary: case report. Cold Spring Harb Mol Case Stud. 2019;5(2):a003434. Published 2019 Apr 1. DOI:10.1101/mcs.a003434; Poddubskaya EV, Baranova MP, Allina DO, et al. Personalized prescription of tyrosine kinase inhibitors in unresectable metastatic cholangiocarcinoma. Exp Hematol Oncol. 2018;7:21. Published 2018 Sep 6. DOI:10.1186/s40164-018-0113-x; Moisseev A, Albert E, Lubarsky D, Schroeder D, Clark J. Transcriptomic and Genomic Testing to Guide Individualized Treatment in Chemoresistant Gastric Cancer Case. Biomedicines. 2020;8(3):67. Published 2020 Mar 23. DOI:10.3390/biomedicines8030067
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10Academic Journal
Συγγραφείς: M. V. Sprindzuk, A. S. Vladyko, L. P. Titov, A. P. Konchits, М. В. Спринджук, А. С. Владыко, Л. П. Титов, А. П. Кончиц
Πηγή: Digital Transformation; № 3 (2021); 47-57 ; Цифровая трансформация; № 3 (2021); 47-57 ; 2524-2822 ; 2522-9613
Θεματικοί όροι: иммуноинформатика, COVID-19, coronavirus, epidemic, pandemic, automated data processing systems, software, medical cybernetics, genomics, transcriptomics, medical systems, immunoinformatics, коронавирус, эпидемия, пандемия, автоматизированные системы обработки данных, программное обеспечение, медицинская кибернетика, геномика, транскриптомика, системы медицинского назначения
Περιγραφή αρχείου: application/pdf
Relation: https://dt.bsuir.by/jour/article/view/635/229; Tomar, Marton. Immunoinformatics. – P [S.l.]: Springer US, 2020. – 409 p.; De, R.K., Tomar, N. Immunoinformatics. – New York: Humana Press, 2014. – P xix, 586 pages.; Schönbach, C., Ranganathan, S., Brusic, V. Immunoinformatics. – New York: Springer, 2008. – xix, 200 p.; Flower, D.R. Bioinformatics for vaccinology. – Chichester, West Sussex, England; Hoboken, NJ: John Wiley & Sons, 2008. – 314 p.; Prabhakar, H., Kapoor, I., Mahajan, C. Clinical synopsis of COVID-19: evolving and challenging. – 1 online resource (XIV, 260 pages).; Chandra, P., Roy, S. Diagnostic strategies for COVID-19 and other coronaviruses. – Singapore: Springer, 2020. – 1 online resource (viii, 199 pages).; Saxena, S.K. Coronavirus disease 2019 (COVID-19): epidemiology, pathogenesis, diagnosis, and therapeutics. – 1 online resource (224 pages).; Plotkin, S.A., Orenstein, W.A., Offit, P.A. Plotkin's vaccines. – Philadelphia, PA: Elsevier, 2018. – xxi, 1691 pages.; Enjuanes, L. Coronavirus replication and reverse genetics. – Berlin; New York: Springer, 2005. – vii, 257 p.; Varshney, D., Singh, M., SpringerLink (Online service). Lyophilized Biologics and Vaccines Modality – Based Approaches. – New York, NY: Springer New York: Imprint: Springer, 2015. – XI, 401 p. 99 illus., 68 illus. in color.; Nunnally, B.K., Turula, V.E., Sitrin, R.D., SpringerLink (Online service). Vaccine Analysis: Strategies, Principles, and Control. – Berlin, Heidelberg: Springer Berlin Heidelberg: Imprint: Springer, 2015. – XII, 665 p. 124 illus., 76 illus. in color.; Maier, H.J., Bickerton, E., Britton, P. Coronaviruses methods and protocols. – New York: Humana Press; Springer, 2015. – xi, 285 pages.; Kiyono, H., Ogra, P.L., McGhee, J.R. Mucosal vaccines. – San Diego: Academic Press, 1996. – xix, 479 p.; Vanniasinkam, T., Tikoo, S.K., Samal, S.K. Viral vectors in veterinary vaccine development: a textbook. – Cham, Switzerland: Springer, 2021. – 1 online resource (xi, 230 pages).; Hwang, W., Lei, W., Katritsis, N.M. et al. Current and prospective computational approaches and challenges for developing COVID-19 vaccines // Advanced Drug Delivery Reviews. – 2021. epub.; Sohail, M.S., Ahmed, S.F., Quadeer, A.A., McKay, M.R. In silico T cell epitope identification for SARS– CoV– 2: Progress and perspectives // Adv Drug Deliv Rev. – 2021. – Vol. 171, – P. 29-47.; Noorimotlagh, Z., Karami, C., Mirzaee, S.A. et al. Immune and bioinformatics identification of T cell and B cell epitopes in the protein structure of SARS–CoV–2: A systematic review // Int Immunopharmacol. – 2020. – Vol. 86. – P. 106738.; Baruah, V., Bose, S. Immunoinformatics aided identification of T cell and B cell epitopes in the surface glycoprotein of 2019 nCoV // Journal of medical virology. – 2020. – Vol. 92, No. 5. – P. 495-500.; Oliveira, S.C., de Magalhães, M.T., Homan, E.J. Immunoinformatic Analysis of SARS–CoV–2 Nucleocapsid Protein and Identification of COVID–19 Vaccine Targets // Frontiers in immunology. – 2020. – Vol. 11. – P. 2758.; Feng, Y., Qiu, M., Zou, S. et al. Multi–epitope vaccine design using an immunoinformatics approach for 2019 novel coronavirus in China (SARS–CoV–2) // BioRxiv. – 2020. – Vol. 1, No. 1. – P. 1-12.; Wang, X., Xu, W., Tong, D. et al. A chimeric multi– epitope DNA vaccine elicited specific antibody response against severe acute respiratory syndrome – associated coronavirus which attenuated the virulence of SARS–CoV in vitro // Immunology letters. – 2008. – Vol. 119, No. 1– 2. – P. 71-77.; Belikova Y., Samsonov Y., Abakushina E. Modern vaccines and coronavirus infections. Research and Practical Medicine Journal, 2020, Vol 7, No. 4. pp. 135-154. (In Russian).; Kononov A., Mishchenko V., Dumova B. et al. Antigenic properties of bovine coronavirus vaccine with various adjuvants. Proceedings of the Federal Center for Animal Health, 2009, Vol 7, pp. 50-55. (In Russian).; Kharchenko E. P. Coronavirus SARS-Cov-2: the complexity of pathogenesis, the search for vaccines and future pandemics. Epidemiology and Vaccine Prevention, 2020, Vol 19, No. 3. pp. 4-20. (In Russian).; Ozharovskaya T., Zubkova O., Dolzhikova I. et al. Immunogenicity of various forms of glycoprotein S of the Middle East respiratory syndrome coronavirus. Acta Naturae (Russian version), 2019, Vol 11, No. 1 (40). pp. 38-47. (In Russian).; Chepurnov A. A., Sharshov K. A., Kazachinskaya E. I. et al. Antigenic properties of SARS-CoV-2 / human / RUS / Nsk-FRCFTM-1/202 coronavirus isolate isolated from a patient in Novosibirsk. Journal of Infectology, 2020, Vol 12, No. 3. pp. 42-50. (In Russian).; Kharchenko E.P. SARS-CoV-2 coronavirus: features of structural proteins, contagiousness and possible immune collisions. Epidemiology and Vaccine Prevention, 2020, Vol 19, No. 2. pp. 13-30. (In Russian).; Titova M.A. Approaches to modeling immunogenic peptides: author. dis. for a job. learned. step. Cand. chem. Sciences: 02.00.10 / Titova Maya Adolfovna. - M., 2003, 23 p. M., 2003.(In Russian).; Hu, T., Li, J., Zhou, H. et al. Bioinformatics resources for SARS–CoV–2 discovery and surveillance // Briefings in Bioinformatics. – 2021. – Vol. 22, No. 2. – P. 631-641.; Hufsky, F., Lamkiewicz, K., Almeida, A. et al. Computational strategies to combat COVID–19: useful tools to accelerate SARS–CoV–2 and coronavirus research // Briefings in Bioinformatics. – 2020. – Vol. 22, No. 2. – P. 642-663.; Kangabam, R., Sahoo, S., Ghosh, A. et al. Next– generation computational tools and resources for coronavirus research: From detection to vaccine discovery // Computers in biology and medicine. – 2020. – Vol. 1, No. 1. – P. 104-115.; Kiyotani, K., Toyoshima, Y., Nemoto, K., Nakamura, Y. Bioinformatic prediction of potential T cell epitopes for SARS-Cov–2 // Journal of human genetics. – 2020. – Vol. 65, No. 7. – P. 569-575.; Ogishi, M., Yotsuyanagi, H. Quantitative Prediction of the Landscape of T Cell Epitope Immunogenicity in Sequence Space // Frontiers in Immunology. – 2019. – Vol. 10, No. 827. – P. 1-20.; Ong, E., Wang, H., Wong, M.U. et al. Vaxign– ML: supervised machine learning reverse vaccinology model for improved prediction of bacterial protective antigens // Bioinformatics. – 2020. – Vol. 36, No. 10. – P. 3185-3191.; Xiang, Z., He, Y. Genome– wide prediction of vaccine targets for human herpes simplex viruses using Vaxign reverse vaccinology // BMC Bioinformatics. – 2013. – Vol. 14, No.1. – P. S2.; He, Y., Xiang, Z., Mobley, H.L. Vaxign: the first web– based vaccine design program for reverse vaccinology and applications for vaccine development // J Biomed Biotechnol. – 2010. – Vol. 10, No. Epub 2010 Jul 4. – P. 297505.; Vita, R., Overton, J.A., Greenbaum, J.A. et al. The immune epitope database (IEDB) 3.0 // Nucleic Acids Res. –Vol. 43, No. 1. – P. D405-12.; El– Manzalawy, Y., Dobbs, D., Honavar, V. Predicting linear B–cell epitopes using string kernels // J Mol Recognit. – 2008. – Vol. 21, No. 4. – P. 243-55.; Seemann, T. Prokka: rapid prokaryotic genome annotation // Bioinformatics. – 2014. – Vol. 30, No. 14. – P. 2068-9.; Huerta-Cepas, J., Szklarczyk, D., Heller, D. et al. eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses // Nucleic Acids Res. – 2018. – Vol. 47, No. D1. – P. D309-D314.; Jones, P., Binns, D., Chang, H.Y. et al. InterProScan 5: genome–scale protein function classification // Bioinformatics. 