Mathematical Modeling Reveals That G2/M Checkpoint Override Creates a Therapeutic Vulnerability in Tsc2-Null Tumors
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A Gene Association Study to Identify Novel Pancreatic Cancer Susceptibility Genes
A Gene Association Study to Identify Novel Pancreatic Cancer Susceptibility Genes Cavin Wong Human Genetics, McGill University, Montreal December 2017 A thesis submitted to McGill University in partial fulfillment of the requirements for the degree of Master of Science. © Cavin Wong, 2017. 1 Abstract Approximately 10% of pancreatic cancer (PAC) cases are hereditary in nature, however, only a fraction is explained by known susceptibility genes. Recent efforts using Next-Generation Sequencing (NGS) data to elucidate novel predisposition genes for PAC risk have been plagued with challenges due to the limitations of the methods in identifying a “faint signal” from a large amount of “noise” created by non-causal variants. Thus, in this dissertation, I have hypothesized that a region-based case-control gene association test, the Mixed-effects Score Test (MiST), will allow for the identification of these “faint signals” in NGS data sets. Compared to previous methods, MiST is a less biased statistical approach which compares mutation frequency across a gene or region for cases versus controls, while accounting for individual variant characteristics. For PAC cases, DNA extracted from circulating lymphocytes or saliva was used as a surrogate for germline DNA. Our case series consists of 109 exomes from high risk PAC cases (familial pancreatic cancer or young onset <50) and 289 prospectively collected PAC cases sequenced for 710 cancer-related genes. Our control series consists of 987 non-cancer cases collected locally from multiple projects. All samples were processed on the same pipeline and variants were limited to exonic and splicing variants. Prior to analysis, we used a principal component analysis to exclude genetic outliers. -
Supplementary Information Method CLEAR-CLIP. Mouse Keratinocytes
Supplementary Information Method CLEAR-CLIP. Mouse keratinocytes of the designated genotype were maintained in E-low calcium medium. Inducible cells were treated with 3 ug/ml final concentration doxycycline for 24 hours before performing CLEAR-CLIP. One 15cm dish of confluent cells was used per sample. Cells were washed once with cold PBS. 10mls of cold PBS was then added and cells were irradiated with 300mJ/cm2 UVC (254nM wavelength). Cells were then scraped from the plates in cold PBS and pelleted by centrifugation at 1,000g for 2 minutes. Pellets were frozen at -80oC until needed. Cells were then lysed on ice with occasional vortexing in 1ml of lysis buffer (50mM Tris-HCl pH 7.4, 100mM NaCl, 1mM MgCl2, 0.1 mM CaCl2, 1% NP-40, 0.5% Sodium Deoxycholate, 0.1% SDS) containing 1X protease inhibitors (Roche #88665) and RNaseOUT (Invitrogen #10777019) at 4ul/ml final concentration. Next, TurboDNase (Invitrogen #AM2238, 10U), RNase A (0.13ug) and RNase T1 (0.13U) were added and samples were incubated at 37oC for 5 minutes with occasional mixing. Samples were immediately placed on ice and then centrifuged at 16,160g at 4oC for 20 minutes to clear lysate. 25ul of Protein-G Dynabeads (Invitrogen #10004D) were used per IP. Dynabeads were pre-washed with lysis buffer and pre- incubated with 3ul of Wako Anti-Mouse-Ago2 (2D4) antibody. The dynabead/antibody mixture was added to the lysate and rocked for 2 hours at 4oC. All steps after the IP were done on bead until samples were loaded into the polyacrylamide gel. -
Under Serratia Marcescens Treatment Kai Feng1,2, Xiaoyu Lu1,2, Jian Luo1,2 & Fang Tang1,2*
