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NetBioSIG2014-Talk by Gerald Quon
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NetBioSIG2014-Talk by Gerald Quon

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NetBioSIG2014 at ISMB in Boston, MA, USA on July 11, 2014

NetBioSIG2014 at ISMB in Boston, MA, USA on July 11, 2014

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  • To disentangle the molecular phenotype predictions on genotype level, we performed qPCR-based gene expression analysis of potential target genes using primary human adipose-derived mesenchymal cells from genotyped patient samples. A longstanding debate has been what exact target gene(s) might underlie the obesity association at FTO. Knock-out of genes flanking the intronic association region, including Fto (Fischer et al., 2009) and Rpgrip1l (Stratigopoulos et al., 2014), show body weight-related phenotypes in mice. Further, studies have pointed to long-range regulation of other flanking genes that could mediate the obesity signal of FTO intron 1 and 2, such as IRX3 (Ragvin et al., 2010)(Smemo et al., 2014) and RBL2 (Jowett et al., 2010).
  • Diverse tissues and cells, from adult, fetal, and progenitor cells
  • Ok, so attached already a knockdown of the TBX15 factor. I would include in your talk the Oil-Red-O staining experiment. Oil-Red-O stains lipids in fat cells. You identified TBX15 as a master regulator of BMI. You see that TBX15 indeed inhibits lipid accumulation in fat cells which is a clear cellular phenotype related to body mass index. <br /> <br /> those are human fat cells/adipocytes isolated from subcutaneous adipose tissue from healthy subjects. I can send you a slide of the isolation process from whole tissue if you want (always impresses ;-). <br /> <br /> Collaborating with jocelyn diabetes center to look at mouse models of conditional knockdowns <br /> <br /> Primary human adopocytes cells -- siRNA
  • A primary driver of increased BMI is an increase in fat cell size (Arner Nature 2008), which involves a reduced lipid removal rate in existing adipocytes due to reduced lipolysis and fatty acid oxidation (Arner Nature 2011). <br /> <br /> To demonstrate the causal effect of this cell autonomous, adipose lineage-specific  regulation of body mass and fat mass on the organismal level, we analyzed a mouse model expressing a dominant negative form of Irx3 specifically in fat (Rosa26EnR-Irx3;ap2-Cre). The loss of Irx3 activity in fat reduced body size compared to Rosa26EnR control mice (Fig. 5a). In contrast to control mice, Irx3-EnR;ap2-Cre mice did not gain body mass on high-fat diet (Fig. 5b-c). This protection against diet-induced obesity was most pronounced in the fat-specific dominant negative Irx3 mouse model when compared to hypothalamic dominant negative Irx3 mice and whole body Irx3 knockout mice (Figure 5 d). This effect could be ascribed to lower mass of adipose tissues (Fig. 5d). Histological analysis confirmed diminished lipid accumulation in individual adipocytes in all measured fat pads resulting in decreased adipocyte size, with no apparent reduction in adipocyte number in Irx3-EnR;ap2-Cre compared to control mice (Fig. 5e). Further, Irx3-EnR;ap2-Cre mice were protected against high-fat diet induced fatty liver. White adipocytes store lipids in the form of a unilocular lipid droplet. Indeed, the expression of several genes involved in lipid accumulation was decreased in the transgenic mice compared to wild-type controls (Fig. 5f). White adipocytes store lipids in the form of a unilocular lipid droplet. The flux of lipids through lipid droplets largely depends on perilipins, an ancient family of droplet-coating proteins14. By qPCR analysis of perigonadal WAT, we observed an x-fold down-regulation of perilipin 2 (Plin2), while there was no effect on Plin1 mRNA (Fig. 5f). Of note, PLIN2 promotes lipid droplet formation for early adipocyte growth 15 and, in contrast to PLIN1, prevents access of adipose triglyceride lipase (ATGL) to the lipid droplet 16. Consistent with decreased lipid accumulation in the fat-specific Irx3 transgenic mice, the mice also showed a marked reduction in leptin mRNA (Fig. 5f). <br /> In line with human adipose cells from risk allele carriers, expression of the adipogenic nuclear receptor Pparγ2 was increased in adipose tissue of Irx3-EnR;ap2-Cre mice, indicating that Irx3 does not promote generation of new adipocytes but instead promotes obesity via increased lipid accumulation in existing adipocytes. An adipocyte autonomous effect of IRX3 and IRX5 on lipid storage was further supported by mouse embryonic fibroblasts (Mefs) obtained from Irx3/5 double knock-out mice, which showed reduced lipid staining after adipogenic differentiation (Fig. 5g). Gene expression analysis of these cell cultures showed consistent effects on genes related to mitochnodrial function and lipid accumulation (Fig. 5h). <br /> Han and CC to edit this paragraph <br /> Han to generate Figure and to include quantification of percentage body fat for all models <br /> <br /> Dominant negative allele === null allele <br /> Promoter driven cre construct <br /> <br />
  • In line with human adipose cells from risk allele carriers, expression of the adipogenic nuclear receptor Pparγ2 was increased in adipose tissue of Irx3-EnR;ap2-Cre mice, indicating that Irx3 does not promote generation of new adipocytes but instead promotes obesity via increased lipid accumulation in existing adipocytes. An adipocyte autonomous effect of IRX3 and IRX5 on lipid storage was further supported by mouse embryonic fibroblasts (Mefs) obtained from Irx3/5 double knock-out mice, which showed reduced lipid staining after adipogenic differentiation (Fig. 5g). Gene expression analysis of these cell cultures showed consistent effects on genes related to mitochnodrial function and lipid accumulation (Fig. 5h). <br /> Han and CC to edit this paragraph <br /> Han to generate Figure and to include quantification of percentage body fat for all models <br /> <br /> Dominant negative allele === null allele <br /> Promoter driven cre construct <br /> <br />