2014. – Vol. 30, No. 9. – P. 1236-40.; Mulder, N., Apweiler, R. InterPro and InterProScan: tools for protein sequence classification and comparison // Methods Mol Biol. – 2007. – Vol.396. – P. 59-70.; Syed, A., Upton, C. Java GUI for InterProScan (JIPS): a tool to help process multiple InterProScans and perform ortholog analysis // BMC Bioinformatics. – 2006. – Vol. 7. – P. 462.; Quevillon, E., Silventoinen, V., Pillai, S. et al. InterProScan: protein domains identifier // Nucleic Acids Res. – 2005. – Vol. 33, No. 1. – P. W116- 20.; Katoh, K., Standley, D.M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability // Mol Biol Evol. – 2013. – Vol. 30, No. 4. – P. 772-80.; Katoh, K., Frith, M.C. Adding unaligned sequences into an existing alignment using MAFFT and LAST // Bioinformatics. – 2012. – Vol. 28, No. 23. – P. 3144-6.; Katoh, K., Toh, H. Parallelization of the MAFFT multiple sequence alignment program // Bioinformatics. – 2010. – Vol. 26, No. 15. – P. 1899-900.; Katoh, K., Asimenos, G., Toh, H. Multiple alignment of DNA sequences with MAFFT // Methods Mol Biol. – 2009. – Vol. 537. – P. 39-64.; Hung, C.L., Lin, Y.S., Lin, C.Y. et al. CUDA ClustalW: An efficient parallel algorithm for progressive multiple sequence alignment on Multi– GPUs // Comput Biol Chem. – Vol. 58. – P. 62-8.; Hung, J.H., Weng, Z. Sequence Alignment and Homology Search with BLAST and ClustalW // Cold Spring Harb Protoc. 2016. – Vol. 16, No. 11. – P. 1-10.; Vangala, R.K., Singh, L., Gupta, R.P. BioParishodhana: A novel graphical interface integrating BLAST, ClustalW, primer3 and restriction digestion tools // Bioinformation. – 2012. – Vol. 8, No. 13. – P. 639-43.; Zaal, D., Nota, B. ADOMA: A Command Line Tool to Modify ClustalW Multiple Alignment Output // Mol Inform. – 2016. – Vol. 35, No. 1. – P. 42-4.; Darling, A.E., Treangen, T.J., Messeguer, X., Perna, N.T. Analyzing patterns of microbial evolution using the mauve genome alignment system // Methods Mol Biol. – 2007. – Vol. 396. – P. 135-52.; Darling, A.C., Mau, B., Blattner, F.R., Perna, N.T. Mauve: multiple alignment of conserved genomic sequence with rearrangements // Genome Res. – 2004. – Vol. 14, No. 7. – P. 1394-403.; Angiuoli, S.V., Salzberg, S.L. Mugsy: fast multiple alignment of closely related whole genomes // Bioinformatics. – 2010. – Vol. 27, No. 3. – P. 33-42.; Cleemput, S., Dumon, W., Fonseca, V. et al. Genome Detective Coronavirus Typing Tool for rapid identification and characterization of novel coronavirus genomes // Bioinformatics. – Vol. 36, No. 11. – P. 3552-3555.; Vilsker, M., Moosa, Y., Nooij, S. et al. Genome Detective: an automated system for virus identification from high-throughput sequencing data // Bioinformatics. – 2018. – Vol. 35, No. 5. – P. 871-873.; Gupta, S., Kapoor, P., Chaudhary, K. et al. Peptide toxicity prediction // Methods Mol Biol. – Vol. 1268. – P. 143-57.; Gupta, S., Kapoor, P., Chaudhary, K. et al. In silico approach for predicting toxicity of peptides and proteins // PLoS One. – 2013. – Vol. 8, No. 9. – P. e. 73957.; Krutz, N.L., Winget, J., Ryan, C.A. et al. Proteomic and Bioinformatic Analyses for the Identification of Proteins With Low Allergenic Potential for Hazard Assessment // Toxicol Sci. – – Vol. 170, No. 1. – P. 210-222.; Maurer– Stroh, S., Krutz, N.L., Kern, P.S. et al. AllerCatPro– prediction of protein allergenicity potential from the protein sequence // Bioinformatics. – 2019. – Vol. 35, No. 17. – P. 3020-3027.; Sharma, N., Patiyal, S., Dhall, A. et al. AlgPred 2.0: an improved method for predicting allergenic proteins and mapping of IgE epitopes // Brief Bioinform. – 2020. – Vol. 1, № 1. – P. 1-7.; Neeharika, D., Sunkar, S. Computational approach for the identification of putative allergens from Cucurbitaceae family members // J Food Sci Technol. – 2021. – Vol 58, No. 1. – P. 267-280.; Sircar, G., Saha, B., Bhattacharya, S.G., Saha, S. In silico prediction of allergenic proteins // Methods Mol Biol. – 2014. Vol. 1184. – P. 375-88.; Saha, S., Raghava, G.P. AlgPred: prediction of allergenic proteins and mapping of IgE epitopes // Nucleic Acids Res. 2006. – Vol. 34, No. Web Server issue. – P. W. 202-9.; Pagadala, N.S., Syed, K., Tuszynski, J. Software for molecular docking: a review // Biophys Rev. – 2017. – Vol. 9, No. 2. – P. 91-102.; Grote, A., Hiller, K., Scheer, M. et al. JCat: a novel tool to adapt codon usage of a target gene to its potential expression host // Nucleic Acids Res. – 2005. – Vol. 33, No. Web Server issue. – P. W526-31.; Zheng, W., Zhang, C., Bell, E.W., Zhang, Y. I – TASSER gateway: A protein structure and function prediction server powered by XSEDE // Future Gener Comput Syst. – 2019. – Vol. 99, – P. 73-85.; Roy, A., Kucukural, A., Zhang, Y. I – TASSER: a unified platform for automated protein structure and function prediction // Nat Protoc. – 2010. – Vol. 5, No. 4. – P. 725-38.; Zhang, Y. I– TASSER server for protein 3D structure prediction // BMC Bioinformatics. – 2008. – Vol. 9. – P. 40.; Raborn, R.T., Brendel, V.P. Using RAMPAGE to Identify and Annotate Promoters in Insect Genomes // Methods Mol Biol. – 2019. – Vol. 1858, No. 1. – P. 99-116.; Collatz, M., Mock, F., Hölzer, M. et al. EpiDope: A Deep neural network for linear B–cell epitope prediction // bioRxiv. 2020. – No. 1. – P. 1-8.; Suprun, M., Ellis, R.J., Sampson, H.A., Suárez– Fariñas, M. bbeaR: an R package and framework for epitope – specific antibody profiling // Bioinformatics. – 2021. – Vol. 37, No. 1. – P. 131-133.; Ogishi, M., Yotsuyanagi, H. Quantitative prediction of the landscape of T cell epitope immunogenicity in sequence space // Frontiers in Immunology. – 2019. – Vol .10. – P. 827.; Pittard, W.S., Li, S. The Essential Toolbox of Data Science: Python, R, Git, and Docker // Methods Mol Biol. – 2020. – Vol. 2104, No. 1. – P. 265-311.; Kwon, C., Kim, J., Ahn, J. DockerBIO: web application for efficient use of bioinformatics Docker images // PeerJ. – 2018. – Vol. 6, No. 1. – P. e. 5954.; Garofoli, A., Paradiso, V., Montazeri, H. et al. PipeIT: A Singularity Container for Molecular Diagnostic Somatic Variant Calling on the Ion Torrent Next– Generation Sequencing Platform // J. Mol Diagn. – 2019. – Vol. 21, No. 5. – P. 884-894.; Samdani, A., Vetrivel, U. POAP: A GNU parallel based multithreaded pipeline of open babel and AutoDock suite for boosted high throughput virtual screening // Comput Biol Chem. – 2018. – Vol. 74, No. 1. – P. 39-48.; Красильников, А.П. Микробиологический словарь-справочник. – 2– е изд., доп. и перераб. – Мн.: ООО "Асар", 1999. – 397 c.; https://dt.bsuir.by/jour/article/view/635
Διαθεσιμότητα: https://dt.bsuir.by/jour/article/view/635
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11Academic Journal
Συγγραφείς: A. M. Afonin, I. V. Leppyanen, O. A. Kulaeva, O. Y. Shtark, I. A. Tikhonovich, E. A. Dolgikh, V. A. Zhukov, А. М. Афонин, И. В. Леппянен, О. А. Кулаева, О. Ю. Штарк, И. А. Тихонович, Е. А. Долгих, В. А. Жуков
Συνεισφορές: The work of A.M. Afonin, O.A. Kulaeva, O.Y. Shtark, I.A. Tikhonovich and V.A. Zhukov on pea transcriptome sequencing, assembly and analysis was supported by the Russian Science Foundation grant # 16-16-00118. The work of I.V. Leppyanen and E.A. Dolgikh on primer design and analysis of expression of the marker genes was supported by the Russian Science Foundation grant # 16-16-10043.
