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Maak databasisse oop vir kopienommer variasies soortgelyk aan TCGA

Maak databasisse oop vir kopienommer variasies soortgelyk aan TCGA


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Die Cancer Genome Atlas (TCGA) het oop data vir kopiegetalvariasie (CNV) van ten minste 10k verskillende kankerpasiënte. Hulle bied twee tipes data, CNV-data van tumor en CNV-data van normale weefselmonsters. Is daar enige ander oop databasisse wat CNV-data van ten minste een kankertipe bied?


Die ICGC het CNV-data vir baie verskillende kankertipes. Dit het baie sowel beperkte as oop datastelle. Die DCC-vrystellingsbladsy sal jou deur hulle laat jag - dié wat publiek is, kan maklik afgelaai word. Hulle het ook ooreenstemmende uitdrukking, SNV, DNA-metilering en strukturele mutasiedata vir baie monsters.


CODEX2: opsporing van volspektrum kopiegetalvariasie deur hoë-deurset DNA-volgordebepaling

Hoë-deurset DNA-volgordebepaling maak die opsporing van kopiegetalvariasies (CNV's) op die genoomwye skaal moontlik met fyner resolusie in vergelyking met skikking-gebaseerde metodes, maar ly aan vooroordele en artefakte wat lei tot vals ontdekkings en lae sensitiwiteit. Ons beskryf CODEX2, as 'n statistiese raamwerk vir volspektrum CNV-profilering wat sensitief is vir variante met beide algemene en seldsame populasiefrekwensies en wat van toepassing is op studieontwerpe met en sonder negatiewe kontrolemonsters. Ons demonstreer en evalueer CODEX2 op heel-eksoom en geteikende volgordebepalingdata, waar vooroordele die mees prominente is. CODEX2 presteer beter as bestaande metodes en verbeter veral sensitiwiteit vir algemene KNV'e aansienlik.


Oop databasisse vir kopienommer variasies soortgelyk aan TCGA - Biologie

Genomiese volgordevariasie

http://www.1000genomes.org/
Data-insameling en 'n katalogus van menslike variasie

dbVar en Databasis van Genomiese Variante

Aanlyn Mendeliese erfenis in die mens

http://www.omim.org/about
OMIM is 'n omvattende, gesaghebbende kompendium van menslike gene en genetiese fenotipes wat vrylik beskikbaar is en daagliks bygewerk word. Die volledige teks, verwysde oorsigte in OMIM bevat inligting oor alle bekende mendeliese afwykings en meer as 12 000 gene. OMIM fokus op die verband tussen fenotipe en genotipe. Dit word daagliks opgedateer, en die inskrywings bevat oorvloedige skakels na ander genetiese hulpbronne.

Die Exome Aggregation Consortium (ExAC)

http://exac.broadinstitute.org/
ExAC is 'n koalisie van ondersoekers wat poog om eksoom-volgordebepalingdata van 'n verskeidenheid grootskaalse volgordebepalingsprojekte saam te voeg en te harmoniseer, en om opsommende data vir die breër wetenskaplike gemeenskap beskikbaar te stel. Die datastel wat op hierdie webwerf verskaf word, strek oor 61 486 onverwante individue wat as deel van verskeie siektespesifieke en bevolkingsgenetiese studies in volgorde geplaas is. Ons het individue wat deur ernstige pediatriese siekte geraak is, verwyder, so hierdie datastel moet dien as 'n nuttige verwysingstel van alleelfrekwensies vir ernstige siektestudies. Al die rou data van hierdie projekte is herverwerk deur dieselfde pyplyn, en gesamentlik variant genoem om konsekwentheid oor projekte te verhoog.

Encyclopedia Of DNA Elements (ENCODE) Projek

http://encodeproject.org/
Skakels na ENCODE2 eenvormig verwerkte histoonmerkdata: https://sites.google.com/site/anshulkundaje/projects/encodehistonemods
Skakels na ander ENCODE2 eenvormig verwerkte data: http://genome.ucsc.edu/ENCODE/downloads.html
Data-insameling, integrerende analise, en 'n omvattende katalogus van
alle volgorde-gebaseerde funksionele elemente

Padkaart Epigenomics Project (NIH Common Fund)

Internasionale Menslike Epigenoom Konsortium (IHEC)

http://www.ihec-epigenomes.org/
Data-insameling en verwysingskaarte van menslike epigenome vir sleutel
sellulêre toestande wat relevant is vir gesondheid en siektes

###Human Body Map sigbaar met Ensemble (http://www.ensembl.org/index.html) of die
Geïntegreerde Genomics Viewer (http://www.broadinstitute.org/igv/)
Geen-uitdrukking databasis van Illumina, van RNA-volg data

###Cancer CellLine Encyclopedia (CCLE) http://www.broadinstitute.org/ccle/home
Skikkingsgebaseerde uitdrukkingsdata, CNV, mutasies, versteurings oor groot versameling sellyne

###FANTOM5-projek http://fantom.gsc.riken.jp/
http://fantom.gsc.riken.jp/5/sstar/Data_source
Groot versameling van CAGE-gebaseerde uitdrukkingsdata oor verskeie spesies (tydreekse en versteurings)

http://www.ebi.ac.uk/gxa/
Databasis ondersteun navrae van toestand-spesifieke geenuitdrukking op
'n saamgestelde subset van die Array Express-argief.

GNF geenuitdrukking Atlas

Bekykbaar by BioGPS (http://biogps.org/#goto=welcome)
GNF (Genomics Institute of the Novartis Research Foundation) menslike en muis geen-uitdrukking skikking data.

http://www.proteinatlas.org/
Proteïenuitdrukkingsprofiele gebaseer op immunohistochemie vir 'n groot aantal menslike weefsels, kankers en sellyne, subsellulêre lokalisering, transkripsie-uitdrukkingsvlakke

http://www.uniprot.org/
'n Omvattende, vrylik toeganklike databasis van proteïenvolgorde en
funksionele inligting

http://www.ebi.ac.uk/interpro/
'n Geïntegreerde databasis van proteïenklassifikasie, funksionele domeine,
en annotasie (insluitend GO-terme).

Proteïenvangreagense-inisiatief

http://commonfund.nih.gov/proteincapture/
Hulpbrongenerering: hernubare, monoklonale teenliggaampies en ander reagense wat die volle reeks proteïene teiken

Knockout Muis Program (KOMP)

Die verbindingskaart (CMAP)

http://www.broadinstitute.org/cmap/
Die Connectivity Map (ook bekend as cmap) is 'n versameling van genoomwye transkripsionele uitdrukkingsdata van gekweekte menslike selle wat behandel is met bioaktiewe klein molekules en eenvoudige patroonbypassende algoritmes wat saam die ontdekking van funksionele verbande tussen dwelms, gene en siektes moontlik maak deur die verbygaande kenmerk van algemene geen-uitdrukking veranderinge. Jy kan meer leer oor cmap uit ons referate in Science and Nature Reviews Cancer.

Biblioteek van Geïntegreerde Netwerk-gebaseerde Sellulêre Handtekeninge (LINCS)

https://commonfund.nih.gov/LINCS/
Data-insameling en ontleding van molekulêre handtekeninge wat beskryf hoe
verskillende tipes selle reageer op 'n verskeidenheid steurende middels

Genomiese van dwelm sensitiwiteit in kanker

http://www.cancerrxgene.org/
Mutasie, CNV, Affy uitdrukking en dwelm sensitiwiteit in

Die Drug Gene Interaction Database (DGIdb)

Molekulêre Biblioteekprogram (MLP)

https://commonfund.nih.gov/molecularlibraries/index.aspx
Toegang tot die grootskaalse siftingskapasiteit wat nodig is om klein molekules te identifiseer wat as chemiese probes geoptimaliseer kan word om die funksies van gene, selle en biochemiese weë in gesondheid en siekte te bestudeer

http://www.brain-map.org/
Data-insameling en 'n aanlyn openbare hulpbronne wat uitgebreide geenuitdrukking en neuroanatomiese data vir mens en muis integreer, insluitend variasie van muisgeenuitdrukking deur stam.

