DOCSLIB.ORG

Multiclass Analysis of Gene Expression Data the Protein Process

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text

Load more

Critical Dynamic Processes in the Pathogenesis of Cancer Multiclass Analysis of Gene Expression Data Robert Stengel March 27, 2006 ! Background ! Small, round, blue-cell tumor example ! Analysis of colon cancer, metastases, and normal tissue presented at the Institute for Advanced Study Putative Paradigm for The Protein Process Microarray Analysis: Expression Level Infers Cell Function & Carcinogenesis ! Up-regulation in tumor cells – Causal input (tumor enhancing gene) – Defensive response (tumor suppressor gene) – Bystander effect – Tissue effect – Artifact ! Down-regulation in tumor cells – Present, but mutated (and, therefore, not detected) – Eliminated or suppressed by tumor growth – Tissue effect – Artifact Models of Gene Microarray Classification Interaction Objectives ! Inhibition and amplification are multigene effects !Class comparison ! Effects of over/underexpression depend on regulatory pathway and sign of interaction – Identify expression profiles (feature sets) for predefined classes !Class prediction – Develop mathematical function/algorithm that predicts class membership for a novel expression profile !Class discovery – Identify new classes, sub-classes, or features related to classification objectives 200 150 Tumor Typical Pairs of Colon Cancer Normal 100 Characteristics of D14657 Microarray Expression Levels 5 0 0 Classification Features -50 (Alon, Notterman, Levine et al, 1999) -20 -10 0 1 0 2 0 3 0 4 0 M94363 ! Samples not well differentiated in individual transcript clusters (overlapping) 250 200 150 Tumor Normal ! Strong feature 100 H57136 – Individual feature provides good classification 5 0 – Minimal overlap of feature values in each class 0 – Significant difference in class mean values -50 – Low variance in class is desirable ! Additional features -100 -80 -60 -40 -20 0 2 0 4 0 6 0 8 0 – Orthogonal feature (low correlation) may add M96839 new information to the set – Co-expressed feature (high correlation) is redundant; averaging may reduce error Two-Sample Student t Test Example of Gene-by-Gene Tumor/Normal Classification by t Test for Gene Selection (data from Alon, Notterman, Levine, et al, 1999) ! 1,151 transcripts are ! t compares mean values of two data sets 2 2 over/under-expressed in # # – |t| is reduced by uncertainty in the data sets ( ) T N ! tumor/normal comparison, p t = (mT " mN )/ + – |t| is increased by number of points in the data sets (n) # 0.003 36 22 ! Genetically dissimilar # 2 # 2 ! m = mean value of data set samples are apparent t = m " m / A + B ! ! = standard deviation of data set ( A B ) ! nA nB ! n = number of points in data set “Cancer-positive probe sets” ! t test applied to mean tumor/normal expression levels of individual genes ! ! |t| > 3, mA ! mB with "99.7% confidence “Cancer-negative probe sets” (error p # 0.003) [n > 25] ! Dukes Stages: A -> B -> C -> D Correlation Matrices Ensemble Mean Values (Metagenes) +1 0 –1 ! Treat each probe set (row) as a redundant, corrupted ! Probe set correlation measurement of the same tumor/normal indicator zij = ki y + "ij , i =1, m, j =1, n x = ! Compute column averages for each sample sub-group (i.e., sum each column and divide by n) 1 n ! Sample correlation ˆ z j = "zij ! n i=1 ! Statistics of random variable sums are normal by central x = limit theorem ! Class Prediction and Evaluation Clustering of Sample Averages for Primary (data from Alon, Notterman, Levine, et al 1999) Colon Cancer vs. Normal Mucosa (PPG, 144-sample set) 800.00 ! 