Induction of an Inflammatory Response in Primary Hepatocyte Cultures from Mice

The liver plays a decisive role in the regulation of systemic inflammation. In chronic kidney disease in particular, the liver reacts in response to the ...

University of Miami Leonard M. Miller School of Medicine

We have created a tool for ortholog and phylogenetic profile retrieval called Roundup. Roundup is backed by a massive repository of orthologs and associated evolutionary distances that was built using the reciprocal smallest distance algorithm, an approach that has been shown to improve upon alternative approaches of ortholog detection, such as reciprocal blast. Presently, the Roundup repository contains all possible pair-wise comparisons for over 250 genomes, including 32 Eukaryotes, more than doubling the coverage of any similar resource. The orthologs are accessible through an intuitive web interface that allows searches by genome or gene identifier, presenting results as phylogenetic profiles together with gene and molecular function annotations. Results may be downloaded as phylogenetic matrices for subsequent analysis, including the construction of whole-genome phylogenies based on gene-content data.

Roundup: a multi-genome repository of orthologs and evolutionary distances.

A phylogenetic profile captures the pattern of gene gain and loss throughout evolutionary time. Proteins that interact directly or indirectly within the cell to perform a biological function will often co-evolve, and this co-evolution should be well reflected within their phylogenetic profiles. Thus similar phylogenetic profiles are commonly used for grouping proteins into functional groups. However, it remains unclear how the size and content of the phylogenetic profile impacts the ability to predict function, particularly in Eukaryotes. Here we developed a straightforward approach to address this question by constructing a complete set of phylogenetic profiles for 31 fully sequenced Eukaryotes. Using Gene Ontology as our gold standard, we compared the accuracy of functional predictions made by a comprehensive array of permutations on the complete set of genomes. Our permutations showed that phylogenetic profiles containing between 25 and 31 Eukaryotic genomes performed equally well and significantly better than all other permuted genome sets, with one exception: we uncovered a core of group of 18 genomes that achieved statistically identical accuracy. This core group contained genomes from each branch of the eukaryotic phylogeny, but also contained several groups of closely related organisms, suggesting that a balance between phylogenetic breadth and depth may improve our ability to use Eukaryotic specific phylogenetic profiles for functional annotations.

Testing the accuracy of eukaryotic phylogenetic profiles for prediction of biological function.

Recent improvements in proteomic technologies have collectively yielded data sets that far exceed the capabilities of typical low-throughput interpretation strategies. Unfortunately, tools designed to leverage the "peptide-centric" content of MS-based proteomics lag the current rate of data production. Here, we describe Pathway Palette (http://blaispathways.dfci.harvard.edu), a freely accessible internet application that enables researchers to easily transition from peptides to biological pathways, while simultaneously retaining the qualitative and quantitative aspects of the underlying MS data.

Pathway Palette: a rich internet application for peptide-, protein- and network-oriented analysis of MS data.

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Genotypic differences greatly influence susceptibility and resistance to disease. Understanding genotype-phenotype relationships requires that phenotypes be viewed as manifestations of network properties, rather than simply as the result of individual genomic variations. Genome sequencing efforts have identified numerous germline mutations, and large numbers of somatic genomic alterations, associated with a predisposition to cancer. However, it remains difficult to distinguish background, or 'passenger', cancer mutations from causal, or 'driver', mutations in these data sets. Human viruses intrinsically depend on their host cell during the course of infection and can elicit pathological phenotypes similar to those arising from mutations. Here we test the hypothesis that genomic variations and tumour viruses may cause cancer through related mechanisms, by systematically examining host interactome and transcriptome network perturbations caused by DNA tumour virus proteins. The resulting integrated viral perturbation data reflects rewiring of the host cell networks, and highlights pathways, such as Notch signalling and apoptosis, that go awry in cancer. We show that systematic analyses of host targets of viral proteins can identify cancer genes with a success rate on a par with their identification through functional genomics and large-scale cataloguing of tumour mutations. Together, these complementary approaches increase the specificity of cancer gene identification. Combining systems-level studies of pathogen-encoded gene products with genomic approaches will facilitate the prioritization of cancer-causing driver genes to advance the understanding of the genetic basis of human cancer.

Interpreting cancer genomes using systematic host network perturbations by tumour virus proteins.

