Evaluating <em>In Vitro</em> DNA Damage Using Comet Assay

DNA damage is a common phenomenon for each cell during its lifespan, and is defined as an alteration of the chemical structure of genomic DNA. Cancer ...

National Cancer Institute

Zunyi Medical College-Zhuhai Campus

The family of STAT proteins consists of seven members that mediate highly specific functions in cytokine signaling. STAT6 is a critical regulator of transcription for interleukin-4 (IL-4)-induced genes. Activation of gene expression involves recruitment of coactivator proteins that function as bridging factors connecting sequence-specific transcription factors to the basal transcription machinery, and as chromatin-modifying enzymes. In this report, we show that the coacitivator p100 protein can interact with STAT6 through its SN domain both in vivo and in vitro, resulting in enhancement of STAT6-mediated gene transcriptional acitivation. Consistent with our previous reports, we identified intracellular localization of p100 and STAT-6 by confocal microscopy examined in response to IL-4. Moreover, in consideration of STAT molecules sharing significant homology in structure and function, we detected whether p100 can associate with STAT-1. In conclusion, this study found no evidence that p100 functions as a transcriptional coactivator for STAT1-dependent gene regulation.

Coactivator P100 protein enhances STAT6-dependent transcriptional activation but has no effect on STAT1-mediated gene transcription.

GSTT1, GSTM1, CYP1A1 are enzymes responsible for the detoxification of the toxicant which may be involved in the development of systemic lupus erythematosus (SLE). We examined the relationship between the risk of SLE and the polymorphisms of these genes in the Chinese population.

Association of GSTT1, GSTM1 and CYP1A1 polymorphisms with susceptibility to systemic lupus erythematosus in the Chinese population.

Epidemiologic studies contribute greatly to evidence-based medicine by identifying risk factors for diseases and determining optimal treatments for clinical practice. However, there is very limited effort on automatic extraction of knowledge from epidemiologic articles, such as exposures, outcomes, and their relations. In this initial study, we developed a system that consists of a natural language processing (NLP) engine and a rule-based classifier, to automatically extract exposure-related terms from titles of epidemiologic articles. The evaluation using 450 titles annotated by an epidemiologist showed the highest F-measure of 0.646 (Precision 0.610 and Recall 0.688) using in-exact matching, which indicated the feasibility of automated methods on mining epidemiologic literature. Further analysis of terms related to epidemiologic exposures suggested that although UMLS would have reasonable coverage, more appropriate semantic classifications of epidemiologic exposures would be required.

Mining Biomedical Literature for Terms related to Epidemiologic Exposures.

Semantic-based sublanguage grammars have been shown to be an efficient method for medical language processing. However, given the complexity of the medical domain, parsers using such grammars inevitably encounter ambiguous sentences, which could be interpreted by different groups of production rules and consequently result in two or more parse trees. One possible solution, which has not been extensively explored previously, is to augment productions in medical sublanguage grammars with probabilities to resolve the ambiguity. In this study, we associated probabilities with production rules in a semantic-based grammar for medication findings and evaluated its performance on reducing parsing ambiguity. Using the existing data set from 2009 i2b2 NLP (Natural Language Processing) challenge for medication extraction, we developed a semantic-based CFG (Context Free Grammar) for parsing medication sentences and manually created a Treebank of 4564 medication sentences from discharge summaries. Using the Treebank, we derived a semantic-based PCFG (Probabilistic Context Free Grammar) for parsing medication sentences. Our evaluation using a 10-fold cross validation showed that the PCFG parser dramatically improved parsing performance when compared to the CFG parser.

Applying semantic-based probabilistic context-free grammar to medical language processing--a preliminary study on parsing medication sentences.

Much epidemiologic information resides in literature, which is not in a computable format. To extract information and build knowledge bases of epidemiologic studies, we developed a system to extract noun phrases about epidemiologic exposures and outcomes. The system consists of two components: a natural language processing (NLP) engine; a machine learning (ML) based classifier. Four ML algorithms were applied and compared over different feature sets. To evaluate the performance of the system, we manually constructed an annotated dataset. The system achieved the highest F-measure of 82.0% for extracting exposure terms, and 70% for extracting outcome terms.

Extracting epidemiologic exposure and outcome terms from literature using machine learning approaches.