Πηγή: Vavilov Journal of Genetics and Breeding; Том 24, № 4 (2020); 331-339 ; Вавиловский журнал генетики и селекции; Том 24, № 4 (2020); 331-339 ; 2500-3259
Θεματικοί όροι: горох посевной, transcriptomics, arbuscular mycorrhiza, garden pea, транскриптомика, арбускулярная микориза
Περιγραφή αρχείου: application/pdf
Relation: https://vavilov.elpub.ru/jour/article/view/2641/1388; Alizadeh O. Mycorrhizal symbiosis. Adv. Stud. Biol. 2011;3(6):273281.; Alves-Carvalho S., Aubert G., Carrère S., Cruaud C., Brochot A.L., Jacquin F., Klein A., Martin C., Boucherot K., Kreplak J., Da Silva C., Moreau S., Gamas P., Wincker P., Gouzy J., Burstin J. Full-length de novo assembly of RNA-seq data in pea (Pisum sativum L.) provides a gene expression atlas and gives insights into root nodulation in this species. Plant J. 2015;84(1):1-19. DOI 10.1111/tpj.12967.; Bankevich A., Nurk S., Antipov D., Gurevich A.A., Dvorkin M., Kulikov A.S., Lesin V.M., Nikolenko S.I., Pham S., Prjibelski A.D., Pyshkin A.V., Sirotkin A.V., Vyahhi N., Tesler G., Alekseyev M.A., Pevzner P.A. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. 2012;19(5): 455-477. DOI 10.1089/cmb.2012.0021.; Borisov A., Vasil’chikov A., Voroshilova V., Danilova T., Zhernakov A., Zhukov V., Koroleva T., Kuznetsova E., Madsen L., Mofett M., Naumkina T., Nemankin T., Ovchinnikova E., Pavlova Z., Petrova N., Pinaev A., Radutoiu S., Rozov S., Rychagova T., Solovov I., Stougaard J., Topunov A., Weeden N., Tsyganov V., Shtark O., Tikhonovich I. Regulatory genes of garden pea (Pisum sativum L.) controlling the development of nitrogen-fixing nodules and arbuscular mycorrhiza: a review of basic and applied aspects. Appl. Biochem. Microbiol. 2007;43(3):237-243. DOI 10.1134/S0003683807030027.; Borisov A.Y., Danilova T.N., Koroleva T.A., Naumkina T.S., Pavlova Z.B., Pinaev A.G., Shtark O.Y., Tsyganov V.E., Voroshilova V.A., Zhernakov A.I., Zhukov V.A., Tikhonovich I.A. Pea (Pisum sativum L.) regulatory genes controlling development of nitrogenfixing nodule and arbuscular mycorrhiza: fundamentals and application. Biologia (Bratislava). 2004;59(13):137-144.; Bushnell B. BBMap. 2014. Available at: sourceforge.net/projects/bbmap/; Davidson N.M., Hawkins A.D.K., Oshlack A. SuperTranscripts: a data driven reference for analysis and visualisation of transcriptomes. Genome Biol. 2017;18:148. DOI 10.1186/s13059-017-1284-1.; Demchenko K., Winzer T., Stougaard J., Parniske M., Pawlowski K. Distinct roles of Lotus japonicus SYMRK and SYM15 in root colonization and arbuscule formation. New Phytol. 2004;163(2):381-392. DOI 10.1111/j.1469-8137.2004.01123.x.; Duc G., Messager A. Mutagenesis of pea (Pisum sativum L.) and the isolation of mutants for nodulation and nitrogen fixation. Plant Sci. 1989;60(2):207-213. DOI 10.1016/0168-9452(89)90168-4.; Engvild K.C. Nodulation and nitrogen fixation mutants of pea, Pisum sativum. Theor. Appl. Genet. 1987;74(6):711-713. DOI 10.1007/BF00247546.; Franssen S.U., Shrestha R.P., Bräutigam A., Bornberg-Bauer E., Weber A.P. Comprehensive transcriptome analysis of the highly complex Pisum sativum genome using next generation sequencing. BMC Genomics. 2011;12(1):227. DOI 10.1186/1471-2164-12-227.; Gobbato E. Recent developments in arbuscular mycorrhizal signaling. Curr. Opin. Plant Biol. 2015;26:1-7. DOI 10.1016/j.pbi.2015.05.006.; Goodstein D.M., Shu S., Howson R., Neupane R., Hayes R.D., Fazo J., Mitros T., Dirks W., Hellsten U., Putnam N., Rokhsar D.S. Phytozome: a comparative platform for green plant genomics. Nucleic Acids Res. 2012;40(D1):D1178-D1186. DOI 10.1093/nar/gkr944.; Grabherr M.G., Haas B.J., Yassour M., Levin J.Z., Thompson D.A., Amit I., Adiconis X., Fan L., Raychowdhury R., Zeng Q., Chen Z., Mauceli E., Hacohen N., Gnirke A., Rhind N., di Palma F., Birren B.W., Nusbaum C., Lindblad-Toh K., Friedman N., Regev A. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat. Biotechnol. 2011;29(7):644-652. DOI 10.1038/nbt.1883.; Grunwald U., Nyamsuren O., Tamasloukht M., Lapopin L., Becker A., Mann P., Gianinazzi-Pearson V., Krajinski F., Franken P. Identification of mycorrhiza-regulated genes with arbuscule development-related expression profile. Plant Mol. Biol. 2004;55(4):553-566. DOI 10.1007/s11103-004-1303-y.; Gutjahr C., Parniske M. Cell and developmental biology of arbuscular mycorrhiza symbiosis. Annu. Rev. Cell Dev. Biol. 2013;29(1):593-617. DOI 10.1146/annurev-cellbio-101512-122413.; Hoagland D.R., Arnon D.I. The water-culture method for growing plants without soil. Circular. California Agric. Exp. Station. 1950; 347(2).; Huerta-Cepas J., Szklarczyk D., Forslund K., Cook H., Heller D., Walter M.C., Rattei T., Mende D.R., Sunagawa S., Kuhn M., Jensen L.J., von Mering C., Bork P. eggNOG 4.5: a hierarchical orthology framework with improved functional annotations for eukaryotic, prokaryotic and viral sequences. Nucleic Acids Res. 2016;44(D1):D286D293. DOI 10.1093/nar/gkv1248.; Jacobi L.M., Kukalev A.S., Ushakov K.V., Tsyganov V.E., Provorov N.A., Borisov A.Y., Tikhonovich I. Genetic variability of garden pea (Pisum sativum L.) for symbiotic capacities. Pisum Genet. 1999; 31:44-45.; Kaschuk G. Sink Stimulation of Leaf Photosynthesis by the Carbon Costs of Rhizobial and Arbuscular Mycorrhizal Fungal Symbioses. Wageningen, Wageningen Univ., S.n., 2009.; Kerr S.C., Gaiti F., Beveridge C.A., Tanurdzic M. De novo transcriptome assembly reveals high transcriptional complexity in Pisum sativum axillary buds and shows rapid changes in expression of diurnally regulated genes. BMC Genomics. 2017;18(1):221. DOI 10.1186/s12864-017-3577-x.; Kreplak J., Madoui M.A., Cápal P., Novák P., Labadie K., Aubert G., Bayer P.E., Gali K.K., Syme R.A., Main D., Klein A., Bérard A., Vrbová I., Fournier C., D’Agata L., Belser C., Berrabah W., Toegelová H., Milec Z., Vrána J., Lee H.T., Kougbeadjo A., Térézol M., Huneau C., Turo C.J., Mohellibi N., Neumann P., Falque M., Gallardo K., McGee R., Tar’an B., Bendahmane A., Aury J.M., Batley J., Le Paslier M.C., Ellis N., Warkentin T.D., Coyne C.J., Salse J., Edwards D., Lichtenzveig J., Macas J., Doležel J., Wincker P., Burstin J. A reference genome for pea provides insight into legume genome evolution. Nat. Genet. 2019;51(9):1411-1422. DOI 10.1038/s41588-019-0480-1.; Leppyanen I.V., Shakhnazarova V.Y., Shtark O.Y., Vishnevskaya N.A., Tikhonovich I.A., Dolgikh E.A. Receptor-like kinase LYK9 in Pisum sativum L. is the CERK1-like receptor that controls both plant immunity and AM symbiosis development. Int. J. Mol. Sci. 2018; 19:8. DOI 10.3390/ijms19010008.; Livak K.J., Schmittgen T.D. Analysis of relative gene expression data using real-time quantitative PCR and the 2-ΔΔCT method. Methods. 2001;25(4):402-408. DOI 10.1006/meth.2001.1262.; Lu G. Vector NTI, a balanced all-in-one sequence analysis suite. Brief. Bioinformatics. 2004;5(4):378-388. DOI 10.1093/bib/5.4.378.; Mamontova T., Afonin A.M., Ihling C., Soboleva A., Lukasheva E., Sulima A.S., Shtark O.Y., Akhtemova G.A., Povydysh M.N., Sinz A., Frolov A., Zhukov V.A., Tikhonovich I.A. Profiling of seed proteome in pea (Pisum sativum L.) lines characterized with high and low responsivity to combined inoculation with nodule bacteria and arbuscular mycorrhizal fungi. Molecules. 