Diff&eacute;rences g&eacute;notypiques influencent grandement la sensibilit&eacute; et la r&eacute;sistance aux maladies. Comprendre les relations g&eacute;notype-ph&eacute;notype, il faut que les ph&eacute;notypes &ecirc;tre consid&eacute;r&eacute;s comme des manifestations des propri&eacute;t&eacute;s r&eacute;seau, plut&ocirc;t que simplement comme le r&eacute;sultat de variations g&eacute;nomiques individuels. Efforts de s&eacute;quen&ccedil;age de g&eacute;nome ont identifi&eacute; de nombreuses mutations germinales et un grand nombre de somatiques alt&eacute;rations g&eacute;nomiques, associ&eacute;es &agrave; une pr&eacute;disposition au cancer. Cependant, il reste difficile de distinguer l&#39;arri&egrave;re-plan, ou &laquo; passager &raquo;, des mutations du cancer de mutations causales, ou &laquo; driver &raquo;, ces ensembles de donn&eacute;es. Virus humain intrins&egrave;quement d&eacute;pendent de leur cellule h&ocirc;te au cours de l&#39;infection et peuvent susciter des ph&eacute;notypes pathologiques semblables &agrave; celles r&eacute;sultant de mutations. Ici, nous testons l&#39;hypoth&egrave;se que les variations g&eacute;nomiques et virus de tumeur peuvent provoquer le cancer par le biais de m&eacute;canismes connexes, en examinant syst&eacute;matiquement les perturbations de r&eacute;seau interactome et transcriptome de h&ocirc;te caus&eacute;es par les prot&eacute;ines du virus ADN tumoral. Les donn&eacute;es r&eacute;sultantes de perturbation virale int&eacute;gr&eacute;e refl&egrave;tent le rec&acirc;blage des r&eacute;seaux cellule h&ocirc;te et met en &eacute;vidence les voies, telles que la signalisation Notch et l&#39;apoptose, qui tournent mal dans le cancer. Nous montrons que les analyses syst&eacute;matiques des cibles de l&#39;h&ocirc;te des prot&eacute;ines virales puissent identifier des g&egrave;nes du cancer avec un taux de r&eacute;ussite sur un pied d&#39;&eacute;galit&eacute; avec leur identification par le biais de g&eacute;nomique fonctionnelle et de catalogage &agrave; grande &eacute;chelle de mutations de la tumeur. Ensemble, ces approches compl&eacute;mentaires augmentent la sp&eacute;cificit&eacute; de l&#39;identification des g&egrave;nes du cancer. Combinant des &eacute;tudes de syst&egrave;mes des produits des g&egrave;nes pathog&egrave;nes cod&eacute; avec des approches g&eacute;nomiques facilitera la priorisation des canc&eacute;rig&egrave;nes des g&egrave;nes de pilote pour faire avancer la compr&eacute;hension de la base g&eacute;n&eacute;tique du cancer chez l&#39;homme.

Interpr&eacute;tation des g&eacute;nomes du cancer utilisant des perturbations r&eacute;seau h&ocirc;te syst&eacute;matique par des prot&eacute;ines de virus de tumeur.

GENOTYPISCHE Unterschiede beeinflussen stark Anf&auml;lligkeit und Widerstandsf&auml;higkeit gegen Krankheiten. Genotyp-Ph&auml;notyp-Beziehungen zu verstehen, erfordert, dass Ph&auml;notypen als Manifestationen der Netzwerkeigenschaften und nicht einfach als das Resultat der individuellen genomischen Variationen betrachtet werden. Genom-Sequenzierung Bem&uuml;hungen haben zahlreiche Keimbahn-Mutationen, und eine gro&szlig;e Anzahl von somatischen genomischen Ver&auml;nderungen, verbunden mit einer Pr&auml;disposition f&uuml;r Krebs identifiziert. Es bleibt jedoch schwierig, einen Hintergrund oder &quot;Fahrgast&quot;, Krebs-Mutationen von kausale oder &#39;Treiber&#39; Mutationen in diesen Datens&auml;tzen zu unterscheiden. Menschliche Viren intrinsisch h&auml;ngen ihre Wirtszelle im Verlauf der Infektion und pathologische Ph&auml;notypen &auml;hnlich denen aus Mutationen hervorrufen k&ouml;nnen. Hier testen wir die Hypothese, die genomischen Variationen und Tumorviren Krebs durch &auml;hnliche Mechanismen verursachen k&ouml;nnen durch Host Interaktom und Transkriptom Netzwerk-St&ouml;rungen, verursacht durch DNA Tumor Virus Proteine systematisch zu untersuchen. Die Ergebnisdaten integrierte virale St&ouml;rung reflektiert Umpolung der Host-Zelle-Netze und zeigt Wege, wie Notch Signalisierung und Apoptosis, die schief bei Krebs gehen. Wir zeigen, dass systematische Analysen von Host-Zielen der viralen Proteine Gene mit einer Erfolgsquote auf einer Stufe mit ihrer Identifikation durch funktionelle Genomik und umfangreichen Katalogisierung von Tumor-Mutationen identifiziert werden k&ouml;nnen. Zusammen, erh&ouml;hen diese komplement&auml;re Ans&auml;tze die Besonderheit des Krebs-gen Identifizierung. Systemebene Studien von Pathogen-codierte Genprodukten mit genomischen Ans&auml;tze kombinieren erleichtert die Priorisierung der krebserregenden Treiber-Gene, das Verst&auml;ndnis f&uuml;r die genetische Grundlage f&uuml;r menschliche Krebs zu f&ouml;rdern.