&#x5316;&#x7597;&#x519B;&#x56E2;&#x5DF2;&#x5E7F;&#x6CDB;&#x5E94;&#x7528;&#x4E8E;&#x5404;&#x79CD;&#x5305;&#x62EC;&#x809D;&#x7EC6;&#x80DE;&#x764C; (HCC) &#x7684;&#x4EBA;&#x7C7B;&#x6076;&#x6027;&#x80BF;&#x7624;&#x7684;&#x6CBB;&#x7597;&#x3002;&#x5728;&#x5316;&#x7597;&#x5931;&#x8D25;&#x7684;&#x4E3B;&#x8981;&#x539F;&#x56E0;&#x662F;&#x764C;&#x7EC6;&#x80DE;&#x7684;&#x6297;&#x836F;&#x6027;&#x3002;&#x6297;&#x963F;&#x9709;&#x7D20; (&#x6CA1;&#x6709;) &#x662F;&#x6709;&#x6548;&#x5730;&#x6CBB;&#x7597;&#x764C;&#x75C7;&#x7684;&#x4E00;&#x79CD;&#x5171;&#x540C;&#x548C;&#x6709;&#x4EE3;&#x8868;&#x6027;&#x7684;&#x969C;&#x788D;&#x3002;&#x6700;&#x8FD1;&#x7684;&#x7814;&#x7A76;&#x5728;&#x5408;&#x540C;&#x4E2D;&#x6CA1;&#x6709;&#x62B5;&#x6297;&#x7684;&#x6F14;&#x53D8;&#x4E86;&#xFF0C;&#x4E2A;&#x522B; microRNA (&#x7C73;&#x5C14;)&#x3002;&#x7136;&#x800C;&#xFF0C;&#x4ECD;&#x7136;&#x7F3A;&#x4E4F;&#x5168;&#x7403;&#x548C;&#x7CFB;&#x7EDF;&#x8BC4;&#x4F30;&#x7684;&#x8D21;&#x732E;&#x6CA1;&#x6709;&#x62B5;&#x6297;&#x7684;&#x7C73;&#x5C14;&#x8868;&#x8FBE;&#x8C31;&#x3002;&#x5728;&#x672C;&#x7814;&#x7A76;&#x4E2D;&#xFF0C;&#x6211;&#x4EEC;&#x91C7;&#x7528;&#x4E86;&#x9AD8;&#x541E;&#x5410;&#x91CF;&#x7167;&#x6392;&#x80F6;&#x7247;&#x6D4B;&#x5E8F;&#x5168;&#x9762;&#x5B9A;&#x6027;&#x7C73;&#x5C14;&#x8868;&#x8FBE;&#x8C31;&#x7684;&#x4EBA;&#x809D;&#x764C;&#x7EC6;&#x80DE;&#x682A; (HepG2) &#x548C;&#x8010;&#x6CA1;&#x6709;&#x5BF9;&#x5E94;&#x7684; (HepG2/&#x6CA1;&#x6709;&#xFF09;&#x3002;&#x5171; 269 &#x5DF2;&#x77E5; Mirna &#x7684;&#x5DEE;&#x5F02;&#x8868;&#x8FBE;&#xFF0C;&#x5176;&#x4E2D; 23 &#x4E0A;&#x8C03;&#xFF0C;246 &#x4E0B;&#x8C03; HepG2/&#x6CA1;&#x6709;&#x5355;&#x5143;&#x683C;&#x4E2D;&#xFF0C;&#x8BE5;&#x503C;&#x5176;&#x4E2D;&#x90E8;&#x5206;&#x53EF;&#x80FD;&#x6D89;&#x53CA;&#x7684;&#x6CA1;&#x6709;&#x6297;&#x836F;&#x6027;&#x7684;&#x53D1;&#x5C55;&#x3002;&#x6B64;&#x5916;&#xFF0C;&#x6211;&#x4EEC;&#x6709; 9 &#x548C; 13 &#x79CD;&#x65B0;&#x578B; Mirna &#x5411;&#x4E0A;-&#x5411;&#x4E0B;-&#x8868;&#x8FBE;&#x548C;&#x5927;&#x5927; HepG2/&#x6CA1;&#x6709;&#x5355;&#x5143;&#x683C;&#xFF0C;&#x5206;&#x522B;&#x786E;&#x5B9A;&#x3002;&#x5B9E;&#x65F6;&#x8367;&#x5149;&#x5B9A;&#x91CF; PCR &#x7528;&#x4E8E;&#x9009;&#x5B9A; Mirna&#xFF0C;&#x5305;&#x62EC; 22 &#x5DF2;&#x77E5;&#x7684; Mirna &#x548C; 6 &#x79CD;&#x65B0;&#x578B; Mirna &#x7136;&#x540E;&#x9A8C;&#x8BC1;&#x7C73;&#x5C14;&#x5206;&#x6790;&#x3002;&#x6B64;&#x5916;&#xFF0C;&#x6211;&#x4EEC;&#x9884;&#x8BA1;&#x4E3A;&#x6837;&#x672C;&#x4E2D;&#x7684;&#x653E;&#x677E;&#x7BA1;&#x5236; Mirna &#x5047;&#x5B9A;&#x76EE;&#x6807;&#x57FA;&#x56E0;&#x3002;&#x51FD;&#x6570;&#x6CE8;&#x91CA;&#x6697;&#x793A;&#x8FD9;&#x4E9B;&#x9009;&#x5B9A;&#x7684; Mirna &#x5F71;&#x54CD;&#x8BB8;&#x591A;&#x4E3B;&#x8981;&#x53C2;&#x4E0E; MAPK &#x4FE1;&#x53F7;&#x8F6C;&#x5BFC;&#x901A;&#x8DEF;&#x7684;&#x7684;&#x9776;&#x57FA;&#x56E0;&#x3002;&#x8FD9;&#x9879;&#x7814;&#x7A76;&#x63D0;&#x4F9B;&#x6211;&#x4EEC;&#x4E00;&#x822C;&#x7684;&#x7C73;&#x5C14;&#x8868;&#x8FBE;&#x8C31;&#xFF0C;&#x8BF4;&#x660E;&#x8FD9;&#x662F;&#x5E2E;&#x52A9;&#x627E;&#x5230;&#x6F5C;&#x5728; Mirna &#x4E3A;&#x514B;&#x670D;&#x6CA1;&#x6709;&#x62B5;&#x6297;&#x7684;&#x672A;&#x6765;&#x809D;&#x764C;&#x5316;&#x7597;&#x7684;&#x8F85;&#x52A9;&#x6CBB;&#x7597;&#x3002;

&#x7C73;&#x5C14;&#x7B7E;&#x540D;&#x8868;&#x8FBE;&#x5728;&#x8010;&#x963F;&#x9709;&#x7D20;&#x548C;&#x7236;&#x6BCD;&#x4EBA;&#x7C7B;&#x809D;&#x764C;&#x7EC6;&#x80DE;&#x682A;&#x7684;&#x5168;&#x57FA;&#x56E0;&#x7EC4;&#x5206;&#x6790;&#x3002;

Chemotherapy regiments have been widely used in the treatment of a variety of human malignancies including hepatocellular carcinoma (HCC). A major cause of failure in chemotherapy is drug resistance of cancer cells. Resistance to doxorubicin (DOX) is a common and representative obstacle to treat cancer effectively. Individual microRNA (miRNA) has been introduced in the evolution of DOX resistance in HCC in recent studies. However, a global and systematic assessment of the miRNA expression profiles contributing to DOX resistance is still lacking. In the present study, we applied high-throughput Illumina sequencing to comprehensively characterize miRNA expression profiles in both human HCC cell line (HepG2) and its DOX-resistant counterpart (HepG2/DOX). A total of 269 known miRNAs were significantly differentially expressed, of which 23 were up-regulated and 246 were down-regulated in HepG2/DOX cells, indicating that part of them might be involved in the development of DOX resistance. In addition, we have identified 9 and 13 novel miRNAs up- and down-expressed significantly in HepG2/DOX cells, respectively. miRNA profiling was then validated by quantitative real-time PCR for selected miRNAs, including 22 known miRNAs and 6 novel miRNAs. Furthermore, we predicted the putative target genes for the deregulated miRNAs in the samples. Function annotation implied that these selected miRNAs affected many target genes mainly involved in MAPK signaling pathway. This study provides us a general description of miRNA expression profiling, which is helpful to find potential miRNAs for adjunct treatment to overcome DOX resistance in future HCC chemotherapy.