144 samples, 200 3,437 probe 700.00 sets analyzed ! Single-feature 180 ! 47 primary prediction 600.00 colon cancer 160 – Cancer-positive Tumors ! 22 normal 140 Normals mucosa genes: 4 errors 500.00 ! Affymetrix – Cancer-negative 120 genes: 2 errors HGU-133A 400.00 100 GeneChip ! Two-feature Primary Colon Cancer Normal Mucosa ! All prediction: 1 error 80 300.00 transcripts (mislabeling?) “Present” in 60 all samples 200.00 40 Average Value for Genes Down-Regulated in Tumors 20 100.00 # Probe sets 0 Average Value of Down-Regulated Genes (Primary Colon Cancer) Up: 1067 0 20 40 60 80 100 120 140 160 180 Down: 290 Average Value for Genes Up-Regulated in Tumors 0.00 0.00 200.00 400.00 600.00 800.00 1000.00 1200.00 1400.00 1600.00 1800.00 2000.00 Constant: 19 Average Value of Up-Regulated Genes (Primary Colon Cancer) Clustering of Sample Averages for Clustering of Sample Averages for Primary Polyp vs. Normal Mucosa Primary Polyp vs. Primary Colon Cancer (PPG, 144-sample set) (PPG, 144-sample set) 800.00 1000.00 900.00 700.00 ! 21 primary polyp 800.00 ! 21 primary polyp 600.00 ! 22 normal mucosa 700.00 ! 47 primary 500.00 colon cancer 600.00 400.00 500.00 Primary Polyp Normal Mucosa 300.00 # Probe sets 400.00 # Probe sets Primary Polyp Up: 1014 Primary Colon Cancer Up: 273 300.00 200.00 Down: 219 Down: 126 Constant: 40 200.00 Constant: 172 Average Value of Down-Regulated Genes (Primary Polyp) 100.00 Average Value of Down-Regulated Genes (Primary Polyp) 100.00 0.00 0.00 500.00 1000.00 1500.00 2000.00 2500.00 0.00 0.00 500.00 1000.00 1500.00 2000.00 2500.00 3000.00 3500.00 Average Value of Up-Regulated Genes (Primary Polyp) Average Value of Up-Regulated Genes (Primary Polyp) Identified Cancer Links in Genetic Distinctions Between Ascending Selected Gene Training Sets and Descending Colon Tumors (PPG, 144 samples) (PPG, 144-sample set) Number of Identified Cancer Genes in Set Over-Expressed (10) Under-Expressed (10) Both (20) 1400.00 Primary Colon Cancer vs. Normal Mucosa 7 (70%) 5 (50%) 12 (60%) 1200.00 Primary Polyp vs. 1000.00 Norma Mucosa 7 (70%) 5 (50%) 12 (60%) Primary Colon Cancer 800.00 vs. Primary Polyp 7 (70%) 6 (60%) 13 (65%) 600.00 Ascending Colon Total 21 (70%) 16 (53%) 37 (62%) Remainder of Colon 400.00 … but a significant number of genes at the bottom of the Average Value of Down-Regulated Genes (Ascending Colon) 200.00 |t| > 3 lists also have been linked to cancer 0.00 ! Bioalma linkages via GeneCards® 0.00 200.00 400.00 600.00 800.00 1000.00 1200.00 1400.00 (Weizmann Institute of Science) Average Value of Up-Regulated Genes (Ascending Colon) Genetic Distinctions Between Cecum, Small, Round Blue-Cell Tumor Sigmoid, and Rectosigmoid Colon Tumors (PPG, 144-sample set) Classification Example ! Childhood cancers, including 1000.00 900.00 – Ewing’s sarcoma (EWS) 900.00 – Burkitt’s Lymphoma (BL) 800.00 – Neuroblastoma (NB) 800.00 700.00 – Rhabdomyosarcoma (RMS) 700.00 600.00 ! cDNA microarray analysis presented in 600.00 RectosigmoidCecum Colon – “Classification and Diagnostic Prediction of 500.00 Remainder of Colon 500.00 Sigmoid Colon Cancers Using Gene Expression Profiling and Remainder of Colon 400.00 Artificial Neural Networks,” J. Khan, et al., 400.00 Nature Medicine, 7(6), June 2001, 673-679. 