Krebs-Genome mit systematischen Host Netzwerk Perturbationen durch Tumor Virus Proteine zu interpretieren.

Genotipica differenze influenzano notevolmente la suscettibilit&agrave; e resistenza alle malattie. Comprendere relazioni genotipo-fenotipo richiede che i fenotipi essere visti come manifestazioni di propriet&agrave; della rete, piuttosto che semplicemente come il risultato di variazioni individuali genomiche. Gli sforzi del sequenziamento del genoma sono identificate numerose mutazioni germinali e un gran numero di alterazioni genomiche somatici, associati a una predisposizione al cancro. Tuttavia, rimane difficile distinguere mutazioni di sfondo, o &#39;passeggeri&#39;, cancro da mutazioni causali, o &#39;driver&#39;, questi insiemi di dati. Virus umano intrinsecamente dipendono dalla loro cellula ospite nel corso dell&#39;infezione e pu&ograve; suscitare fenotipi patologici simili a quelli derivanti da mutazioni. Qui ci prova l&#39;ipotesi che varianti genomiche e tumore virus pu&ograve; provocare il cancro attraverso meccanismi correlati, esaminando sistematicamente host interactome e transcriptome rete perturbazioni causate da proteine del virus del DNA tumorale. I dati risultanti perturbazione virale integrato riflette ricablaggio delle reti cellula ospite e mette in evidenza percorsi, ad esempio segnalazione Notch e l&#39;apoptosi, che saltano nel cancro. Mostriamo che analisi sistematiche degli obiettivi ospite delle proteine virali &egrave; in grado di identificare geni del cancro con un tasso di successo su un par con loro identificazione attraverso la genomica funzionale e catalogazione su larga scala delle mutazioni del tumore. Insieme, questi approcci complementari aumentano la specificit&agrave; dell&#39;identificazione del gene cancro. Combinando gli studi di livello di sistemi di prodotti genici codificati in agente patogeno con approcci genomici faciliter&agrave; la prioritizzazione dei geni di driver che causano il cancro per far avanzare la comprensione delle basi genetiche del cancro umano.

Interpretariato genomi cancro mediante perturbazioni di rete host sistematica delle proteine del virus tumorale.