Genome-Wide Analysis of miRNA Signature Differentially Expressed in Doxorubicin-Resistant and Parental Human Hepatocellular Carcinoma Cell Lines.

R&eacute;giments de chimioth&eacute;rapie ont &eacute;t&eacute; largement utilis&eacute;s dans le traitement de divers cancers humains y compris le carcinome h&eacute;patocellulaire (CHC). Des principales causes d&#39;&eacute;chec lors de la chimioth&eacute;rapie sont la r&eacute;sistance des cellules canc&eacute;reuses. R&eacute;sistance &agrave; la doxorubicine (DOX) est un obstacle commun et repr&eacute;sentatif pour traiter efficacement le cancer. Les microARN (miARN) a &eacute;t&eacute; introduit dans l&#39;&eacute;volution de la r&eacute;sistance de DOX &agrave; HCC dans des &eacute;tudes r&eacute;centes. Cependant, il manque encore une &eacute;valuation globale et syst&eacute;matique des profils d&#39;expression des miRNA qui contribuent &agrave; la r&eacute;sistance de DOX. Dans la pr&eacute;sente &eacute;tude, nous avons appliqu&eacute; le s&eacute;quen&ccedil;age Illumina haut d&eacute;bit afin de caract&eacute;riser globalement miRNA expression profiles en la lign&eacute;e cellulaire humaine HCC (HepG2) et son homologue DOX-r&eacute;sistant (HepG2/DOX). Un total de 269 miARN connus ont &eacute;t&eacute; significativement diff&eacute;rentiellement exprim&eacute;s, dont 23 ont &eacute;t&eacute; r&eacute;gul&eacute;e et 246 &eacute;taient diminu&eacute;es dans les cellules HepG2/DOX, indiquant que la partie d&#39;entre eux peut-&ecirc;tre &ecirc;tre impliqu&eacute; dans le d&eacute;veloppement de la r&eacute;sistance de DOX. En outre, nous avons identifi&eacute; 9 et 13 miARN roman haut - et bas-exprim&eacute; sensiblement dans les cellules HepG2/DOX, respectivement. miRNA profilage a &eacute;t&eacute; valid&eacute; ensuite par des PCR quantitative en temps r&eacute;el des miARN s&eacute;lectionn&eacute;s, dont 22 miARN connus et 6 miARN roman. En outre, nous avons pr&eacute;dit les g&egrave;nes putatifs de cible pour les miARN d&eacute;r&eacute;glement&eacute; dans les &eacute;chantillons. Annotation de fonction implicite que ces miARN s&eacute;lectionn&eacute;s affect&eacute; de nombreux g&egrave;nes cibles principalement impliqu&eacute;s dans la voie de signalisation MAPK. Cette &eacute;tude nous donne une description g&eacute;n&eacute;rale du profilage de l&#39;expression de miRNA, qui est utile pour trouver des miARN potentielle pour le traitement auxiliaire de vaincre la r&eacute;sistance de DOX dans le future chimioth&eacute;rapie HCC.

Analyse du g&eacute;nome-large de miRNA Signature diff&eacute;rentiellement exprim&eacute;s en r&eacute;sistant &agrave; la doxorubicine et Parental humain lign&eacute;es de cellules carcinome h&eacute;patocellulaire.

Chemotherapie-Regimenter wurden weitgehend in der Behandlung einer Vielzahl von menschlichen Krebserkrankungen einschlie&szlig;lich Hepatozellul&auml;res Karzinom (HCC) verwendet. Eine wesentliche Ursache des Scheiterns in Chemotherapie ist Medikamentenresistenz von Krebszellen. Widerstand gegen Doxorubicin (DOX) ist eine gemeinsame und repr&auml;sentativen Hindernis f&uuml;r Krebs wirksam zu behandeln. Individuelle MicroRNA (MiRNA) wurde in der Entwicklung der DOX Widerstand im HCC in neueren Studien eingef&uuml;hrt. Allerdings fehlt es noch an eine globale und systematische Bewertung der MiRNA Ausdruck Profile zur DOX Widerstand. In der vorliegenden Studie angewendet wir Hochdurchsatz-Illumina Sequenzierung um MiRNA Ausdruck Profile in menschlichen HCC-Zelllinie (HepG2) und DOX-best&auml;ndig das Gegenst&uuml;ck (HepG2/DOX) umfassend zu charakterisieren. Insgesamt 269 bekannten MiRNAs wurden signifikant differentiell ausgedr&uuml;ckt, von denen 23 wurden oben-geregelt und 246 in HepG2/DOX Zellen, darauf hinweist, dass der Teil von ihnen bei der Entwicklung von DOX Widerstand beteiligt sein k&ouml;nnte waren unten-geregelt. Dar&uuml;ber hinaus haben wir 9 und 13 Roman MiRNAs oben - und -ab-deutlich in ausgedr&uuml;ckt HepG2/DOX Zellen, bzw. identifiziert. MiRNA profiling wurde dann von quantitative Echtzeit-PCR f&uuml;r ausgew&auml;hlte MiRNAs, darunter 22 bekannte MiRNAs und 6 neuartige MiRNAs validiert. Dar&uuml;ber hinaus vorhergesagt wir die vermeintliche Zielgene f&uuml;r den deregulierten MiRNAs in den Proben. Funktion Anmerkung angedeutet, dass diese ausgew&auml;hlten MiRNAs viele Zielgene haupts&auml;chlich beteiligt MAPK Signalisierung Weg betroffen. Diese Studie liefert uns eine allgemeine Beschreibung der MiRNA Expression profiling, die hilfreich, potenzielle MiRNAs au&szlig;erordentlicher Behandlung DOX Widerstand in zuk&uuml;nftigen HCC-Chemotherapie zu finden ist.

Genomweiten Analyse der MiRNA Signatur differentiell in Doxorubicin-resistenten und elterliche menschlichen Hepatozellul&auml;res Karzinom Zelllinien exprimiert.