http://braincloud.jhmi.edu/
BrainCloud is 'n vrylik-beskikbare, bioloog-vriendelike, selfstandige toepassing vir die ondersoek van die temporale dinamika en genetiese beheer van transkripsie in die menslike prefrontale korteks oor die leeftyd. BrainCloud is ontwikkel deur samewerking tussen die Lieber-instituut en NIMH

Die Human Connectome-projek

http://www.humanconnectomeproject.org/
Data-insameling en -integrasie om 'n volledige kaart te skep van die strukturele en funksionele neurale verbindings, binne en oor individue heen

Geuvadis RNA-volgordebepalingsprojek van 1000 genome-monsters

http://www.geuvadis.org/web/geuvadis
mRNA- en klein-RNA-volgordebepaling op 465 limfoblasoïede sellynmonsters (LCL) van 5 populasies van die 1000 Genomes Project: die CEPH (CEU), Finne (FIN), Britte (GBR), Toscani (TSI) en Yoruba (YRI).

http://www.broadinstitute.org/achilles Projek Achilles is 'n sistematiese poging wat daarop gemik is om genetiese kwesbaarhede oor honderde genomies gekarakteriseerde kankersellyne te identifiseer en te katalogiseer. Die projek gebruik 'n genoomwye shRNA-biblioteek om individuele gene stil te maak en daardie gene te identifiseer wat seloorlewing beïnvloed. Grootskaalse funksionele sifting van kankersellyne bied 'n komplementêre benadering tot daardie studies wat daarop gemik is om die molekulêre veranderinge (mutasies, kopiegetalveranderings, ens.) van primêre gewasse, soos The Cancer Genome Atlas, te karakteriseer. Die oorhoofse doel van die projek is om kankergenetiese afhanklikhede aan hul molekulêre eienskappe te koppel ten einde molekulêre teikens te identifiseer en terapeutiese ontwikkeling te lei.

Menslike veroudering genomiese hulpbronne

Die Kanker Genoom Atlas (TCGA)

http://cancergenome.nih.gov/
Data-insameling en 'n databewaarplek, insluitend kankergenoomvolgordedata

Internasionale Kankergenoomkonsortium (ICGC)

http://www.icgc.org/
Data-insameling en 'n databewaarplek vir 'n omvattende beskrywing van genomiese, transkriptomiese en epigenomiese veranderinge van kanker

Genotipe-weefsel-uitdrukking (GTEx)-projek

https://commonfund.nih.gov/GTEx/
Data-insameling, databewaarplek en monsterbank vir menslike geenuitdrukking en regulering in veelvuldige weefsels, in vergelyking met genetiese variasie

Knockout Muis-fenotipering-program (KOMP2)

https://commonfund.nih.gov/KOMP2/
Data-insameling vir gestandaardiseerde fenotipering van 'n genoom-wye versameling van muis uitklophoue

Databasis van genotipes en fenotipes (dbGaP)

http://www.ncbi.nlm.nih.gov/gap
Databewaarplek vir resultate van studies wat die interaksie van genotipe en fenotipe ondersoek

NHGRI Katalogus van Gepubliseerde GWAS

http://www.genome.gov/gwastudies/
Openbare katalogus van gepubliseerde Genome-Wide Association Studies

Kliniese Genomiese Databasis

http://research.nhgri.nih.gov/CGD/
'n Handmatig saamgestelde databasis van toestande met bekende genetiese oorsake, wat fokus op medies beduidende genetiese data met beskikbare intervensies.

NHGRI se borskanker inligting kern

http://www.ncbi.nlm.nih.gov/clinvar/
ClinVar is ontwerp om 'n vry toeganklike, publieke argief van verslae van die verwantskappe tussen menslike variasies en fenotipes te verskaf, met ondersteunende bewyse. ClinVar versamel verslae van variante wat in pasiëntmonsters gevind word, bewerings wat gemaak word rakende hul kliniese betekenis, inligting oor die indiener en ander ondersteunende data. Die allele wat in voorleggings beskryf word, word na verwysingsvolgorde gekarteer en volgens die HGVS-standaard gerapporteer. ClinVar bied dan die data aan vir interaktiewe gebruikers sowel as diegene wat ClinVar in daaglikse werkvloeie en ander plaaslike toepassings wil gebruik. ClinVar werk in samewerking met belangstellende organisasies om so doeltreffend en effektief moontlik in die behoeftes van die mediese genetika-gemeenskap te voorsien.

Menslike geenmutasiedatabasis (HGMD)

http://www.hgmd.cf.ac.uk/ac/
Die Human Gene Mutation Database (HGMD®) verteenwoordig 'n poging om bekende (gepubliseerde) geenletsels wat verantwoordelik is vir menslike oorgeërfde siektes te versamel

NHLBI Exome Sequencing Project (ESP) Exome Variant Server

http://evs.gs.washington.edu/EVS/
Die doel van die NHLBI GO Exome Sequencing Project (ESP) is om nuwe gene en meganismes te ontdek wat bydra tot hart-, long- en bloedafwykings deur baanbrekerswerk te doen met die toepassing van volgende generasie volgordebepaling van die proteïenkoderende streke van die menslike genoom oor diverse, ryk- fenotipeerde populasies en om hierdie datastelle en bevindings met die wetenskaplike gemeenskap te deel om die diagnose, bestuur en behandeling van hart-, long- en bloedafwykings uit te brei en te verryk.

http://ghr.nlm.nih.gov/
Genetics Home Reference is die Nasionale Biblioteek vir Geneeskunde se webwerf vir verbruikersinligting oor genetiese toestande en die gene of chromosome wat met daardie toestande verband hou.

http://www.ncbi.nlm.nih.gov/books/NBK1116/
GeneReviews is kundige-outeur, eweknie-geëvalueerde siektebeskrywings wat in 'n gestandaardiseerde formaat aangebied word en gefokus op klinies relevante en medies uitvoerbare inligting oor die diagnose, bestuur en genetiese berading van pasiënte en gesinne met spesifieke oorgeërfde toestande.

Global Alzheimer's Association Interactive Network (GAAIN)

http://www.gaain.org/
Die Global Alzheimer's Association Interactive Network (GAAIN) is 'n samewerkende projek wat navorsers regoor die wêreld toegang sal bied tot 'n groot versameling van navorsingsdata oor Alzheimer se siekte en die gesofistikeerde analitiese gereedskap en rekenaarkrag wat nodig is om met daardie data te werk. Ons doel is om die manier waarop wetenskaplikes saamwerk om sleutelvrae te beantwoord wat verband hou met die begrip van die oorsake, diagnose, behandeling en voorkoming van Alzheimer se en ander neurodegeneratiewe siektes te verander.
In 2013 het WGS-data vir die grootste groep van 800 Alzheimer-pasiënte verkry

Die kohorte vir hart- en verouderingsnavorsing in genomiese epidemiologie (CHARGE)-konsortium

http://web.chargeconsortium.com/
Die kohorte vir hart- en verouderingsnavorsing in genomiese epidemiologie (CHARGE)-konsortium is gevorm om genoomwye assosiasiestudie-meta-analises en replikasiegeleenthede tussen veelvuldige groot en goed-fenotipeerde longitudinale kohortstudies te fasiliteer. Hulle het ook DNA-metileringsdata langs WGS en Exome Seq.

Die NIMH-sentrum vir samewerkende genomiese studies oor geestesversteurings


Resultate

Omvattende epigenomiese profilering in beide BLCA-lyne en primêre gewasse

In hierdie projek het ons RNA-Seq, ChIP-Seq vir Histon 3 lysine 27 asetilering (H3K27ac), Assay vir Transposase-toeganklike Chromatien uitgevoer deur gebruik te maak van volgordebepaling (ATAC-Seq), en genoomwye chromatien bevestiging vang eksperimente (Hi-C) op 4 blaaskankersellyne (Fig. 1a), waarvan twee (RT4 en SW780) voorheen as luminaal geannoteer is en die twee ander (SCABER en HT1376) wat as basaal gekarakteriseer is [8, 25]. Gebaseer op die RNA-Seq-data wat in hierdie studie gegenereer is, het ons 'n voorheen gerapporteerde molekulêre subtiperingsbenadering [26] gebruik om toewysing aan luminale en basale toestande te bevestig. Ons resultate het bevestig dat RT4 en SW780 aan die Luminale-papillêre subtipe behoort, terwyl SCABER en HT1376 aan die Basale/Plaveiselagtige subtipe behoort (Bykomende lêer 1: Tabel S1). Elke eksperiment in blaaskanker sellyne het ten minste twee biologiese herhalings (Bykomende lêer 2: Tabel S2) en ons het 'n hoë korrelasie tussen die twee herhalings waargeneem (Bykomende lêer 3: Tabel S3). Belangriker nog, ons het ook dieselfde stel eksperimente op vier pasiënt-spier-indringende blaasgewasse uitgevoer. Deur dieselfde molekulêre subtiperingsmetode te gebruik, het ons hul subtipes as die volgende bepaal: T1 is Luminaal-papillêr, T3 is Stroma-ryk, en T4 en T5 is basaal/platewerig.