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DNA &#x816B;&#x760D;&#x30A6;&#x30A4;&#x30EB;&#x30B9;&#x86CB;&#x767D;&#x8CEA;&#x306B;&#x3088;&#x3063;&#x3066;&#x5F15;&#x304D;&#x8D77;&#x3053;&#x3055;&#x308C;&#x308B;&#x30DB;&#x30B9;&#x30C8; &#x30A4;&#x30F3;&#x30BF;&#x30E9;&#x30AF;&#x30C8;&#x30FC;&#x30E0;&#x3068;&#x30C8;&#x30E9;&#x30F3;&#x30B9;&#x30AF;&#x30EA;&#x30D7;&#x30C8;&#x30FC;&#x30E0; &#x30CD;&#x30C3;&#x30C8;&#x30EF;&#x30FC;&#x30AF;&#x64FE;&#x4E71;&#x3092;&#x4F53;&#x7CFB;&#x7684;&#x306B;&#x8ABF;&#x3079;&#x308B;&#x3053;&#x3068;&#x306B;&#x3088;&#x3063;&#x3066;&#x95A2;&#x9023;&#x306E;&#x30E1;&#x30AB;&#x30CB;&#x30BA;&#x30E0;&#x3092;&#x901A;&#x3058;&#x3066;&#x3092;&#x5F15;&#x304D;&#x8D77;&#x3053;&#x3059;&#x3053;&#x3068;&#x304C;&#x3042;&#x308A;&#x307E;&#x3059;&#x4EEE;&#x8AAC;&#x30C6;&#x30B9;&#x30C8;&#x3057;&#x307E;&#x3059;&#x3002;&#x7D50;&#x679C;&#x306E;&#x7D71;&#x5408;&#x30A6;&#x30A4;&#x30EB;&#x30B9;&#x6442;&#x52D5;&#x30C7;&#x30FC;&#x30BF;&#x306F;&#x3001;&#x30DB;&#x30B9;&#x30C8;&#x306E;&#x7D30;&#x80DE;&#x306E;&#x30CD;&#x30C3;&#x30C8;&#x30EF;&#x30FC;&#x30AF;&#x306E;&#x518D;&#x914D;&#x7DDA;&#x3092;&#x53CD;&#x6620;&#x3057;&#x3001;&#x30CE;&#x30C3;&#x30C1; &#x30B7;&#x30B0;&#x30CA;&#x30EB;&#x4F1D;&#x9054;&#x3068;&#x764C;&#x306B;&#x3086;&#x304C;&#x3093;&#x3067;&#x884C;&#x304F;&#x30A2;&#x30DD;&#x30C8;&#x30FC;&#x30B7;&#x30B9;&#x306A;&#x3069;&#x306E;&#x7D4C;&#x8DEF;&#x306E;&#x30CF;&#x30A4;&#x30E9;&#x30A4;&#x30C8;&#x3057;&#x307E;&#x3059;&#x3002;&#x30DB;&#x30B9;&#x30C8; &#x30BF;&#x30FC;&#x30B2;&#x30C3;&#x30C8;&#x306E;&#x30A6;&#x30A4;&#x30EB;&#x30B9;&#x86CB;&#x767D;&#x8CEA;&#x306E;&#x7CFB;&#x7D71;&#x7684;&#x89E3;&#x6790;&#x6210;&#x529F;&#x7387;&#x306B;&#x81EA;&#x5206;&#x306E;&#x8B58;&#x5225;&#x6A5F;&#x80FD;&#x30B2;&#x30CE;&#x30E0;&#x5B66;&#x3068;&#x5927;&#x898F;&#x6A21;&#x306A;&#x816B;&#x760D;&#x907A;&#x4F1D;&#x5B50;&#x5909;&#x7570;&#x306E;&#x76EE;&#x9332;&#x3092;&#x901A;&#x3058;&#x3066;&#x4E26;&#x3076;&#x304C;&#x3093;&#x907A;&#x4F1D;&#x5B50;&#x3092;&#x8B58;&#x5225;&#x3067;&#x304D;&#x308B;&#x3053;&#x3068;&#x3092;&#x793A;&#x3057;&#x307E;&#x3059;&#x3002;&#x4E00;&#x7DD2;&#x306B;&#x3001;&#x3053;&#x308C;&#x3089;&#x306E;&#x76F8;&#x88DC;&#x7684;&#x306A;&#x30A2;&#x30D7;&#x30ED;&#x30FC;&#x30C1;&#x306F;&#x304C;&#x3093;&#x907A;&#x4F1D;&#x5B50;&#x540C;&#x5B9A;&#x306E;&#x7279;&#x7570;&#x6027;&#x3092;&#x5897;&#x3084;&#x3057;&#x307E;&#x3059;&#x3002;&#x30B7;&#x30B9;&#x30C6;&#x30E0; &#x30EC;&#x30D9;&#x30EB;&#x306E;&#x7814;&#x7A76;&#x75C5;&#x539F;&#x4F53;&#x30A8;&#x30F3;&#x30B3;&#x30FC;&#x30C9;&#x3055;&#x308C;&#x305F;&#x907A;&#x4F1D;&#x5B50;&#x306E;&#x88FD;&#x54C1;&#x306E;&#x30B2;&#x30CE;&#x30E0;&#x306E;&#x30A2;&#x30D7;&#x30ED;&#x30FC;&#x30C1;&#x3092;&#x7D44;&#x307F;&#x5408;&#x308F;&#x305B;&#x308B;&#x3053;&#x3068;&#x3068;&#x3001;&#x30C9;&#x30E9;&#x30A4;&#x30D0;&#x30FC;&#x907A;&#x4F1D;&#x5B50;&#x306E;&#x764C;&#x3092;&#x5F15;&#x304D;&#x8D77;&#x3053;&#x3059;&#x3072;&#x3068;&#x764C;&#x306E;&#x907A;&#x4F1D;&#x306E;&#x57FA;&#x790E;&#x306E;&#x7406;&#x89E3;&#x3092;&#x9032;&#x3081;&#x308B;&#x305F;&#x3081;&#x306E;&#x512A;&#x5148;&#x9806;&#x4F4D;&#x4ED8;&#x3051;&#x304C;&#x5BB9;&#x6613;&#x306B;&#x306A;&#x308A;&#x307E;&#x3059;&#x3002;