Chemioterapia reggimenti sono stati ampiamente utilizzati nel trattamento di una variet&agrave; di tumori maligni umani tra cui il carcinoma epatocellulare (HCC). Delle principali cause di fallimento in chemioterapia &egrave; farmaco resistenza delle cellule tumorali. Resistenza alla doxorubicina (DOX) &egrave; un ostacolo comune e rappresentativo per trattare efficacemente il cancro. Singolo microRNA (miRNA) &egrave; stato introdotto nell&#39;evoluzione della resistenza DOX in HCC negli studi recenti. Tuttavia, una valutazione globale e sistematica dei profili di espressione di miRNA che contribuiscono alla resistenza DOX &egrave; ancora carente. Nel presente studio, abbiamo applicato il sequenziamento Illumina high-throughput per caratterizzare esaurientemente profili di espressione di miRNA in sia variet&agrave; di cellula umane HCC (HepG2) e la sua controparte DOX-resistenti (HepG2/DOX). Un totale di 269 miRNAs noti erano significativamente differenzialmente espressi, di cui 23 sono up-regolati e 246 erano down-regolato nelle cellule HepG2/DOX, che indica che la parte di loro possa essere coinvolti nello sviluppo della resistenza di DOX. Inoltre, abbiamo identificato 9 e 13 miRNAs romanzo up e down-espresso significativamente nelle cellule HepG2/DOX, rispettivamente. miRNA profilatura &egrave; stato poi convalidato mediante PCR quantitativa in tempo reale per i miRNAs selezionati, tra cui 22 miRNAs noto e 6 miRNAs romanzo. Inoltre, abbiamo previsto i geni bersaglio putativo per i miRNAs deregolamentato nei campioni. Annotazione di funzione implicita che questi miRNAs selezionato influenzato molti geni bersaglio principalmente coinvolti nel signaling pathway MAPK. Questo studio ci fornisce una descrizione generale del profilo di espressione di miRNA, che &egrave; utile per trovare potenziali miRNAs per trattamento adiuvante superare la resistenza di DOX in futura chemioterapia HCC.

Analisi Genome-Wide di miRNA Signature differenzialmente espressi in doxorubicina-resistente e parentale umano Hepatocellular Carcinoma Cell Lines.