300.00 http://www.medstudents.com.br/patoclin/courses/ 300.00 » 96 transcripts chosen from 2308 probes for training sarcomas/dsrct/dsrct.htm » 63 EWS, BL, NB, and RMS training samples 200.00200.00 Average Value of Down-Regulated Genes (Cecum) Average Value of Down-Regulated Genes (Sigmoid) Desmoplastic small, » Classification with principal component analysis Average Value of Down-Regulated Genes (Rectosigmoid) 100.00100.00 round blue-cell tumors and a linear neural network 0.00 – Source of data for my analysis 0.00 100.00200.00100.00200.00400.00200.00300.00 600.00400.00300.00500.00800.00 400.00600.001000.00 700.00500.001200.00800.00 1400.00600.00900.00 AverageAverageAverage Value Value Value of of Up-Regulatedof Up-Regulated Up-Regulated Genes Genes Genes (Rectosigmoid) (Sigmoid) (Cecum) Overview of Present Ranking by EWS t Values SRBCT Analysis (Top and Bottom 12) ! 24 genes selected from 12 highest and lowest t values for EWS vs. remainder ! Gene selection by t test Sort by EWS t Value EWS BL NB RMS – 96 genes, 12 highest and lowest t values for Image ID Gene Description t Value t Value t Value t Value 770394 Fc fragment of IgG, receptor, transporter, alpha 12.04 -6.67 -6.17 -4.79 each class 1435862 antigen identified by monoclonal antibodies 12E7, F21 and O13 9.09 -6.75 -5.01 -4.03 377461 caveolin 1, caveolae protein, 22kD 8.82 -5.97 -4.91 -4.78 – Overlap with Khan set: 32 genes 814260 follicular lymphoma variant translocation 1 8.17 -4.31 -4.70 -5.48 ! Samples 491565 Cbp/p300-interacting transactivator, with Glu/Asp-rich carboxy-terminal domain 7.60 -5.82 -2.62 -3.68 ! Ensemble averaging of highest and 841641 cyclin D1 (PRAD1: parathyroid adenomatosis 1) 6.84 -9.93 0.56 -4.30 – EWS (13 tumor, 10 cell-line 1471841 ATPase, Na+/K+ transporting, alpha 1 polypeptide 6.65 -3.56 -2.72 -4.69 lowest t values for each class 866702 protein tyrosine phosphatase, non-receptor type 13 6.54 -4.99 -4.07 -4.84 samples) vs. remainder 713922 glutathione S-transferase M1 6.17 -5.61 -5.16 -1.97 308497 KIAA0467S proteintrong t-value distinctions 5.99 -6.69 -6.63 -1.11 ! Correlations of training genes and – BL (8 cell-line samples) vs. 770868 NGFI-A binding protein 2 (ERG1 binding protein 2) 5.93 -6.74 -3.88 -1.21 samples remainder 345232 lymphotoxinb ealphatw (TNFe esuperfamily,n cla membersse 1)s 5.61 -8.05 -2.49 -1.19 – NB (12 cell-line samples) vs. 786084 chromobox homolog 1 (Drosophila HP1 beta) -5.04 -1.05 9.65 -0.62 ! Clustering of ensemble averages remainder 796258 sarcoglycan, alpha (50kD dystrophin-associated glycoprotein) -5.04 -3.31 -3.86 6.83 431397 -5.04 2.64 2.19 0.64 ! Classification by sigmoidal neural – RMS (10 tumor, 10 cell-line 825411 N-acetylglucosamine receptor 1 (thyroid) -5.06 -1.45 5.79 0.76 859359 quinone oxidoreductase homolog -5.23 -7.27 0.78 5.40 network samples) vs.
Recommended publications
  • Download PDF (Inglês)

