preparation-of-raav9-to-overexpress-or-knockdown-genes-in-mouse-hearts

Preparation of rAAV9 to Overexpress or Knockdown Genes in Mouse Hearts

Controlling the expression or activity of specific genes through the myocardial delivery of genetic materials in murine models permits the investigation ...

Boston Children's Hospital

Harvard Medical School

To explore CD34(+) antigen expression in new diagnosed acute myeloid leukemia (AML) and analyze the prognosis for CD34(+) AML patients, the expression of antigen CD34 in 238 AML patients was detected by indirect immunofluorescence assay. The results showed that CD34 in 92 out of the 238 patients (38.7%) were positive, there was relationship between the CD34(+) expression and FAB subtypes (M(0), M(1)), and no CD34(+) expression was observed in M(3) subtypes. The complete remission rate of CD34(+) AML patients was 32%, which was lower than that of CD34(-) AML (61%). The lymphoid-associated antigen (CD7) was significantly increased in CD34(+) AML patients, compared with CD34(-) patients (P < 0.05). It is concluded that CD34(+) AML patients show poor prognosis and lower CR rate. The detection of CD34 expression is of some value in predicting prognosis in AML.

[CD34+ antigen expression relating to prognosis in acute myeloid leukemia].

Apoptosis repressor with caspase recruitment domain (ARC) is abundantly expressed in cardiomyocytes. Protein kinase CK2 can phosphorylate ARC at threonine-149, thereby enabling ARC to antagonize apoptosis. ARC phosphorylation occurs in a constitutive manner. Nevertheless, cardiomyocytes still undergo apoptosis that is related to cardiac diseases such as myocardial infarction and heart failure. Whether the occurrence of apoptosis is related to the loss of protection by ARC under pathological conditions remains unknown.

Novel cardiac apoptotic pathway: the dephosphorylation of apoptosis repressor with caspase recruitment domain by calcineurin.

MicroRNAs (miRNAs) are a class of small noncoding RNAs that mediate post-transcriptional gene silencing. Myocardial hypertrophy is frequently associated with the development of heart failure. A variety of miRNAs are involved in the regulation of cardiac hypertrophy, however, the molecular targets of miRNAs in the cardiac hypertrophic cascades remain to be fully identified. We produced miR-23a transgenic mice, and these mice exhibit exaggerated cardiac hypertrophy in response to the stimulation with phenylephrine or pressure overload by transverse aortic banding. The endogenous miR-23a is up-regulated upon treatment with phenylephrine, endothelin-1, or transverse aortic banding. Knockdown of miR-23a attenuates hypertrophic responses. To identify the downstream targets of miR-23a, we found that transcription factor Foxo3a is suppressed by miR-23a. Luciferase assay indicates that miR-23a directly inhibits the translation activity of Foxo3a 3' UTR. Introduction or knockdown of miR-23a leads to the alterations of Foxo3a protein levels. Enforced expression of the constitutively active form of Foxo3a counteracts the provocative effect of miR-23a on hypertrophy. Furthermore, we observed that miR-23a is able to alter the expression levels of manganese superoxide dismutase and the consequent reactive oxygen species, and this effect is mediated by Foxo3a. In addition, our results show that miR-23a and Foxo3a bi-transgenic mice exhibit a reduced hypertrophic response compared with the miR-23a transgenic mice alone. Our present study reveals that miR-23a can mediate the hypertrophic signal through regulating Foxo3a. They form an axis in hypertrophic machinery and can be targets for the development of hypertrophic treatment.

Cardiac hypertrophy is positively regulated by MicroRNA miR-23a.