&#x5316;&#x5B66;&#x7642;&#x6CD5;&#x306E;&#x9023;&#x968A;&#x306F;&#x3001;&#x5E83;&#x304F;&#x69D8;&#x3005; &#x306A;&#x809D;&#x7D30;&#x80DE;&#x764C; (HCC) &#x3092;&#x542B;&#x3080;&#x30D2;&#x30C8;&#x306E;&#x60AA;&#x6027;&#x816B;&#x760D;&#x306E;&#x6CBB;&#x7642;&#x306B;&#x4F7F;&#x7528;&#x3055;&#x308C;&#x3066;&#x3044;&#x307E;&#x3059;&#x3002;&#x5316;&#x5B66;&#x7642;&#x6CD5;&#x306E;&#x5931;&#x6557;&#x306E;&#x4E3B;&#x306A;&#x539F;&#x56E0;&#x306F;&#x3001;&#x85AC;&#x5264;&#x8010;&#x6027;&#x764C;&#x7D30;&#x80DE;&#x3067;&#x3059;&#x3002;&#x30C9;&#x30AD;&#x30BD;&#x30EB;&#x30D3;&#x30B7;&#x30F3; (DOX) &#x3078;&#x306E;&#x62B5;&#x6297;&#x306F;&#x3001;&#x304C;&#x3093;&#x3092;&#x52B9;&#x679C;&#x7684;&#x306B;&#x6CBB;&#x7642;&#x3059;&#x308B;&#x305F;&#x3081;&#x306B;&#x5171;&#x901A;&#x3067;&#x4EE3;&#x8868;&#x7684;&#x306A;&#x969C;&#x5BB3;&#x3067;&#x3059;&#x3002;&#x500B;&#x3005; &#x306E;&#x30DE;&#x30A4;&#x30AF;&#x30ED; Rna &#xFF08;miRNA&#xFF09; &#x306F;&#x3001;&#x6700;&#x8FD1;&#x306E;&#x7814;&#x7A76;&#x3067; HCC &#x306E; DOX &#x8010;&#x6027;&#x306E;&#x9032;&#x5316;&#x3067;&#x5C0E;&#x5165;&#x3055;&#x308C;&#x3066;&#x3044;&#x307E;&#x3059;&#x3002;&#x305F;&#x3060;&#x3057;&#x3001;DOX &#x62B5;&#x6297;&#x306B;&#x8CA2;&#x732E; miRNA &#x767A;&#x73FE;&#x30D7;&#x30ED;&#x30D5;&#x30A1;&#x30A4;&#x30EB;&#x306E;&#x30B0;&#x30ED;&#x30FC;&#x30D0;&#x30EB;&#x304B;&#x3064;&#x4F53;&#x7CFB;&#x7684;&#x306A;&#x8A55;&#x4FA1;&#x306F;&#x307E;&#x3060;&#x6B20;&#x3051;&#x3066;&#x3044;&#x307E;&#x3059;&#x3002;&#x672C;&#x7814;&#x7A76;&#x3067;&#x306F;, &#x7DCF;&#x5408;&#x7684;&#x3072;&#x3068;&#x809D;&#x7D30;&#x80DE;&#x764C;&#x7D30;&#x80DE;&#x682A; (HepG2) &#x3068; DOX &#x8010;&#x6027;&#x306E;&#x5BFE;&#x5FDC; (HepG2/DOX) &#x306E;&#x4E21;&#x65B9;&#x3067;&#x30DE;&#x30A4;&#x30AF;&#x30ED; Rna &#x767A;&#x73FE;&#x30D7;&#x30ED;&#x30D5;&#x30A1;&#x30A4;&#x30EB;&#x3092;&#x7279;&#x5FB4;&#x3065;&#x3051;&#x308B;&#x9AD8;&#x30B9;&#x30EB;&#x30FC;&#x30D7;&#x30C3;&#x30C8; &#x30A4;&#x30EB;&#x30DF;&#x30CA; &#x30B7;&#x30FC;&#x30B1;&#x30F3;&#x30B9;&#x3092;&#x9069;&#x7528;&#x3057;&#x307E;&#x3059;&#x3002;&#x5408;&#x8A08; 269 &#x65E2;&#x77E5; miRNAs &#x306E;&#x7279;&#x7570;&#x7684;&#x306B;&#x8868;&#x73FE;&#x3055;&#x308C;&#x305F;&#x5927;&#x5E45;&#x306F; 23 &#x30A2;&#x30C3;&#x30D7;&#x898F;&#x5236;&#x3001;246 HepG2/DOX &#x7D30;&#x80DE;&#x3067;&#x306F;&#x3001;&#x305D;&#x308C;&#x3089;&#x306E;&#x4E00;&#x90E8; DOX &#x62B5;&#x6297;&#x306E;&#x958B;&#x767A;&#x306B;&#x95A2;&#x4E0E;&#x3059;&#x308B;&#x3053;&#x3068;&#x3092;&#x793A;&#x3059;&#x30C0;&#x30A6;&#x30F3;&#x898F;&#x5236;&#x3060;&#x3063;&#x305F;&#x3002;&#x3055;&#x3089;&#x306B;&#x3001;&#x6211;&#x3005; &#x305D;&#x308C;&#x305E;&#x308C; 9 &#x3068; 13 &#x5C0F;&#x8AAC; miRNAs &#x30A2;&#x30C3;&#x30D7;&#x3068;&#x30C0;&#x30A6;&#x30F3;-&#x767A;&#x73FE; HepG2/DOX &#x7D30;&#x80DE;&#x3067;&#x6709;&#x610F;&#x3092;&#x8B58;&#x5225;&#x3057;&#x3066;&#x3044;&#x307E;&#x3059;&#x3002;&#x30DE;&#x30A4;&#x30AF;&#x30ED; Rna &#x30D7;&#x30ED;&#x30D5;&#x30A1;&#x30A4;&#x30EA;&#x30F3;&#x30B0;&#x3057;&#x306F; 22 &#x65E2;&#x77E5; Mirna&#x3001;6 &#x5C0F;&#x8AAC; miRNAs &#x306A;&#x3069;&#x3001;&#x9078;&#x629E;&#x3057;&#x305F; miRNAs &#x306E;&#x305F;&#x3081;&#x5B9A;&#x91CF;&#x7684;&#x30EA;&#x30A2;&#x30EB;&#x30BF;&#x30A4;&#x30E0; PCR &#x3067;&#x691C;&#x8A3C;&#x3055;&#x308C;&#x307E;&#x3057;&#x305F;&#x3002;&#x3055;&#x3089;&#x306B;&#x3001;&#x898F;&#x5236;&#x7DE9;&#x548C;&#x306E; Mirna &#x30B5;&#x30F3;&#x30D7;&#x30EB;&#x3067;&#x306E;&#x63A8;&#x5B9A;&#x3055;&#x308C;&#x308B;&#x30BF;&#x30FC;&#x30B2;&#x30C3;&#x30C8;&#x907A;&#x4F1D;&#x5B50;&#x3092;&#x4E88;&#x6E2C;&#x3057;&#x307E;&#x3057;&#x305F;&#x3002;&#x6A5F;&#x80FD;&#x30A2;&#x30CE;&#x30C6;&#x30FC;&#x30B7;&#x30E7;&#x30F3;&#x3053;&#x308C;&#x3089;&#x9078;&#x629E;&#x3057;&#x305F; miRNAs MAPK &#x30B7;&#x30B0;&#x30CA;&#x30EB;&#x4F1D;&#x9054;&#x7D4C;&#x8DEF;&#x306E;&#x4E3B;&#x306B;&#x304B;&#x304B;&#x308F;&#x308B;&#x591A;&#x304F;&#x306E;&#x30BF;&#x30FC;&#x30B2;&#x30C3;&#x30C8;&#x907A;&#x4F1D;&#x5B50;&#x306E;&#x5F71;&#x97FF;&#x3092;&#x53D7;&#x3051;&#x308B;&#x3053;&#x3068;&#x3092;&#x6697;&#x793A;&#x3057;&#x307E;&#x3057;&#x305F;&#x3002;&#x3053;&#x306E;&#x7814;&#x7A76;&#x79C1;&#x305F;&#x3061;&#x306F;&#x5C06;&#x6765;&#x809D;&#x7D30;&#x80DE;&#x764C;&#x5316;&#x5B66;&#x7642;&#x6CD5;&#x306B;&#x304A;&#x3051;&#x308B; DOX &#x62B5;&#x6297;&#x3092;&#x514B;&#x670D;&#x3059;&#x308B;&#x975E;&#x5E38;&#x52E4;&#x306E;&#x6CBB;&#x7642;&#x306E;&#x6F5C;&#x5728;&#x7684;&#x306A; Mirna &#x3092;&#x898B;&#x3064;&#x3051;&#x308B;&#x4FBF;&#x5229;&#x3067;&#x3059;&#x30DE;&#x30A4;&#x30AF;&#x30ED; Rna &#x767A;&#x73FE;&#x30D7;&#x30ED;&#x30D5;&#x30A1;&#x30A4;&#x30EB;&#x306E;&#x4E00;&#x822C;&#x7684;&#x306A;&#x8AAC;&#x660E;&#x3092;&#x63D0;&#x4F9B;&#x3057;&#x307E;&#x3059;&#x3002;

MiRNA &#x7F72;&#x540D;&#x3092;&#x7279;&#x7570;&#x7684;&#x306B;&#x8868;&#x73FE;&#x30C9;&#x30AD;&#x30BD;&#x30EB;&#x30D3;&#x30B7;&#x30F3;&#x8010;&#x6027;&#x3068;&#x4FDD;&#x8B77;&#x8005;&#x306B;&#x3088;&#x308B;&#x3072;&#x3068;&#x809D;&#x7D30;&#x80DE;&#x764C;&#x7D30;&#x80DE;&#x682A;&#x306E;&#x30B2;&#x30CE;&#x30E0;&#x89E3;&#x6790;&#x3002;