&#x4F53;&#x7CFB;&#x7684;&#x306A;&#x30DB;&#x30B9;&#x30C8; &#x30CD;&#x30C3;&#x30C8;&#x30EF;&#x30FC;&#x30AF;&#x6442;&#x52D5;&#x306B;&#x3088;&#x308B;&#x816B;&#x760D;&#x30A6;&#x30A4;&#x30EB;&#x30B9;&#x86CB;&#x767D;&#x8CEA;&#x3092;&#x7528;&#x3044;&#x305F;&#x764C;&#x306E;&#x30B2;&#x30CE;&#x30E0;&#x3092;&#x89E3;&#x91C8;&#x3057;&#x307E;&#x3059;&#x3002;

Genotypic &#xCC28;&#xC774; &#xBBFC;&#xAC10;&#xC131;&#xACFC; &#xC9C8;&#xBCD1;&#xC5D0; &#xB300; &#xD55C; &#xC800;&#xD56D;&#xC5D0; &#xD06C;&#xAC8C; &#xC601;&#xD5A5;&#xC744; &#xBBF8;&#xCE69;&#xB2C8;&#xB2E4;. &#xC720;&#xC804;&#xC790; &#xD615; &#xAD00;&#xACC4;&#xB97C; &#xC774;&#xD574; &#xD558;&#xB824;&#xBA74; &#xACE0;&#xAE30; &#xC544;&#xB2CC; &#xB2E8;&#xC21C;&#xD788; &#xAC1C;&#xC778; &#xAC8C;&#xB188; &#xBCC0;&#xC774;&#xC758; &#xACB0;&#xACFC;&#xB85C; &#xB124;&#xD2B8;&#xC6CC;&#xD06C; &#xC18D;&#xC131;&#xC758; &#xBC1C;&#xD604;&#xC73C;&#xB85C; &#xBCFC; &#xC218; &#xC788;&#xC2B5;&#xB2C8;&#xB2E4;. &#xAC8C;&#xB188; &#xC2DC;&#xD000;&#xC2F1; &#xB178;&#xB825; &#xC218;&#xB9CE;&#xC740; &#xBC88;&#xC9F8; &#xB3CC;&#xC5F0;&#xBCC0;&#xC774; &#xBC0F; &#xC554; &#xCCB4;&#xC9C8;&#xACFC; &#xAD00;&#xB828; &#xB41C; &#xCCB4;&#xC138;&#xD3EC; &#xAC8C;&#xB188; &#xBCC0;&#xACBD;&#xC758; &#xB2E4; &#xC218; &#xBC1C;&#xACAC; &#xD588;&#xB2E4;. &#xADF8;&#xB7EC;&#xB098;, &#xC774;&#xB7EC;&#xD55C; &#xB370;&#xC774;&#xD130; &#xC138;&#xD2B8;&#xC758; &#xC778;&#xACFC; &#xAD00;&#xACC4;, &#xB610;&#xB294; &#39;&#xB4DC;&#xB77C;&#xC774;&#xBC84;&#39; &#xB3CC;&#xC5F0;&#xBCC0;&#xC774;&#xC5D0;&#xC11C; &#xBC30;&#xACBD;, &#xB610;&#xB294; &#39;&#xC2B9;&#xAC1D;&#39; &#xC554; &#xB3CC;&#xC5F0;&#xBCC0;&#xC774; &#xAD6C;&#xBD84; &#xD558;&#xAE30; &#xC5B4;&#xB824;&#xC6B4; &#xB0A8;&#xC544; &#xC788;&#xB2E4;. &#xBCF8;&#xC9C8;&#xC801;&#xC73C;&#xB85C; &#xC778;&#xAC04; &#xBC14;&#xC774;&#xB7EC;&#xC2A4; &#xAC10;&#xC5FC;&#xC758; &#xACFC;&#xC815;&#xC5D0;&#xC11C; &#xADF8;&#xB4E4;&#xC758; &#xC8FC;&#xC778; &#xC138;&#xD3EC;&#xC5D0; &#xC758;&#xC874; &#xD558; &#xACE0; &#xBCD1; &#xC801;&#xC778; &#xACE0;&#xAE30; &#xB3CC;&#xC5F0;&#xBCC0;&#xC774;&#xC5D0;&#xC11C; &#xBC1C;&#xC0DD; &#xD558;&#xB294; &#xAC83;&#xACFC; &#xC720;&#xC0AC;&#xD55C; &#xC720;&#xB3C4; &#xC218; &#xC788;&#xC2B5;&#xB2C8;&#xB2E4;. &#xC5EC;&#xAE30; &#xC6B0;&#xB9AC;&#xB294; &#xAC8C;&#xB188; &#xBCC0;&#xD654;&#xC640; &#xC885;&#xC591; &#xBC14;&#xC774;&#xB7EC;&#xC2A4; &#xB098;&#xD0C0;&#xB0A0; &#xC218; &#xC788;&#xB294; &#xC554; &#xAD00;&#xB828;&#xB41C; &#xBA54;&#xCEE4;&#xB2C8;&#xC998;&#xC744; &#xD1B5;&#xD574; &#xCCB4;&#xACC4;&#xC801;&#xC73C;&#xB85C; DNA &#xC885;&#xC591; &#xBC14;&#xC774;&#xB7EC;&#xC2A4; &#xB2E8;&#xBC31;&#xC9C8;&#xC5D0; &#xC758;&#xD574; &#xBC1C;&#xC0DD; &#xD638;&#xC2A4;&#xD2B8; &#xC704;&#xD558;&#xC5EC; &#xBC0F; transcriptome &#xB124;&#xD2B8;&#xC6CC;&#xD06C; &#xBB3C;&#xACB0;&#xC744; &#xAC80;&#xC0AC; &#xD558; &#xC5EC; &#xAC00;&#xC124;&#xC744; &#xD14C;&#xC2A4;&#xD2B8;. &#xACB0;&#xACFC; &#xD1B5;&#xD569;&#xB41C; &#xBC14;&#xC774;&#xB7EC;&#xC2A4; &#xC131; &#xC12D; &#xB3D9; &#xB370;&#xC774;&#xD130; &#xD638;&#xC2A4;&#xD2B8; &#xC140; &#xB124;&#xD2B8;&#xC6CC;&#xD06C;&#xC758; &#xBC30;&#xC120;&#xC744; &#xBC18;&#xC601; &#xD558; &#xACE0; &#xB178;&#xCE58; &#xC2E0;&#xD638; &#xC804;&#xB2EC; &#xBC0F; apoptosis, &#xC554;&#xC5D0; &#xBE44;&#xC2A4;&#xB4EC;&#xD788;&#xAC00; &#xAC19;&#xC740; &#xACBD;&#xB85C; &#xAC15;&#xC870; &#xD45C;&#xC2DC; &#xD569;&#xB2C8;&#xB2E4;. &#xC6B0;&#xB9AC;&#xB294; &#xBC14;&#xC774;&#xB7EC;&#xC2A4; &#xC131; &#xB2E8;&#xBC31;&#xC9C8;&#xC758; &#xD638;&#xC2A4;&#xD2B8; &#xB300;&#xC0C1;&#xC758; &#xCCB4;&#xACC4;&#xC801;&#xC778; &#xBD84;&#xC11D; &#xAE30;&#xB2A5; &#xC720;&#xC804;&#xCCB4;&#xD559; &#xBC0F; &#xC885;&#xC591; &#xBCC0;&#xC774;&#xC758; &#xB300;&#xADDC;&#xBAA8; &#xCE74;&#xD0C8;&#xB85C;&#xADF8;&#xB97C; &#xD1B5;&#xD574; &#xADF8;&#xB4E4;&#xC758; &#xC2DD;&#xBCC4;&#xACFC; &#xD30C;&#xC758; &#xC131;&#xACF5;&#xB960;&#xC744; &#xAC00;&#xC9C4; &#xC554; &#xC720;&#xC804;&#xC790;&#xB97C; &#xD655;&#xC778;&#xD560; &#xC218; &#xC788;&#xC2B5;&#xB2C8;&#xB2E4; &#xBCF4;&#xC5EC;&#xC90D;&#xB2C8;&#xB2E4;. &#xD568;&#xAED8;, &#xC774;&#xB7EC;&#xD55C; &#xC0C1;&#xD638; &#xBCF4;&#xC644;&#xC801;&#xC778; &#xC811;&#xADFC; &#xC554; &#xC720;&#xC804;&#xC790; &#xC2DD;&#xBCC4;&#xC758; &#xD2B9;&#xC774;&#xC131;&#xC744; &#xC99D;&#xAC00;. &#xBCD1;&#xC6D0; &#xCCB4;&#xB85C; &#xC778;&#xCF54;&#xB529;&#xB41C; &#xC720;&#xC804;&#xC790; &#xC81C;&#xD488;&#xC758; &#xC2DC;&#xC2A4;&#xD15C; &#xC218;&#xC900;&#xC758; &#xC5F0;&#xAD6C;&#xB97C; &#xACB0;&#xD569; &#xD558; &#xC5EC; &#xAC8C;&#xB188; &#xC811;&#xADFC; &#xC778;&#xAC04;&#xC758; &#xC554; &#xC720;&#xC804;&#xC790;&#xC758; &#xC774;&#xD574;&#xB97C; &#xC0AC;&#xC804; &#xB4DC;&#xB77C;&#xC774;&#xBC84; &#xC720;&#xC804;&#xC790; &#xC554; &#xBC1C;&#xC0DD;&#xC758; &#xC6B0;&#xC120; &#xC21C;&#xC704; &#xC9C0;&#xC815;&#xC744; &#xCD09;&#xC9C4; &#xD55C;&#xB2E4;.