Luminale en basale transkripsionele BLCA subtipes word geassosieer met duidelike promotor en distale versterkers se aktiwiteit op die epigenetiese vlak. a Algehele ontwerp van die studie. b Differensiële uitdrukking geen (DEG) analise van luminale sellyne (RT4 en SW780) en basale sellyne (SCABER en HT1376) toon 427 basaal-spesifieke opgereguleerde gene en 524 luminale-spesifieke opgereguleerde gene. c Hittekaart van differensiële H3K27ac ChIP-Seq by promotors (links). Sein H3K27ac intensiteit profiele vir elke groep BLCA selle (regs). d Genoomblaaier seinspore vir 'n paneel van luminale en basale gene. Hier word die spore van H3K27ac ChIP-Seq-, ATAC-Seq- en RNA-Seq-data in RT4-, SW780-, SCABER- en HT1376-selle getoon. e Promotor H3K27ac en sy geassosieerde RNA-Seq seine vir geselekteerde luminale en basale gene toon merkwaardige ooreenkoms. f Geïntegreerde H3K27ac-pieke by distale versterkers en RNA-Seq geenuitdrukking assosiasiemodel identifiseer vermeende versterkers en geenregulering. Top 10 000 mees veranderlike versterkers (linker hittekaart) word saam met hul ooreenstemmende geenuitdrukking (regter hittekaart) geplot. g Korrelasies van genoomwye H3K27ac-seine tussen die blaaskankersellyne en tumormonsters toon ooreenkomste van versterkerlandskap

Luminale en basale transkripsie BLCA subtipes word geassosieer met duidelike promotor en distale versterkers aktiwiteit op die epigenetiese vlak

Verryking van H3K27ac seine is gebruik om beide aktiewe promotors en distale versterkers te voorspel [27, 28]. Daarom het ons eers ChIP-Seq vir H3K27ac uitgevoer in al vier seltipes en vier pasiëntmonsters. Ons het opgemerk dat biologiese replikate wat volg op H3K27ac ChIP-seq, altyd saamgegroepeer is, wat aandui dat ons resultate hoogs reproduceerbaar is (Bykomende lêer 4: Figuur S1A). Verder het ons gevind dat twee luminale subtipes (RT4 en SW780) saam gegroepeer het, terwyl twee basale (SCABER en HT1376) sellyne ook saam gegroepeer is (Addisionele lêer 4: Figuur S1A). Hierdie groeperingsresultate dui daarop dat globale epigenomiese profiele selidentiteit akkuraat weerspieël. Die hiërargiese groepering in die sellyne gebaseer op H3K27ac seine is ook weerspieël deur globale mRNA uitdrukking deur RNA-Seq data (Bykomende lêer 4: Figuur S1B). Ons het differensiële geenuitdrukking-analise op die twee groepe seltipes (RT4 en SW780 vs. SCABER en HT1376) uitgevoer en 427 basaalspesifieke (Addisionele lêer 5: Tabel S4) en 524 luminale spesifieke gene (Fig. 1b, Addisionele lêer) geïdentifiseer 6: Tabel S5).

Vervolgens het ons promotorgebruik ondersoek gebaseer op H3K27ac seine by bekende gene. Ons het bevestig dat promotor H3K27ac intensiteite merkwaardig soortgelyk is aan geenuitdrukking (Fig. 1c), en groeperingsanalise gebaseer op promotor H3K27ac intensiteit was in staat om luminale en basale modelle van BLCA te onderskei (Bykomende lêer 4: Figuur S1C). Ons het byvoorbeeld waargeneem dat twee luminale subtipe BLCA-sellyne RT4 en SW780 soortgelyke H3K27ac-patrone by luminale gene het FOXA1, GATA3, en PPARG (Fig. 1d, e), terwyl die twee basale sellyne soortgelyke promotormerke deel by gene wat vir die basale/plaveiselmerkers kodeer KRT5/14. Interessant genoeg, hoewel gebaseer op globale geenuitdrukking, HT1376 geklassifiseer word as 'n basale / plaveiselsubtipe, toon dit 'n soortgelyke promotor H3K27ac patroon by luminale gene (GATA3, KRT7/8/18, Fig. 1e).

Distale H3K27ac pieke van geen promotor streke is gebruik as merkers vir aktiewe versterkers [27, 29]. Ons het dieselfde benadering hier gevolg, en ons het gemiddeld 59,466 (40,731–78,506) versterkers in elke sellyn voorspel (Bykomende lêer 7: Tabel S6). Om die distale versterkers aan hul teikengene te koppel, het ons 'n korrelasie-gebaseerde distale-enhancer piek-geen assosiasie uitgevoer soos beskryf in [30] en die top 10,000 veranderlike distale versterkers geïdentifiseer wat beduidende korrelasie met sy gekoppelde geen toon (korrelasie ≥0.5, bl < 0,01 'n totaal van 58 509 voldoen aan ons kriteria Fig. 1f en Addisionele lêer 8: Tabel S7). Ons het waargeneem dat die versterkers duidelike groepering toon volgens verskillende seltipes, en hul teikengene toon soortgelyke seltipe-spesifieke patrone (Fig. 1f en Addisionele lêer 4: Figuur S1D). Verder, om die kliniese relevansie van ons bevindings te verstaan, het ons H3K27ac ChIP-Seq uitgevoer in vier spierindringende blaaspasiëntmonsters. Ons resultate toon 'n merkwaardige korrelasie van tumorsellyne (Fig. 1g). Samevattend wys ons in hierdie sellyne en in 'n beperkte tumorkohort dat epigenetiese regulering gekorreleer is met molekulêre subtipe toewysing.

Afsonderlike stelle transkripsiefaktormotiewe is verryk in luminale en basale BLCA-geassosieerde cis DNA-regulerende streke

Ons het ATAC-Seq in RT4-, SW780-, SCABER- en HT1376-sellyne uitgevoer om hul oop chromatienstatus in die genoom te evalueer. Ons het gemiddeld in elke sellyn 32 000 oop chromatienstreke geïdentifiseer (Fig. 2a en Addisionele lêer 9: Tabel S8). Onder hulle was 40.8% van oop chromatienstreke by promotorstreke geleë, terwyl 59.2% by distale streke geleë was. In die algemeen oorvleuel > 90% van die oop chromatien promotor streke met H3K27ac (Bykomende lêer 4: Figuur S2A, S2C-D). Die oorvleueling van distale ATAC-Seq pieke en H3K27ac is laer (Addisionele lêer 4: Figuur S2A en Addisionele lêer 10: Tabel S9), ten minste gedeeltelik as gevolg van die verskillende getalle pieke in verskillende datastelle. Genoomwye korrelasie van ATAC-Seq het getoon dat HT1376 en SCABER saamgegroepeer het met 80% ooreenkoms (Bykomende lêer 4: Figuur S2E) in vergelyking met luminale RT4 (

65%). Ons het opgemerk dat hierdie waarneming ooreenstem met die RNA-Seq-gebaseerde groepering en H3K27ac-gebaseerde groepering (Bykomende lêer 4: Figuur S1A en B).