&#xD654;&#xD559; &#xC694;&#xBC95; &#xC5F0;&#xB300; &#xB2E4;&#xC591; &#xD55C; &#xAC04;&#xC138;&#xD3EC; &#xC554; (HCC)&#xB97C; &#xD3EC;&#xD568; &#xD558; &#xC5EC; &#xC778;&#xAC04;&#xC758; &#xC545;&#xC131; &#xC885;&#xC591;&#xC758; &#xCE58;&#xB8CC;&#xC5D0; &#xB110;&#xB9AC; &#xC0AC;&#xC6A9; &#xB418;&#xC5C8;&#xC2B5;&#xB2C8;&#xB2E4;. &#xD654;&#xD559; &#xC694;&#xBC95;&#xC5D0; &#xC2E4;&#xD328;&#xC758; &#xC8FC;&#xC694; &#xC6D0;&#xC778;&#xC740; &#xC554; &#xC138;&#xD3EC;&#xC758; &#xC57D;&#xBB3C; &#xC800;&#xD56D;. &#xB3C5; &#xC18C; &#xB8E8;&#xBE44; (DOX)&#xC5D0; &#xC800;&#xD56D;&#xC744; &#xD6A8;&#xACFC;&#xC801;&#xC73C;&#xB85C; &#xC554; &#xCE58;&#xB8CC;&#xB97C; &#xC77C;&#xBC18;&#xC801;&#xC774; &#xACE0; &#xB300;&#xD45C;&#xC801;&#xC778; &#xC7A5;&#xC560; &#xC774;&#xB2E4;. &#xAC1C;&#xBCC4; microRNA (&#xBBF8;&#xB974;)&#xC758; &#xCD5C;&#xADFC; &#xC5F0;&#xAD6C;&#xC5D0;&#xC11C; HCC&#xC5D0; DOX &#xC800;&#xD56D; &#xC9C4;&#xD654;&#xC5D0; &#xB3C4;&#xC785; &#xB418;&#xC5C8;&#xC2B5;&#xB2C8;&#xB2E4;. &#xADF8;&#xB7EC;&#xB098;, DOX &#xC800;&#xD56D;&#xC5D0; &#xAE30;&#xC5EC; &#xD558;&#xB294; miRNA &#xC2DD; &#xD504;&#xB85C; &#xD30C;&#xC77C;&#xC758; &#xC804;&#xC5ED;&#xC774; &#xACE0; &#xCCB4;&#xACC4;&#xC801;&#xC778; &#xD3C9;&#xAC00; &#xC5EC;&#xC804;&#xD788; &#xBD80;&#xC871; &#xD55C;. &#xD604;&#xC7AC;&#xC758; &#xC5F0;&#xAD6C;&#xC5D0;&#xC11C; &#xC6B0;&#xB9AC;&#xB294; &#xB192;&#xC740; &#xCC98;&#xB9AC;&#xB7C9; Illumina &#xC2DC;&#xD000;&#xC2F1; &#xC885;&#xD569;&#xC801;&#xC73C;&#xB85C; &#xC778;&#xAC04;&#xC758; HCC &#xC140; &#xB77C;&#xC778; (HepG2)&#xC640; DOX &#xBC29;&#xC9C0; &#xB300;&#xC751; (HepG2/DOX) &#xBBF8;&#xB974; &#xC2DD; &#xD504;&#xB85C;&#xD544;&#xC758; &#xD2B9;&#xC131;&#xC744; &#xC801;&#xC6A9;. &#xCD1D; 269 &#xC54C;&#xB824;&#xC9C4;&#xB41C; miRNAs &#xCC28;&#xB3D9; &#xD45C;&#xD604; &#xD06C;&#xAC8C; &#xD588;&#xB2E4;&#xB294; 23 &#xC704;&#xB85C; &#xD588;&#xB2E4; &#xBC0F; 246 &#xD588;&#xB2E4; &#xC544;&#xB798;&#xB85C; HepG2/DOX &#xC140;&#xC5D0; &#xB098;&#xD0C0;&#xB0B4;&#xB294; &#xADF8;&#xB4E4;&#xC758; &#xC77C;&#xBD80; DOX &#xC800;&#xD56D;&#xC758; &#xAC1C;&#xBC1C;&#xC5D0; &#xAD00;&#xC5EC; &#xB420; &#xC218;&#xB3C4;. &#xB610;&#xD55C;, &#xC6B0;&#xB9AC;&#xB294; &#xC2DD;&#xBCC4; 9 &#xBC0F; 13 &#xC18C;&#xC124; miRNAs &#xCD5C;&#xB300;-&#xBC0F; &#xB2E4;&#xC6B4;-&#xB2E8;&#xC704;&#xB85C; &#xD06C;&#xAC8C; HepG2/DOX &#xC140; &#xAC01;&#xAC01;. &#xBBF8;&#xB974; &#xD504;&#xB85C; &#xD30C;&#xC77C;&#xB9C1; &#xB2E4;&#xC74C; 22 &#xC54C;&#xB824;&#xC9C4;&#xB41C; miRNAs &#xB4F1; 6 &#xC18C;&#xC124; miRNAs &#xC120;&#xD0DD;&#xD55C; Mirnas&#xC5D0; &#xB300; &#xD55C; &#xC2E4;&#xC2DC;&#xAC04; &#xC591;&#xC774; &#xB9CE;&#xC740; PCR&#xC5D0; &#xC758;&#xD574; &#xAC80;&#xC99D; &#xB418;&#xC5C8;&#xB2E4;. &#xB610;&#xD55C;, &#xC6B0;&#xB9AC;&#xB294; &#xC0D8;&#xD50C;&#xC5D0;&#xC11C; &#xADDC;&#xC81C; &#xC644;&#xD654; Mirnas&#xC758; &#xC0C1; &#xB300;&#xC0C1; &#xC720;&#xC804;&#xC790; &#xC608;&#xCE21;. &#xD568;&#xC218; &#xC8FC;&#xC11D;&#xC774; &#xC120;&#xD0DD;&#xD55C; miRNAs MAPK &#xC2E0;&#xD638; &#xD1B5;&#xB85C;&#xC5D0; &#xC8FC;&#xB85C; &#xAD00;&#xB828; &#xB41C; &#xB9CE;&#xC740; &#xB300;&#xC0C1; &#xC720;&#xC804;&#xC790; &#xC601;&#xD5A5;&#xC744; &#xC554;&#xC2DC;. &#xC774; &#xC5F0;&#xAD6C;&#xB294; &#xC6B0;&#xB9AC; &#xBBF8;&#xB798;&#xC758; HCC &#xD654;&#xD559; &#xC694;&#xBC95;&#xC5D0; DOX &#xC800;&#xD56D;&#xC744; &#xADF9;&#xBCF5; &#xD558;&#xAE30; &#xC704;&#xD574; &#xC678;&#xB798; &#xCE58;&#xB8CC;&#xC5D0; &#xB300; &#xD55C; &#xC7A0;&#xC7AC;&#xC801;&#xC778; miRNAs &#xCC3E;&#xC744; &#xC720;&#xC6A9; &#xC2DD; &#xD504;&#xB85C; &#xD30C;&#xC77C;&#xB9C1; Mirna&#xC758; &#xC77C;&#xBC18;&#xC801;&#xC778; &#xC124;&#xBA85;&#xC744; &#xC81C;&#xACF5; &#xD569;&#xB2C8;&#xB2E4;.