&#xC885;&#xC591; &#xBC14;&#xC774;&#xB7EC;&#xC2A4; &#xB2E8;&#xBC31;&#xC9C8;&#xC5D0; &#xC758;&#xD574; &#xCCB4;&#xACC4;&#xC801;&#xC778; &#xD638;&#xC2A4;&#xD2B8; &#xB124;&#xD2B8;&#xC6CC;&#xD06C; &#xBB3C;&#xACB0;&#xC744; &#xC0AC;&#xC6A9; &#xD558; &#xC5EC; &#xC554; &#xC720;&#xC804;&#xC790; &#xD574;&#xC11D;.

Diferencias genot&iacute;picas influyen mucho la susceptibilidad y resistencia a la enfermedad. Entender Relaciones genotipo-fenotipo requiere que los fenotipos considerarse como manifestaciones de propiedades de la red, en lugar de simplemente como resultado de variaciones gen&oacute;micas individuales. Esfuerzos de secuenciaci&oacute;n del genoma han identificado numerosas mutaciones de l&iacute;nea germinal y gran n&uacute;mero de alteraciones gen&oacute;micas som&aacute;ticas, asociadas a una predisposici&oacute;n al c&aacute;ncer. Sin embargo, es dif&iacute;cil distinguir las mutaciones de c&aacute;ncer de fondo, o &quot;pasajero&quot;, de mutaciones causales o &#39;driver&#39;, en estos conjuntos de datos. Virus humanos intr&iacute;nsecamente dependen de su c&eacute;lula hu&eacute;sped durante el curso de la infecci&oacute;n y pueden provocar fenotipos patol&oacute;gicos similares a las que se derivan de las mutaciones. Aqu&iacute; ponemos a prueba la hip&oacute;tesis de que variaciones gen&oacute;micas y virus del tumor pueden causar c&aacute;ncer a trav&eacute;s de mecanismos relacionados, examinando sistem&aacute;ticamente las host interactoma y transcriptoma red perturbaciones causadas por las prote&iacute;nas del virus del tumor ADN. Los datos resultantes de la perturbaci&oacute;n viral integrado reflejan el aumento de las redes de la c&eacute;lula hu&eacute;sped y destacan las v&iacute;as, como se&ntilde;alizaci&oacute;n Notch y apoptosis, que van mal en c&aacute;ncer. Mostramos que an&aacute;lisis sistem&aacute;ticos de objetivos de anfitri&oacute;n de las prote&iacute;nas virales pueden identificar genes de c&aacute;ncer con una tasa de &eacute;xito a la par con su identificaci&oacute;n a trav&eacute;s de la gen&oacute;mica funcional y catalogaci&oacute;n a gran escala de las mutaciones del tumor. Juntos, estos enfoques complementarios aumentan la especificidad de la identificaci&oacute;n de genes de c&aacute;ncer. La combinaci&oacute;n de estudios a nivel de sistemas de productos g&eacute;nicos codificados pat&oacute;geno con enfoques gen&oacute;micos facilitar&aacute; la priorizaci&oacute;n de genes cancer&iacute;genos de controlador para avanzar en el entendimiento de las bases gen&eacute;ticas del c&aacute;ncer humano.