&#x7EFC;&#x5408;&#x4EE3;&#x8C22;&#x9176;&#x53CA;&#x8F6C;&#x8FD0;&#x86CB;&#x767D;&#x7684;&#x4FE1;&#x606F;&#x3001; &#x52A8;&#x7269;&#x7684;&#x751F;&#x7406;&#x53C2;&#x6570;&#x548C;&#x4E2D;&#x56FD;&#x4EBA;&#x7684;&#x4EBA;&#x53E3;&#x7EDF;&#x8BA1;&#x6570;&#x5B57;&#x7684;&#x809D;&#x6A21;&#x578B;&#x5EFA;&#x7ACB; (DLM) &#x57FA;&#x4E8E;&#x4EE3;&#x8C22;&#x548C;&#x519C;&#x7528;&#x8FD0;&#x8F93;&#x8F66;&#x548C; vba &#x7F16;&#x7801;&#x3002;&#x6E05;&#x5173;&#x548C;&#x5019;&#x9009;&#x836F;&#x7269;&#x5728;&#x52A8;&#x7269;&#x548C;&#x4EBA;&#x7C7B;&#x7684;&#x836F;&#x7269;&#x4E0E;&#x836F;&#x7269;&#x76F8;&#x4E92;&#x4F5C;&#x7528; (DDI) &#x53EF;&#x4EE5;&#x57FA;&#x4E8E;&#x4ECE;&#x4F53;&#x5916;&#x548C;&#x4F53;&#x5185;&#x5B9E;&#x9A8C;&#x83B7;&#x53D6;&#x7684;&#x836F;&#x4EE3;&#x52A8;&#x529B;&#x5B66;&#x6570;&#x636E;&#x9884;&#x6D4B;&#x3002;&#x5339;&#x4F10;&#x4ED6;&#x6C40;&#x548C;&#x666E;&#x4F10;&#x4ED6;&#x6C40;&#x6311;&#x9009;&#x4E86;&#x4F5C;&#x4E3A;&#x6837;&#x672C;&#xFF0C;&#x68C0;&#x67E5;&#x6B64;&#x6A21;&#x578B;&#x4E2D;&#xFF0C;&#x9884;&#x8BA1;&#x5176;&#x95F4;&#x9699;&#x548C;&#x5176;&#x4E0E;&#x73AF;&#x5B62;&#x7D20;&#x7684; DDI&#x3002;&#x7ED3;&#x679C;&#x8868;&#x660E;&#x9884;&#x6D4B;&#x7684;&#x53C2;&#x6570;&#x503C;&#x7684;&#x4E2D;&#x4F4D;&#x6570;&#x5728;&#x540C;&#x4E00;&#x7269;&#x79CD;&#x5185;&#x4E24;&#x4E2A;&#x65B9;&#x9762;&#x7684;&#x89C2;&#x5BDF;&#x503C;&#x4E3A; 83.3%(5/6)&#x3002;&#x8BE5;&#x7A0B;&#x5E8F;&#x6210;&#x529F;&#x9884;&#x6D4B;&#x5728; DDI &#x8D8B;&#x52BF;&#x53EF;&#x80FD;&#x8868;&#x660E;&#x5728;&#x4F18;&#x5316;&#x7ED9;&#x836F;&#x65B9;&#x6848;&#x548C;&#x51CF;&#x5C11;&#x98CE;&#x9669;&#x7684;&#x4E34;&#x5E8A;&#x8BD5;&#x9A8C;&#x4E2D;&#x7684;&#x5E94;&#x7528;&#x3002;

[&#x4E0E;&#x73AF;&#x5B62;&#x83CC;&#x7D20;&#x4EE3;&#x8C22;&#x548C;&#x519C;&#x7528;&#x8FD0;&#x8F93;&#x8F66;&#x7684;&#x57FA;&#x7840;&#x7684;&#x6570;&#x5B57;&#x809D;&#x6A21;&#x578B;&#x5339;&#x4F10;&#x4ED6;&#x6C40;&#x548C;&#x666E;&#x4F10;&#x4ED6;&#x6C40;&#x836F;&#x7269;&#x7684;&#x836F;&#x4EE3;&#x52A8;&#x529B;&#x5B66;&#x836F;&#x7269;&#x76F8;&#x4E92;&#x4F5C;&#x7528;&#x7684;&#x9884;&#x6D4B;]&#x3002;

Information of metabolic enzymes and transporters, physiological parameters of animals and demography of Chinese people were integrated to establish a digital liver model (DLM) based on metabolism and transporter and coded with VBA. Clearance and drug-drug interaction (DDI) of candidate drugs in animal and human could be predicted based on the pharmacokinetic data obtained from in vitro and in vivo experiments. Pravastatin and pitavastatin were selected as the samples to examine this model, where their clearance and their DDI with cyclosporine were predicted. The results showed that the predicted values of median parameters in same species were within twofold of observed values for 83.3% (5/6). The program's successful prediction in DDI tendency might indicate its application in optimizing the dosage regimen and reducing the risk of clinical trial.

