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PARSANA-DISSERTATION-2020.Pdf
DECIPHERING TRANSCRIPTIONAL PATTERNS OF GENE REGULATION: A COMPUTATIONAL APPROACH by Princy Parsana A dissertation submitted to The Johns Hopkins University in conformity with the requirements for the degree of Doctor of Philosophy Baltimore, Maryland July, 2020 © 2020 Princy Parsana All rights reserved Abstract With rapid advancements in sequencing technology, we now have the ability to sequence the entire human genome, and to quantify expression of tens of thousands of genes from hundreds of individuals. This provides an extraordinary opportunity to learn phenotype relevant genomic patterns that can improve our understanding of molecular and cellular processes underlying a trait. The high dimensional nature of genomic data presents a range of computational and statistical challenges. This dissertation presents a compilation of projects that were driven by the motivation to efficiently capture gene regulatory patterns in the human transcriptome, while addressing statistical and computational challenges that accompany this data. We attempt to address two major difficulties in this domain: a) artifacts and noise in transcriptomic data, andb) limited statistical power. First, we present our work on investigating the effect of artifactual variation in gene expression data and its impact on trans-eQTL discovery. Here we performed an in-depth analysis of diverse pre-recorded covariates and latent confounders to understand their contribution to heterogeneity in gene expression measurements. Next, we discovered 673 trans-eQTLs across 16 human tissues using v6 data from the Genotype Tissue Expression (GTEx) project. Finally, we characterized two trait-associated trans-eQTLs; one in Skeletal Muscle and another in Thyroid. Second, we present a principal component based residualization method to correct gene expression measurements prior to reconstruction of co-expression networks. -
Mouse Spock1 Knockout Project (CRISPR/Cas9)
https://www.alphaknockout.com Mouse Spock1 Knockout Project (CRISPR/Cas9) Objective: To create a Spock1 knockout Mouse model (C57BL/6J) by CRISPR/Cas-mediated genome engineering. Strategy summary: The Spock1 gene (NCBI Reference Sequence: NM_009262 ; Ensembl: ENSMUSG00000056222 ) is located on Mouse chromosome 13. 12 exons are identified, with the ATG start codon in exon 2 and the TAG stop codon in exon 12 (Transcript: ENSMUST00000185502). Exon 5 will be selected as target site. Cas9 and gRNA will be co-injected into fertilized eggs for KO Mouse production. The pups will be genotyped by PCR followed by sequencing analysis. Note: Mice homozygous for a targeted null mutation display no obvious morphological or behavioral abnormalities, are fertile, and have normal life spans. Adult homozygotes exhibit normal brain morphology and EEG recordings. Exon 5 starts from about 18.25% of the coding region. Exon 5 covers 8.67% of the coding region. The size of effective KO region: ~115 bp. The KO region does not have any other known gene. Page 1 of 8 https://www.alphaknockout.com Overview of the Targeting Strategy Wildtype allele gRNA region 5' gRNA region 3' 1 5 12 Legends Exon of mouse Spock1 Knockout region Page 2 of 8 https://www.alphaknockout.com Overview of the Dot Plot (up) Window size: 15 bp Forward Reverse Complement Sequence 12 Note: The 2000 bp section upstream of Exon 5 is aligned with itself to determine if there are tandem repeats. Tandem repeats are found in the dot plot matrix. The gRNA site is selected outside of these tandem repeats. -