www.nature.com/scientificreports OPEN SMRT sequencing of the full‑length transcriptome of Odontotermes formosanus (Shiraki) under Serratia marcescens treatment Kai Feng1,2, Xiaoyu Lu1,2, Jian Luo1,2 & Fang Tang1,2* Odontotermes formosanus (Shiraki) is an important pest in the world. Serratia marcescens have a high lethal efect on O. formosanus, but the specifc insecticidal mechanisms of S. marcescens on O. formosanus are unclear, and the immune responses of O. formosanus to S. marcescens have not been clarifed. At present, genetic database resources of O. formosanus are extremely scarce. Therefore, using O. formosanus workers infected by S. marcescens and the control as experimental materials, a full-length transcriptome was sequenced using the PacBio Sequel sequencing platform. A total of 10,364 isoforms were obtained as the fnal transcriptome. The unigenes were further annotated with the Nr, Swiss-Prot, EuKaryotic Orthologous Groups (KOG), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) Ortholog public databases. In a comparison between the control group and a Serratia marcescens-infected group, a total of 259 diferentially expressed genes (DEGs) were identifed, including 132 upregulated and 127 downregulated genes. Pathway enrichment analysis indicated that the expression of the mitogen-activated protein kinase (MAPK) pathway, oxidative stress genes and the AMP-activated protein kinase (AMPK) pathway in O. formosanus may be associated with S. marcescens treatment. This research intensively studied O. formosanus at the high-throughput full-length transcriptome level, laying a foundation for further development of molecular markers and mining of target genes in this species and thereby promoting the biological control of O. -
Crit. Rev. in Biochem. and Molec. Biol. 2011
Critical Reviews in Biochemistry and Molecular Biology Critical Reviews in Biochemistry and Molecular Biology, 2011, 1–21, Early Online 2011 © 2011 Informa Healthcare USA, Inc. ISSN 1040-9238 print/ISSN 1549-7798 online 00 DOI: 10.3109/10409238.2011.618113 00 000 REVIEW 000 18 June 2011 Mammalian TOR signaling to the AGC kinases 15 August 2011 Bing Su1 and Estela Jacinto2 24 August 2011 1Department of Immunobiology and The Vascular Biology and Therapeutics Program, Yale University School of Medicine, New Haven, CT, USA and 2Department of Physiology and Biophysics, UMDNJ-RWJMS, Piscataway, NJ, USA 1040-9238 1549-7798 Abstract The mechanistic (or mammalian) target of rapamycin (mTOR), an evolutionarily conserved protein kinase, orchestrates © 2011 Informa Healthcare USA, Inc. cellular responses to growth, metabolic and stress signals. mTOR processes various extracellular and intracellular inputs as part of two mTOR protein complexes, mTORC1 or mTORC2. The mTORCs have numerous cellular targets but 10.3109/10409238.2011.618113 members of a family of protein kinases, the protein kinase (PK)A/PKG/PKC (AGC) family are the best characterized direct mTOR substrates. The AGC kinases control multiple cellular functions and deregulation of many members of BBMG this family underlies numerous pathological conditions. mTOR phosphorylates conserved motifs in these kinases to allosterically augment their activity, influence substrate specificity, and promote protein maturation and 618113 stability. Activation of AGC kinases in turn triggers the phosphorylation of diverse, often overlapping, targets that ultimately control cellular response to a wide spectrum of stimuli. This review will highlight recent findings on how mTOR regulates AGC kinases and how mTOR activity is feedback regulated by these kinases. -
Chronic Exposure of Humans to High Level Natural Background Radiation Leads to Robust Expression of Protective Stress Response Proteins S
www.nature.com/scientificreports OPEN Chronic exposure of humans to high level natural background radiation leads to robust expression of protective stress response proteins S. Nishad1,2, Pankaj Kumar Chauhan3, R. Sowdhamini3 & Anu Ghosh1,2* Understanding exposures to low doses of ionizing radiation are relevant since most environmental, diagnostic radiology and occupational exposures lie in this region. However, the molecular mechanisms that drive cellular responses at these doses, and the subsequent health outcomes, remain unclear. A local monazite-rich high level natural radiation area (HLNRA) in the state of Kerala on the south-west coast of Indian subcontinent show radiation doses extending from ≤ 1 to ≥ 45 mGy/y and thus, serve as a model resource to understand low dose mechanisms directly on healthy humans. We performed quantitative discovery proteomics based on multiplexed isobaric tags (iTRAQ) coupled with LC–MS/MS on human peripheral blood mononuclear cells from HLNRA individuals. Several proteins