[Prediction of the pharmacokinetic drug-drug interaction of pravastatin and pitavastatin with cyclosporine by a digital liver model based on metabolism and transporter].

Information des enzymes m&eacute;taboliques et des transporteurs, des param&egrave;tres physiologiques des animaux et la d&eacute;mographie de la population chinoise ont &eacute;t&eacute; int&eacute;gr&eacute;s afin d&#39;&eacute;tablir un mod&egrave;le num&eacute;rique du foie (DLM) bas&eacute; sur le m&eacute;tabolisme et le transporteur et cod&eacute; avec VBA. D&eacute;gagement et interaction m&eacute;dicament-m&eacute;dicament (DDI) de m&eacute;dicaments candidats dans les animaux et les humains pouvaient &ecirc;tre pr&eacute;dite bas&eacute;e sur les donn&eacute;es pharmacocin&eacute;tiques obtenues &agrave; partir des exp&eacute;riences in vitro et in vivo. Pravastatine et pitavastatin ont &eacute;t&eacute; s&eacute;lectionn&eacute;s comme les &eacute;chantillons &agrave; examiner ce mod&egrave;le, o&ugrave; leur d&eacute;douanement et leur DDI avec la cyclosporine ont &eacute;t&eacute; pr&eacute;dites. Les r&eacute;sultats font appara&icirc;tre que les valeurs estim&eacute;es des param&egrave;tres m&eacute;dians dans la m&ecirc;me esp&egrave;ce au double des valeurs observ&eacute;es pour 83,3 % (5/6). Pr&eacute;diction de r&eacute;ussite du programme dans la tendance de la DDI peut indiquer sa demande en optimisant la posologie et de r&eacute;duction des risques de l&#39;essai clinique.

[La pr&eacute;diction de l&#39;interaction pharmacocin&eacute;tique m&eacute;dicamenteuse de pravastatine et pitavastatin avec la cyclosporine par un mod&egrave;le de foie num&eacute;rique bas&eacute; sur le m&eacute;tabolisme et le transporteur].

Informationen von metabolischen Enzymen und Transporteure, physiologische Parameter von Tieren und Demographie des chinesischen Volks wurden integriert, um ein digitales Modell der Leberkrebs herzustellen (DLM) basierend auf Stoffwechsel und Transporter und kodiert mit VBA. Abstands- und Drogen-Drogen-Interaktion (DDI) Kandidat Drogen in tierischen und menschlichen konnte auf der Grundlage der pharmakokinetischen Daten aus in-vitro und in vivo Experimente vorhergesagt werden. Pravastatin und Pitavastatin wurden wie die Beispiele ausgew&auml;hlt, um dieses Modell zu pr&uuml;fen, wo ihre Abstands- und ihre DDI mit Cyclosporin vorhergesagt wurden. Die Ergebnisse zeigten, dass die vorhergesagten Werte der Median Parameter in derselben Art innerhalb der zweifachen der beobachteten Werte f&uuml;r 83,3 % waren (5/6). Das Programm erfolgreich Vorhersage in DDI Tendenz k&ouml;nnte seine Anwendung bei der Optimierung der Dosierungsschema und Verringerung des Risikos von klinischen Studie hinweisen.

[Vorhersage der pharmakokinetischen Drogen-Drogen-Interaktion von Pravastatin und Pitavastatin mit Cyclosporin durch ein digital-Leber-Modell basierend auf Stoffwechsel und Transporter].

Informazioni degli enzimi metabolici e i trasportatori, i parametri fisiologici degli animali e la demografia del popolo cinese sono stati integrati per stabilire un modello digitale del fegato (DLM) basato sul metabolismo e transporter e codificato con VBA. Gioco e interazione farmaco-farmaco (DDI) di farmaci candidati in animale e umana potrebbe essere previsto sulla base dei dati farmacocinetici ottenuti da esperimenti in vitro e in vivo. Pravastatin e pitavastatina sono stati selezionati come campioni per esaminare questo modello, dove sono stati previsti loro liquidazione e loro DDI con ciclosporina. I risultati hanno mostrato che i valori stimati dei parametri mediani nella stessa specie sono stati all&#39;interno della duplice di valori osservati per 83,3% (5/6). Previsione di successo del programma nella tendenza DDI potrebbe indicare la sua applicazione nell&#39;ottimizzazione del regime di dosaggio e riducendo il rischio di trial clinici.

