ESC+ CIS Vs Oo,Gon,NT Significant up in ESC+CIS Input

Total Page:16

File Type:pdf, Size:1020Kb

ESC+ CIS Vs Oo,Gon,NT Significant up in ESC+CIS Input ESC+ CIS vs Oo,Gon,NT Significant up in ESC+CIS Input parameters Study Design: Two Class Unpaired Imputation Engine: K-Nearest Neighbors Number of K-Nearest Neighbors: 10 Delta: 1.232 Upper Cutoff: 3.462057 Lower Cutoff: -4.2173448 All permutations unique? false Number of Permutations: 100 Fold Change Criterion Used: No HCL: no linkage Time: 14901 ms Computed Quantiles Computed Exchangeability Factor s0: 0.18402982 s0 Percentile: 1.0 Pi0Hat: 0.82013094 Num. False Sig. Genes (Median): 1.64026 Num. False Sig. Genes (90th %ile): 20.50327 False Discovery Rate (Median): 0.92670 % False Discovery Rate (90th %ile): 11.58377 % Results Positive Significant# of PositiveGenes Significant Genes: 162 % of Positive Significant Genes: 0% Negative Significant# Genes of Negative Significant Genes: 15 % of Negative Significant Genes: 0% All Significant GenesTotal # of Significant Genes: 177 % of Genes that are Significant: 0% Non-Significant GenesTotal # of Non-Significant Genes: 43199 % of Genes that are Not Significant: 100% Expected score Observed Numerator Denominator ProbeID Acc No HUGO Name Fold change q-value (%) (dExp) score (d) (r) (s+s0) A_32_P81674 AK091593 LOC157627 0.8224995 -5.522918 -6.855621 1.241304 0.014933664 0.7009666 A_23_P145724 NM_006658 C7orf16 -0.84460914 -4.971844 -5.4247065 1.0910853 0.04294284 0.56560755 A_32_P149288 ENST00000354271 ENST00000354271 -0.3369156 -4.8684034 -2.1406336 0.4396993 0.23353566 0.56560755 A_23_P250122 NM_020223 FAM20C 0.614345 -4.705595 -1.4391937 0.30584735 0.3723636 0.9881096 A_32_P32406 X15675 X15675 -0.24166806 -4.6141524 -6.5238457 1.4138774 0.002122805 0.9881096 A_23_P124619 NM_020672 S100A14 1.002037 -4.552065 -3.48658 0.7659337 0.08215146 0.9881096 A_24_P351283 NM_018000 MREG 0.373876 -4.432416 -2.6824012 0.6051781 0.12981768 0.9881096 A_24_P252310 NM_014690 FAM131B 0.8797154 -4.3596897 -4.691379 1.0760809 0.09375565 0.9881096 A_23_P90273 NM_022467 CHST8 0.6046405 -4.354433 -2.531434 0.5813464 0.170064 0.9881096 A_23_P135595 NM_032329 ING5 -1.3220888 -4.336276 -3.1131902 0.7179409 0.10597495 0.9881096 A_23_P17673 NM_013369 DNMT3L 1.4538249 -4.327721 -4.479163 1.0349935 0.044912152 0.9881096 A_23_P119778 NM_020342 SLC39A10 -0.7632112 -4.2425666 -3.418871 0.8058497 0.084524214 0.95364064 A_23_P103269 AF242772 LOC51336 -0.35310197 -4.2316866 -3.7269282 0.88071936 0.0535082 0.95364064 A_23_P134484 NM_001742 CALCR 0.20234203 -4.2243314 -5.074961 1.2013644 0.049506035 0.95364064 A_23_P204246 NM_004426 PHC1 -0.104349 -4.2173448 -2.4296188 0.57610154 0.18633255 0.95364064 CIS+Oo vs ESC,Gon,NT Significant up in CIS + Oo Input parameters Study Design: Two Class Unpaired Imputation Engine: K-Nearest Neighbors Number of K-Nearest Neighbors: 10 Delta: 1.193 Upper Cutoff: 3.9326658 Lower Cutoff: -4.0489993 All permutations unique? false Number of Permutations: 100 Fold Change Criterion Used: No HCL: no linkage Time: 24083 ms Computed Quantiles Computed Exchangeability Factor s0: 0.1809463 s0 Percentile: 1.0 Pi0Hat: 0.6370343 Num. False Sig. Genes (Median): 0.63703 Num. False Sig. Genes (90th %ile): 14.65179 False Discovery Rate (Median): 0.92324 % False Discovery Rate (90th %ile): 21.23448 % Results Positive Significant #Genes of Positive Significant Genes: 42 % of Positive Significant Genes: 0% Negative Significant# Genes of Negative Significant Genes: 27 % of Negative Significant Genes: 0% All Significant GenesTotal # of Significant Genes: 69 % of Genes that are Significant: 0% Non-Significant GenesTotal # of Non-Significant Genes: 43307 % of Genes that are Not Significant: 100% Expected score Observed Numerator Denominator ProbeID Acc No HUGO Name Fold change q-value (%) (dExp) score (d) (r) (s+s0) A_23_P76799 