2019;24(8):1603. DOI 10.3390/molecules24081603.; Manck-Götzenberger J., Requena N. Arbuscular mycorrhiza symbiosis induces a major transcriptional reprogramming of the potato SWEET sugar transporter family. Front. Plant Sci. 2016;7:487. DOI 10.3389/fpls.2016.00487.; Morandi D., Sagan M., Prado-Vivant E., Duc G. Influence of genes determining supernodulation on root colonization by the mycorrhizal fungus Glomus mosseae in Pisum sativum and Medicago truncatula mutants. Mycorrhiza. 2000;10(1):37-42. DOI 10.1007/s005720050285.; Murray J.D., Muni R.R.D., Torres-Jerez I., Tang Y., Allen S., Andriankaja M., Li G., Laxmi A., Cheng X., Wen J., Vaughan D., Schultze M., Sun J., Charpentier M., Oldroyd G., Tadege M., Ratet P., Mysore K.S., Chen R., Udvardi M.K. Vapyrin, a gene essential for intracellular progression of arbuscular mycorrhizal symbiosis, is also essential for infection by rhizobia in the nodule symbiosis of Medicago truncatula. Plant J. 2011;65(2):244-252. DOI 10.1111/j.1365-313X.2010.04415.x.; Park H.J., Floss D.S., Levesque-Tremblay V., Bravo A., Harrison M.J. Hyphal branching during arbuscule development requires Reduced Arbuscular Mycorrhiza1. Plant Physiol. 2015;169(4):2774-2788. DOI 10.1104/pp.15.01155.; Pimprikar P., Gutjahr C. Transcriptional regulation of arbuscular mycorrhiza development. Plant Cell Physiol. 2018;59(4):876. DOI 10.1093/pcp/pcy024.; Pumplin N., Mondo S.J., Topp S., Starker C.G., Gantt J.S., Harrison M.J. Medicago truncatula Vapyrin is a novel protein required for arbuscular mycorrhizal symbiosis. Plant J. 2010;61(3):482-494. DOI 10.1111/j.1365-313X.2009.04072.x.; Shtark O.Y., Danilova T.N., Naumkina T.S., Vasilchikov A.G., Chebotar V.K., Kazakov A.E., Zhernakov A.I., Nemankin T.A., Prilepskaya N.A., Borisov A.U., Tikhonovich I.A. Analysis of pea (Pisum sativum L.) source material for breeding of cultivars with high symbiotic potential and choice of criteria for its evaluation. Ecol. Genet. 2006;4(2):22-28. DOI 10.17816/ecogen4222-28.; Shtark O.Y., Sulima A.S., Zhernakov A.I., Kliukova M.S., Fedorina J.V., Pinaev A.G., Kryukov A.A., Akhtemova G.A., Tikhonovich I.A., Zhukov V.A. Arbuscular mycorrhiza development in pea (Pisum sativum L.) mutants impaired in five early nodulation genes including putative orthologs of NSP1 and NSP2. Symbiosis. 2016; 68(1-3):129-144. DOI 10.1007/s13199-016-0382-2.; Siddiqui Z.A., Akhtar M.S., Futai K. (Eds.). Mycorrhizae: Sustainable Agriculture and Forestry. Springer, 2008. DOI 10.1007/978-1-4020-8770-7.; Simão F.A., Waterhouse R.M., Ioannidis P., Kriventseva E.V., Zdobnov E.M. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics. 2015;31(19): 3210-3212. DOI 10.1093/bioinformatics/btv351.; Solaiman Z., Abbott L.K., Varma A. (Eds.). Mycorrhizal Fungi: Use in Sustainable Agriculture and Land Restoration. Springer, Berlin, Heidelberg, 2014. DOI 10.1007/978-3-662-45370-4.; Sudheesh S., Sawbridge T.I., Cogan N.O., Kennedy P., Forster J.W., Kaur S. De novo assembly and characterisation of the field pea transcriptome using RNA-Seq. BMC Genomics. 2015;16(1):611. DOI 10.1186/s12864-015-1815-7.; Tikhonovich I.A., Andronov E.E., Borisov A.Y., Dolgikh E.A., Zhernakov A.I., Zhukov V.A., Provorov N.A., Roumiantseva M.L., Simarov B.V. The principle of genome complementarity in the enhancement of plant adaptive capacities. Russ. J. Genet. 2015;51(9): 831-846. DOI 10.1134/S1022795415090124.; Tisserant E., Kohler A., Dozolme-Seddas P., Balestrini R., Benabdellah K., Colard A., Croll D., … Shachar-Hill Y., Tuskan G., Young J.P.W., Gianinazzi-Pearson V., Martin F. The transcriptome of the arbuscular mycorrhizal fungus Glomus intraradices (DAOM 197198) reveals functional tradeoffs in an obligate symbiont. New Phytol. 2012;193(3):755-769. DOI 10.1111/j.1469-8137.2011.03948.x.; Tisserant E., Malbreil M., Kuo A., Kohler A., Symeonidi A., Balestrini R., Charron P., … Rensing S.A., Grigoriev I.V., Corradi N., Roux C., Martin F. Genome of an arbuscular mycorrhizal fungus provides insight into the oldest plant symbiosis. Proc. Natl. Acad. Sci. USA. 2013;110(50):20117-20122. DOI 10.1073/pnas.1313452110.; Trapnell C., Williams B.A., Pertea G., Mortazavi A., Kwan G., Van Baren M.J., Salzberg S.L., Wold B.J., Pachter L. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 2010;28(5):511-515. DOI 10.1038/nbt.1621.; Trouvelot A., Kough J.L., Gianinazzi-Pearson V. Mesure du taux de mycorhization VA d’un système radiculaire. Recherche de méthodes d’estimation ayant une signification fonctionnelle. Mycorhizae: Physiologie et Génétique. 1986;217-221.; Young N.D., Debellé F., Oldroyd G.E.D., Geurts R., Cannon S.B., Udvardi M.K., Benedito V.A., … Schwartz D.C., Rogers J., Quétier F., Town C.D., Roe B.A. The Medicago genome provides insight into the evolution of rhizobial symbioses. Nature. 2011;480:520-524. DOI 10.1038/nature10625.; Zhernakov A.I., Shtark O.Y., Kulaeva O.A., Fedorina J.V., Afonin A.M., Kitaeva A.B., Tsyganov V.E., Afonso-Grunz F., Hoffmeier K., Rotter B., Winter P., Tikhonovich I.A., Zhukov V.A. Mapping-by-sequencing using NGS-based 3′-MACE-Seq reveals a new mutant allele of the essential nodulation gene Sym33 (IPD3) in pea (Pisum sativum L.). PeerJ. 2019;7. DOI 10.7717/peerj.6662.; Zhukov V.A., Akhtemova G.A., Zhernakov A.I., Sulima A.S., Shtark O.Yu., Tikhonovich I.A. Evaluation of the symbiotic effectiveness of pea (Pisum sativum L.) genotypes in pot experiment. Sel’skokhozyaistvennaya Biologiya = Agricultural Biology. 2017; 52(3):607-614. DOI 10.15389/agrobiology.2017.3.607eng.; Zhukov V.A., Shtark O.Y., Nemankin T.A., Kryukov A.A., Borisov A.Y., Tikhonovich I.A. Genetic mapping of pea (Pisum sativum L.) genes involved in symbiosis. Sel’skokhozyaistvennaya Biologiya = Agricultural Biology. 2016;51(5):593-601. DOI 10.15389/agrobiology.2016.5.593eng.; Zhukov V.A., Shtark O.Y., Puzanskiy R.K., Avdeeva G.S., Yurkov A.P., Smolikova G.N., Yemelyanov V.V., Kliukova M.S., Shavarda A.L., Kirpichnikova A.A., Zhernakov A.I., Afonin A.M., Tikhonovich I.A., Shishova M.F. Metabolic alterations in pea leaves during arbuscular mycorrhiza development. PeerJ. 2019; DOI 10.7717/peerj.7495.; Zhukov V.A., Zhernakov A.I., Kulaeva O.A., Ershov N.I., Borisov A.Y., Tikhonovich I.A. De novo assembly of the pea (Pisum sativum L.) nodule transcriptome. Int. J. Genomics. 2015:695947. DOI 10.1155/2015/695947.; Zorin E.A., Kliukova M.S., Kulaeva O.A., Afonin A.M., Tikhonovich I.A., Zhukov V.A. Identification of sequences encoding for NCR-peptides and defensins in the “meta-assembly” of transcriptome of pea (Pisum sativum L.) nitrogen-fixing nodules. Ecol. Genet. 2019;17(3):39-46. DOI 10.17816/ecogen17339-17346.; https://vavilov.elpub.ru/jour/article/view/2641
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12Academic Journal
Συγγραφείς: D. A. Sychev, Д. А. Сычёв
Πηγή: Pharmacogenetics and Pharmacogenomics; № 1 (2015); 3 ; Фармакогенетика и фармакогеномика; № 1 (2015); 3 ; 2686-8849 ; 2588-0527
Θεματικοί όροι: микробиомика, геномика, транскриптомика, протеомика, метаболомика
Περιγραφή αρχείου: application/pdf
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13Academic Journal
Συγγραφείς: Д. А. Сычёв
Πηγή: Pharmacogenetics and Pharmacogenomics; № 2 (2018); 3-3 ; Фармакогенетика и фармакогеномика; № 2 (2018); 3-3 ; 2686-8849 ; 2588-0527
Θεματικοί όροι: метаболомика, омиксные технологии, геномика, транскриптомика, протеомика
Περιγραφή αρχείου: application/pdf
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14Academic Journal
Συγγραφείς: E. A. Vodiasova, E. S. Chelebieva, O. N. Kuleshova, Е. А. Водясова, Э. С. Челебиева, О. Н. Кулешова
Συνεισφορές: The research were supported by the Government of the Russian Federation, grant No. 14.W03.31.0015.