[Previsione delle interazioni farmacocinetiche farmaco-farmaco di Pravastatina e pitavastatina con ciclosporina da un modello digitale del fegato basato sul metabolismo e transporter].

&#x4EE3;&#x8B1D;&#x9175;&#x7D20;&#x3068;&#x30C8;&#x30E9;&#x30F3;&#x30B9;&#x30DD;&#x30FC;&#x30BF;&#x30FC;&#x306E;&#x60C5;&#x5831;&#x3001;&#x52D5;&#x7269;&#x306E;&#x751F;&#x7406;&#x5B66;&#x7684;&#x30D1;&#x30E9;&#x30E1;&#x30FC;&#x30BF;&#x30FC;&#x3068;&#x4E2D;&#x56FD;&#x306E;&#x4EBA;&#x3005; &#x306E;&#x4EBA;&#x53E3;&#x7D71;&#x8A08;&#x5B66;&#x30C7;&#x30B8;&#x30BF;&#x30EB;&#x809D;&#x30E2;&#x30C7;&#x30EB;&#x3092;&#x78BA;&#x7ACB;&#x3059;&#x308B;&#x305F;&#x3081;&#x306B;&#x7D71;&#x5408;&#x3055;&#x308C;&#x307E;&#x3057;&#x305F; (DLM) &#x4EE3;&#x8B1D;&#x3068;&#x8F38;&#x9001;&#x306B;&#x57FA;&#x3065;&#x304F;&#x3001;VBA &#x3067;&#x30B3;&#x30FC;&#x30C9;&#x5316;&#x3055;&#x308C;&#x307E;&#x3057;&#x305F;&#x3002;&#x30AF;&#x30EA;&#x30A2;&#x30E9;&#x30F3;&#x30B9;&#x3068;&#x85AC;&#x7269;&#x76F8;&#x4E92;&#x4F5C;&#x7528; (DDI) &#x306E;&#x5019;&#x88DC;&#x85AC;&#x52D5;&#x7269;&#x3068;&#x4EBA;&#x9593;&#x306F;&#x3001;in vitro &#x304A;&#x3088;&#x3073; in vivo &#x306E;&#x5B9F;&#x9A13;&#x304B;&#x3089;&#x5F97;&#x3089;&#x308C;&#x305F;&#x85AC;&#x7269;&#x52D5;&#x614B;&#x5B66;&#x7684;&#x30C7;&#x30FC;&#x30BF;&#x306B;&#x57FA;&#x3065;&#x304F;&#x4E88;&#x6E2C;&#x53EF;&#x80FD;&#x6027;&#x304C;&#x3002;&#x30D7;&#x30E9;&#x30D0;&#x30B9;&#x30BF;&#x30C1;&#x30F3;&#x3068;&#x30D4;&#x30BF;&#x30D0;&#x30B9;&#x30BF;&#x30C1;&#x30F3; &#x30B5;&#x30F3;&#x30D7;&#x30EB;&#x3068;&#x3057;&#x3066;&#x5F7C;&#x3089;&#x306E;&#x30AF;&#x30EA;&#x30A2;&#x30E9;&#x30F3;&#x30B9;&#x3068;&#x30B7;&#x30AF;&#x30ED;&#x30B9;&#x30DD;&#x30EA;&#x30F3;&#x3068;&#x5F7C;&#x3089;&#x306E; DDI &#x304C;&#x4E88;&#x6E2C;&#x3055;&#x308C;&#x305F;&#x3053;&#x306E;&#x30E2;&#x30C7;&#x30EB;&#x3092;&#x691C;&#x67FB;&#x306B;&#x9078;&#x3070;&#x308C;&#x307E;&#x3057;&#x305F;&#x3002;&#x305D;&#x306E;&#x7D50;&#x679C;&#x3067;&#x306F;&#x3001;&#x4E88;&#x6E2C;&#x5024;&#x306E;&#x4E2D;&#x592E;&#x5024;&#x306E;&#x30D1;&#x30E9;&#x30E1;&#x30FC;&#x30BF;&#x30FC;&#x306E;&#x540C;&#x3058;&#x7A2E;&#x306E; 83.3 &#xFF05; &#x306E;&#x89B3;&#x6E2C;&#x5024;&#x306E; 2 &#x500D;&#x4EE5;&#x5185;&#x793A;&#x3057;&#x305F; (5/6)&#x3002;&#x30D7;&#x30ED;&#x30B0;&#x30E9;&#x30E0;&#x306E;&#x6210;&#x529F;&#x3057;&#x305F;&#x4E88;&#x6E2C; DDI &#x50BE;&#x5411;&#x306E;&#x7528;&#x6CD5;&#x7528;&#x91CF;&#x3092;&#x6700;&#x9069;&#x5316;&#x3057;&#x3001;&#x81E8;&#x5E8A;&#x8A66;&#x9A13;&#x306E;&#x30EA;&#x30B9;&#x30AF;&#x3092;&#x4F4E;&#x6E1B;&#x3078;&#x306E;&#x5FDC;&#x7528;&#x53EF;&#x80FD;&#x6027;&#x304C;&#x3042;&#x308A;&#x307E;&#x3059;&#x3002;