Afsonderlike stelle transkripsiefaktormotiewe is verryk in luminale en basale BLCA-geassosieerde cis DNA-regulerende streke. a 'n Omvattende en 'n duidelike stel distale ATAC-Seq seine by drie trosse (luminale spesifiek, basaal spesifiek en gedeeld) en ooreenstemmende H3K27ac seine. b TF-motief-ontledingsresultate word hier getoon as 'n gerangorde plot (links) en motiewe (regs), waar vir luminaal-spesifieke (bo) en basaal-plaveisel-spesifieke oop chromatienversterkers (onder). c FOXA1 en GATA3 gebonde oop chromatiene geleë by distale versterkers van RT4/luminale sellyn word hier in drie groepe uitgebeeld: slegs FOXA1, slegs GATA3 en FOXA1 en GATA3 bindingsplekke. d Gene-ontologie-analise van paaie vir elke groep bindingsplekke (slegs FOXA1, FOXA1 en GATA3, en GATA3 slegs). e Waargenome voorkoms van TF-motiewe (AP-1, FOX Forkhead en GATA) word hier getoon by distale versterkers en promotors van drie groepe. f Genoomwye oop chromatiene van BLCA-sellyne toon ooreenkoms met TCGA-blaasgewasse [30]

Vervolgens het ons motiefanalise van hierdie oop chromatienstreke uitgevoer (Bykomende lêer 11: Tabel S10). Ons het waargeneem dat bindingsplekke vir CTCF en AP-1-kompleks verryk is in alle sellyne (Fig. 2b en Addisionele lêer 4: Figuur S2G). Verdere rangorde van TF-motiewe deur verryking bl-waarde geopenbaarde luminale oop chromatienstreke (gedeel tussen RT4 en SW780) is verryk met bindingsmotiewe vir GRHL2, TP53 en TP63 terwyl basale oop chromatiene (gedeel tussen SCABER en HT1376) verryk is vir TEAD1/4 en KLF2 faktor (Fig. ) bindmotiewe. Daar is voorheen berig dat GRHL2 [31] 'n luminale geen is, wat ons bevindinge bekragtig het. Interessant genoeg was bindingsmotiewe vir AP-1-komplekse proteïene FOSL1/2, JUN/JUNB, ATF3 en BATF TF's [32] die mees verrykte motiewe vir beide luminale en basaal-plaveiselagtige oop chromatiene. Ons het toe al die verrykte TF-motiewe in luminale, basaal-plaveiselagtige en gedeelde oop chromatiene van distale versterkers omvattend gekarteer om die verhouding tussen TF's en BLCA-subtipes te ondersoek (Bykomende lêer 11: Tabel S10). Ons het ontdek dat by distale versterkers die luminale BLCA-subtipes geassosieer word met voorheen gerapporteerde steroïedhormoonreseptor TF's. Aan die ander kant toon basaal-plaveiselagtige oop chromatienareas by versterkers verryking van voorheen ongerapporteerde faktore MADS box TF MEF2C en die homeobox TF OTX2. Nie verrassend nie, luminale baanbrekende TF's soos vurkkop-transkripsiefaktore (FOXA1/2/3, FOXF1, FOXK1, FOXM1), en GATA TF's (GATA3/4/6) is verryk in luminale-geassosieerde versterkers met 'n oop chromatienkonformasie. Meer verbasend is dat vurkkop- en GATA-motiewe ook geïdentifiseer is as geassosieer met oop chromatien by versterkerelemente oor sellyne (Bykomende lêer 11: Tabel S10). Alhoewel dit bekend is dat FOXA1 en GATA3 lae uitdrukking in basale blaaskankersellyne en gewasse het, dui die verryking van vurkkop- en GATA-motiewe in oop chromatiene oor BLCA-sellyne kompensasie deur Forkhead- en GATA-faktore anders as FOXA1 en GATA3 voor. Daarbenewens kan Forkhead- en GATA-motiefverryking oor sellyne in areas van oop chromatien aandui dat luminale-spesifieke TF's gereed is om aan hierdie areas van oop chromatien te bind. Verder is dit bekend dat FOXA1 en GATA3 'n rol speel in die ontwikkeling van urotheel [31] wat daarop dui dat hul bindingsplekke vroeg tydens ontwikkeling voorberei kan word. Ons het ook ontdek dat die stamsel-geassosieerde baanbreker-TF's soos KLF-faktore (KLF10/14), ATF-faktore (ATF1/2/4/7) en NANOG verryk is in basaal-geassosieerde versterkers. Dit is interessant omdat daar 'n stammoederselpopulasie binne basale urotheel bestaan ​​wat kan bydra tot uroteliale ontwikkeling en differensiasie [33, 34].

FOXA1 en GATA3 bind by luminale oop chromatiene by regulatoriese distale versterkers om uitdrukking van luminale spesifieke gene aan te dryf

Ons het veronderstel dat TF's soos FOXA1 en GATA3 by die oop chromatienstreek bind om luminale versterkers te baan en geassosieerde geenuitdrukking te aktiveer. Om hierdie hipotese te toets, het ons GATA3 ChIP-Seq in die RT4 luminale BLCA-sellyn uitgevoer en FOXA1 ChIP-Seq in RT4-selle verkry uit ons voorheen gepubliseerde werk (Bykomende lêer 12: Tabel S11) [8]. Soos voorspel, het luminale TF's FOXA1 en GATA3 verrykte binding by die oop chromatien-lokusse van luminaal-geassosieerde (FOXA1, GATA3, PPARG, FGFR3, en FABP4) distale versterkers (Fig. 2c). Meer spesifiek, ons het 1325 distale versterkers ontdek wat medebinding van beide FOXA1 en GATA3 in RT4 toon (Fig. 2c). Net so het FOXA1 en GATA3 verrykte binding getoon by oop chromatien lokusse van luminale merkergene (FOXA1, ERBB3, KRT19, GPX2, en FABP4) promotors (Bykomende lêer 4: Figuur S2F).

GO-termanalise van gene proksimaal tot hierdie distale versterker-plekke het regulering van TGF-beta-produksie, epiteelontwikkeling, regulering van transkripsie betrokke by sellot-verbintenis en sel-sel adhesie biologiese prosesse (cadherinbinding en adherens-aansluitingsamestelling) getoon as terme wat met FOXA1 geassosieer word. . Daarbenewens was regulering van sellulêre komponent, selgrootte en apikale plasmamembraan biologiese prosesse terme wat geïdentifiseer is met GATA3-gebonde gene proksimaal aan hierdie distale versterkers, wat 'n sterk betrokkenheid van beide TF's in verbintenis tot sellelot en luminale differensiasie voorstel (Fig. 2d) ). Met betrekking tot proksimale gene wat verband hou met distale versterkers gebind deur beide FOXA1 en GATA3, terme wat geïdentifiseer is, is geassosieer met verskeie ontwikkelingsprosesse en die regulering van slymafskeiding en vetseldifferensiasie, beide belangrike metaboliese eienskappe van gedifferensieerde urotheel (Fig. 2d).

Ons het toe voortgegaan met die motiefanalise van slegs FOXA1, slegs GATA3 en mede-gebonde terreine. Verbasend genoeg is AP1-komplekse spesifiek verryk in alle distale versterkers bykomend tot FOXA- of GATA-motiewe (Fig. 2e). Die volgorde van binding van hierdie drie faktore moet nog ondersoek word. Ten slotte, om die kliniese relevansie van ons bevindings te verstaan, het ons ons vier BLCA-sellyne vergelyk met die TCGA-spier-indringende blaastumor ATAC-Seq-data [30] en ontdek dat die genoomwye oop chromatienprofiel in ons sellyne saamgevoeg is met duidelike trosse gewasse (Fig. 2f), wat daarop dui dat die oop chromatienstreke in hierdie sellyne soortgelyke patrone met pasiëntgewasse deel.