Πηγή: Vavilov Journal of Genetics and Breeding; Том 23, № 5 (2019); 508-518 ; Вавиловский журнал генетики и селекции; Том 23, № 5 (2019); 508-518 ; 2500-3259
Θεματικοί όροι: единичные клетки, transcriptomics, Chromium 10XGenomics, sequencing, single cell, транскриптомика, секвенирование
Περιγραφή αρχείου: application/pdf
Relation: https://vavilov.elpub.ru/jour/article/view/2200/1246; Adamson B., Norman T.M., Jost M., Cho M.Y., Nunez J.K., Chen Y., Villalta J.E., Gilbert L.A., Horlbeck M.A., Hein M.Y., Pak R.A., Gray A.N., Gross C.A., Dixit A., Parnas O., Regev A., Weissman J.S. A multiplexed single-cell CRISPR screening platform enables systematic dissection of the unfolded protein response. Cell. 2016;167(7):1867-1882.e21. DOI 10.1016/j.cell.2016.11.048.; Andrews T.S., Hemberg M. Identifying cell populations with scRNASeq. Mol. Aspects Med. 2018;59:114-122. DOI 10.1016/j.mam.2017.07.002.; Bacher R., Chu L.-F., Leng N., Gasch A.P., Thomson J.A., Stewart R.M., Newton M., Kendziorski C. SCnorm: robust normalization of single-cell RNA-seq data. Nat. Methods. 2017;14(6):584586. DOI 10.1038/nmeth.4263.; Belton J.M., McCord R.P., Gibcus J.H., Naumova N., Zhan Y., Dekker J. Hi-C: a comprehensive technique to capture the conformation of genomes. Methods. 2012;58(3):268-276. DOI 10.1016/j.ymeth.2012.05.001.; Brehm-Stecher B.F., Johnson E.A. Single-cell microbiology: tools, technologies, and applications. Microbiol. Mol. Biol. Rev. 2004; 68(3):538-559.; Brennecke P., Anders S., Kim J.K., Kolodziejczyk A.A., Zhang X., Proserpio V., Baying B., Benes V., Teichmann S.A., Marioni J.C., Heisler M.G. Accounting for technical noise in single-cell RNA-seq experiments. Nat. Methods. 2013;10:1093-1095.; Butler A., Hoffman P., Smibert P., Papalexi E., Satija R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 2018;36(5):411-420. DOI 10.1038/nbt.4096.; Callaway A. The trickiest family tree in biology. Nature. 2017;547 (7661):20-22. DOI 10.1038/547020a.; Chen Y.R., Zheng Y., Liu B., Zhong S., Giovannoni J., Fei Z. A costeffective method for Illumina small RNA-Seq library preparation using T4 RNA ligase 1 adenylated adapters. Plant Methods. 2012;8(1):41. DOI 10.1186/1746-4811-8-41.; Coombe L., Warren R.L., Jackman S.D., Yang C., Vandervalk B.P., Moore R.A., Pleasance S., Coope R.J., Bohlmann J., Holt R.A., Jones S.J.M., Birol I. Assembly of the complete Sitka spruce chloroplast genome using 10× Genomics’ GemCode sequencing data. PLoS One. 2016;11(9):e0163059. DOI 10.1371/journal.pone.0163059.; Deng Q., Ramsköld D., Reinius B., Sandberg R. Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells. Science. 2014;343(6167):193-196. DOI 10.1126/science.1245316.; Der E., Ranabothu S., Suryawanshi H., Akat K.M., Clancy R., Morozov P., Kustagi M., Czuppa M., Izmirly P., Belmont H.M., Wang T., Jordan N., Bornkamp N., Nwaukoni J., Martinez J., Goilav B., Buyon J.P., Tuschl T., Putterman C. Single cell RNA sequencing to dissect the molecular heterogeneity in lupus nephritis. JCI Insight. 2017;2(9). pii: 93009. DOI 10.1172/jci.insight.93009.; Derrien T., Johnson R., Bussotti G., Tanzer A., Djebali S., Tilgner H., Guernec G., Martin D., Merkel A., Knowles D.G., Lagarde J., Veeravalli L., Ruan X., Ruan Y., Lassmann T., Carninci P., Brown J.B., Lipovich L., Gonzalez J.M., Thomas M., Davis C.A., Shiekhattar R., Gingeras T.R., Hubbard T.J., Notredame C., Harrow J., Guigo R. The GENCODE v7 catalog of human long noncoding RNAs: analysis of their gene structure, evolution, and expression. Genome Res. 2012;22(9):1775-1789. DOI 10.1101/gr.132159.111.; Eisenstein M. Startups use short-read data to expand long-read sequencing market. Nat. Biotechnol. 2015;33(5):433-435. DOI 10.1038/nbt0515-433.; Fan J., Salathia N., Liu R., Kaeser G.E., Yung Y.C., Herman J.L., Kaper F., Fan J.-B., Zhang K., Chun J., Kharchenko P.V. Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis. Nat. Methods. 2016;13:241-244. DOI 10.1038/nmeth.3734.; Frieda K.L., Linton J.M., Hormoz S., Choi J., Chow K.-H.K., Singer Z.S., Budde M.W., Elowitz M.B., Cai L. Synthetic recording and in situ readout of lineage information in single cells. Nature. 2017;541(7635):107-111. DOI 10.1038/nature20777.; Fustin J.M., Dоi M., Yamaguchi Y., Hida H., Nishimura S., Yoshida M., Isagawa T., Morioka M.S., Kakeya H., Manabe I., Okamura H. RNA-methylation-dependent RNA processing controls the speed of the circadian clock. Cell. 2013;155(4):793-806. DOI 10.1016/j.cell.2013.10.026.; Gawad C., Koh W., Quake S.R. Dissecting the clonal origins of childhood acute lymphoblastic leukemia by single-cell genomics. Proc. Natl. Acad. Sci. USA. 2014;111(50):17947-17952. DOI 10.1073/pnas.1420822111.; Gawad C., Koh W., Quake S.R. Single-cell genome sequencing: current state of the science. Nat. Rev. Genet. 2016;17(3):175-188. DOI 10.1038/nrg.2015.16.; Gerber T., Willscher E., Loeffler-Wirth H., Hopp L., Schadendorf D., Schartl M., Anderegg U., Camp G., Treutlein B., Binder H., Kunz M. Mapping heteroge-neity in patient-derived melanoma cultures by single-cell RNA-seq. Oncotarget. 2017;8(1):846-862. DOI 10.18632/oncotarget.13666.; Goldberg A.D., Allis C.D., Bernstein E. Epigenetics: a landscape takes shape. Cell. 2007;128(4):635-638.; Grun D., Lyubimova A., Kester L., Wiebrands K., Basak O., Sasaki N., Clevers H., Oudenaarden A. Single-cell messenger RNA sequencing reveals rare intestinal cell types. Nature. 2015;525(7568):251-255. DOI 10.1038/nature14966.; Hafner M., Landgraf P., Ludwig J., Rice A., Ojo T., Lin C., Holoch D., Lim C., Tuschl T. Identification of microRNAs and other small regulatory RNAs using cDNA library sequencing. Methods. 2008;44(1):3-12.; Haghverdi L., Büttner M., Wolf F.A., Buettner F., Theis F.J. Diffusion pseudotime robustly reconstructs lineage branching. Nat. Methods. 2016;13:845-848.; Ham R.G. Clonal growth of mammalian cells in a chemically defined, synthetic medium. Proc. Natl. Acad. Sci. USA. 1965;53:288-293.; Haque A., Engel J., Teichmann S.A., Lonnberg T. A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications. Genome Med. 2017;9:75.; Hashimshony T., Senderovich N., Avital G., Klochendler A., Leeuw Y., Anavy L., Gennert D., Li S., Livak K.J., Rozenblatt-Rosen O., Dor Y., Regev A., Yanai I. CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq. Genome Biol. 2016;17:77. DOI 10.1186/s13059-016-0938-8.; Hashimshony T., Wagner F., Sher N., Yanai I. CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. Cell Rep. 2012;2(3): 666-673. DOI 10.1016/j.celrep.2012.08.003.; Hwang B., Lee J.H., Bang D. Single-cell RNA sequencing technologies and bioinformatics pipelines. Exp. Mol. Med. 2018;50:96.; Islam S., Zeisel A., Joost S., Manno G.L., Zajac P., Kasper M., Lonnerberg P., Linnarsson S. Quantitative single-cell RNA-seq with unique molecular identifiers. Nat. Methods. 2014;11(2):163-166. DOI 10.1038/nmeth.2772.; Jaitin D.A., Kenigsberg E., Keren-Shaul H., Elefant N., Paul F., Zaretsky I., Mildner A., Cohen N., Jung S., Tanay A., Amit I. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science. 2014;343(6172):776-779. DOI 10.1126/science.1247651.; Jaitin D.A., Weiner A., Yofe I., Lara-Astiaso D., Kern-Shaul H., David E., Salame T.M., Tanay A., Oudenaarden A., Amit I. Dissecting immune circuits by linking CRISPR-pooled screens with single-cell RNA-seq. Cell. 2016;167(7):1883-1896. DOI 10.1016/j.cell.2016.11.039.; Janes K.A., Wang C.C., Holmberg K.J., Cabral K., Brugge J.S. Identifying single-cell molecular programs by stochastic profiling. Nat. Methods. 2010;7(4):311-317. DOI 10.1038/nmeth.1442.; Jiang L., Schlesinger F., Davis C.A., Zhang Y., Li R., Salit M., Gingeras T.R., Oliver B. Synthetic spike-in standards for RNA-seq experiments. Genome Res. 2011;21:1543-1551.; Junker J.P., Oudenaarden A. Every cell is special: genome-wide studies add a new dimension to single-cell biology. Cell. 2014;157(1):8-11. DOI 10.1016/j.cell.2014.02.010.; Kalhor R., Mali P., Church G.M. Rapidly evolving homing CRISPR barcodes. Nat. Methods. 