NM_013448 BAZ1A 0.46986562 3.9326658 2.308405 0.58698225 4.327397 0.93681514 A_23_P76799 NM_013448 BAZ1A 0.09361901 3.9758356 2.1509 0.54099315 4.0580716 1.0443186 A_23_P76799 NM_013448 BAZ1A -0.8589524 3.979695 2.2844458 0.57402533 4.2801247 1.0443186 A_24_P278172 NM_006007 ZFAND5 0.58774894 3.9851449 2.1909375 0.54977614 4.16514 1.0443186 A_23_P12643 NM_020682 AS3MT 0.673183 3.994299 3.1313505 0.783955 7.6182137 1.0443186 A_23_P57268 NM_001338 CXADR 0.31080467 4.00446 4.6864944 1.1703187 16.739403 1.0443186 A_24_P53051 NM_171846 LACTB 0.38155034 4.0306673 4.0658584 1.0087308 6.8292794 1.1375612 A_24_P70993 NM_002414 CD99 -0.93209964 4.0330005 4.1292267 1.0238597 20.236265 1.1375612 A_24_P345123 NM_003953 MPZL1 0.09538944 4.0588956 3.717463 0.91588044 9.837251 1.1796931 A_23_P140821 NM_016948 PARD6A -1.125216 4.0678334 3.0458312 0.74876004 9.568967 1.1796931 A_32_P230138 CA431053 CA431053 -0.29207706 4.117919 2.5994096 0.6312435 5.5683007 1.225066 A_24_P350576 AB011123 TNIK -0.063313715 4.13281 4.511723 1.0916841 13.763751 1.2490869 A_24_P184803 NM_004086 COCH 0.944961 4.146195 3.8934016 0.93903005 19.15364 1.2740686 A_24_P185709 NM_012156 EPB41L1 1.1146014 4.166986 2.7647743 0.663495 5.1433406 1.3848572 A_32_P233727 CK570365 SLMO2 -0.10615703 4.1697197 3.3043451 0.79246217 6.0204782 1.3848572 A_23_P369948 NM_024955 FOXRED2 0.6002224 4.20234 3.937982 0.93709266 13.645968 0 A_23_P364024 NM_006851 GLIPR1 1.5337305 4.212257 3.645565 0.865466 13.528672 0 A_23_P138308 NM_001779 CD58 0.24719526 4.232161 4.16426 0.9839559 8.590657 0 A_32_P205139 CA943539 PTEN 0.3610789 4.2392774 3.9079561 0.9218449 6.4094152 0 A_24_P846755 A_24_P846755 A_24_P846755 -0.17360649 4.2752304 5.1019964 1.1933851 19.499683 0 A_32_P202977 THC2383225 THC2383225 -0.31859857 4.286073 3.6653152 0.8551686 8.618072 0 A_24_P388033 NM_033055 HIAT1 -0.44128892 4.371553 4.8173065 1.1019669 31.216665 0 A_23_P133438 NM_019018 FAM105A 0.6740633 4.477504 3.8056273 0.849944 7.5414762 0 A_23_P137381 NM_002167 ID3 0.12693632 4.491157 4.779557 1.0642151 18.842564 0 A_32_P225355 NM_182485 CPEB2 -0.18965065 4.5685916 3.7825208 0.8279402 21.46156 0 A_24_P297680 BC063633 PDXDC2 -0.7803692 4.6100025 2.947515 0.63937384 9.399402 0 A_23_P11372 NM_000194 HPRT1 1.176598 4.637163 1.9753094 0.42597365 4.1474614 0 A_24_P81691 NM_014758 SNX19 0.51488096 4.677278 3.6612558 0.7827749 14.103683 0 A_23_P11372 NM_000194 HPRT1 0.6278126 4.8065267 1.964181 0.40864873 4.0886083 0 A_23_P11372 NM_000194 HPRT1 -0.6977348 4.816077 2.0018444 0.4156587 4.205325 0 A_23_P11372 NM_000194 HPRT1 -0.33900505 4.8235087 1.8776293 0.38926628 3.8249865 0 A_23_P11372 NM_000194 HPRT1 0.9855167 4.8401003 1.9373827 0.40027738 4.0033126 0 A_23_P11372 NM_000194 HPRT1 -0.34054983 4.8763022 1.8405819 0.37745443 3.671119 0 A_23_P11372 NM_000194 HPRT1 -0.82863426 4.929704 1.9700308 0.39962456 4.074263 0 A_23_P11372 NM_000194 HPRT1 2.145265 4.9763246 1.9509687 0.39205015 4.0284076 0 A_23_P11372 NM_000194 HPRT1 0.9160912 4.977657 2.0412054 0.41007355 4.309906 0 A_23_P11372 NM_000194 HPRT1 -1.1129072 5.009658 2.0302725 0.40527168 4.245344 0 A_24_P216968 ENST00000367142 ENST00000367142 0.5217399 5.1173162 4.8014116 0.9382675 43.281456 0 A_24_P130952 NM_032435 KIAA1804 -0.29949218 5.125391 5.0454373 0.98440045 56.106506 0 A_24_P349117 NM_020752 GPR158 -1.7036519 5.2812567 4.6603026 0.88242304 28.546621 0 A_23_P139434 NM_006248 PRB2 -0.58351964 5.3396435 5.423881 1.0157759 92.38469 0 A_32_P133072 NM_006108 SPON1 0.3362832 5.468335 3.2894049 0.6015368 9.235191 0 CIS + Gon vs ESC,Oo,NT Significant up in CIS + Gon Input parameters Study Design: Two Class Unpaired Imputation Engine: K-Nearest Neighbors Number of K-Nearest Neighbors: 10 Delta: 1.1 Upper Cutoff: 2.6732464 Lower Cutoff: -2.457588 All permutations unique? false Number of Permutations: 100 Fold Change Criterion Used: No HCL: no linkage Time: 