[&#x30B7;&#x30AF;&#x30ED;&#x30B9;&#x30DD;&#x30EA;&#x30F3;&#x4EE3;&#x8B1D;&#x3068;&#x8F38;&#x9001;&#x306B;&#x57FA;&#x3065;&#x304F;&#x30C7;&#x30B8;&#x30BF;&#x30EB;&#x809D;&#x30E2;&#x30C7;&#x30EB;&#x306B;&#x3088;&#x3063;&#x3066;&#x3068;&#x30D7;&#x30E9;&#x30D0;&#x30B9;&#x30BF;&#x30C1;&#x30F3;&#x3068;&#x30D4;&#x30BF;&#x30D0;&#x30B9;&#x30BF;&#x30C1;&#x30F3;&#x306E;&#x85AC;&#x7269;&#x52D5;&#x614B;&#x85AC;&#x7269;&#x30FB;&#x85AC;&#x7269;&#x76F8;&#x4E92;&#x4F5C;&#x7528;&#x306E;&#x4E88;&#x6E2C;]&#x3002;

&#xB300;&#xC0AC; &#xD6A8;&#xC18C;&#xC640; &#xC804;&#xC1A1;&#xAE30;&#xC758; &#xC815;&#xBCF4;, &#xB3D9;&#xBB3C;&#xC758; &#xC0DD;&#xB9AC; &#xC801; &#xB9E4;&#xAC1C; &#xBCC0;&#xC218; &#xBC0F; &#xC778;&#xAD6C; &#xC911;&#xAD6D; &#xC0AC;&#xB78C;&#xB4E4;&#xC758; &#xB514;&#xC9C0;&#xD138; &#xAC04; &#xBAA8;&#xB378; &#xD655;&#xB9BD;&#xC744; &#xD1B5;&#xD569; &#xD588;&#xB2E4; (DLM) &#xB300;&#xC0AC; &#xBC0F; &#xC804;&#xC1A1; &#xAE30;&#xBC18; &#xBC0F; VBA&#xB85C; &#xCF54;&#xB529; &#xB41C;. &#xD074;&#xB9AC;&#xC5B4;&#xB7F0;&#xC2A4; &#xBC0F; &#xC57D;&#xBB3C;-&#xC57D;&#xBB3C; &#xC0C1;&#xD638;&#xC791;&#xC6A9; (DDI) &#xB3D9;&#xBB3C; &#xBC0F; &#xC778;&#xAC04;&#xC758; &#xD6C4;&#xBCF4; &#xC57D;&#xBB3C;&#xC758; &#xC0DD;&#xCCB4; &#xC678; &#xBC0F; &#xC0DD;&#xCCB4; &#xC870;&#xAC74; &#xC2E4;&#xD5D8;&#xC5D0;&#xC11C; &#xC5BB;&#xC740; pharmacokinetic &#xB370;&#xC774;&#xD130;&#xB97C; &#xAE30;&#xBC18;&#xC73C;&#xB85C; &#xC608;&#xCE21; &#xB420; &#xC218; &#xC788;&#xC2B5;&#xB2C8;&#xB2E4;. Pravastatin &#xBC0F; pitavastatin&#xC758; &#xD5C8;&#xAC00; cyclosporine&#xACFC; &#xADF8;&#xB4E4;&#xC758; DDI &#xC608;&#xCE21; &#xD588;&#xB2E4;&#xC774; &#xBAA8;&#xB378;&#xC744; &#xAC80;&#xC0AC; &#xD558;&#xB294; &#xC0D8;&#xD50C;&#xC73C;&#xB85C; &#xC120;&#xC815; &#xB410;&#xB2E4;. &#xACB0;&#xACFC; &#xAC19;&#xC740; &#xC885;&#xC871;&#xC758; &#xC911;&#xAC04; &#xB9E4;&#xAC1C; &#xBCC0;&#xC218; &#xC608;&#xCE21;&#xB41C; &#xAC12; 83.3%&#xC5D0; &#xB300; &#xD55C; &#xAD00;&#xCC30; &#xB41C; &#xAC12;&#xC758; &#xB450; &#xAC00;&#xC9C0; &#xC774;&#xB0B4; &#xD588;&#xB2E4; &#xBCF4;&#xC5EC;&#xC8FC;&#xC5C8;&#xB2E4; (5/6). &#xD504;&#xB85C;&#xADF8;&#xB7A8;&#xC758; &#xC131;&#xACF5;&#xC801;&#xC778; &#xC608;&#xCE21; DDI &#xACBD;&#xD5A5;&#xC5D0; &#xD22C;&#xC5EC;&#xB97C; &#xCD5C;&#xC801;&#xD654; &#xD558; &#xACE0; &#xC784;&#xC0C1; &#xC2DC;&#xD5D8;&#xC758; &#xC704;&#xD5D8;&#xC744; &#xC904;&#xC77C; &#xC218; &#xC788;&#xB294; &#xC751;&#xC6A9; &#xD504;&#xB85C;&#xADF8;&#xB7A8;&#xC744; &#xB098;&#xD0C0;&#xB0BC; &#xC218; &#xC788;&#xC2B5;&#xB2C8;&#xB2E4;.

[&#xC2E0;&#xC9C4; &#xB300;&#xC0AC; &#xBC0F; &#xC804;&#xC1A1; &#xAE30;&#xBC18; &#xB514;&#xC9C0;&#xD138; &#xAC04; &#xBAA8;&#xB378;&#xC5D0; &#xC758;&#xD574; cyclosporine&#xACFC; pravastatin pitavastatin&#xC758; pharmacokinetic &#xC57D;&#xBB3C;-&#xC57D;&#xBB3C; &#xC0C1;&#xD638;&#xC791;&#xC6A9;&#xC758; &#xC608;&#xCE21;].

Informaci&oacute;n de enzimas metab&oacute;licas y transportadores, par&aacute;metros fisiol&oacute;gicos de los animales y la demograf&iacute;a de la poblaci&oacute;n China se integraron para establecer un modelo digital de h&iacute;gado (DLM) basado en el metabolismo y transportador y codificados con VBA. Separaci&oacute;n y la interacci&oacute;n de f&aacute;rmacos (DDI) de f&aacute;rmacos candidatos en animales y humanos pueden predecirse basado en los datos farmacocin&eacute;ticos obtenidos de experimentos in vitro e in vivo. Pravastatina y pitavastatina fueron seleccionados como las muestras para examinar este modelo, donde se predijeron su separaci&oacute;n y su DDI con ciclosporina. Los resultados mostraron que los valores predichos de medianos par&aacute;metros en la misma especie fueron en doble de los valores observados de 83,3% (5/6). Predicci&oacute;n exitosa del programa en la tendencia DDI podr&iacute;a indicar su aplicaci&oacute;n en optimizando el r&eacute;gimen de dosificaci&oacute;n y reduciendo el riesgo de ensayo cl&iacute;nico.

[La predicci&oacute;n de la interacci&oacute;n de f&aacute;rmacos farmacocin&eacute;ticos de pravastatina y Pitavastatina con ciclosporina por un modelo de h&iacute;gado digital basado en metabolismo y transportador].