2017;14(2):195-200. DOI 10.1038/nmeth.4108.; Kamme F., Salunga R., Yu J., Tran D.T., Zhu J., Luo L., Bittner A., Guo H.Q., Miller N., Wan J., Erlander M. Single-cell microarray analysis in hippocampus CA1: demonstration and validation of cellular heterogeneity. J. Neurosci. 2003;23(9):3607-3615.; Klein A.M., Mazutis L., Akartuna I., Tallapragada N., Veres A., Li V., Peshkin L., Weitz D.A., Kirschner M.W. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell. 2015;161(5):1187-1201. DOI 10.1016/j.cell.2015.04.044.; Kolodziejczyk A.A., Kim J.K., Svensson V., Marioni J.C., Teichmann S.A. The technology and biology of single-cell RNA sequencing. Mol. Cell. 2015;58(4):610-620. DOI 10.1016/j.molcel.2015.04.005.; Kumar P., Tan Y., Cahan P. Understanding development and stem cells using single cell-based analyses of gene expression. Development. 2017;144:17-32. DOI 10.1242/dev.133058.; Landry Z.C., Giovanonni S.J., Quake S.R., Blainey P.C. Optofluidic cell selection from complex microbial communities for single-genome analysis. Methods Enzymol. 2013;531:61-90. DOI 10.1016/B978-0-12-407863-5.00004-6.; Lavin Y., Kobayashi S., Leader A., Amir E.D., Elefant N., Bigenwald C., Remark R., Sweeney R., Becker C.D., Levine J.H., Meinhof K., Chow A., Kim-Shulze S., Wolf A., Medaglia C., Li H., Rytlewski J.A., Emerson R.O., Merad M. Innate immune landscape in early lung adenocarcinoma by paired single-cell analyses. Cell. 2017;169(4):750-765.e17. DOI 10.1016/j.cell.2017.04.014.; Leung M.L., Davis A., Gao R., Casasent A., Wang Y., Sei E., Vilar E., Maru D., Kopetz S., Navin N.E. Single-cell DNA sequencing reveals a late-dissemination model in metastatic colorectal cancer. Genome Res. 2017;27(8):1287-1299. DOI 10.1101/gr.209973.116.; Li B., Ruotti V., Stewart R.M., Thomson J.A., Dewey C.N. RNA-seq gene expression estimation with read mapping uncertainty. Bioinformatics. 2010;26:493-500.; Li H., Courtois E.T., Sengupta D., Tan Y., Chen K.H., Goh J.J.L., Kong S.L., Chua C., Hon L.K., Tan W.S., Wong M., Choi P.J., Wee L.J.K., Hillmer A.M., Tan I.B., Robson P., Prabhakar S. Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors. Nat. Genet. 2017; 49:708-718.; Li J., Witten D.M., Johnstone I.M., Tibshirani R. Normalization, testing, and false discovery rate estimation for RNA-sequencing data. Biostatistics. 2012;13:523-538.; Lindström S., Andersson-Svahn H. Overview of single-cell analyses: microdevices and applications. Lab. Chip. 2010;10(24):3363-3372. DOI 10.1039/c0lc00150c.; Liu L., Li Y., Li S., Hu N., He Y., Pong R., Lin D., Lu L., Law M. Comparison of next-generation sequencing systems. J. Biomed. Biotechnol. 2012;2012:251364. DOI 10.1155/2012/251364.; Ludwig L.S., Lareau C.A., Ulirsch J.C., Christian E., Muus C., Li L.H., Pelka K., Ge W., Oren Y., Brack A., Law T., Rodman C., Chen J.H., Boland G.M., Hacohen N., Rozenblatt-Rosen O., Aryee M.J., Buenrostro J.D., Regev A., Sankaran V.G. Lineage tracing in humans enabled by mitochondrial mutations and single-cell genomics. Cell. 2019;176(6);1325-1339. DOI 10.1016/j.cell.2019.01.022.; Lun A.T.L., Bach K., Marioni J.C. Pooling across cells to normalize single-cell RNA sequencing data with many zero counts. Genome Biol. 2016;17:75. DOI 10.1186/s13059-016-0947-7.; Maaten L. van der, Hinton G. Visualizing data using t-SNE. J. Mach. Learn. Res. 2008;9: 2579-2605.; Macosko E.Z., Basu A., Satija R., Nemesh J., Shekhar K., Goldman M., Tirosh I., Bialas A.R., Kamitaki N., Martersteck E.M., Trombetta J.J., Weitz D.A., Sanes J.R., Shalek A.K., Regev A., McCarroll S.A. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell. 2015;161(5):1202-1214. DOI 10.1016/j.cell.2015.05.002.; Mahata B., Zhang X., Kolodziejczyk A.A., Proserpio V., Haim-Vilmovsky L., Taylor A.E., Hebenstreit D., Dingler F.A., Moignard V., Gottgens B., Arlt W., McKenzie A.N.J., Teichmann S.A. Single-cell RNA sequencing reveals T helper cells synthesizing steroids de novo to contribute to immune homeostasis. Cell Rep. 2014;7(4):11301142. DOI 10.1016/j.celrep.2014.04.011.; Marcus J.S., Anderson W.F., Quake S.R. Microfluidic single-cell mRNA isolation and analysis. Anal. Chem. 2006;78(9):3084-3089.; Mazutis L., Gilbert J., Ung W.L., Weitz D.A., Griffiths A.D., Heyman J.A. Single-cell analysis and sorting using droplet-based microfluidics. Nat. Protoc. 2013;8(5):870-891. DOI 10.1038/nprot.2013.046.; McKenna A., Findlay G.M., Gagnon J.A., Horwitz M.S., Schier A.F., Shendure J. Whole-organism lineage tracing by combinatorial and cumulative genome editing. Science. 2016;353(6298):aaf7907. DOI 10.1126/science.aaf7907.; Mi D., Li Z., Lim L., Li M., Moissidis M., Yang Y., Gao T., Hu T.X., Pratt T., Price D.J., Sestan N., Marin O. Early emergence of cortical interneuron diversity in the mouse embryo. Science. 2018;360 (6384):81-85. DOI 10.1126/science.aar6821.; Moon K.R., Dijk D., Wang Z., Burkhardt D., Chen W.S., Yim K., Elen A., Hirn M.J., Coifman R.R., Ivanova N.B., Wolf G., Krishnaswamy S. Visualizing structure and transitions for biological data exploration. Cell. 2018;65. http://dx.doi.org/10.2139/ssrn.3155891. Moroz L.L. NeuroSystematics and periodic system of neurons: model vs reference species at single-cell resolution. ACS Chem. Neurosci. 2018;9:1884-1903. DOI 10.1021/acschemneuro.8b00100.; Moroz L.L., Edwards J.R., Puthanveettil S.V., Kohn A.B., Ha T., Heyland A., Knudsen B., Sahni A., Yu F., Liu L., Jezzini S., Lovell P., Iannucculli W., Chen M., Nguyen T., Sheng H., Shaw R., Kalachikov S., Panchin Y.V., Farmerie W., Russo J.J., Ju J., Kandel E.R. Neuronal transcriptome of Aplysia: neuronal compartments and circuitry. Cell. 2006;127(7):1453-1467.; Mortazavi A., Williams B.A., McCue K., Schaeffer L., Wold B. Mapping and quantifying mammalian transcriptomes by RNA-seq. Nat. Methods. 2008;5:621-628.; Nagano T., Lubling Y., Stevens T.J., Schoenfelder S., Yaffe E., Dean W., Laue E.D., Tanay A., Fraser P. Single-cell Hi-C reveals cell-to-cell variability in chromosome structure. Nature. 2013;502(7469):59-64. DOI 10.1038/nature12593.; Nagano T., Lubling Y., Varnai C., Dudley C., Leung W., Baran Y., Cohen N.M., Wingett S., Fraser P., Tanay A. Cell-cycle dynamics of chromosomal organization at single-cell resolution. Nature. 2017; 547(7661):61-67. DOI 10.1038/nature23001.; Navin N., Kendall J., Troge J., Andrews P., Rodgers L., McIndoo J., Cook K., Stepansky A., Levy D., Esposito D., Muthuswamy L., Krasnitz A., McCombie W.R., Hicks J., Wingler M. Tumour evolution inferred by single-cell sequencing. Nature. 2011;472(7341): 90-94. DOI 10.1038/nature09807.; Nikolenko S.I., Korobeynikov A.I., Alekseyev M.A. BayesHammer: Bayesian clustering for error correction in single-cell sequencing. BMC Genomics. 2013;14(Suppl. 1):S7. DOI 10.1186/1471-216414-S1-S7.; Nowogrodzki A. The cell seeker. Nature. 2017;547:24-26.; Okaty B.W., Freret M.E., Rood B.D., Brust R.D., Hennessy M.L., Bairos D., Kim J.C., Cook M.N., Dymecki S.M. Multi-scale molecular deconstruction of the serotonin neuron system. Neuron. 2015; 88(4):774-791. DOI 10.1016/j.neuron.2015.10.007.; Parekh S., Ziegenhain C., Vieth B., Enard W., Hellmann I. zUMIs – A fast and flexible pipeline to process RNA sequencing data with UMIs. GigaScience. 2018;7(6). DOI 10.1093/gigascience/giy059.; Pierson E., Yau C., ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis. Genome Biol. 2015;16:241. DOI 10.1186/s13059-015-0805-z.; Pijuan-Sala B., Griffiths J.A., Guibentif C., Hiscock T.W., Jawaid W., Calero-Nieto F.J., Mulas C., Ibarra-Soria X., Tyser R.C.V., Ho D.L.L., Reik W., Srinivas S., Simons B.D., Nihols J., Marioni J.C., Gottgens B. A single-cell molecular map of mouse gastrulation and early organogenesis. Nature. 