Luminale en basale subtipes van BLCA toon potensieel verskillende 3D-genoomorganisasies

Vorige studies het getoon dat 3D-chromatienorganisasie geassosieer word met epigenetiese aktivering of stilmaak van gene in selle [35]. Byvoorbeeld, dit is bekend dat die meerderheid van heterochromatien in kerne saamgepers word en naby die lamina-geassosieerde periferie van die kernomhulsel geleë is [35]. Om aanvanklike insigte in die genoomwye 3D-landskap van luminale en basale BLCA te verkry, het ons hoë-resolusie Hi-C eksperimente op al vier sellyne (ten minste 800 M lees, elk) en vyf blaas tumor pasiënte (> 800 M leese) uitgevoer , elk) (Bykomende lêer 4: Figuur S3). Ons het ons onlangs ontwikkelde sagteware, Peakachu [36], wat 'n masjienleer-gebaseerde chromatienlus-opsporingsbenadering is, gebruik om lusse teen 10Kb bin-resolusie te voorspel. Eerstens het ons 'n gemiddeld van 56 315 lusse (reeks tussen 38 271 en 69 032) in die vier sellyne geïdentifiseer (waarskynlik 0.8 Addisionele lêer 13: Tabel S12). Dan, deur die waarskynlikheidtelling-uitset van Peakachu te gebruik, het ons subtipe-spesifieke chromatienlusse toegewys soos getoon in die Aggregate Peak Analysis (APA, Fig. 3a en Addisionele lêer 14: Tabel S13) [37]. Op grond van ons benadering het ons meer potensieel luminale-spesifieke lusse in RT4 en SW780 (2299) waargeneem relatief tot die basale BLCA-modelle SCABER en HT1376 (2144). Ons het toe elk van hierdie kategorieë vergelyk met lusse wat in vyf pasiëntmonsters opgespoor is (Fig. 3b):

30-40% van luminaal-toegewysde en basaal-toegewysde 3D-chromatienlusse wat in die sellyne geïdentifiseer is, is in hierdie vyf tumormonsters waargeneem.

Luminale en basale subtipes van blaaskanker toon potensieel verskillende 3D-genoomorganisasies. a Hi-C lus analise van luminale en basaal-plaveiselagtige sellyne toon duidelike luminale lusse en basaal-plaveiselagtige lusse. b Kontakte wat in luminale en basaal-plaveiselagtige sellyne geïdentifiseer word, word gedeel en bekragtig in vyf blaaskanker tumormonsters. c Genoomblaaierspore vir geselekteerde luminale geen (FOXA1) en basale geen (KRT5) wat versterker-promotorlusse bevat, word hier gewys. Boë dui die voorspelde chromatienlusse aan met behulp van Hi-C data. d Die tipe kontakte gebaseer op die oorvleueling van kontakligging by óf versterker (H3K27ac by distale streek) óf promoter (H3K27ac en H3K4me3 by promoter) in elke sellyn word getoon. E-P, versterker-promotor-lusse E-E, versterker-enhancer-lusse P-P, promotor-promotor-lusse E-N, versterker-nie-regulerende lusse P-N, promotor-nie-regulerende lusse Geen, nie-regulerende lusse. e Verryking van FOXA1 (linker-as) en GATA3 (regter-as) bindingsplekke in RT4 (luminale) selle word hier by sy lusankers getoon

Laastens het ons versterker- en promotorlusse in elke kategorie ondersoek vir hul assosiasie met subtipe-spesifieke geenuitdrukking. Voorbeelde word getoon in Fig. 3c, waarin ons gevind het dat die luminale geen FOXA1 en die basale geen KRT5 het verhoogde aantal versterker-promotorlusse in onderskeidelik luminale en basale sellyne getoon. Oor die algemeen het ons dit waargeneem

40% van die chromatien-lusse bestaan ​​tussen versterkers en promotors (Fig. 3d). Verder het ons 'n beduidende verryking van FOXA1 en GATA3 bindingsplekke by hierdie lusankers gevind, wat die betrokkenheid van hierdie pionierfaktore in die regulering van die 3D genoom aandui (Fig. 3e). Hierdie bevinding stem ooreen met vorige studies wat die verryking van FOXA1-bindingsplekke in versterker-promotor-lusse [38] rapporteer.

Kopieergetalvariasie (CNV) en chromatienlusse in blaaskanker

'n Kenmerk van kanker is groot strukturele variasies (SV's), wat inversies, delesies, dupliserings en translokasies insluit. Onlangs is daar getoon dat verandering in CNV's en SV's kan lei tot die veranderinge in 3D-genoomstruktuur, insluitend die vorming van nuwe topologies geassosieerde domeine ("neo-TAD's") [39] en gevolglike "enhancer kaping [40]." Neo-TAD's verwys na scenario's waar 'n SV-gebeurtenis lei tot die vorming van nuwe chromatiendomeine, wat weer die uitdrukkingsprofiele van die gene wat in daardie streke geleë is, kan beïnvloed. In die "enhancer-hijacking"-model lei veranderde 3D-genoomorganisasie tot abnormale versterkerinteraksie, met versterkers wat in die nabyheid van die verkeerde teikengeen (gewoonlik 'n onkogeen) gebring word, wat lei tot onvanpaste teikenaktivering.

Ons het eers sistematies kopiegetalvariasies (CNV's) en SV-gebeurtenisse geïdentifiseer deur die Hi-C-data met HiNT [41] en die Hi-Cbreakfinder [42]-sagteware te gebruik. Ons het tientalle groot SV's geïdentifiseer, insluitend inversies, skrappings en translokasies (Fig. 4a, b, Addisionele lêer 4: Figure S4-S5, Addisionele lêer 15: Tabel 14). Soos verwag kan word, het ons minder CNV's in die pasiëntmonsters waargeneem as in sellyne. Nog belangriker, ons kon die plaaslike Hi-C-kaart rondom die breekpunte van die SV's herkonstrueer. Ons kan interessante versterker-kapingsgebeure en die vorming van neo-TAD's in hierdie plaaslike Hi-C-kaarte waarneem (Fig. 4c-h). These observations provide an important resource to further study the function of the re-arranged enhancers in the context of bladder cancer.

Chromatin interactions induced by structure variation (SV) events. a, b Circos plot showing intra- and inter-chromosome SVs in SCABER (a) and SW780 (b). c A large intra-chromosomal translocation on chr9. dh Inter-chromosomal translocations. The breakpoints were identified by the HiCBreakfinder software. We then reconstructed the local Hi-C maps across the breakpoints. RNA-Seq and H3K27ac ChIP-Seq tracks from the same cell type are shown below the Hi-C maps

Neuronal PAS Domain Protein 2 (NPAS2) is a novel luminal BLCA TF which regulates luminal gene expression and cell migration

Genome-wide open chromatin analysis of BLCA cell lines provides an ideal platform for the identification of novel transcriptional regulators of BLCA cell fate and phenotype. Here we performed motif analysis of luminal-associated, basal-associated, and shared open chromatin regions, resulting in the identification of distinct TFs in each cluster. Among them, many represent known families of subtype-specific regulators, such as the GATA, FOX, and ETS families at luminal-associated ATAC-Seq peaks. Among them, we noticed a potential novel bHLH containing regulator, NPAS2, which is enriched in the luminal-associated and shared clusters, but not enriched in basal-associated ATAC-Seq peaks (Fig. 5a). We examined its binding profile using the latest ENCODE data (HEPG2 cells) [43] and found that NPAS2 binds at the FOXA1 promoter region (Fig. 5b), but not at regulatory regions for basal marker genes. This suggests the possibility that NPAS2 may be an upstream regulator of FOXA1. We then checked the TCGA data and found that high expression level of NPAS2 is significantly correlated to overall patient survival (Fig. 5c).

NPAS2 is a novel bladder cancer regulator. a bl-values of NPAS2 motif in luminal-associated (RT4, SW780), basal-associated (SCABER, HT1376), and shared open chromatin regions. b NPAS2 ChIP-seq signal near luminal marker genes FOXA1, GATA3, en PPARG in HEPG2 cell line. c NPAS2 Kaplan-Meier curve is shown here for 2000 days with log-rank statistics and hazards ratio. d Transwell migration assay representative crystal violet staining (left) and quantification of differences in transwell migration (right) are shown following overexpression of NPAS2 in SCABER. e RT-qPCR results for basal marker genes KRT5, KRT6A, STAT3, en TFAP2C are shown here for wild-type and NPAS2 overexpressed SCABER basal cell line. f NPAS2, FOXA1/GATA3, en PPARG RT-qPCR are shown here for wildtype and FOXA1/GATA3 overexpressed SCABER basal cell line

To further determine whether NPAS2 expression influences the downstream target expression and phenotype, we overexpressed NPAS2 in the basal-squamous BLCA cell line SCABER. First, we performed trans-well migration assays and found that overexpression of NPAS2 in SCABER cells decreased cell trans-well migration (Fig. 5d). We then performed RT-qPCR experiments and found that the basal marker genes (such as KRT5, KRT6A, en TFAP2C) are significantly downregulated (Fig. 5e) following NPAS2 overexpression, suggesting NPAS2 represses the expression of a subset of basal marker genes.