Novel Tumor Subgroups of Urothelial Carcinoma of the Bladder Defined by Integrated Genomic Analysis
Published OnlineFirst August 29, 2012; DOI: 10.1158/1078-0432.CCR-12-1807 Clinical Cancer Human Cancer Biology Research Novel Tumor Subgroups of Urothelial Carcinoma of the Bladder Defined by Integrated Genomic Analysis Carolyn D. Hurst, Fiona M. Platt, Claire F. Taylor, and Margaret A. Knowles Abstract Purpose: There is a need for improved subclassification of urothelial carcinoma (UC) at diagnosis. A major aim of this study was to search for novel genomic subgroups. Experimental design: We assessed 160 tumors for genome-wide copy number alterations and mutation in genes implicated in UC. These comprised all tumor grades and stages and included 49 high-grade stage T1 (T1G3) tumors. Results: Our findings point to the existence of genomic subclasses of the "gold-standard" grade/stage groups. The T1G3 tumors separated into 3 major subgroups that differed with respect to the type and number of copy number events and to FGFR3 and TP53 mutation status. We also identified novel regions of copy number alteration, uncovered relationships between molecular events, and elucidated relationships between molecular events and clinico-pathologic features. FGFR3 mutant tumors were more chromosom- ally stable than their wild-type counterparts and a mutually exclusive relationship between FGFR3 mutation and overrepresentation of 8q was observed in non-muscle-invasive tumors. In muscle-invasive (MI) tumors, metastasis was positively associated with losses of regions on 10q (including PTEN), 16q and 22q, and gains on 10p, 11q, 12p, 19p, and 19q. Concomitant copy number alterations positively associated with TP53 mutation in MI tumors were losses on 16p, 2q, 4q, 11p, 10q, 13q, 14q, 16q, and 19p, and gains on 1p, 8q, 10q, and 12q. -
Accumulation of Rare Coding Variants in Genes Implicated in Risk of Human Cleft Lip with Or Without Cleft Palate
HHS Public Access Author manuscript Author ManuscriptAuthor Manuscript Author Am J Med Manuscript Author Genet A. Author Manuscript Author manuscript; available in PMC 2020 July 01. Published in final edited form as: Am J Med Genet A. 2019 July ; 179(7): 1260–1269. doi:10.1002/ajmg.a.61183. Accumulation of Rare Coding Variants in Genes Implicated in Risk of Human Cleft Lip with or without Cleft Palate Nicholas J. Marini1,*, Kripa Asrani1, Wei Yang2, Jasper Rine1, Gary M Shaw2 1California Institute for Quantitative Biosciences, Department of Molecular and Cellular Biology, University of California, Berkeley, CA 94720, USA 2Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA Abstract Cleft lip with/without cleft palate (CLP) is a common craniofacial malformation with complex etiologies, reflecting both genetic and environmental factors. Most of the suspected genetic risk for CLP has yet to be identified. To further classify risk loci and estimate the contribution of rare variants, we sequenced the exons in 49 candidate genes in 323 CLP cases and 211 non-malformed controls. Our findings indicated that rare, protein-altering variants displayed markedly higher burdens in CLP cases at relevant loci. First, putative loss-of-function mutations (nonsense, frameshift) were significantly enriched among cases: 13 of 323 cases (~4%) harbored such alleles within these 49 genes, versus one such change in controls (p = 0.01). Second, in gene-level analyses, the burden of rare alleles showed greater case-association for several genes previously implicated in cleft risk. For example, BHMT displayed a 10-fold increase in protein-altering variants in CLP cases (p = 0.03), including multiple case occurrences of a rare frameshift mutation (K400fs). -