involved in diverse biological processes such as DNA repair, RNA processing, chromatin modifcations and cytoskeletal organization showed distinct expression in HLNRA individuals, suggestive of both recovery and adaptation to low dose radiation. In protein–protein interaction (PPI) networks, YWHAZ (14-3-3ζ) emerged as the top-most hub protein that may direct phosphorylation driven pro- survival cellular processes against radiation stress. PPI networks also identifed an integral role for the cytoskeletal protein ACTB, signaling protein PRKACA; and the molecular chaperone HSPA8. The data will allow better integration of radiation biology and epidemiology for risk assessment [Data are available via ProteomeXchange with identifer PXD022380]. Te basic principles of low linear energy transfer (LET) ionizing radiation (IR) induced efects on mammalian systems have been broadly explored and there exists comprehensive knowledge on the health efects of high doses of IR delivered at high dose rates. -
Supporting Information
Supporting Information Pouryahya et al. SI Text Table S1 presents genes with the highest absolute value of Ricci curvature. We expect these genes to have significant contribution to the network’s robustness. Notably, the top two genes are TP53 (tumor protein 53) and YWHAG gene. TP53, also known as p53, it is a well known tumor suppressor gene known as the "guardian of the genome“ given the essential role it plays in genetic stability and prevention of cancer formation (1, 2). Mutations in this gene play a role in all stages of malignant transformation including tumor initiation, promotion, aggressiveness, and metastasis (3). Mutations of this gene are present in more than 50% of human cancers, making it the most common genetic event in human cancer (4, 5). Namely, p53 mutations play roles in leukemia, breast cancer, CNS cancers, and lung cancers, among many others (6–9). The YWHAG gene encodes the 14-3-3 protein gamma, a member of the 14-3-3 family proteins which are involved in many biological processes including signal transduction regulation, cell cycle pro- gression, apoptosis, cell adhesion and migration (10, 11). Notably, increased expression of 14-3-3 family proteins, including protein gamma, have been observed in a number of human cancers including lung and colorectal cancers, among others, suggesting a potential role as tumor oncogenes (12, 13). Furthermore, there is evidence that loss Fig. S1. The histogram of scalar Ricci curvature of 8240 genes. Most of the genes have negative scalar Ricci curvature (75%). TP53 and YWHAG have notably low of p53 function may result in upregulation of 14-3-3γ in lung cancer Ricci curvatures. -
Biomarkers, Master Regulators and Genomic Fabric Remodeling in a Case of Papillary Thyroid Carcinoma
G C A T T A C G G C A T genes Article Biomarkers, Master Regulators and Genomic Fabric Remodeling in a Case of Papillary Thyroid Carcinoma Dumitru A. Iacobas Personalized Genomics Laboratory, CRI Center for Computational Systems Biology, Roy G Perry College of Engineering, Prairie View A&M University, Prairie View, TX 77446, USA; [email protected]; Tel.: +1-936-261-9926 Received: 1 August 2020; Accepted: 1 September 2020; Published: 2 September 2020 Abstract: Publicly available (own) transcriptomic data have been analyzed to quantify the alteration in functional pathways in thyroid cancer, establish the gene hierarchy, identify potential gene targets and predict the effects of their manipulation. The expression data have been generated by profiling one case of papillary thyroid carcinoma (PTC) and genetically manipulated BCPAP (papillary) and 8505C (anaplastic) human thyroid cancer cell lines. The study used the genomic fabric paradigm that considers the transcriptome as a multi-dimensional mathematical object based on the three independent characteristics that can be derived for each gene from the expression data. We found remarkable remodeling of the thyroid hormone synthesis, cell cycle, oxidative phosphorylation and apoptosis pathways. Serine peptidase inhibitor, Kunitz type, 2 (SPINT2) was identified as the Gene Master Regulator of the investigated PTC. The substantial increase in the expression synergism of SPINT2 with apoptosis genes in the cancer nodule with respect to the surrounding normal tissue (NOR) suggests that SPINT2 experimental overexpression may force the PTC cells into apoptosis with a negligible effect on the NOR cells. The predictive value of the expression coordination for the expression regulation was validated with data from 8505C and BCPAP cell lines before and after lentiviral transfection with DDX19B. -