    Download PDF (Inglês)

    Genetics and Molecular Biology, 44, 1, e20200158 (2021) Copyright © Sociedade Brasileira de Genética. DOI: https://doi.org/10.1590/1678-4685-GMB-2020-0158 Research Article Cellular, Molecular and Developmental Genetics Human DDX56 protein interacts with influenza A virus NS1 protein and stimulates the virus replication 1 2 Ayşegül Pirinçal and Kadir Turan 1Marmara University, Institute of Health Sciences, Istanbul, Turkey. 2Marmara University, Faculty of Pharmacy, Department of Basic Pharmaceutical Sciences, Istanbul, Turkey. Abstract Influenza A viruses (IAV) are enveloped viruses carrying a single-stranded negative-sense RNA genome. Detection of host proteins having a relationship with IAV and revealing of the role of these proteins in the viral replication are of great importance in keeping IAV infections under control. Consequently, the importance of human DDX56, which is determined to be associated with a viral NS1 with a yeast two-hybrid assay, was investigated for IAV replication. The viral replication in knocked down cells for the DDX56 gene was evaluated. The NS1 was co-precipitated with the DDX56 protein in lysates of cells transiently expressing DDX56 and NS1 or infected with the viruses, showing that NS1 and DDX56 interact in mammalian cells. Viral NS1 showed a tendency to co-localize with DDX56 in the cells, transiently expressing both of these proteins, which supports the IP and two-hybrid assays results. The data obtained with in silico predictions supported the in vitro protein interaction results. The viral replication was significantly reduced in the DDX56-knockdown cells comparing with that in the control cells. In conclusion, human DDX56 protein interacts with the IAV NS1 protein in both yeast and mammalian cells and has a positive regulatory effect on IAV replication.
  • DDX56 Mouse Monoclonal Antibody [Clone ID: OTI1G6] Product Data

    DDX56 Mouse Monoclonal Antibody [Clone ID: OTI1G6] Product Data

    OriGene Technologies, Inc. 9620 Medical Center Drive, Ste 200 Rockville, MD 20850, US Phone: +1-888-267-4436 [email protected] EU: [email protected] CN: [email protected] Product datasheet for TA802941 DDX56 Mouse Monoclonal Antibody [Clone ID: OTI1G6] Product data: Product Type: Primary Antibodies Clone Name: OTI1G6 Applications: WB Recommended Dilution: WB 1:2000 Reactivity: Human, Mouse, Rat Host: Mouse Isotype: IgG1 Clonality: Monoclonal Immunogen: Human recombinant protein fragment corresponding to amino acids 323-547 of human DDX56 (NP_061955) produced in E.coli. Formulation: PBS (PH 7.3) containing 1% BSA, 50% glycerol and 0.02% sodium azide. Concentration: 1 mg/ml Purification: Purified from mouse ascites fluids or tissue culture supernatant by affinity chromatography (protein A/G) Conjugation: Unconjugated Storage: Store at -20°C as received. Stability: Stable for 12 months from date of receipt. Predicted Protein Size: 61.4 kDa Gene Name: DEAD-box helicase 56 Database Link: NP_061955 Entrez Gene 54606 Human Q9NY93 This product is to be used for laboratory only. Not for diagnostic or therapeutic use. View online » ©2021 OriGene Technologies, Inc., 9620 Medical Center Drive, Ste 200, Rockville, MD 20850, US 1 / 2 DDX56 Mouse Monoclonal Antibody [Clone ID: OTI1G6] – TA802941 Background: This gene encodes a member of the DEAD box protein family. DEAD box proteins, characterized by the conserved motif Asp-Glu-Ala-Asp (DEAD), are putative RNA helicases. They are implicated in a number of cellular processes involving alteration of RNA secondary structure such as translation initiation, nuclear and mitochondrial splicing, and ribosome and spliceosome assembly. Based on their distribution patterns, some members of this family are believed to be involved in embryogenesis, spermatogenesis, and cellular growth and division.
  • DDX56 (N-13): Sc-104189