Downloaded the Rnaseq and Mirnaseq Profiling Taining DAPI (Invitrogen) Following the Manufacturer’S Datasets on 12 OTSCC and Paired Normal Tissue Samples Protocol
He et al. BMC Cancer (2016) 16:685 DOI 10.1186/s12885-016-2716-0 RESEARCH ARTICLE Open Access MicroRNA-21 regulates prostaglandin E2 signaling pathway by targeting 15- hydroxyprostaglandin dehydrogenase in tongue squamous cell carcinoma Qianting He1,2†, Zujian Chen1†, Qian Dong2, Leitao Zhang1,3, Dan Chen1,2, Aditi Patel1, Ajay Koya1, Xianghong Luan4, Robert J. Cabay5, Yang Dai6,7, Anxun Wang2* and Xiaofeng Zhou1,7,8* Abstract Background: Oral tongue squamous cell carcinoma (OTSCC) is one of the most aggressive forms of head and neck/oral cancer (HNOC), and is a complex disease with extensive genetic and epigenetic defects, including microRNA deregulation. Identifying the deregulation of microRNA-mRNA regulatory modules (MRMs) is crucial for understanding the role of microRNA in OTSCC. Methods: A comprehensive bioinformatics analysis was performed to identify MRMs in HNOC by examining the correlation among differentially expressed microRNA and mRNA profiling datasets and integrating with 12 different sequence-based microRNA target prediction algorithms. Confirmation experiments were performed to further assess the correlation among MRMs using OTSCC patient samples and HNOC cell lines. Functional analyses were performed to validate one of the identified MRMs: miR-21-15-Hydroxyprostaglandin Dehydrogenase (HPGD) regulatory module. Results: Our bioinformatics analysis revealed 53 MRMs that are deregulated in HNOC. Four high confidence MRMs were further defined by confirmation experiments using OTSCC patient samples and HNOC cell lines, including miR-21-HPGD regulatory module. HPGD is a known anti-tumorigenic effecter, and it regulates the tumorigenic actions of Prostaglandin E2 (PGE2) by converts PGE2 to its biologically inactive metabolite. Ectopic transfection of miR-21 reduced the expression of HPGD in OTSCC cell lines, and the direct targeting of the miR-21 to the HPGD mRNA was confirmed using a luciferase reporter gene assay. -
Long Non‑Coding Rnas in Small Cell Lung Cancer: a Potential Opening to Combat the Disease (Review)
ONCOLOGY REPORTS 40: 1831-1842, 2018 Long non‑coding RNAs in small cell lung cancer: A potential opening to combat the disease (Review) TIAN-TIAN LI1, RONG-QUAN HE1, JIE MA1, ZU-YUN LI2, XIAO-HUA HU1 and GANG CHEN2 Departments of 1Medical Oncology and 2Pathology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530021, P.R. China Received February 16, 2018; Accepted August 1, 2018 DOI: 10.3892/or.2018.6635 Abstract. Lung cancer is the top cause of cancer-associated 3. Expression of lncRNAs in SCLC mortality in men and women worldwide. Small cell lung cancer 4. Conclusions and future directions (SCLC) is a subtype that constitutes ~15% of all lung cancer cases. Long non-coding RNAs (lncRNAs), possessing no or limited protein-coding ability, have gained extensive attention 1. Introduction as a potentially promising avenue by which to investigate the biological regulation of human cancer. lncRNAs can modulate Lung cancer, recognized as the leading cause of cancer- gene expression at the transcriptional, post-transcriptional and associated mortality worldwide, is classified into small epigenetic levels. The current review highlights the developing cell lung cancer (SCLC) and non-SCLC (NSCLC). SCLC clinical implications and functional roles of lncRNAs in constitutes ~15% of all confirmed cases of lung cancer SCLC, and provides directions for their future utilization in worldwide (1-3). Distinct from NSCLC, SCLC is unique in the diagnosis and treatment of SCLC. its inclination for quick metastasis and sensitivity to initial systemic cytotoxic chemotherapy. Systemic chemotherapy is the solid foundation of treatment for the limited and extensive Contents stages of this disease. -