Because our functional genomics analysis suggests that FOXA1 and GATA3 cooperate to regulate luminal target genes [8], we individually overexpressed FOXA1 and GATA3 in SCABER cells to test their ability to regulate NPAS2 expression. We observed increased expression of NPAS2 by both FOXA1 and GATA3 overexpression (Fig. 5f).


Discussions

Advances in single-cell technologies present new challenges and opportunities for making biological discovery. Single-cell studies often involve large numbers of cells, which are powerful at characterizing cellular heterogeneity, but small numbers of biological samples, which are underpowered for discovering common disease genes. It has been shown by recent genome-wide association analysis that it is possible to enable new discovery by performing association analysis at cell-type resolutions [55]. For cancer and genetic diseases driven by somatic mutations, being able to obtain genetic footprint at various time and conditions can enable discovery of genes responsible for disease progression and resistance to therapy.

However, it remains unclear what analytical strategies should be deployed to achieve the benefits. Even more challenging it gets when CNAs are being considered, as CNAs affect large regions of the genome and are difficult to trace using phylogenetics methods.

In our study, we demonstrated that it is possible to achieve the benefit by reconstructing copy number evolution history as a lineage tree, i.e., MEDALT, and performing permutation-based statistical analysis, i.e., LSA, to identify fitness-associated CNAs and genes.

We have learned several important lessons in our study.

First, it is important to perform accurate lineage tracing. Although the single-copy gain and loss model that we implemented in deriving MEDALTs is limited in complexity, it already performed substantially better than conventional phylogenetics algorithms such as MP that assumes infinite sites and NJ that employs naïve distance metrics, as shown in our simulation and in real data analysis. It is conceivable that further development of methodology that incorporates more complex genome evolution mechanisms such as chromothripsis [56] can lead to better results.

An important goal was to represent convergent evolution that is likely prevalent in the lens of CNAs [10, 57]. Conventional phylogenetics algorithms strictly prohibit the expression of convergent evolution by disallowing an alteration to occur multiple times in a course of evolution [28]. Several new algorithms relaxed such limitation but were designed for analyzing point mutation data [58]. As shown in our analysis of the TNBC patients, genes identified based on convergent evolution analysis (i.e., PLSA) had an even higher fraction of known cancer genes than those identified based on cohort-level single-lineage LSA. Our result suggests that examining convergent evolution is likely a key component towards fully unleashing the power of single-cell studies.

Unlike canonical phylogenetic trees, MEDALTs are minimal spanning trees that do not contain unobserved internal ancestral nodes. Representing evolution using minimal spanning trees instead of phylogenetics trees was our deliberate choice, as it allowed us to develop polynomial-runtime solutions that are scalable to real datasets containing thousands of cells. It also allowed us to conveniently implement biologically meaningful MED and enforce directionality constraints. Phylogenetics algorithms are likely effective when the numbers of cells are small and that the alterations are simple to trace. None of these conditions apply to available SCCN datasets that have CNAs evolving non-linearly in hundreds of cells. Moreover, we have shown in our simulation that for the purpose of detecting fitness-association alterations, our method outperformed phylogenetics approaches in a wide range of sample sizes.

A particular challenge in developing and evaluating computational lineage tracing methods is the lack of exact ground truth. Although various experimental technologies have been developed [59, 60], we are not aware of any that can be applied to trace copy number evolution in patient samples. To circumvent this, we utilized in silico simulation that mimics several prevalent CNA mechanisms to evaluate the accuracies of the reconstructed lineages and fitness-associated alterations. We also utilized longitudinal datasets on which we knew the biological stages of the cells to evaluate the chronological accuracy of the inference results. Although these strategies are unlikely sufficient to validate all the edges and lengths in the trees, they are objective and sufficient to discriminate various approaches.

Second, it is important to control biases in statistical inference. It is challenging to detect fitness-associated genes, as CNAs often affect a large number of genes and that the sample sizes are often small. Passenger CNAs that occur naturally in non-functional regions such as those near fragile sites or repeats could easily cloud the discovery. In addition, lineage tracing algorithms are unlikely to be perfect and could introduce distinct biases. To address these challenges, we employed LSA, which randomly permutes SCCN profiles into different cells to reduce the biases introduced by background genomic variations and technical noises. And we reconstructed trees from permutated datasets to alleviate biases introduced by the lineage tracing algorithms. The evolutionarily meaningful MED metrics and constraints help our analyses to focus on biologically relevant hypotheses, given limited computational resources. These procedures appeared important to achieve the accuracy. Further exploration of different ways to permute the data and to estimate the background distribution will likely lead to better results.

We assessed the functional impact of the identified genes using cell-line CRISPR essentiality screen data. We confirmed that the set of fitness-associated, amplified genes discovered by our methods are significantly more essential than other control gene sets in cancer cell lines. We also nominated novel genes that appear to have prognostic values in TCGA and the METABRIC datasets. These assessment strategies likely have false positives and negatives. Further comprehensive, well-controlled and targeted experiments will likely be required to fully assess the functional impact and clinical values of these genes.

Lastly, it was exciting to observe benefits of our methods on both the scDNA-seq and the scRNA-seq data. Although RNA-derived copy number profiles may not be as accurate as those derived from DNAs, previous studies [61] suggested that they can reasonably distinguish tumor clones. Our study further revealed the value of scRNA-seq data in lineage tracing and supported the notion that genomic profiles, even approximations, are more accurate than transcriptomic profiles in determining biological timing of cells. Our results opened doors towards utilizing scRNA-seq as a platform to understand genetics underlying developmental processes and perform gene discovery.


AFSLUITING

The number of users proves that MEXPRESS, through its ease of use and unique, integrative data overview, found its place in the toolbox of many researchers. By combining a comprehensive visualization and statistical analysis in a single figure, MEXPRESS helps researchers quickly identify dysregulations and their clinical relevance in cancer. With this major, feedback-driven update, we aim to consolidate MEXPRESS’s place in the set of open source web tools available to researchers and clinicians.


Metodes

Haploproficient genes and orthology analysis

The set of S.cerevisiae genes which are haploproficient in turbidostat culture was obtained using the growth data of [8] and an FDR cutoff of 0.02. This stringent FDR cut-off rigorously defines those genes for which heterozygosity confers a strong fitness advantage, but has no effect on the functional enrichment of genes identified as haploproficient. Genes defined as ‘haploproficient’ for the purposes of this study are listed in Additional file 1: Table S1. The set of chromosome maintenance-associated HP genes described in [8] overlaps, but is not coincident, with the HPGI set studied here, since the current set also includes DNA damage-response genes.

Orthology assignments were made using the InParanoid algorithm [50] and compared with the results of a BLAST [51] reciprocal best-hits search. GO enrichment searches were performed using the Babelomics 4 FatiGO tool [52]. To assess the significance of HP gene conservation, the number of HP genes having orthologs in a given Ascomycete species, given the number of S. cerevisiae HP genes, was compared against the whole-genome conserved proportion using a χ 2 or Fisher exact test (depending on sample size), with the null hypothesis of identical distribution. All findings of significance were reiterated using a Z test for difference of proportions. Where necessary, P values were corrected for multiple testing using the Bonferroni correction. Cell cycle and DNA damage repair pathways were obtained from the KEGG pathway database [53].

Expression data for S.cerevisae genes was obtained from the Saccharomyces Genome Database [54] and protein expression levels from [55]. A list of human cancer genes/oncogenes was obtained from the Cancer Gene Index [17] enrichment of HP genes amongst the orthologs was determined using a χ 2 test as above. CNV incidence across eight tumour types (breast invasive carcinoma, rectum adencarcinoma colon adenocarcinoma, kidney renal cell clear carcinoma, uterine corpus endometrioid carcinoma, glioblastoma multiforme, acute myeloid leukemia, lung adenocarcinoma, lung squamous cell carcinoma, serous cystadenocarcinoma) as measured by comparative genomic hybridisation, was obtained from the NCI Cancer Genome Atlas online data browser [17] with a copy number (log2 ratio) of magnitude >0.5 taken as the significance threshold. Details of the sampling and analysis of the tumour samples are described in [17]. A P-value for HP ortholog overrepresentation was calculated using a χ 2 test .The TGCA database was also used to perform a pathway search for overrepresentation of HP orthologs.