NetBioSIG2014-Talk by Gerald Quon NetBioSIG2014-Talk by Gerald Quon Presentation Transcript

  • A framework for identifying key regulators of complex traits Gerald Quon Manolis Kellis Melina Claussnitzer Soheil Feizi Michal Grzadkowski Daniel Marbach
  • Identifying functional mechanisms of GWAS variants is challenging chr16 position SNP-log10(p) 0 70 Recombinationrate (cM/Mb) 0 100 • > 90% of GWAS variants do not tag a coding variant (Welter et al., 2014) • Mechanism of action (target gene, or disrupted regulatory element) is typically unknown
  • Segrè et al. (2010) PLoS Genet 6(8): Rossin EJ et al. (2011) PLoS Genet 7(1): e1001273. Most network and gene set approaches explicitly map SNPs to genes Gene set enrichment analysis (MAGENTA) Network enrichment analysis DAPPLE
  • Segrè et al. (2010) PLoS Genet 6(8): Rossin EJ et al. (2011) PLoS Genet 7(1): e1001273. Most network and gene set approaches explicitly map SNPs to genes MAGENTA DAPPLE How do we assign variants to genes? Do physical interaction networks make sense?
  • Maurano et al., 2012 Few regulatory motifs are directly disrupted GWAS variants Cardiovasc.Cancer Cancer Cardiovasc. TranscriptionFactors
  • Finding key regulators of complex traits • Goal: identify the key regulators driving complex traits (obesity and cholesterol) • Previous work attempt to identify regulators whose binding is directly disrupted • We relax the constraint that regulator motifs have to be directly disrupted • We also expand the analysis to be cell type specific • Our networks do not depend on mapping variants to target genes TFs Regulatory elements (blue = GWAS target) GWAS variants
  • Epigenomics Roadmap profiles 127 tissues/cell types Art: Rae Senarighi, Richard Sandstrom
  • Combinations of chromatin marks are associated with regulatory elements • H3K4me3 • H3K9ac • DNase •H3K36me3 •H3K79me2 •H4K20me1 •H3K4me1 •H3K27ac •DNase •H3K9me3 •H3K27me3 •DNAmethyl Enhancers Promoters Transcribed Repressed FTO intron SNPr2 127Roadmapcelltypes ChromHMM (Ernst et al,. 2010)
  • 659 motifs (Kheradpour et al., 2014) (JASPAR, TRANSFAC, ENCODE) CTCF (H1 ESC) 0 0.2 0.4 0 1 Precision Recall 0.6 USF1 (H1 ESC) 0 0.1 0 1 Precision Recall 0.2 Cell type specific regulatory network construction Sum all motif instances in a given regulatory element Generate shuffled motifs Estimate background expected # motif hits Estimate # motif instances above background (threshold at 0.5)
  • #SNPs # tagged enhancers (Liver) 0 30 0 100 GWAS SNPs can tag more than one regulatory element • ~50% of 197 total cholesterol variants tagging liver regulatory elements, tag >1 element Regulatory elements SNPs LD block Lead GWAS hit GWAS variants Regulatory element targets