Worldwide influenza caused by influenza virus is a respiratory disease which threats the public health by seasonal epidemics or global influenza outbreak. Vaccines and drugs are current therapies, but there are many restricted factors such as neurotoxicity, side effects of gastrointestinal, and drug resistance. New technologies, particularly RNAi mediated by small RNAs, has become a potential and robust method in influenza antiviral research for its high efficiency, specific, and speedy. Following the spread and epidemic of the influenza virus, application of small RNAs into influenza antiviral research has been reported increasingly. The small RNAs, PA-2087, NP-1496, and M-950, which targets PA, NP, and M2 genes, respectively, are the most effective anti-influenza siRNAs up to now. siRNA of targeting conservative region of different influenza viral genes has broader effect on virus inhibition. The combination of siRNAs of targeting different genes can achieve better virus inhibition. In this review, we mainly described the progress of siRNAs and miRNAs for anti-influenza virus, and the prospects and hurdles of influenza RNAi therapy as well.

[Research progress on influenza antiviral small RNAs].

Sulforaphane is a common antioxidant selectively abundant in cruciferous plants, which exhibits effective anti-cancer actions in control of tumorigenesis or progression of various cancers. A recent study has shown that sulforaphane attenuates the EGFR signaling pathway in non-small cell lung cancer (NSCLC), suggesting its potential anti-metastatic effects. In this study we assessed the involvement of sulforaphane and miR-616-5p in epithelial-mesenchymal transition (EMT) and NSCLC metastasis. Sulforaphane suppressed the cell proliferation in human NSCLC cell lines H1299, 95C and 95D with IC50 values of 9.52±1.23, 9.04±1.90 and 17.35±2.03 μmol/L, respectively. At low concentrations (1-5 μmol/L), sulforaphane dose-dependently inhibited the migration and invasion of 95D and H1299 cells with relatively high metastatic potential. The anti-metastatic action of sulforaphane was confirmed in 95D and H1299 cell xenografts in vivo. In fresh NSCLC tissue samples from 179 patients, miR-616-5p levels were upregulated in late-stage NSCLCs, and strongly correlated with risk of NSCLC recurrence and metastasis. Consistent with the clinic observation, miR-616-5p levels in the 3 NSCLC cell lines were correlated with their metastatic ability, and were decreased by sulforaphane treatment. Silencing miR-616-5p markedly suppressed the migration and invasion of 95D cells in vitro and NSCLC metastasis in vivo. Further studies revealed that miR-616-5p directly targeted GSK3β and decreased its expression, whereas sulforaphane decreased miR-616-5p levels by histone modification, and followed by inactivation of the GSK3β/β-catenin signaling pathway and inhibition of EMT, which was characterized by loss of epithelial markers and acquisition of a mesenchymal phenotype in NSCLC cells. Our findings suggest that sulforaphane is a potential adjuvant chemotherapeutic agent for the prevention of NSCLC recurrence and metastasis, and miR-616-5p can be clinically utilized as a biomarker or therapeutic target to inhibit metastasis.

Article	Year
[CD34+ antigen expression relating to prognosis in acute myeloid leukemia]. Zhongguo shi yan xue ye xue za zhi\| PubMed ID: 16277848	2005
Novel cardiac apoptotic pathway: the dephosphorylation of apoptosis repressor with caspase recruitment domain by calcineurin. Circulation\| PubMed ID: 19001025	2008
Cardiac hypertrophy is positively regulated by MicroRNA miR-23a. The Journal of biological chemistry\| PubMed ID: 22084234	2012
[Prediction of the pharmacokinetic drug-drug interaction of pravastatin and pitavastatin with cyclosporine by a digital liver model based on metabolism and transporter]. Yao xue xue bao = Acta pharmaceutica Sinica\| PubMed ID: 22121784	2011
[Research progress on influenza antiviral small RNAs]. Yi chuan = Hereditas\| PubMed ID: 22659424	2012
Sulforaphane suppresses EMT and metastasis in human lung cancer through miR-616-5p-mediated GSK3β/β-catenin signaling pathways. Acta pharmacologica Sinica\| PubMed ID: 27890917	2016

Zhi-Qiang Lin

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