Evidence for the Role of Ywha in Mouse Oocyte Maturation
EVIDENCE FOR THE ROLE OF YWHA IN MOUSE OOCYTE MATURATION A thesis submitted To Kent State University in partial Fulfillment of the requirements for the Degree of Master of Science By Ariana Claire Detwiler August, 2015 © Copyright All rights reserved Except for previously published materials Thesis written by Ariana Claire Detwiler B.S., Pennsylvania State University, 2012 M.S., Kent State University, 2015 Approved by ___________________________________________________________ Douglas W. Kline, Professor, Ph.D., Department of Biological Sciences, Masters Advisor ___________________________________________________________ Laura G. Leff, Professor, PhD., Chair, Department of Biological Sciences ___________________________________________________________ James L. Blank, Professor, Dean, College of Arts and Sciences i TABLE OF CONTENTS List of Figures ……………………………………………………………………………………v List of Tables ……………………………………………………………………………………vii Acknowledgements …………………………………………………………………………….viii Abstract ……………………………………………………………………………………….....1 Chapter I Introduction…………………………………………………………………………………..2 1.1 Introduction …………………………………………………………………………..2 1.2 Ovarian Function ……………………………………………………………………..2 1.3 Oogenesis and Folliculogenesis ………………………………………………………3 1.4 Oocyte Maturation ……………………………………………………………………5 1.5 Maternal to Embryonic Messenger RNA Transition …………………………………8 1.6 Meiotic Spindle Formation …………………………………………………………...9 1.7 YWHA Isoforms and Oocyte Maturation …………………………………………...10 Aim…………………………………………………………………………………………..15 Chapter II Methods……………………………………………………………………………………...16 -
Cryo-EM Reconstructions of Inhibitor-Bound SMG1 Kinase Reveal An
bioRxiv preprint doi: https://doi.org/10.1101/2021.07.28.454180; this version posted July 28, 2021. 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. 1 Cryo-EM reconstructions of inhibitor-bound SMG1 kinase reveal an 2 autoinhibitory state dependent on SMG8 3 4 5 Lukas M. Langer, Fabien Bonneau, Yair Gat and Elena Conti 6 7 Max Planck Institute of Biochemistry, Martinsried, Germany 8 9 Abstract 10 11 The PI3K-related kinase (PIKK) SMG1 monitors progression of metazoan nonsense- 12 mediated mRNA decay (NMD) by phosphorylating the RNA helicase UPF1. Previous 13 work has shown that the activity of SMG1 is impaired by small molecule inhibitors, is 14 reduced by the SMG1 interactors SMG8 and SMG9, and is downregulated by the so- 15 called SMG1 insertion domain. However, the molecular basis for this complex regulatory 16 network has remained elusive. Here, we present cryo-electron microscopy reconstructions 17 of human SMG1-9 and SMG1-8-9 complexes bound to either a SMG1 inhibitor or a non- 18 hydrolyzable ATP analogue at overall resolutions ranging from 2.8 to 3.6 Å. These 19 structures reveal the basis with which a small molecule inhibitor preferentially targets 20 SMG1 over other PIKKs. By comparison with our previously reported substrate-bound 21 structure (Langer et al. 2020), we show that the SMG1 insertion domain can exert an 22 autoinhibitory function by directly blocking the substrate binding path as well as overall 23 access to the SMG1 kinase active site. -
Autism and Cancer Share Risk Genes, Pathways, and Drug Targets