11422 ms Computed Quantiles Computed Exchangeability Factor s0: 0.32644853 s0 Percentile: 9.0 Pi0Hat: 0.5430192 Num. False Sig. Genes (Median): 17.91963 Num. False Sig. Genes (90th %ile): 89.05515 False Discovery Rate (Median): 0.93918 % False Discovery Rate (90th %ile): 4.66746 % Results Positive Significant# ofGenes Positive Significant Genes: 632 % of Positive Significant Genes: 1% Negative Significant# Genes of Negative Significant Genes: 1276 % of Negative Significant Genes: 3% All Significant GenesTotal # of Significant Genes: 1908 % of Genes that are Significant: 4% Non-Significant GenesTotal # of Non-Significant Genes: 41468 % of Genes that are Not Significant: 96% Expected score Observed Numerator Denominator ProbeID Acc No HUGO Name Fold change q-value (%) (dExp) score (d) (r) (s+s0) A_23_P423331 NM_032536 NTNG2 -0.003585179 -4.6608396 -4.859108 1.0425392 0.03549816 0.15971152 A_24_P634768 AK026416 FLJ22763 0.9634413 -4.506478 -4.881654 1.0832525 0.028646223 0.15971152 A_24_P883577 THC2442727 THC2442727 0.23539987 -4.4966207 -4.706494 1.0466735 0.048275832 0.15971152 A_24_P784203 AI885390 AI885390 -1.1508468 -4.4069467 -2.5972404 0.5893515 0.16187035 0.15971152 A_23_P250444 NM_000166 GJB1 -0.12834935 -4.11679 -3.6554203 0.8879298 0.078782864 0.49166727 A_23_P149441 AF172850 LOC51152 0.23428312 -4.096271 -5.5022974 1.3432454 0.046524018 0.49166727 A_23_P64611 NM_176798 P2RY6 0.16516803 -4.0752826 -2.9416566 0.7218289 0.13749719 0.49166727 A_23_P421032 NM_174977 SEC14L4 -0.04271415 -3.982618 -4.898387 1.2299414 0.092557006 0.49166727 A_32_P170454 AK094730 LOC283454 -1.4287357 -3.9782026 -4.345393 1.0923007 0.091263354 0.49166727 A_24_P25544 NM_000514 GDNF -0.17861782 -3.9261417 -6.025608 1.5347403 0.00460199 0.49166727 A_24_P929202 AA814903 AA814903 -0.3886899 -3.9222445 -4.532756 1.1556536 0.08932942 0.49166727 A_32_P121674 A_32_P121674 A_32_P121674 -0.33721867 -3.903812 -3.7885904 0.97048485 0.067726225 0.49166727 A_32_P5086 BU632168 STOM 0.18238331 -3.9001167 -4.041771 1.0363206
Recommended publications
  • Genetic Variation Across the Human Olfactory Receptor Repertoire Alters Odor Perception
    bioRxiv preprint doi: https://doi.org/10.1101/212431; this version posted November 1, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Genetic variation across the human olfactory receptor repertoire alters odor perception Casey Trimmer1,*, Andreas Keller2, Nicolle R. Murphy1, Lindsey L. Snyder1, Jason R. Willer3, Maira Nagai4,5, Nicholas Katsanis3, Leslie B. Vosshall2,6,7, Hiroaki Matsunami4,8, and Joel D. Mainland1,9 1Monell Chemical Senses Center, Philadelphia, Pennsylvania, USA 2Laboratory of Neurogenetics and Behavior, The Rockefeller University, New York, New York, USA 3Center for Human Disease Modeling, Duke University Medical Center, Durham, North Carolina, USA 4Department of Molecular Genetics and Microbiology, Duke University Medical Center, Durham, North Carolina, USA 5Department of Biochemistry, University of Sao Paulo, Sao Paulo, Brazil 6Howard Hughes Medical Institute, New York, New York, USA 7Kavli Neural Systems Institute, New York, New York, USA 8Department of Neurobiology and Duke Institute for Brain Sciences, Duke University Medical Center, Durham, North Carolina, USA 9Department of Neuroscience, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA *[email protected] ABSTRACT The human olfactory receptor repertoire is characterized by an abundance of genetic variation that affects receptor response, but the perceptual effects of this variation are unclear. To address this issue, we sequenced the OR repertoire in 332 individuals and examined the relationship between genetic variation and 276 olfactory phenotypes, including the perceived intensity and pleasantness of 68 odorants at two concentrations, detection thresholds of three odorants, and general olfactory acuity.