    DDX56 (N-13): Sc-104189

    SAN TA C RUZ BI OTEC HNOL OG Y, INC . DDX56 (N-13): sc-104189 BACKGROUND APPLICATIONS DEAD-box proteins, characterized by the conserved motif Asp-Glu-Ala-Asp, DDX56 (N-13) is recommended for detection of DDX56 of mouse and human are putative RNA helicases implicated in several cellular processes involving origin by Western Blotting (starting dilution 1:200, dilution range 1:100- modifications of RNA secondary structure and ribosome/spliceosome assembly. 1:1000), immunoprecipitation [1-2 µg per 100-500 µg of total protein (1 ml Based on their distribution patterns, some members of this family may be of cell lysate)], immunofluorescence (starting dilution 1:50, dilution range involved in embryogenesis, spermatogenesis, and cellular growth and division. 1:50-1:500) and solid phase ELISA (starting dilution 1:30, dilution range 1:30- DDX56 (DEAD box polypeptide 56), also known as DDX21 or NOH61, contains 1:3000); non cross-reactive with other DDX family members. DDX56 (N-13) a helicase core region, a leucine zipper motif in its N-terminus, two putative is also recommended for detection of DDX56 in additional species, including C-terminal nuclear localization signals and several potential phosphorylation equine, bovine and porcine. sites. DDX56 may be involved in ribosome synthesis, specifically during Suitable for use as control antibody for DDX56 siRNA (h): sc-89835, DDX56 assembly of the large 60S ribosomal subunit. siRNA (m): sc-105281, DDX56 shRNA Plasmid (h): sc-89835-SH, DDX56 shRNA Plasmid (m): sc-105281-SH, DDX56 shRNA (h) Lentiviral Particles: REFERENCES sc- 89835-V and DDX56 shRNA (m) Lentiviral Particles: sc-105281-V.
  • DEAD-Box RNA Helicases in Cell Cycle Control and Clinical Therapy

    DEAD-Box RNA Helicases in Cell Cycle Control and Clinical Therapy

    cells Review DEAD-Box RNA Helicases in Cell Cycle Control and Clinical Therapy Lu Zhang 1,2 and Xiaogang Li 2,3,* 1 Department of Nephrology, Renmin Hospital of Wuhan University, Wuhan 430060, China; [email protected] 2 Department of Internal Medicine, Mayo Clinic, 200 1st Street, SW, Rochester, MN 55905, USA 3 Department of Biochemistry and Molecular Biology, Mayo Clinic, 200 1st Street, SW, Rochester, MN 55905, USA * Correspondence: [email protected]; Tel.: +1-507-266-0110 Abstract: Cell cycle is regulated through numerous signaling pathways that determine whether cells will proliferate, remain quiescent, arrest, or undergo apoptosis. Abnormal cell cycle regula- tion has been linked to many diseases. Thus, there is an urgent need to understand the diverse molecular mechanisms of how the cell cycle is controlled. RNA helicases constitute a large family of proteins with functions in all aspects of RNA metabolism, including unwinding or annealing of RNA molecules to regulate pre-mRNA, rRNA and miRNA processing, clamping protein complexes on RNA, or remodeling ribonucleoprotein complexes, to regulate gene expression. RNA helicases also regulate the activity of specific proteins through direct interaction. Abnormal expression of RNA helicases has been associated with different diseases, including cancer, neurological disorders, aging, and autosomal dominant polycystic kidney disease (ADPKD) via regulation of a diverse range of cellular processes such as cell proliferation, cell cycle arrest, and apoptosis. Recent studies showed that RNA helicases participate in the regulation of the cell cycle progression at each cell cycle phase, including G1-S transition, S phase, G2-M transition, mitosis, and cytokinesis.
  • The Helicase Activity of DDX56 Is Required for Its Role in Assembly of Infectious West Nile Virus Particles