TIGS 1255 No. of Pages 8 Forum Table 1 summarizes the characteristics of unclear whether this is related to its signal- Autism and Cancer risk genes for ASD that are also risk genes ing function or a consequence of a second for cancers, extending the original finding independent PTEN activity, but this dual Share Risk Genes, that the PI3K-Akt-mTOR signaling axis function may provide the rationale for the (involving PTEN, FMR1, NF1, TSC1, and dominant role of PTEN in cancer and Pathways, and Drug TSC2) was associated with inherited risk autism. Other genes encoding common Targets for both cancer and ASD [6–9]. Recent tumor signaling pathways include MET8[1_TD$IF],[2_TD$IF] genome-wide exome-sequencing studies PTK7, and HRAS, while p53, AKT, mTOR, Jacqueline N. Crawley,1,2,* of de novo variants in ASD and cancer WNT, NOTCH, and MAPK are compo- Wolf-Dietrich Heyer,3,4 and have begun to uncover considerable addi- nents of signaling pathways regulating Janine M. LaSalle1,4,5 tional overlap. What is surprising about the the nuclear factors described above. genes in Table 1 is not necessarily the Autism is a neurodevelopmental number of risk genes found in both autism Autism is comorbid with several mono- and cancer, but the shared functions of genic neurodevelopmental disorders, disorder, diagnosed behaviorally genes in chromatin remodeling and including Fragile X (FMR1), Rett syndrome by social and communication genome maintenance, transcription fac- (MECP2), Phelan-McDermid (SHANK3), fi de cits, repetitive behaviors, tors, and signal transduction pathways 15q duplication syndrome (UBE3A), and restricted interests. Recent leading to nuclear changes [7,8]. -
Xo PANEL DNA GENE LIST
xO PANEL DNA GENE LIST ~1700 gene comprehensive cancer panel enriched for clinically actionable genes with additional biologically relevant genes (at 400 -500x average coverage on tumor) Genes A-C Genes D-F Genes G-I Genes J-L AATK ATAD2B BTG1 CDH7 CREM DACH1 EPHA1 FES G6PC3 HGF IL18RAP JADE1 LMO1 ABCA1 ATF1 BTG2 CDK1 CRHR1 DACH2 EPHA2 FEV G6PD HIF1A IL1R1 JAK1 LMO2 ABCB1 ATM BTG3 CDK10 CRK DAXX EPHA3 FGF1 GAB1 HIF1AN IL1R2 JAK2 LMO7 ABCB11 ATR BTK CDK11A CRKL DBH EPHA4 FGF10 GAB2 HIST1H1E IL1RAP JAK3 LMTK2 ABCB4 ATRX BTRC CDK11B CRLF2 DCC EPHA5 FGF11 GABPA HIST1H3B IL20RA JARID2 LMTK3 ABCC1 AURKA BUB1 CDK12 CRTC1 DCUN1D1 EPHA6 FGF12 GALNT12 HIST1H4E IL20RB JAZF1 LPHN2 ABCC2 AURKB BUB1B CDK13 CRTC2 DCUN1D2 EPHA7 FGF13 GATA1 HLA-A IL21R JMJD1C LPHN3 ABCG1 AURKC BUB3 CDK14 CRTC3 DDB2 EPHA8 FGF14 GATA2 HLA-B IL22RA1 JMJD4 LPP ABCG2 AXIN1 C11orf30 CDK15 CSF1 DDIT3 EPHB1 FGF16 GATA3 HLF IL22RA2 JMJD6 LRP1B ABI1 AXIN2 CACNA1C CDK16 CSF1R DDR1 EPHB2 FGF17 GATA5 HLTF IL23R JMJD7 LRP5 ABL1 AXL CACNA1S CDK17 CSF2RA DDR2 EPHB3 FGF18 GATA6 HMGA1 IL2RA JMJD8 LRP6 ABL2 B2M CACNB2 CDK18 CSF2RB DDX3X EPHB4 FGF19 GDNF HMGA2 IL2RB JUN LRRK2 ACE BABAM1 CADM2 CDK19 CSF3R DDX5 EPHB6 FGF2 GFI1 HMGCR IL2RG JUNB LSM1 ACSL6 BACH1 CALR CDK2 CSK DDX6 EPOR FGF20 GFI1B HNF1A IL3 JUND LTK ACTA2 BACH2 CAMTA1 CDK20 CSNK1D DEK ERBB2 FGF21 GFRA4 HNF1B IL3RA JUP LYL1 ACTC1 BAG4 CAPRIN2 CDK3 CSNK1E DHFR ERBB3 FGF22 GGCX HNRNPA3 IL4R KAT2A LYN ACVR1 BAI3 CARD10 CDK4 CTCF DHH ERBB4 FGF23 GHR HOXA10 IL5RA KAT2B LZTR1 ACVR1B BAP1 CARD11 CDK5 CTCFL DIAPH1 ERCC1 FGF3 GID4 HOXA11 -
Integrin Αvβ6-EGFR Crosstalk Regulates Bidirectional Force Transmission and Controls Breast Cancer Invasion
bioRxiv preprint doi: https://doi.org/10.1101/407908; this version posted September 4, 2018. 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-NC-ND 4.0 International license. Integrin αVβ6-EGFR crosstalk regulates bidirectional force transmission and controls breast cancer invasion Joanna R. Thomas1#, Kate M. Moore2#, Caroline Sproat2, Horacio J. Maldonado-Lorca1, Stephanie Mo1, Syed Haider3, Dean Hammond1, Gareth J. Thomas5, Ian A. Prior1, Pedro R. Cutillas2, Louise J. Jones2, John F. Marshall2†, Mark R. Morgan1† 1 Institute of Translational Medicine, University of Liverpool, Crown Street, Liverpool, L69 3BX, UK. 2 Centre for Tumour Biology, Barts Cancer Institute, Queen Mary University London, John Vane Science Centre, Charterhouse Square, London EC1M 6BQ, UK. 3 The Weatherall Institute of Molecular Medicine, Department of Oncology, University of Oxford, Oxford OX3 9DS, UK. 4 Cancer Research UK Centre for Epidemiology, Mathematics and Statistics, Wolfson Institute of Preventative Medicine, Queen Mary University London, Charterhouse Square, London EC1M 6BQ, UK. 5 Cancer Sciences Division, Somers Building, Southampton General Hospital, Southampton, SO16 6YA, UK. # Denotes equal contribution † Corresponding author Correspondence to: Dr Mark R. Morgan, PhD, Cellular & Molecular Physiology, Institute of Translational Medicine, University of Liverpool, Crown Street, Liverpool, L69 3BX,