4-6 Weeks Old Female C57BL/6 Mice Obtained from Jackson Labs Were Used for Cell Isolation
Methods Mice: 4-6 weeks old female C57BL/6 mice obtained from Jackson labs were used for cell isolation. Female Foxp3-IRES-GFP reporter mice (1), backcrossed to B6/C57 background for 10 generations, were used for the isolation of naïve CD4 and naïve CD8 cells for the RNAseq experiments. The mice were housed in pathogen-free animal facility in the La Jolla Institute for Allergy and Immunology and were used according to protocols approved by the Institutional Animal Care and use Committee. Preparation of cells: Subsets of thymocytes were isolated by cell sorting as previously described (2), after cell surface staining using CD4 (GK1.5), CD8 (53-6.7), CD3ε (145- 2C11), CD24 (M1/69) (all from Biolegend). DP cells: CD4+CD8 int/hi; CD4 SP cells: CD4CD3 hi, CD24 int/lo; CD8 SP cells: CD8 int/hi CD4 CD3 hi, CD24 int/lo (Fig S2). Peripheral subsets were isolated after pooling spleen and lymph nodes. T cells were enriched by negative isolation using Dynabeads (Dynabeads untouched mouse T cells, 11413D, Invitrogen). After surface staining for CD4 (GK1.5), CD8 (53-6.7), CD62L (MEL-14), CD25 (PC61) and CD44 (IM7), naïve CD4+CD62L hiCD25-CD44lo and naïve CD8+CD62L hiCD25-CD44lo were obtained by sorting (BD FACS Aria). Additionally, for the RNAseq experiments, CD4 and CD8 naïve cells were isolated by sorting T cells from the Foxp3- IRES-GFP mice: CD4+CD62LhiCD25–CD44lo GFP(FOXP3)– and CD8+CD62LhiCD25– CD44lo GFP(FOXP3)– (antibodies were from Biolegend). In some cases, naïve CD4 cells were cultured in vitro under Th1 or Th2 polarizing conditions (3, 4). -
University of Florida Thesis Or Dissertation Formatting Template
STATISTICAL METHODS FOR ANALYZING GENOMICS DATA By SINJINI SIKDAR A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2017 © 2017 Sinjini Sikdar Dedicated to my husband, Sandipan, who has been a constant source of support and encouragement during the challenges of my PhD life. ACKNOWLEDGMENTS I am deeply grateful to my advisor Professor Susmita Datta for her unyielding support and invaluable guidance throughout my graduate work. It would not have been possible for me to reach this point without her support. I also want to express my sincere thanks to Professor Somnath Datta as I learnt a lot through my interactions with him and benefited from his expert suggestions. I am also thankful to my other dissertation committee members Professor Fei Zou, and Professor Lauren McIntyre for all their kind support and constructive comments regarding my dissertation. I want to thank Professor Ryan Gill from University of Louisville for his helpful contributions to my research works. I also want to thank all the faculty, staff, and students of the Department of Biostatistics at University of Florida as well as all the faculty and students of the Department of Bioinformatics and Biostatistics at University of Louisville who have helped me reach this point. I would like to thank my sister Shreejata Sikdar for her invaluable friendship and constant support throughout my PhD life. Finally, I want to thank my parents for their immense encouragement that have helped me in moving forward in my academic life. -