    [Show full text]
  • Supplementary Table 1
    Supplemental Table 1. Genes that were increased or decreased more than two-fold in anti- FGFR2IIIc monoclonal antibody-treated cells Fold change Gene Symbol Description HCT-15 LoVo NKD1 Protein naked cuticle homolog 1 (Naked-1) (hNkd1) 24.74 2.18 (hNkd) [Source: UniProtKB/Swiss-Prot; Acc: Q969G9] [ENST00000268459] SAA1 Homo sapiens serum amyloid A1 (SAA1), transcript 21.09 3.84 variant 1, mRNA [NM_000331] LRRC18 Homo sapiens leucine-rich repeat containing 18 13.70 4.60 (LRRC18), mRNA [NM_001006939] ABCB5 Homo sapiens ATP-binding cassette, sub-family B 11.78 2.54 (MDR/TAP), member 5 (ABCB5), transcript variant 2, mRNA [NM_178559] CXCL1 Homo sapiens chemokine (C-X-C motif) ligand 1 10.61 7.05 (melanoma growth-stimulating activity, alpha) (CXCL1), mRNA [NM_001511] SERPINA9 Homo sapiens serpin peptidase inhibitor, clade A 9.63 4.14 (alpha-1 antiproteinase, antitrypsin), member 9 (SERPINA9), transcript variant A, mRNA [NM_175739] LCN2 Homo sapiens lipocalin 2 (LCN2), mRNA 9.00 4.78 [NM_005564] HS6ST3 Homo sapiens heparan sulfate 6-O-sulfotransferase 3 8.53 2.70 (HS6ST3), mRNA [NM_153456] CXCL3 Homo sapiens chemokine (C-X-C motif) ligand 3 7.89 4.55 (CXCL3), mRNA [NM_002090] IL8 Homo sapiens interleukin 8 (IL8), mRNA 7.34 9.61 [NM_000584] CXorf18 PREDICTED: Homo sapiens chromosome X open 6.55 2.06 reading frame 18 (CXorf18), miscRNA [XR_040313] BEST1 Homo sapiens bestrophin 1 (BEST1), transcript variant 6.54 2.43 1, mRNA [NM_004183] CXCL2 Homo sapiens chemokine (C-X-C motif) ligand 2 6.39 5.52 (CXCL2), mRNA [NM_002089] USH2A Homo sapiens Usher
    [Show full text]
  • Table S4. RAE Analysis of Dedifferentiated Liposarcoma
    Table S4. RAE analysis of dedifferentiated liposarcoma Model Chromosome Region start Region end Size q value freqX0* # genes genes Amp 1 57809872 60413476 2603605 0.00026 34.6 10 DAB1,RPS26P15,OMA1,TACSTD2,MYSM1,JUN,FGGY,HOOK1,CYP2J2,C1orf87 Amp 1 158619146 158696968 77823 0.053 25 1 VANGL2 Amp 1 158883523 158922841 39319 0.081 23.1 2 SLAMF1,CD48 Amp 1 162042586 162118557 75972 0.072 25 0 [Nearest:NUF2] Amp 1 162272460 162767627 495168 0.017 26.9 0 [Nearest:PBX1] Amp 1 165486554 165532374 45821 0.057 25 1 POU2F1 Amp 1 167138282 167483267 344986 0.024 26.9 2 ATP1B1,NME7 Amp 1 167612872 167708844 95973 0.041 25 3 BLZF1,C1orf114,SLC19A2 Amp 1 167728199 167808161 79963 0.076 21.2 1 F5 Amp 1 168436370 169233893 797524 0.018 26.9 3 GORAB,PRRX1,C1orf129 Amp 1 169462231 170768440 1306210 1.3E-06 38.5 10 FMO1,FMO4,TOP1P1,BAT2D1,MYOC,VAMP4,METTL13,DNM3,C1orf105,PIGC Amp 1 171026247 171291427 265181 0.015 26.9 1 TNFSF18 Del 1 201860394 202299299 438906 0.0047 25 6 ATP2B4,SNORA77,LAX1,ZC3H11A,SNRPE,C1orf157 Del 1 210909187 212021116 1111930 0.017 19.2 8 BATF3,NSL1,TATDN3,C1orf227,FLVCR1,VASH2,ANGEL2,RPS6KC1 Del 1 215937857 216049214 111358 0.079 23.1 1 SPATA17 Del 1 218237257 218367476 130220 0.0063 26.9 3 EPRS,BPNT1,IARS2 Del 1 222100886 222727238 626353 5.2E-05 32.7 5 FBXO28,DEGS1,NVL,CNIH4,WDR26 Del 1 223166548 224519805 1353258 0.0063 26.9 15 DNAH14,LBR,ENAH,SRP9,EPHX1,TMEM63A,LEFTY1,PYCR2,LEFTY2,C1orf55,H3F3A,LOC440926 ,ACBD3,MIXL1,LIN9 Del 1 225283136 225374166 91031 0.054 23.1 1 CDC42BPA Del 1 227278990 229012661 1733672 0.091 21.2 13 RAB4A,SPHAR,C1orf96,ACTA1,NUP133,ABCB10,TAF5L,URB2,GALNT2,PGBD5,COG2,AGT,CAP
    [Show full text]
  • Gnomad Lof Supplement