&#xC11C;&#xBA85; &#xCC28;&#xB3D9; &#xB3C5; &#xC18C; &#xB8E8;&#xBE44; &#xC800;&#xD56D; &#xD558;&#xB294; &#xBD80;&#xBAA8;&#xC758; &#xC778;&#xAC04;&#xC758; &#xAC04;&#xC138;&#xD3EC; &#xC554; &#xC885; &#xC138;&#xD3EC; &#xB77C;&#xC778;&#xC5D0; &#xD45C;&#xD604; &#xD558;&#xB294; Mirna&#xC758; &#xAC8C;&#xB188; &#xB113;&#xC740; &#xBD84;&#xC11D;.

Regimientos de quimioterapia han sido ampliamente utilizados en el tratamiento de una variedad de tumores malignos humanos incluyendo el carcinoma hepatocelular (HCC). Una causa importante del fracaso de la quimioterapia es la resistencia a los medicamentos de las c&eacute;lulas cancerosas. Resistencia a la doxorrubicina (DOX) es un obst&aacute;culo com&uacute;n y representante para tratar el c&aacute;ncer con eficacia. Los microARN (miARN) se ha introducido en la evoluci&oacute;n de la resistencia de DOX en HCC en estudios recientes. Sin embargo, todav&iacute;a carece de una evaluaci&oacute;n global y sistem&aacute;tica de los perfiles de expresi&oacute;n de miRNA que contribuyen a la resistencia de DOX. En el presente estudio, aplicamos la secuencia de Illumina de alto rendimiento para caracterizar exhaustivamente miRNA perfiles de expresi&oacute;n en l&iacute;nea celular humana de HCC (HepG2) y su contraparte DOX-resistente (HepG2/DOX). Un total de 269 miRNAs conocidos significativamente fueron expresados diferencialmente, de que 23 fueron regulados por arriba y 246 regula en c&eacute;lulas HepG2/DOX, indicando que parte de ellos podr&iacute;a participar en el desarrollo de resistencia de DOX. Adem&aacute;s, hemos identificado 9 y 13 nuevos miRNAs arriba y abajo-expresados significativamente en las c&eacute;lulas HepG2/DOX, respectivamente. miRNA perfilado entonces fue validada por PCR cuantitativa en tiempo real para miRNAs seleccionados, incluyendo 22 miRNAs conocidos y 6 nuevos miRNAs. Adem&aacute;s, nos predijo los genes putativos de destino para los miRNAs desregulado en las muestras. Anotaci&oacute;n de la funci&oacute;n impl&iacute;cita que estos seleccionados miRNAs afectados muchos genes diana implicados principalmente en la v&iacute;a de se&ntilde;alizaci&oacute;n de MAPK. Este estudio nos proporciona una descripci&oacute;n general del perfil de expresi&oacute;n de miRNA, que es &uacute;til para encontrar miRNAs potenciales para el tratamiento adyuvante vencer la resistencia de DOX en futura quimioterapia HCC.

An&aacute;lisis del genoma de miRNA firma expresadas diferencialmente en Doxorubicin-resistente y Parental humana hepatocelular l&iacute;neas celulares de Carcinoma.

In the brain and heart, auxiliary Kv channel-interacting proteins (KChIPs) co-assemble with pore-forming Kv4 α-subunits to form a native K(+) channel complex and regulate the expression and gating properties of Kv4 currents. Among the KChIP1-4 members, KChIP4a exhibits a unique N terminus that is known to suppress Kv4 function, but the underlying mechanism of Kv4 inhibition remains unknown. Using a combination of confocal imaging, surface biotinylation, and electrophysiological recordings, we identified a novel endoplasmic reticulum (ER) retention motif, consisting of six hydrophobic and aliphatic residues, 12-17 (LIVIVL), within the KChIP4a N-terminal KID, that functions to reduce surface expression of Kv4-KChIP complexes. This ER retention capacity is transferable and depends on its flanking location. In addition, adjacent to the ER retention motif, the residues 19-21 (VKL motif) directly promote closed-state inactivation of Kv4.3, thus leading to an inhibition of channel current. Taken together, our findings demonstrate that KChIP4a suppresses A-type Kv4 current via ER retention and enhancement of Kv4 closed-state inactivation.

Auxiliary KChIP4a suppresses A-type K+ current through endoplasmic reticulum (ER) retention and promoting closed-state inactivation of Kv4 channels.

Emerging evidence indicate that microRNAs (miRNAs) may play important roles in cancer. Aberrant expression of miRNAs has been frequently identified in different human malignancies, including colorectal cancer (CRC). However, the mechanism by which deregulated miRNAs impact the development of CRC remains largely elusive. In this study, we show that miR-124 is significantly down-regulated in CRC compared to adjacent non-tumor colorectal tissues. MiR-124 suppresses the expression of STAT3 by directly binding to its 3'-untranslated region (3'-UTR). Overexpression of miR-124 led to increased apoptosis of CRC cells and reduced tumor growth in vitro and in vivo. Knocking down STAT3 expression by specific siRNA suppressed the growth of CRC cells in vitro and in vivo, resembling that of miR-124 overexpression. Moreover, overexpression of STAT3 in miR-124-transfected CRC cells effectively rescued the inhibition of cell proliferation caused by miR-124. These data suggest that miR-124 serves as a tumor suppressor by targeting STAT3, and call for the use of miR-124 as a potential therapeutic tool for CRC, where STAT3 is often hyper-activated.

MiR-124 suppresses growth of human colorectal cancer by inhibiting STAT3.

The aberrant expression of microRNAs is associated with development and progression of cancers. Down-regulation of miR-124 has been demonstrated in the hepatocellular carcinoma (HCC), but the underlying mechanism by which miR-124 suppresses tumorigenesis in HCC remains elusive. In this study, we found that miR-124 suppresses the tumor growth of HCC through targeting the signal transducers and activators of transcription 3 (STAT3). Overexpression of miR-124 suppressed proliferation and induced apoptosis in HepG-2 cells. Luciferase assay confirmed that miR-124 binding to the 3'-UTR region of STAT3 inhibited the expression of STAT3 and phosphorylated STAT3 proteins in HepG-2 cells. Knockdown of STAT3 by siRNA in HepG-2 cells mimicked the effect induced by miR-124. Overexpression of STAT3 in miR-124-transfected HepG-2 cells effectively rescued the inhibition of cell proliferation caused by miR-124. Furthermore, miR-124 suppressed xenograft tumor growth in nude mice implanted with HepG-2 cells by reducing STAT3 expression. Taken together, our findings show that miR-124 functions as tumor suppressor in HCC by targeting STAT3, and miR-124 may therefore serve as a biomarker for diagnosis and therapeutics in HCC.