Interpretaci&oacute;n de genomas del c&aacute;ncer mediante las perturbaciones de red host sistem&aacute;tico de las prote&iacute;nas del virus de tumor.

Chronic kidney disease (CKD) is a worldwide public health threat that increases risk of death due to cardiovascular complications, including left ventricular hypertrophy (LVH). Novel therapeutic targets are needed to design treatments to alleviate the cardiovascular burden of CKD. Previously, we demonstrated that circulating concentrations of fibroblast growth factor (FGF) 23 rise progressively in CKD and induce LVH through an unknown FGF receptor (FGFR)-dependent mechanism. Here, we report that FGF23 exclusively activates FGFR4 on cardiac myocytes to stimulate phospholipase Cγ/calcineurin/nuclear factor of activated T cell signaling. A specific FGFR4-blocking antibody inhibits FGF23-induced hypertrophy of isolated cardiac myocytes and attenuates LVH in rats with CKD. Mice lacking FGFR4 do not develop LVH in response to elevated FGF23, whereas knockin mice carrying an FGFR4 gain-of-function mutation spontaneously develop LVH. Thus, FGF23 promotes LVH by activating FGFR4, thereby establishing FGFR4 as a pharmacological target for reducing cardiovascular risk in CKD.

Activation of Cardiac Fibroblast Growth Factor Receptor 4 Causes Left Ventricular Hypertrophy.

Patients with chronic kidney disease (CKD) develop increased levels of the phosphate-regulating hormone, fibroblast growth factor (FGF) 23, that are associated with a higher risk of mortality. Increases in inflammatory markers are another common feature that predicts poor clinical outcomes. Elevated FGF23 is associated with higher circulating levels of inflammatory cytokines in CKD, which can stimulate osteocyte production of FGF23. Here, we studied whether FGF23 can directly stimulate hepatic production of inflammatory cytokines in the absence of α-klotho, an FGF23 coreceptor in the kidney that is not expressed by hepatocytes. By activating FGF receptor isoform 4 (FGFR4), FGF23 stimulated calcineurin signaling in cultured hepatocytes, which increased the expression and secretion of inflammatory cytokines, including C-reactive protein. Elevating serum FGF23 levels increased hepatic and circulating levels of C-reactive protein in wild-type mice, but not in FGFR4 knockout mice. Administration of an isoform-specific FGFR4 blocking antibody reduced hepatic and circulating levels of C-reactive protein in the 5/6 nephrectomy rat model of CKD. Thus, FGF23 can directly stimulate hepatic secretion of inflammatory cytokines. Our findings indicate a novel mechanism of chronic inflammation in patients with CKD and suggest that FGFR4 blockade might have therapeutic anti-inflammatory effects in CKD.

Saurav Singh

Katz Family Drug Discovery Center and Division of Nephrology and Hypertension,
Department of Medicine,
Department of Cell Biology and Anatomy,
Katz Family Drug Discovery Center and Division of Nephrology and Hypertension, Department of Medicine

University of Miami Leonard M. Miller School of Medicine

Induction of an Inflammatory Response in Primary Hepatocyte Cultures from Mice