    1 gnomAD supplement gnomAD supplement 1 Data processing 4 Alignment and read processing 4 Variant Calling 4 Coverage information 5 Data processing 5 Sample QC 7 Hard filters 7 Supplementary Table 1 | Sample counts before and after hard and release filters 8 Supplementary Table 2 | Counts by data type and hard filter 9 Platform imputation for exomes 9 Supplementary Table 3 | Exome platform assignments 10 Supplementary Table 4 | Confusion matrix for exome samples with Known platform labels 11 Relatedness filters 11 Supplementary Table 5 | Pair counts by degree of relatedness 12 Supplementary Table 6 | Sample counts by relatedness status 13 Population and subpopulation inference 13 Supplementary Figure 1 | Continental ancestry principal components. 14 Supplementary Table 7 | Population and subpopulation counts 16 Population- and platform-specific filters 16 Supplementary Table 8 | Summary of outliers per population and platform grouping 17 Finalizing samples in the gnomAD v2.1 release 18 Supplementary Table 9 | Sample counts by filtering stage 18 Supplementary Table 10 | Sample counts for genomes and exomes in gnomAD subsets 19 Variant QC 20 Hard filters 20 Random Forest model 20 Features 21 Supplementary Table 11 | Features used in final random forest model 21 Training 22 Supplementary Table 12 | Random forest training examples 22 Evaluation and threshold selection 22 Final variant counts 24 Supplementary Table 13 | Variant counts by filtering status 25 Comparison of whole-exome and whole-genome coverage in coding regions 25 Variant annotation 30 Frequency and context annotation 30 2 Functional annotation 31 Supplementary Table 14 | Variants observed by category in 125,748 exomes 32 Supplementary Figure 5 | Percent observed by methylation.
    [Show full text]
  • Supplementary Data
    SUPPLEMENTARY METHODS 1) Characterisation of OCCC cell line gene expression profiles using Prediction Analysis for Microarrays (PAM) The ovarian cancer dataset from Hendrix et al (25) was used to predict the phenotypes of the cell lines used in this study. Hendrix et al (25) analysed a series of 103 ovarian samples using the Affymetrix U133A array platform (GEO: GSE6008). This dataset comprises clear cell (n=8), endometrioid (n=37), mucinous (n=13) and serous epithelial (n=41) primary ovarian carcinomas and samples from 4 normal ovaries. To build the predictor, the Prediction Analysis of Microarrays (PAM) package in R environment was employed (http://rss.acs.unt.edu/Rdoc/library/pamr/html/00Index.html). When more than one probe described the expression of a given gene, we used the probe with the highest median absolute deviation across the samples. The dataset from Hendrix et al. (25) and the dataset of OCCC cell lines described in this manuscript were then overlaid on the basis of 11536 common unique HGNC gene symbols. Only the 99 primary ovarian cancers samples and the four normal ovary samples were used to build the predictor. Following leave one out cross-validation, a predictor based upon 126 genes was able to identify correctly the four distinct phenotypes of primary ovarian tumour samples with a misclassification rate of 18.3%. This predictor was subsequently applied to the expression data from the 12 OCCC cell lines to determine the likeliest phenotype of the OCCC cell lines compared to primary ovarian cancers. Posterior probabilities were estimated for each cell line in comparison to the following phenotypes: clear cell, endometrioid, mucinous and serous epithelial.