Genome-Wide DNA Methylation Analysis of KRAS Mutant Cell Lines Ben Yi Tew1,5, Joel K
www.nature.com/scientificreports OPEN Genome-wide DNA methylation analysis of KRAS mutant cell lines Ben Yi Tew1,5, Joel K. Durand2,5, Kirsten L. Bryant2, Tikvah K. Hayes2, Sen Peng3, Nhan L. Tran4, Gerald C. Gooden1, David N. Buckley1, Channing J. Der2, Albert S. Baldwin2 ✉ & Bodour Salhia1 ✉ Oncogenic RAS mutations are associated with DNA methylation changes that alter gene expression to drive cancer. Recent studies suggest that DNA methylation changes may be stochastic in nature, while other groups propose distinct signaling pathways responsible for aberrant methylation. Better understanding of DNA methylation events associated with oncogenic KRAS expression could enhance therapeutic approaches. Here we analyzed the basal CpG methylation of 11 KRAS-mutant and dependent pancreatic cancer cell lines and observed strikingly similar methylation patterns. KRAS knockdown resulted in unique methylation changes with limited overlap between each cell line. In KRAS-mutant Pa16C pancreatic cancer cells, while KRAS knockdown resulted in over 8,000 diferentially methylated (DM) CpGs, treatment with the ERK1/2-selective inhibitor SCH772984 showed less than 40 DM CpGs, suggesting that ERK is not a broadly active driver of KRAS-associated DNA methylation. KRAS G12V overexpression in an isogenic lung model reveals >50,600 DM CpGs compared to non-transformed controls. In lung and pancreatic cells, gene ontology analyses of DM promoters show an enrichment for genes involved in diferentiation and development. Taken all together, KRAS-mediated DNA methylation are stochastic and independent of canonical downstream efector signaling. These epigenetically altered genes associated with KRAS expression could represent potential therapeutic targets in KRAS-driven cancer. Activating KRAS mutations can be found in nearly 25 percent of all cancers1. -
Supplementary Figure S4
18DCIS 18IDC Supplementary FigureS4 22DCIS 22IDC C D B A E (0.77) (0.78) 16DCIS 14DCIS 28DCIS 16IDC 28IDC (0.43) (0.49) 0 ADAMTS12 (p.E1469K) 14IDC ERBB2, LASP1,CDK12( CCNE1 ( NUTM2B SDHC,FCGR2B,PBX1,TPR( CD1D, B4GALT3, BCL9, FLG,NUP21OL,TPM3,TDRD10,RIT1,LMNA,PRCC,NTRK1 0 ADAMTS16 (p.E67K) (0.67) (0.89) (0.54) 0 ARHGEF38 (p.P179Hfs*29) 0 ATG9B (p.P823S) (0.68) (1.0) ARID5B, CCDC6 CCNE1, TSHZ3,CEP89 CREB3L2,TRIM24 BRAF, EGFR (7p11); 0 ABRACL (p.R35H) 0 CATSPER1 (p.P152H) 0 ADAMTS18 (p.Y799C) 19q12 0 CCDC88C (p.X1371_splice) (0) 0 ADRA1A (p.P327L) (10q22.3) 0 CCNF (p.D637N) −4 −2 −4 −2 0 AKAP4 (p.G454A) 0 CDYL (p.Y353Lfs*5) −4 −2 Log2 Ratio Log2 Ratio −4 −2 Log2 Ratio Log2 Ratio 0 2 4 0 2 4 0 ARID2 (p.R1068H) 0 COL27A1 (p.G646E) 0 2 4 0 2 4 2 EDRF1 (p.E521K) 0 ARPP21 (p.P791L) ) 0 DDX11 (p.E78K) 2 GPR101, p.A174V 0 ARPP21 (p.P791T) 0 DMGDH (p.W606C) 5 ANP32B, p.G237S 16IDC (Ploidy:2.01) 16DCIS (Ploidy:2.02) 14IDC (Ploidy:2.01) 14DCIS (Ploidy:2.9) -3 -2 -1 -3 -2 -1 -3 -2 -1 -3 -2 -1 -3 -2 -1 -3 -2 -1 Log Ratio Log Ratio Log Ratio Log Ratio 12DCIS 0 ASPM (p.S222T) Log Ratio Log Ratio 0 FMN2 (p.G941A) 20 1 2 3 2 0 1 2 3 2 ERBB3 (p.D297Y) 2 0 1 2 3 20 1 2 3 0 ATRX (p.L1276I) 20 1 2 3 2 0 1 2 3 0 GALNT18 (p.F92L) 2 MAPK4, p.H147Y 0 GALNTL6 (p.E236K) 5 C11orf1, p.Y53C (10q21.2); 0 ATRX (p.R1401W) PIK3CA, p.H1047R 28IDC (Ploidy:2.0) 28DCIS (Ploidy:2.0) 22IDC (Ploidy:3.7) 22DCIS (Ploidy:4.1) 18IDC (Ploidy:3.9) 18DCIS (Ploidy:2.3) 17q12 0 HCFC1 (p.S2025C) 2 LCMT1 (p.S34A) 0 ATXN7L2 (p.X453_splice) SPEN, p.P677Lfs*13 CBFB 1 2 3 4 5 6 7 8 9 10 11 -