  • TFs Infer regulators of GWAS target elements M-step: Refine target elements of variants E-step: GWAS variants (input) Regulatory element targets Pruned GWAS variants  Apply to 47 traits: CARDIoGRAM: LDL, HDL, total cholesterol, triglycerides, CAD GLGC: LDL, HDL, total cholesterol, triglycerides WTCCC2: Multiple sclerosis IBDG: Crohn’s, Ulcerative colitis MAGIC: Glycemic traits DIAGRAM: T2D ICBP: Systolic and diastolic blood pressure GIANT: BMI, weight, height Network (input)
  • Subsetof127celltypes HDL LDL Totalchol. Alzheimer’s BMI T2D Schizophrenia T2D/pancreatic islets SZ/brain Alzheimer’s/immune Cholesterol/liver, adipose BMI/adipocyte progenitors Enriched (genome-wide+subthresh) Enriched (subthresh 10-6) Noenrich enrich NumberofGWASloci Identifying relevant cell types requires 100’s of GWAS loci
  • PWM-regulatory element incidence matrix 111targetregulatoryelements Recurring PWMs • More recurring PWMs compared to previous incidence matrix for disrupted binding elements (but also by design.) Total cholesterol
  • Example: BMI • 41 candidate regulators identified regulator Relative weight0 60 * * ** * * ** * * * * * * * * * * * * * * * Implicated regulator Of adipocyte differentiation or lipid accumulation followup followup
  • TBX15 over-expression yields decrease in lipid accumulation in isolated human adipocyte cells • TBX15 still reduces lipid accumulation even after constitutive upregulation of a key adipocyte differentiation factor (PPARG) • Now looking into mouse models for TBX15 knockdown as well to look for closer connection to BMI Control TBX15++ Control TBX15++ (++PPARG) Gesta et al., 2011
  • IRX3 KD yields an increase in fat accumulation • Lipid accumulation in fat cells is a clear cellular phenotype related to BMI WT IRX3 KD Perigonadal (visceral) fat
  • Whole body KD Hypothalamus- specific KD Adipocyte- Specific KD Fat Mass Ratio (% of control) 0 9050 IRX3 KD effect is adipocyte-specific • Lipid accumulation is only inhibited when knocked down in adipocytes Normal diet High fat diet IRX3 KD, Normal diet IRX3 KD, High fat diet
  • Mobilized CD34 CD4 Memory CD4 Naive CD8 Naive ATHL1 H3K4me1 signal 30kb Predicted target genes of disrupted enhancers are involved in cholesterol related abnormalities • Distal elements linked to target promoters by correlation in activity (Jianrong Wang)
  • Summary • Using only enhancer and promoter maps, we can recover known regulators and have prioritized new ones for followup • Cell type specific regulatory networks are still far from complete – ~25% of enhancers have no predicted binding – PWM library is incomplete (~3,000+ regulators) – Linking regulatory elements to genes is still a huge challenge
  • MIT Computational Biology Group Wouter Meuleman Jason ErnstSoheil FeiziGerald Quon Daniel Marbach Bob Altshuler Anshul Kundaje Matt Eaton Abhishek Sarkar Pouya KheradpourMariana Mendoza Jessica Wu Manasi Vartak David Hendrix Mukul Bansal Matt Rasmussen Stefan Washietl Andreas Pfenning Hayden Metsky Luis Barrera Manolis Kellis ENCODE Project Consortium Roadmap Epigenome Mapping Consortium