    [Show full text]
  • Us 2018 / 0305689 A1
    US 20180305689A1 ( 19 ) United States (12 ) Patent Application Publication ( 10) Pub . No. : US 2018 /0305689 A1 Sætrom et al. ( 43 ) Pub . Date: Oct. 25 , 2018 ( 54 ) SARNA COMPOSITIONS AND METHODS OF plication No . 62 /150 , 895 , filed on Apr. 22 , 2015 , USE provisional application No . 62/ 150 ,904 , filed on Apr. 22 , 2015 , provisional application No. 62 / 150 , 908 , (71 ) Applicant: MINA THERAPEUTICS LIMITED , filed on Apr. 22 , 2015 , provisional application No. LONDON (GB ) 62 / 150 , 900 , filed on Apr. 22 , 2015 . (72 ) Inventors : Pål Sætrom , Trondheim (NO ) ; Endre Publication Classification Bakken Stovner , Trondheim (NO ) (51 ) Int . CI. C12N 15 / 113 (2006 .01 ) (21 ) Appl. No. : 15 /568 , 046 (52 ) U . S . CI. (22 ) PCT Filed : Apr. 21 , 2016 CPC .. .. .. C12N 15 / 113 ( 2013 .01 ) ; C12N 2310 / 34 ( 2013. 01 ) ; C12N 2310 /14 (2013 . 01 ) ; C12N ( 86 ) PCT No .: PCT/ GB2016 /051116 2310 / 11 (2013 .01 ) $ 371 ( c ) ( 1 ) , ( 2 ) Date : Oct . 20 , 2017 (57 ) ABSTRACT The invention relates to oligonucleotides , e . g . , saRNAS Related U . S . Application Data useful in upregulating the expression of a target gene and (60 ) Provisional application No . 62 / 150 ,892 , filed on Apr. therapeutic compositions comprising such oligonucleotides . 22 , 2015 , provisional application No . 62 / 150 ,893 , Methods of using the oligonucleotides and the therapeutic filed on Apr. 22 , 2015 , provisional application No . compositions are also provided . 62 / 150 ,897 , filed on Apr. 22 , 2015 , provisional ap Specification includes a Sequence Listing . SARNA sense strand (Fessenger 3 ' SARNA antisense strand (Guide ) Mathew, Si Target antisense RNA transcript, e . g . NAT Target Coding strand Gene Transcription start site ( T55 ) TY{ { ? ? Targeted Target transcript , e .
    [Show full text]
  • MOL #82305 TITLE PAGE Title: Induced CYP3A4 Expression In
    Downloaded from molpharm.aspetjournals.org at ASPET Journals on September 28, 2021 1 This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted. The final version may differ from this version. This article has not been copyedited and formatted.
    [Show full text]
  • Explorations in Olfactory Receptor Structure and Function by Jianghai
    Explorations in Olfactory Receptor Structure and Function by Jianghai Ho Department of Neurobiology Duke University Date:_______________________ Approved: ___________________________ Hiroaki Matsunami, Supervisor ___________________________ Jorg Grandl, Chair ___________________________ Marc Caron ___________________________ Sid Simon ___________________________ [Committee Member Name] Dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Neurobiology in the Graduate School of Duke University 2014 ABSTRACT Explorations in Olfactory Receptor Structure and Function by Jianghai Ho Department of Neurobiology Duke University Date:_______________________ Approved: ___________________________ Hiroaki Matsunami, Supervisor ___________________________ Jorg Grandl, Chair ___________________________ Marc Caron ___________________________ Sid Simon ___________________________ [Committee Member Name] An abstract of a dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Neurobiology in the Graduate School of Duke University 2014 Copyright by Jianghai Ho 2014 Abstract Olfaction is one of the most primitive of our senses, and the olfactory receptors that mediate this very important chemical sense comprise the largest family of genes in the mammalian genome. It is therefore surprising that we understand so little of how olfactory receptors work. In particular we have a poor idea of what chemicals are detected by most of the olfactory receptors in the genome, and for those receptors which we have paired with ligands, we know relatively little about how the structure of these ligands can either activate or inhibit the activation of these receptors. Furthermore the large repertoire of olfactory receptors, which belong to the G protein coupled receptor (GPCR) superfamily, can serve as a model to contribute to our broader understanding of GPCR-ligand binding, especially since GPCRs are important pharmaceutical targets.