SUPPLEMENTARY MATERIAL Bone Morphogenetic Protein 4 Promotes
www.intjdevbiol.com doi: 10.1387/ijdb.160040mk SUPPLEMENTARY MATERIAL corresponding to: Bone morphogenetic protein 4 promotes craniofacial neural crest induction from human pluripotent stem cells SUMIYO MIMURA, MIKA SUGA, KAORI OKADA, MASAKI KINEHARA, HIROKI NIKAWA and MIHO K. FURUE* *Address correspondence to: Miho Kusuda Furue. Laboratory of Stem Cell Cultures, National Institutes of Biomedical Innovation, Health and Nutrition, 7-6-8, Saito-Asagi, Ibaraki, Osaka 567-0085, Japan. Tel: 81-72-641-9819. Fax: 81-72-641-9812. E-mail: [email protected] Full text for this paper is available at: http://dx.doi.org/10.1387/ijdb.160040mk TABLE S1 PRIMER LIST FOR QRT-PCR Gene forward reverse AP2α AATTTCTCAACCGACAACATT ATCTGTTTTGTAGCCAGGAGC CDX2 CTGGAGCTGGAGAAGGAGTTTC ATTTTAACCTGCCTCTCAGAGAGC DLX1 AGTTTGCAGTTGCAGGCTTT CCCTGCTTCATCAGCTTCTT FOXD3 CAGCGGTTCGGCGGGAGG TGAGTGAGAGGTTGTGGCGGATG GAPDH CAAAGTTGTCATGGATGACC CCATGGAGAAGGCTGGGG MSX1 GGATCAGACTTCGGAGAGTGAACT GCCTTCCCTTTAACCCTCACA NANOG TGAACCTCAGCTACAAACAG TGGTGGTAGGAAGAGTAAAG OCT4 GACAGGGGGAGGGGAGGAGCTAGG CTTCCCTCCAACCAGTTGCCCCAAA PAX3 TTGCAATGGCCTCTCAC AGGGGAGAGCGCGTAATC PAX6 GTCCATCTTTGCTTGGGAAA TAGCCAGGTTGCGAAGAACT p75 TCATCCCTGTCTATTGCTCCA TGTTCTGCTTGCAGCTGTTC SOX9 AATGGAGCAGCGAAATCAAC CAGAGAGATTTAGCACACTGATC SOX10 GACCAGTACCCGCACCTG CGCTTGTCACTTTCGTTCAG Suppl. Fig. S1. Comparison of the gene expression profiles of the ES cells and the cells induced by NC and NC-B condition. Scatter plots compares the normalized expression of every gene on the array (refer to Table S3). The central line -
Association Weight Matrix for the Genetic Dissection of Puberty in Beef Cattle
Association weight matrix for the genetic dissection of puberty in beef cattle Marina R. S. Fortesa,b,c, Antonio Revertera,b, Yuandan Zhanga,d, Eliza Collisa,b, Shivashankar H. Nagarajb,NickN.Jonssona,c,e, Kishore C. Prayagaa,b,1, Wes Barrisa,b, and Rachel J. Hawkena,b,2 aCooperative Research Centre for Beef Genetic Technologies; bCommonwealth Scientific and Industrial Research Organization, division of Livestock Industries, Queensland Bioscience Precinct, Brisbane QLD 4067, Australia; cThe University of Queensland, School of Veterinary Science, Gatton QLD 4343, Australia; dAnimal Genetics and Breeding Unit, University of New England, Armidale NSW 2351, Australia; and eFaculty of Veterinary Medicine, University of Glasgow, Glasgow G61 1QH, United Kingdom Edited by George Seidel, Colorado State University, Fort Collins, CO, and approved June 21, 2010 (received for review February 23, 2010) We describe a systems biology approach for the genetic dissection tional data on traits related to puberty are available. For example, of complex traits based on applying gene network theory to the re- weight and condition score are often measured on occasions sults from genome-wide associations. The associations of single- throughout an animal’s development. Hence, understanding ge- nucleotide polymorphisms (SNP) that were individually associated netics of cattle puberty and its biology serves two purposes: as with a primary phenotype of interest, age at puberty in our study, a strategy to develop efficient livestock resources and as a model were explored across 22 related traits. Genomic regions were sur- for human biology. veyed for genes harboring the selected SNP. As a result, an asso- The focus of this work is to demonstrate a unique systems ap- ciation weight matrix (AWM) was constructed with as many rows proach, which we call an association weight matrix (AWM), ap- as genes and as many columns as traits.