Yeast strains

In total, 30 HP genes were chosen for analysis, based upon the criteria discussed in the Results above. The heterozygous deletion mutant of each gene was obtained from the heterozygous diploid deletion library (Open Biosystems), in the BY4743 (MAT a /α, his3D1/his3D1, leu2D0/leu2D0, LYS2/lys2D0, met15D0/MET15, ura3D0/ura3D0) genetic background. For non-essential genes, the homozygous deletant was retrieved from the analogous homozygous diploid deletion library (Open Biosystems).

Control strains were the BY4743 WT, along with the heterozygous deletion mutant of the non-functional his3 locus the non-HP, non-cell cycle ho/HO heterozygous deletion strain and the heterozygous deletion mutant of the non-HP, cell cycle gene HSL1. In addition, heterozygous deletion mutants of the G1 and G2 cyclins were included in several of the experiments. A complete list of the strains used is provided in Additional file 6: Table S6.

Cell-cycle profiling

Flow cytometric analysis of the deletion strains’ cell cycle profiles was carried about following the method of [56]. Kortliks,

10 7 cells in mid-exponential phase were harvested, washed, and fixed in absolute ethanol at 4C overnight. Fixed cells were then collected, washed, and boiled for 15 minutes in 2 mg/mL RNAse in 50 mM Tris-Cl (pH 8), and incubated at 37C for 2–12 hours. Cells were resuspended in protease solution (5 mg/mL pepsin, 4.5 μL/mL concentrated HCl), incubated for 15 minutes at 37C and resuspended in 50 mM Tris (pH 7.5). For analysis, 50 mL of cell suspension was added to 1 mL of 1 mM Sytox Green in 50 mM Tris pH 7.5), vortexed and analysed using a Cyan flow cytometer (Beckman Coulter). FlowJo (Tree Star) analysis software was used to fit histograms to the peaks representing 1C and 2C DNA content, and thereby calculate the number of cells in the G1 and G2 phases, and infer the number in S phase from the remaining fraction of the population.

Chronological lifespan assay

Cultures were inoculated from frozen stocks, grown overnight in YPD at 3°C, and 200mL of each was transferred into a well of a 96-well microtiter plate (Corning). Strains were present in duplicate on each plate, with a buffer of WT in the wells around the edge of the plate, so edge effects would not impact test colony measurements. A Singer Rotor HDA colony pinning robot was used to spot four replicates of each well onto a YPD + 10 μg/mL phloxine B (Sigma) plate. Phloxine B is a fluorescein derivative taken up when the cell membrane is disrupted upon cell death [57]. Plates were incubated for 48 hours at 3°C and photographed using an Epson 1240 Scanner. The colony images were analysed using a custom image-analysis code written in MatLab, with colony size measured by pixel count, and fraction of dead cells by the intensity of colony redness [10]. Since these parameters are independent, this allowed the dissection of the effect of cell viability upon colony growth from that of growth rate variation. The 96-well liquid cultures were incubated at 3°C, and, every second day over a period of three weeks, the colony-pinning onto YPD + phloxine B and image analysis repeated. For each plate, the median culture intensity for each strain was compared with the growth of the WT on that plate, and also with the strain growth and viability after the initial 48-hour period. The experiment was performed twice.

At several points throughout the 3-week period, several strains were selected at random, and viability assayed by performing serial dilutions and counting colony-forming units. These results were checked for compatibility with the microplate viability results.

Apoptosis assays

The rate of occurrence of apoptosis in the different strain populations was measured in two ways. Apoptosis was first induced by pretreating cells with 0.001%, 0.01% MMS, 0.0001% or 0.001% TBHP in overnight culture keeping a negative, non-induced WT control sample.

The translocation of phosphatidyl serine to the cell surface, a marker of apoptosis [58], was measured using an Annexin V-FITC Apoptosis Detection kit. (Sigma). Cells were harvested, washed in 1.2M sorbitol, 0.5 mM MgCl2, 35 mM K phosphate (pH 6.8) and then digested in 5.5% glusulase (Sigma) and 15 U/mL lyticase (Sigma) for 2 hours at 28C. Spheroplasts were harvested, washed in binding buffer (10 mM Hepes/NaOH pH 7.4, 140 mM NaCl, 2.5 mM CaCl2 in 1.2 M sorbitol buffer) and resuspended in binding buffer/sorbitol. 5 mL of FITC-labelled annexin V, and 10 mL of 10010 mg/mL propidium iodide were added to each sample, with control samples containing 1.) no label, 2.) FITC-annexin V only, and 3.) PI only. Fluorescence was quantified using a CyAn (Beckman Coulter). Gates were fitted on the basis of the the control samples, dividing a log PI versus log FITC plot into four quadrants: lower left (neither FITC nor PI-stained) – viable cells upper left (PI stain only) – necrotic cells lower right (FITC only) – early apoptotic cells and upper right (PI and FITC-stained) – late apoptotic cells. FlowJo software (TreeStar) was used to count the fraction of the total cell population in each quadrant. The proportion of both necrotic and apoptotic cells for each strain was normalised to strain viability (i.e. on the basis of the proportion of cells assigned to the lower-left FITC/PI quadrant), and the ratio of necrotic:apoptotic cells calculated. Ratios for each strain were normalised to the WT value, and the standard deviation across all samples calculated. Strains having a necrosis:apoptosis ratio further than 1.5x this standard deviation from WT levels were deemed to demonstrate abnormal apoptosis rates.

Growth rate and drug sensitivity assays

Growth and drug sensitivity assays were performed both on solid media and in liquid cultures. For solid assays, the required drug concentration was added to YPD-agar containing 10μg/m/mL phloxine B. Overnight cultures of the strains were spotted onto the (drug-containing) plates using a Singer rotor, as above. Plates were incubated at 3°C and photographed at 24 and 48 hours and analysed using an image-processing code as described above. Strain growth and viability was compared both with WT growth on the same plate, and with growth on YPD-agar (or YPD-agar plus DMSO, where the drug is DMSO-soluble). The ratio of viability and size with and without drug was calculated for every strain on a plate, and the standard deviation of all ratios calculated. Strains having a drug:untreated ratio greater than or less than two standard deviations from that of the WT were deemed to be resistant and sensitive, respectively.

Assays in liquid culture were performed by transferring 5mL of overnight culture into each well of a 96-well microtitre plate, containing 200 μL of YPD plus the required concentration of drug. Absorbance was measured for 30 hours at 3°C using a BMG Optima platereader, maximum growth rate calculated using a curve-fitting script written in R, and the growth rate for each strain compared with that of the WT in the same plate, and growth in YPD/YPD + DMSO.


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Metodes

Haploproficient genes and orthology analysis

The set of S.cerevisiae genes which are haploproficient in turbidostat culture was obtained using the growth data of [8] and an FDR cutoff of 0.02. This stringent FDR cut-off rigorously defines those genes for which heterozygosity confers a strong fitness advantage, but has no effect on the functional enrichment of genes identified as haploproficient. Genes defined as ‘haploproficient’ for the purposes of this study are listed in Additional file 1: Table S1. The set of chromosome maintenance-associated HP genes described in [8] overlaps, but is not coincident, with the HPGI set studied here, since the current set also includes DNA damage-response genes.

Orthology assignments were made using the InParanoid algorithm [50] and compared with the results of a BLAST [51] reciprocal best-hits search. GO enrichment searches were performed using the Babelomics 4 FatiGO tool [52]. To assess the significance of HP gene conservation, the number of HP genes having orthologs in a given Ascomycete species, given the number of S. cerevisiae HP genes, was compared against the whole-genome conserved proportion using a χ 2 or Fisher exact test (depending on sample size), with the null hypothesis of identical distribution. All findings of significance were reiterated using a Z test for difference of proportions. Where necessary, P values were corrected for multiple testing using the Bonferroni correction. Cell cycle and DNA damage repair pathways were obtained from the KEGG pathway database [53].