    [Show full text]
  • Online Supporting Information S2: Proteins in Each Negative Pathway
    Online Supporting Information S2: Proteins in each negative pathway Index Proteins ADO,ACTA1,DEGS2,EPHA3,EPHB4,EPHX2,EPOR,EREG,FTH1,GAD1,HTR6, IGF1R,KIR2DL4,NCR3,NME7,NOTCH1,OR10S1,OR2T33,OR56B4,OR7A10, Negative_1 OR8G1,PDGFC,PLCZ1,PROC,PRPS2,PTAFR,SGPP2,STMN1,VDAC3,ATP6V0 A1,MAPKAPK2 DCC,IDS,VTN,ACTN2,AKR1B10,CACNA1A,CHIA,DAAM2,FUT5,GCLM,GNAZ Negative_2 ,ITPA,NEU4,NTF3,OR10A3,PAPSS1,PARD3,PLOD1,RGS3,SCLY,SHC1,TN FRSF4,TP53 Negative_3 DAO,CACNA1D,HMGCS2,LAMB4,OR56A3,PRKCQ,SLC25A5 IL5,LHB,PGD,ADCY3,ALDH1A3,ATP13A2,BUB3,CD244,CYFIP2,EPHX2,F CER1G,FGD1,FGF4,FZD9,HSD17B7,IL6R,ITGAV,LEFTY1,LIPG,MAN1C1, Negative_4 MPDZ,PGM1,PGM3,PIGM,PLD1,PPP3CC,TBXAS1,TKTL2,TPH2,YWHAQ,PPP 1R12A HK2,MOS,TKT,TNN,B3GALT4,B3GAT3,CASP7,CDH1,CYFIP1,EFNA5,EXTL 1,FCGR3B,FGF20,GSTA5,GUK1,HSD3B7,ITGB4,MCM6,MYH3,NOD1,OR10H Negative_5 1,OR1C1,OR1E1,OR4C11,OR56A3,PPA1,PRKAA1,PRKAB2,RDH5,SLC27A1 ,SLC2A4,SMPD2,STK36,THBS1,SERPINC1 TNR,ATP5A1,CNGB1,CX3CL1,DEGS1,DNMT3B,EFNB2,FMO2,GUCY1B3,JAG Negative_6 2,LARS2,NUMB,PCCB,PGAM1,PLA2G1B,PLOD2,PRDX6,PRPS1,RFXANK FER,MVD,PAH,ACTC1,ADCY4,ADCY8,CBR3,CLDN16,CPT1A,DDOST,DDX56 ,DKK1,EFNB1,EPHA8,FCGR3A,GLS2,GSTM1,GZMB,HADHA,IL13RA2,KIR2 Negative_7 DS4,KLRK1,LAMB4,LGMN,MAGI1,NUDT2,OR13A1,OR1I1,OR4D11,OR4X2, OR6K2,OR8B4,OXCT1,PIK3R4,PPM1A,PRKAG3,SELP,SPHK2,SUCLG1,TAS 1R2,TAS1R3,THY1,TUBA1C,ZIC2,AASDHPPT,SERPIND1 MTR,ACAT2,ADCY2,ATP5D,BMPR1A,CACNA1E,CD38,CYP2A7,DDIT4,EXTL Negative_8 1,FCER1G,FGD3,FZD5,ITGAM,MAPK8,NR4A1,OR10V1,OR4F17,OR52D1,O R8J3,PLD1,PPA1,PSEN2,SKP1,TACR3,VNN1,CTNNBIP1 APAF1,APOA1,CARD11,CCDC6,CSF3R,CYP4F2,DAPK1,FLOT1,GSTM1,IL2
    [Show full text]
  • Characterization of the Genomic Features and Expressed Fusion Genes In
    1 SUPPLEMENTARY INFORMATION (ONLINE SUPPORTING INFORMATION) Characterization of the genomic features and expressed fusion genes in micropapillary carcinomas of the breast Natrajan et al. Supplementary Methods Supplementary Figures S1-S6 Supplementary Tables S1-S7 2 SUPPLEMENTARY METHODS Tumor samples Two cohorts of micropapillary carcinomas (MPCs) were analyzed; the first cohort comprised 16 consecutive formalin fixed paraffin embedded (FFPE) MPCs, 11 pure and 5 mixed, which were retrieved from the authors' institutions (Table 1), and a second, validation cohort comprised 14 additional consecutive FFPE MPCs, retrieved from Molinette Hospital, Turin, Italy. Frozen samples were available from five out of the 16 cases from the first cohort of MPCs. As a comparator for the results of the Sequenom mutation profiling, a cohort of 16 consecutive IC-NSTs matched to the first cohort of 16 MPCs according to ER and HER2 status and histological grade were retrieved from a series of breast cancers previously analyzed by aCGH[1]. In addition, 14 IC-NSTs matched according to grade, and ER and HER2 status to tumors from the second cohort of 14 MPCs, and 48 grade 3 IC-NSTs were retrieved from Hospital La Paz, Madrid, Spain[1] (Supplementary Table S1). Power calculation For power calculations, we have assumed that if MPCs were driven by a recurrent fusion gene in a way akin to secretory carcinomas (which harbor the ETV6-NTRK3 fusion gene in >95% of cases[2-4]) or adenoid cystic carcinomas of the breast (which harbor the MYB-NFIB fusion gene in >90% of cases[5]), a ‘pathognomonic’ driver event would be present in at least ≥70% of cases (an estimate that is conservative).