MicroRNA-124 suppresses growth of human hepatocellular carcinoma by targeting STAT3.

Protein-protein interactions (PPIs) play a key role in understanding the mechanisms of cellular processes. The availability of interactome data has catalyzed the development of computational approaches to elucidate functional behaviors of proteins on a system level. Gene Ontology (GO) and its annotations are a significant resource for functional characterization of proteins. Because of wide coverage, GO data have often been adopted as a benchmark for protein function prediction on the genomic scale.

M-Finder: Uncovering functionally associated proteins from interactome data integrated with GO annotations.

Aberrant microRNA (miRNA) expression has been frequently observed in colorectal cancer (CRC), the third most common human cancer in the world. However, the roles of miRNAs in CRC remain poorly understood. The present study explored, identified and characterized the miRNAs that correlate with CRC progression. Deregulated level of microRNA-223 (miR-223) was screened out by miRNA microarray in colorectal tumor tissues and further confirmed by quantitative real-time PCR in a large cohort of CRC samples (n=90). After silencing miR-223 by artificial anti‑miR-223 (miR-223-inhibitor), WST-1 and colony formation assays were employed to assess cell proliferation, and cell migration and invasion were evaluated by wound healing test and Transwell assays, respectively. Compared with adjacent non-tumor tissues, 22 miRNAs were screened out in CRC tissues with significance (>2-fold), of which 13 were upregulated and 9 were downregulated. miR-223 is one of the noticeably upregulated miRNAs. Large cohort CRC sample analyses showed that a higher level of miR-223 correlated with a higher clinical stage. Reducing miR-223 expression resulted in a decreased cell proliferation, migration and invasion in CRC cells. miR-223 functions as an oncomiR in CRC, therefore serving as a potential diagnostic and therapeutic target for the treatment of CRC.

MicroRNA-223 functions as an oncogene in human colorectal cancer cells.

Mexiletine and lidocaine are widely used class IB anti-arrhythmic drugs that are considered to act by blocking voltage-gated open sodium currents for treatment of ventricular arrhythmias and relief of pain. To gain mechanistic insights into action of anti-arrhythmics, we characterized biophysical properties of Nav1.5 and Nav1.7 channels stably expressed in HEK293 cells and compared their use-dependent block in response to mexiletine and lidocaine using whole-cell patch clamp recordings. While the voltage-dependent activation of Nav1.5 or Nav1.7 was not affected by mexiletine and lidocaine, the steady-state fast and slow inactivation of Nav1.5 and Nav1.7 were significantly shifted to hyperpolarized direction by either mexiletine or lidocaine in dose-dependent manner. Both mexiletine and lidocaine enhanced the slow component of closed-state inactivation, with mexiletine exerting stronger inhibition on either Nav1.5 or Nav1.7. The recovery from inactivation of Nav1.5 or Nav1.7 was significantly prolonged by mexiletine compared to lidocaine. Furthermore, mexiletine displayed a pronounced and prominent use-dependent inhibition of Nav1.5 than lidocaine, but not Nav1.7 channels. Taken together, our findings demonstrate differential responses to blockade by mexiletine and lidocaine that preferentially affect the gating of Nav1.5, as compared to Nav1.7; and mexiletine exhibits stronger use-dependent block of Nav1.5. The differential gating properties of Nav1.5 and Nav1.7 in response to mexiletine and lidocaine may help explain the drug effectiveness and advance in new designs of safe and specific sodium channel blockers for treatment of cardiac arrhythmia or pain.

Comparison of Gating Properties and Use-Dependent Block of Nav1.5 and Nav1.7 Channels by Anti-Arrhythmics Mexiletine and Lidocaine.

We propose a novel method to achieve tunable plasmon focusing in graphene/photonic-crystal hybrid structure exhibiting all-angle negative refraction at terahertz frequencies. A two-dimensional photonic crystal composed of a square lattice of dielectric rods is constructed on the substrate of a graphene sheet to provide the hyperbolic dispersion relations of the graphene plasmon, giving rise to the all-angle plasmonic negative refraction. Plasmon lensing induced from the negative refraction is observed. We show that the ultracompact graphene-based system can produce sub-diffraction-limited images with the resolution significant smaller than the wavelength of the incident terahertz wave. Moreover, by adjusting the Fermi energy of the graphene, the imaging performance of the proposed system can remain almost invariant for different frequencies. Our results may find applications in diverse fields such as subwavelength spatial light manipulation, biological imaging, and so forth.

Tunable plasmon lensing in graphene-based structure exhibiting negative refraction.

Mutations in isocitrate dehydrogenase (IDH) are the most prevalent genetic abnormalities in lower grade gliomas. The presence of these mutations in glioma is prognostic for better clinical outcomes with longer patient survival. In the present study, we found that defects in oxidative metabolism and 2-HG production confer chemosensitization in IDH1-mutated glioma cells. In addition, temozolomide (TMZ) treatment induced greater DNA damage and apoptotic changes in mutant glioma cells. The PARP1-associated DNA repair pathway was extensively compromised in mutant cells due to decreased NAD+ availability. Targeting the PARP DNA repair pathway extensively sensitized IDH1-mutated glioma cells to TMZ. Our findings demonstrate a novel molecular mechanism that defines chemosensitivity in IDH mutant gliomas. Targeting PARP-associated DNA repair may represent a novel therapeutic strategy for gliomas.

Yanxin Lu

Neuro-Oncology Branch,
Center for Cancer Research,
Neuro-Oncology Branch, Center for Cancer Research

National Cancer Institute

Evaluating In Vitro DNA Damage Using Comet Assay

Yanxin Lu

.css-o250i3{font-size:18px;font-family:Open Sans,sans-serif;line-height:21.6px;}Neuro-Oncology Branch,Center for Cancer Research,Neuro-Oncology Branch, Center for Cancer Research

National Cancer Institute

Evaluating In Vitro DNA Damage Using Comet Assay

Neuro-Oncology Branch,
Center for Cancer Research,
Neuro-Oncology Branch, Center for Cancer Research