2019;566;490-495. DOI 10.1038/s41586-019-0933-9.; Poulin J.F., Tasic B., Hjerling-Leffler J., Trimarchi J.M., Awatramani R. Disentangling neural cell diversity using single-cell transcriptomics. Nat. Neurosci. 2016;19(9):1131-1141. DOI 10.1038/nn.4366.; Qiu X., Mao Q., Tang Y., Wang L., Chawla R., Pliner H., Trapnell C. Reversed graph embedding resolves complex single-cell developmental trajectories. Nat. Methods. 2017;14(10):979-982. DOI 10.1038/nmeth.4402.; Ramsköld D., Luo S., Wang Y.C., Li R., Deng Q., Faridani O.R., Daniels G.A., Khrebtukova I., Loring J.F., Laurent L.C., Schroth G.P., Sandberg R. Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat. Biotechnol. 2012; 30(8):777-782.; Robinson M.D., Oshlack A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 2010; 11:R25. DOI 10.1186/gb-2010-11-3-r25.; Rotem A., Ram O., Shoresh N., Sperling R.A., Goren A., Weitz D.A., Bernstein B.E. Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state. Nat. Biotechnol. 2015;33(11):1165-1172. DOI 10.1038/nbt.3383.; Salafi T., Zeming K.K., Zhang Y. Advancements in microfluidics for nanoparticle separation. Lab. Chip. 2016;17(1):11-33.; Saliba A.E., Westermann A.J., Gorski S.A., Vogel J. Single-cell RNAseq: advances and future challenges. Nucleic Acids Res. 2014; 42(14):8845-8860. DOI 10.1093/nar/gku555.; Sassagawa Y., Danno H., Takada H., Ebisawa M., Tanaka K., Hayashi T., Kurisaki A., Nikaido I. Quartz-Seq2: a high-throughput single-cell RNA-sequencing method that effectively uses limited sequence reads. Genome Biol. 2018;19:29. DOI 10.1186/s13059018-1407-3.; Schwartz S., Agarwala S.D., Mumbach M.R., Jovanovic M., Mertins P., Shishkin A., Tabach Y., Mikkelsen T.S., Satija R., Ruvkun G., Carr S.A., Lander E.S., Fink G.R., Regev A. High-resolution mapping reveals a conserved, widespread, dynamic mRNA methylation program in yeast meiosis. Cell. 2013;155(6):1409-1421. DOI 10.1016/j.cell.2013.10.047.; Segal E., Friedman N., Koller D., Regev A. A module map showing conditional activity of expression modules in cancer. Nat. Genet. 2004;36(10):1090-1098.; Segerstolpe A., Palasantza A., Eliasson P., Andersson E.M., Andreasson A.C., Sun X., Picelli S., Sabirsh A., Clausen M., Bjursell M.K., Smith D.M., Kasper M., Ammala C., Sandberg R. Single-cell transcriptome profiling of human pancreatic islets in health and type 2 diabetes. Cell Metab. 2016;24:593-607. DOI 10.1016/j.cmet.2016.08.020.; Shalek A.K., Satija R., Adiconis X., Gertner R.S., Gaublomme J.T., Raychowdhury R., Schwartz S., Yosef N., Malboeuf C., Lu D., Trombetta J.J., Gennert D., Gnirke A., Goren A., Hacohen N., Levin J.Z., Park H., Regev A. Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature. 2013; 498(7453):236-240. DOI 10.1038/nature12172.; Soumillon M., Cacchiarelli D., Semrau S., van Oudenaarden A., Mikkelsen T.S. Characterization of directed differentiation by high-throughput single-cell RNA-seq. bioRxiv. 2014. DOI 10.1101/003236.; Stegle O., Teichmann S.A., Marioni J.C. Computational and analytical challenges in single-cell transcriptomics. Nat. Rev. Genet. 2015;16(3):133-145. DOI 10.1038/nrg3833.; Stevens T.J., Lando D., Basu S., Atkinson L.P., Cao Y., Lee S.F., Leeb M., Wohlfahrt K.J., Boucher W., O’Shaughnessy-Kirwan A., Cramard J., Faure A.J., Ralser M., Blanco E., Morey L., Sanso M., Palayret M.G.S., Lehner B., Croce L.D., Wutz A., Hendrich B., Klenerman D., Laue E.D. 3D structures of individual mammalian genomes studied by single-cell Hi-C. Nature. 2017;544(7648):59-64. DOI 10.1038/nature21429.; Subkhankulova T., Gilchrist M.J., Livesey F.J. Modelling and measuring single cell RNA expression levels find considerable transcriptional differences among phenotypically identical cells. BMC Genomics. 2008;9:268. DOI 10.1186/1471-2164-9-268.; Tang F., Barbacioru C., Wang Y., Nordman E., Lee C., Xu N., Wang X., Bodeau J., Tuch B.B., Siddiqui A., Lao K., Surani M.A. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods. 2009; 6(5):377-382. DOI 10.1038/nmeth.1315. Epub 2009 Apr 6.; Taylor D.M., Aronow B.J. The pediatric cell atlas: defining the growth phase of human development at single-cell resolution. Dev. Cell. 2019;49. DOI 10.1016/j.devcel.2019.03.001.; Tian L., Su S., Dong X., Amann-Zalcenstein D., Biben C., Seidi A., Hilton D.J., Naik S.H., Ritchie M.E. scPipe: a flexible R/Bioconductor preprocessing pipeline for single-cell RNA-sequencing data. PLoS Comput. Biol. 2018;14(8):e100636. DOI 10.1371/journal.pcbi.1006361.; Tirosh I., Izar B., Prakadan S.M., Wadsworth M.H., Treacy D., Trombetta J.J., Rotem A., Rodman C., Lian C., Murphy G., Fallahi-Sichani M., Dutton-Regester K., Lin J.-R., Cohen O., Shah P., Lu D., Genshaft A.S., Hughes T.K., Ziegler C.G.K., Kazer S.W., Gaillard A., Kolb K.E., Villani A.C., Johannessen C.M., Andreev A.Y., Allen E.M.V., Bertagnolli M., Sorger P.K., Sullivan R.J., Flaherty K.T., Frederick D.T., Jane-Valbuena J., Yoon C.H., Rozenblatt-Rosen O., Shalek A.K., Regev A., Garraway L.A. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science. 2016;352(6282):189-196. DOI 10.1126/science.aad0501.; Tokunaga T., Hirose O., Kawaguchi S., Toyoshima Y., Teramoto T., Ikebata H., Kuge S., Ishihara T., Iino Y., Yoshida R. Automated detection and tracking of many cells by using 4D live-cell imaging data. Bioinformatics. 2014;30:43-51.; Trapnell C., Cacchiarelli D., Grimsby J., Pokharel P., Li S., Morse M., Lennon N.J., Livak K.J., Mikkelsen T.S., Rinn J.L. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 2014;32(4):381-386. DOI 10.1038/nbt.2859.; Treutlein B., Brownfield D.G., Wu A.R., Neff N., Mantalas G.L., Espinoza F.H., Desai T.J., Krasnow M.A., Quake S.R. Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Nature. 2014;509(7500):371-375. DOI 10.1038/nature13173.; Valihrach L., Androvic P., Kubista M. Platforms for single-cell collection and analysis. Int. J. Mol. Sci. 2018;19:807.; Wang J., Song Y. Single cell sequencing: a distinct new field. Clin. Transl. Med. 2017;6(1):10. DOI 10.1186/s40169-017-0139-4.; Wang Y., Navin N.E. Advances and applications of single-cell sequencing technologies. Mol. Cell. 2015;58(4):598-609. DOI 10.1016/j.molcel.2015.05.005.; Whitesides G.M. The origins and the future of microfluidics. Nature. 2006;442(7101):368-373.; Wolf F.A., Angerer P., Theis F.J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 2018;19(1):15. DOI 10.1186/s13059-017-1382-0.; Yu P., Lin W. Single-cell transcriptome study as big data. Genom. Proteom. Bioinf. 2016;14:21-30.; Zeisel A., Munoz-Manchado A.B., Codeluppi S., Lonnerberg P., Manno G., Jureus A., Margues S., Munguba H., He L., Betsholtz C., Rolny C., Castelo-Branco G., Hjerling-Leffler J., Linnarsson S. Cell types in the mouse cortex and hippocampus revealed by singlecell RNA-seq. Science. 2015;347(6226):1138-1142. DOI 10.1126/science.aaa1934.; Zhao C., Hu S., Huo X., Zhang Y. Dr.seq2: a quality control and analysis pipeline for parallel single cell transcriptome and epigenome data. PLoS One. 2017;12(7):e0180583. DOI 10.1371/journal.pone.0180583.; Zheng G.X., Terry J.M., Belgrader P., Ryvkin P., Bent Z.W., Wilson R., Ziraldo S.B., Wheeler T.D., McDermott G.P., Zhu J., Gregory M.T., Shuga J., Montesclaros L., Underwood J.G., Masquelier D.A.; Nishimura S.Y., Schnall-Levin M., Wyatt P.W., Hindson C.M., Bharadwaj R., Wong A., Ness K.D., Beppu L.W., Deeg H.J., McFarland C., Loeb K.R., Valente W.J., Ericson N.G., Stevens E.A., Radich J.P., Mikkelsen T.S., Hindson B.J., Bielas J.H. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 2017;8:14049. DOI 10.1038/ncomms14049.; Ziegenhain C., Vieth B., Parekh S., Reinius B., Guillaumet-Adkins A., Smets M., Leonhardt H., Heyn H., Hellmann I., Enard W. Comparative analysis of single-cell RNA sequencing methods. Mol. Cell. 2017;65:631-643. DOI 10.1015/j.molcel.2017.01.023.; https://vavilov.elpub.ru/jour/article/view/2200
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15Academic Journal