    [Show full text]
  • Convergent Evolution of Chicken Z and Human X Chromosomes by Expansion and Gene Acquisition
    Convergent Evolution of Chicken Z and Human X Chromosomes by Expansion and Gene Acquisition Daniel W. Bellott1, Helen Skaletsky1, Tatyana Pyntikova1, Elaine R. Mardis2, Tina Graves2, Colin Kremitzki2, Laura G. Brown1, Steve Rozen1, Wesley C. Warren2, Richard K. Wilson2 & David C Page1 1. Howard Hughes Medical Institute, Whitehead Institute, and Department of Biology, Massachusetts Institute of Technology, 9 Cambridge Center, Cambridge, Massachusetts 02142, USA 2. The Genome Center, Washington University School of Medicine, 4444 Forest Park Boulevard, St. Louis Missouri 63108, USA 2 In birds, as in mammals, one pair of chromosomes differs between the sexes. In birds, males are ZZ and females ZW. In mammals, males are XY and females XX. Like the mammalian XY pair, the avian ZW pair is believed to have evolved from autosomes, with most change occurring in the chromosomes found in only one sex – the W and Y chromosomes1-5. By contrast, the sex chromosomes found in both sexes – the Z and X chromosomes – are assumed to have diverged little from their autosomal progenitors2. Here we report findings that overturn this assumption for both the chicken Z and human X chromosomes. The chicken Z chromosome, which we sequenced essentially to completion, is less gene-dense than chicken autosomes but contains a massive tandem array containing hundreds of duplicated genes expressed in testes. A comprehensive comparison of the chicken Z chromosome to the finished sequence of the human X chromosome demonstrates that each evolved independently from different portions of the ancestral genome. Despite this independence, the chicken Z and human X chromosomes share features that distinguish them from autosomes: the acquisition and amplification of testis-expressed genes, as well as a low gene density resulting from an expansion of intergenic regions.
    [Show full text]
  • Key Genes Associated with Diabetes Mellitus and Hepatocellular Carcinoma T ⁎ ⁎ Gao-Min Liu , Hua-Dong Zeng, Cai-Yun Zhang, Ji-Wei Xu
    Pathology - Research and Practice 215 (2019) 152510 Contents lists available at ScienceDirect Pathology - Research and Practice journal homepage: www.elsevier.com/locate/prp Key genes associated with diabetes mellitus and hepatocellular carcinoma T ⁎ ⁎ Gao-Min Liu , Hua-Dong Zeng, Cai-Yun Zhang, Ji-Wei Xu Department of Hepatobiliary Surgery, Meizhou People’s Hospital, No. 38 Huangtang Road, Meizhou 514000, China ARTICLE INFO ABSTRACT Keywords: Accumulating evidence indicates a strong correlation between type 2 diabetes mellitus (T2DM) and hepato- Hepatocellular carcinoma cellular carcinoma (HCC), but the underlying pathophysiology is still elusive. We aimed to identify unrecognized Diabetes but important genes and pathways related to T2DM and HCC by bioinformatic analysis. The GSE64998 and TCGA GSE15653 datasets (for T2DM), the GSE121248 dataset and the Cancer Genome Atlas-Liver Hepatocellular GEO Carcinoma (TCGA-LIHC) dataset (for HCC) were downloaded. Differential expression analysis, functional and Bioinformatic pathway enrichment analysis, protein–protein interaction (PPI) network construction, survival analysis, tran- scription factor (TF) prediction, and correlation of gene expression with methylation and tumour-infiltrating immune cells were conducted. Nine genes, namely, CDNF, CRELD2, DNAJB11, DTL, GINS2, MANF, PDIA4, PDIA6, and VCP, were recognized as hub genes. Enrichment analysis revealed several enriched terms and pathways. Transcription factors such as Kruppel-like factor 6, abnormal methylation and immune dysregulation might help explain the dysregulation of hub genes. Our study identified nine hub genes that might play a critical role in both T2DM and HCC. However, more studies are warranted to clarify the mechanisms of these genes. 1. Introduction 2. Materials and methods Hepatocellular carcinoma (HCC) remains a great challenge in public 2.1.
    [Show full text]