Expression data for S.cerevisae genes was obtained from the Saccharomyces Genome Database [54] and protein expression levels from [55]. A list of human cancer genes/oncogenes was obtained from the Cancer Gene Index [17] enrichment of HP genes amongst the orthologs was determined using a χ 2 test as above. CNV incidence across eight tumour types (breast invasive carcinoma, rectum adencarcinoma colon adenocarcinoma, kidney renal cell clear carcinoma, uterine corpus endometrioid carcinoma, glioblastoma multiforme, acute myeloid leukemia, lung adenocarcinoma, lung squamous cell carcinoma, serous cystadenocarcinoma) as measured by comparative genomic hybridisation, was obtained from the NCI Cancer Genome Atlas online data browser [17] with a copy number (log2 ratio) of magnitude Ϡ.5 taken as the significance threshold. Details of the sampling and analysis of the tumour samples are described in [17]. A P-value for HP ortholog overrepresentation was calculated using a χ 2 test .The TGCA database was also used to perform a pathway search for overrepresentation of HP orthologs.

Yeast strains

In total, 30 HP genes were chosen for analysis, based upon the criteria discussed in the Results above. The heterozygous deletion mutant of each gene was obtained from the heterozygous diploid deletion library (Open Biosystems), in the BY4743 (MATa/α, his3D1/his3D1, leu2D0/leu2D0, LYS2/lys2D0, met15D0/MET15, ura3D0/ura3D0) genetic background. For non-essential genes, the homozygous deletant was retrieved from the analogous homozygous diploid deletion library (Open Biosystems).

Control strains were the BY4743 WT, along with the heterozygous deletion mutant of the non-functional his3 locus the non-HP, non-cell cycle ho/HO heterozygous deletion strain and the heterozygous deletion mutant of the non-HP, cell cycle gene HSL1. In addition, heterozygous deletion mutants of the G1 and G2 cyclins were included in several of the experiments. A complete list of the strains used is provided in Additional file 6: Table S6.

Cell-cycle profiling

Flow cytometric analysis of the deletion strains’ cell cycle profiles was carried about following the method of [56]. Kortliks,

10 7 cells in mid-exponential phase were harvested, washed, and fixed in absolute ethanol at 4C overnight. Fixed cells were then collected, washed, and boiled for 15 minutes in 2 mg/mL RNAse in 50 mM Tris-Cl (pH 8), and incubated at 37C for 2� hours. Cells were resuspended in protease solution (5 mg/mL pepsin, 4.5 μL/mL concentrated HCl), incubated for 15 minutes at 37C and resuspended in 50 mM Tris (pH 7.5). For analysis, 50 mL of cell suspension was added to 1 mL of 1 mM Sytox Green in 50 mM Tris pH 7.5), vortexed and analysed using a Cyan flow cytometer (Beckman Coulter). FlowJo (Tree Star) analysis software was used to fit histograms to the peaks representing 1C and 2C DNA content, and thereby calculate the number of cells in the G1 and G2 phases, and infer the number in S phase from the remaining fraction of the population.

Chronological lifespan assay

Cultures were inoculated from frozen stocks, grown overnight in YPD at 3ଌ, and 200mL of each was transferred into a well of a 96-well microtiter plate (Corning). Strains were present in duplicate on each plate, with a buffer of WT in the wells around the edge of the plate, so edge effects would not impact test colony measurements. A Singer Rotor HDA colony pinning robot was used to spot four replicates of each well onto a YPD +� μg/mL phloxine B (Sigma) plate. Phloxine B is a fluorescein derivative taken up when the cell membrane is disrupted upon cell death [57]. Plates were incubated for 48 hours at 3ଌ and photographed using an Epson 1240 Scanner. The colony images were analysed using a custom image-analysis code written in MatLab, with colony size measured by pixel count, and fraction of dead cells by the intensity of colony redness [10]. Since these parameters are independent, this allowed the dissection of the effect of cell viability upon colony growth from that of growth rate variation. The 96-well liquid cultures were incubated at 3ଌ, and, every second day over a period of three weeks, the colony-pinning onto YPD + phloxine B and image analysis repeated. For each plate, the median culture intensity for each strain was compared with the growth of the WT on that plate, and also with the strain growth and viability after the initial 48-hour period. The experiment was performed twice.

At several points throughout the 3-week period, several strains were selected at random, and viability assayed by performing serial dilutions and counting colony-forming units. These results were checked for compatibility with the microplate viability results.

Apoptosis assays

The rate of occurrence of apoptosis in the different strain populations was measured in two ways. Apoptosis was first induced by pretreating cells with 0.001%, 0.01% MMS, 0.0001% or 0.001% TBHP in overnight culture keeping a negative, non-induced WT control sample.

The translocation of phosphatidyl serine to the cell surface, a marker of apoptosis [58], was measured using an Annexin V-FITC Apoptosis Detection kit. (Sigma). Cells were harvested, washed in 1.2M sorbitol, 0.5 mM MgCl2, 35 mM K phosphate (pH 6.8) and then digested in 5.5% glusulase (Sigma) and 15 U/mL lyticase (Sigma) for 2 hours at 28C. Spheroplasts were harvested, washed in binding buffer (10 mM Hepes/NaOH pH 7.4, 140 mM NaCl, 2.5 mM CaCl2 in 1.2 M sorbitol buffer) and resuspended in binding buffer/sorbitol. 5 mL of FITC-labelled annexin V, and 10 mL of 10010 mg/mL propidium iodide were added to each sample, with control samples containing 1.) no label, 2.) FITC-annexin V only, and 3.) PI only. Fluorescence was quantified using a CyAn (Beckman Coulter). Gates were fitted on the basis of the the control samples, dividing a log PI versus log FITC plot into four quadrants: lower left (neither FITC nor PI-stained) – viable cells upper left (PI stain only) – necrotic cells lower right (FITC only) – early apoptotic cells and upper right (PI and FITC-stained) – late apoptotic cells. FlowJo software (TreeStar) was used to count the fraction of the total cell population in each quadrant. The proportion of both necrotic and apoptotic cells for each strain was normalised to strain viability (i.e. on the basis of the proportion of cells assigned to the lower-left FITC/PI quadrant), and the ratio of necrotic:apoptotic cells calculated. Ratios for each strain were normalised to the WT value, and the standard deviation across all samples calculated. Strains having a necrosis:apoptosis ratio further than 1.5x this standard deviation from WT levels were deemed to demonstrate abnormal apoptosis rates.

Growth rate and drug sensitivity assays

Growth and drug sensitivity assays were performed both on solid media and in liquid cultures. For solid assays, the required drug concentration was added to YPD-agar containing 10μg/m/mL phloxine B. Overnight cultures of the strains were spotted onto the (drug-containing) plates using a Singer rotor, as above. Plates were incubated at 3ଌ and photographed at 24 and 48 hours and analysed using an image-processing code as described above. Strain growth and viability was compared both with WT growth on the same plate, and with growth on YPD-agar (or YPD-agar plus DMSO, where the drug is DMSO-soluble). The ratio of viability and size with and without drug was calculated for every strain on a plate, and the standard deviation of all ratios calculated. Strains having a drug:untreated ratio greater than or less than two standard deviations from that of the WT were deemed to be resistant and sensitive, respectively.

Assays in liquid culture were performed by transferring 5mL of overnight culture into each well of a 96-well microtitre plate, containing 200 μL of YPD plus the required concentration of drug. Absorbance was measured for 30 hours at 3ଌ using a BMG Optima platereader, maximum growth rate calculated using a curve-fitting script written in R, and the growth rate for each strain compared with that of the WT in the same plate, and growth in YPD/YPD +𠂝MSO.


2. Metodes

This section proposes an expanded graph database model that includes the gene expression, miRNA expression, DNA methylation, copy number gain and loss information, tissue slide information, and mutation data from TCGA. It also outlines the steps performed to create the proposed graph database model.

2.1. Data

For this study, we have specifically added copy number information, miRNA expression, and image information of the tissue slide to the previously stored clinical information, gene expression (log2 counts per million), hyper and hypomethylation information, and mis-sense mutation data from the Genomics Data Commons (GDC) for breast cancer (BRCA), prostate adenocarcinoma (PRAD), and the pancreatic adenocarcinoma (PAAD). Table 1 shows the summary information about the data set used for this study.


Kyk die video: TCGA data analysis using GEPIA2, Part-1. Hindi (Oktober 2022).