Συγγραφείς: S. L. Morozov, A. S. Voronkova, V. V. Dlin, T. I. Turkina, V. S. Sukhorukov, С. Л. Морозов, А. С. Воронкова, В. В. Длин, Т. И. Туркина, В. С. Сухоруков
Πηγή: Rossiyskiy Vestnik Perinatologii i Pediatrii (Russian Bulletin of Perinatology and Pediatrics); Том 64, № 1 (2019); 110-115 ; Российский вестник перинатологии и педиатрии; Том 64, № 1 (2019); 110-115 ; 2500-2228 ; 1027-4065 ; 10.21508/1027-4065-2019-64-1
Θεματικοί όροι: транскриптомика, nephrotic syndrome, RNA, DNA, expression, nephrology, kidney, transcriptomics, нефротический синдром, РНК, ДНК, экспрессия, нефрология, почка
Περιγραφή αρχείου: application/pdf
Relation: https://www.ped-perinatology.ru/jour/article/view/830/754; Benjamin A., Michael M. Molecular nephropathology: ready for prime time? Am J Physiol Renal Physiol 2015; 309: F185– F188. DOI:10.21508/1027-4065-2017-62-3-32-36; Морозов С.Л., Длин В.В., Сухоруков В.С., Воронкова А.С. Молекулярная нефропатология: новые возможности диагностики заболеваний почек. Российский вестник перинатологии и педиатрии 2017; 62(3): 32–36. DOI:10.21508/1027-4065-2017-62-3-32-36; Mengel M., Campbell P.M., Gebel H., Randhawa P., Rodriguez E.R., Colvin R. et al. Precision diagnostics in transplantation: from bench to bedside. Am J Transplant 2013; 13: 562–568. DOI:10.1111/j.1600-6143.2012.04344.x; Traitanon O., Poggio E., Fairchild R. Molecular monitoring of alloimmune-mediated injury in kidney transplant patients. Curr Opin Nephrol Hypertens 2014; 23: 625–630. DOI:10.1097/MNH.0000000000000064; Sis B., Jhangri G.S., Bunnag S., Allanach K., Kaplan B., Halloran P.F. Endothelial gene expression in kidney transplants with alloantibody indicates antibody-mediated damage despite lack of C4d staining. Am J Transplant 2009; 9: 2312–2323. DOI:10.1111/j.1600-6143.2009.02761.x; Hidalgo L.G., Sis B., Sellares J., Campbell P.M., Mengel M., Einecke G. et al. NK cell transcripts and NK cells in kidney biopsies from patients with donorspecific antibodies: evidence for NK cell involvement in antibody mediated rejection. Am J Transplant 2010; 10: 1812–1822. DOI:10.1111/j.1600-6143.2010.03201.x.; Hidalgo L.G., Sellares J., Sis B., Mengel M., Chang J., Halloran P.F. Interpreting NK cell transcripts versus T cell transcripts in renal transplant biopsies. Am J Transplant 2012; 12: 1180–1191. DOI:10.1111/j.1600-6143.2011.03970.x; Haas M., Sis B., Racusen L.C., Solez K., Glotz D., Colvin R.B. et al. Banff 2013 meeting report: inclusion of C4d-negative antibody-mediated rejection and antibody-associated arterial lesions. Am J Transplant 2014; 14: 272–283. DOI:10.1111/ajt.12590; Geiss G.K., Bumgarner R.E., Birditt B., Dahl T., Dowidar N., Dunaway D.L. et al. Direct multiplexed measurement of gene expression with color-coded probe pairs. Nat Biotechnol 2008; 26: 317–325. DOI:10.1038/nbt1385; Adam B., Afzali B., Dominy K.M., Chapman E., Gill R., Hidalgo L.G. et al. Multiplexed color-coded probe-based gene expression assessment for clinical molecular diagnostics in formalin-fixed paraffin-embedded human renal allograft tissue. Clin Transplant 2016; 30: 295–305. DOI:10.1111/ctr.12689; Reis P.P., Waldron L., Goswami R.S., Xu W., Xuan Y., Perez-Ordonez B. et al. mRNA transcript quantification in archival samples using multiplexed, color-coded probes. BMC biotechnology 2011; 11: 46. DOI:10.1186/1472-6750-11-46; Sánchez-Navarro I., Gámez-Pozo A., González-Barón M., Pin-to-Marín A., Hardisson D., López R. et al. Comparison of gene expression profiling by reverse transcription quantitative PCR between fresh frozen and formalin-fixed, paraffin-embedded breast cancer tissues. Biotechniques 2010; 48(5): 389–397. DOI:10.2144/000113388; Pascoe C.D., Obeidat M., Arsenault B.A., Nie Y., Warner S., Stefanowicz D. et al. Gene expression analysis in asthma using a targeted multiplex array. BMC Pulmonary Medicine 2017; 17: 189. DOI:10.1186/s12890-017-0545-9; Terakado M., Gon Y., Sekiyama A., Takeshita I., Kozu Y., Matsumoto K. et al. The Rac1/JNK pathway is critical for EGFR-dependent barrier formation in human airway epithelial cells. Am J Physiol Lung Cell Mol Physiol 2011; 300: 56–63. DOI:10.1152/ajplung.00159.2010; Ong C.-T., Corces V.G. CTCF: an architectural protein bridging genome topology and function. Nat Rev Genet 2014; 15: 234–246. DOI:10.1038/nrg3663; Youssef D.M., Elbehidy R.M., Abdelhalim H.S., Amr G.E. Soluble Interleukine-2 Receptor and MDR1 Gene Expression Levels as Inflammatory Biomarkers for Prediction of Steroid Response in Children With Nephrotic Syndrome. Iran J Kidney Dis 2011; 5(3): 154–161.; Хундерякова Н.В., Захарченко М.В., Ячкула Т.В., Плясунова С.В., Сухоруков В.С., Баранич Т.И. и др. Неповреждающие высокочувствительные цито²биохимические показатели состояния митохондрий у человека для клинических исследований. В книге: Теоретическая и экспериментальная биофизика. Материалы конференции, посвященной 65-летию ИТЭБ РАН. М., 2017; 43.
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16Academic Journal
Συγγραφείς: Bobrovsky, P.A., Larin, A.K., Polina, N.F., Lazarev, V.N.
Πηγή: Biomedical Chemistry: Research and Methods; Vol. 2 No. 4 (2019); e00113 ; Biomedical Chemistry: Research and Methods; Том 2 № 4 (2019); e00113 ; 2618-7531
Θεματικοί όροι: Chlamydia trachomatis, PGLYRP, рекомбинантные белки, транскриптомика, recombinant proteins, transcriptomics
Περιγραφή αρχείου: application/pdf; application/zip; text/html
Relation: http://www.bmc-rm.org/index.php/BMCRM/article/view/113/235; http://www.bmc-rm.org/index.php/BMCRM/article/view/113/236; http://www.bmc-rm.org/index.php/BMCRM/article/view/113/237; http://www.bmc-rm.org/index.php/BMCRM/article/view/113/238
Διαθεσιμότητα: http://www.bmc-rm.org/index.php/BMCRM/article/view/113
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17Academic Journal
Συγγραφείς: Souchelnytskyi, S.
Πηγή: Biotechnologia Acta, Vol 6, Iss 4, Pp 105-117 (2013)
Θεματικοί όροι: 0301 basic medicine, transcriptomics, 0303 health sciences, 03 medical and health sciences, proteomics, genomics, diagnostic, ПЕРСОНАЛіЗОВАНЕ ЛіКУВАННЯ РАКУ, ДіАГНОСТИКА, ГЕНОМіКА, ТРАНСКРИПТОМіКА, ПРОТЕОМіКА, МЕТАБОЛОМіКА, ПЕРСОНАЛИЗИРОВАННОЕ ЛЕЧЕНИЕ РАКА, ДИАГНОСТИКА, ГЕНОМИКА, ТРАНСКРИПТОМИКА, ПРОТЕОМИКА, МЕТАБОЛОМИКА, personalized cancer medicine, metabolomics, TP248.13-248.65, Biotechnology, 3. Good health
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Σύνδεσμος πρόσβασης: http://biotechnology.kiev.ua/index.php?view=article&catid=85%3A2013-4&id=445%3A-omic-&format=pdf&option=com_content&Itemid=110&lang=en
https://doaj.org/article/c6bb49adbb314682bb93dd5454066563
https://cyberleninka.ru/article/n/individualization-of-cancer-treatment-contribution-of-omics-technologies-to-cancer-diagnostic
http://cyberleninka.ru/article/n/individualization-of-cancer-treatment-contribution-of-omics-technologies-to-cancer-diagnostic
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18Academic Journal
Συγγραφείς: Arora Sanjeevani
Συνεισφορές: Казанский (Приволжский) федеральный университет
Θεματικοί όροι: рак почек, однонуклеотидные полиморфизмы, транскриптомика
Σύνδεσμος πρόσβασης: https://openrepository.ru/article?id=190784
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19Academic Journal
Συνεισφορές: Казанский (Приволжский) федеральный университет
Θεματικοί όροι: внутриклеточные молекулярные пути, персонализированная онкология, секвенирование нового поколения, таргетные противораковые препараты, транскриптомика
Σύνδεσμος πρόσβασης: https://openrepository.ru/article?id=190649
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20Academic Journal
Συνεισφορές: Казанский (Приволжский) федеральный университет
Θεματικοί όροι: системная биология, биоинформатика, транскрипционные факторы, ATRA, клетки линии HL-60, транскриптомика, протеомика
Σύνδεσμος πρόσβασης: https://openrepository.ru/article?id=190613