    The Helicase Activity of DDX56 Is Required for Its Role in Assembly of Infectious West Nile Virus Particles

    Virology 433 (2012) 226–235 Contents lists available at SciVerse ScienceDirect Virology journal homepage: www.elsevier.com/locate/yviro The helicase activity of DDX56 is required for its role in assembly of infectious West Nile virus particles Zaikun Xu a, Tom C. Hobman a,b,c,n a Department of Cell Biology, University of Alberta, 5-14 Medical Sciences Building, Edmonton, Canada T6G 2H7 b Department of Medical Microbiology and Immunology, University of Alberta, Canada T6G 2H7 c Li Ka Shing Institute of Virology, University of Alberta, Canada T6G 2H7 article info abstract Article history: Although flaviviruses encode their own helicases, evidence suggests that cellular helicases are also Received 26 April 2012 required for replication and/or assembly of these viruses. By and large, the mechanisms of action for Returned to author for revisions viral and cellular helicases are not known. Moreover, in some cases, enzymatic activity is not even 1 June 2012 required for their roles in virus biology. Recently, we showed that expression of the host nucleolar Accepted 3 August 2012 helicase DDX56 is important for infectivity of West Nile virus (WNV) particles. In the present study, we Available online 25 August 2012 demonstrate that the helicase activity of this enzyme is essential for its role in assembly of infectious Keywords: WNV virions. Over-expression of the capsid-binding region of DDX56 also reduces infectivity of WNV West Nile virus suggesting that interaction of DDX56 and capsid protein is an important step in the virion assembly Flavivirus pathway. To our knowledge, this is the first study showing that enzymatic activity of a cellular helicase Helicase is critical for infectivity of flaviviruses.
  • Noelia Díaz Blanco

    Noelia Díaz Blanco

    Effects of environmental factors on the gonadal transcriptome of European sea bass (Dicentrarchus labrax), juvenile growth and sex ratios Noelia Díaz Blanco Ph.D. thesis 2014 Submitted in partial fulfillment of the requirements for the Ph.D. degree from the Universitat Pompeu Fabra (UPF). This work has been carried out at the Group of Biology of Reproduction (GBR), at the Department of Renewable Marine Resources of the Institute of Marine Sciences (ICM-CSIC). Thesis supervisor: Dr. Francesc Piferrer Professor d’Investigació Institut de Ciències del Mar (ICM-CSIC) i ii A mis padres A Xavi iii iv Acknowledgements This thesis has been made possible by the support of many people who in one way or another, many times unknowingly, gave me the strength to overcome this "long and winding road". First of all, I would like to thank my supervisor, Dr. Francesc Piferrer, for his patience, guidance and wise advice throughout all this Ph.D. experience. But above all, for the trust he placed on me almost seven years ago when he offered me the opportunity to be part of his team. Thanks also for teaching me how to question always everything, for sharing with me your enthusiasm for science and for giving me the opportunity of learning from you by participating in many projects, collaborations and scientific meetings. I am also thankful to my colleagues (former and present Group of Biology of Reproduction members) for your support and encouragement throughout this journey. To the “exGBRs”, thanks for helping me with my first steps into this world. Working as an undergrad with you Dr.
  • Curcumin Alters Gene Expression-Associated DNA Damage, Cell Cycle, Cell Survival and Cell Migration and Invasion in NCI-H460 Human Lung Cancer Cells in Vitro

    Curcumin Alters Gene Expression-Associated DNA Damage, Cell Cycle, Cell Survival and Cell Migration and Invasion in NCI-H460 Human Lung Cancer Cells in Vitro

    ONCOLOGY REPORTS 34: 1853-1874, 2015 Curcumin alters gene expression-associated DNA damage, cell cycle, cell survival and cell migration and invasion in NCI-H460 human lung cancer cells in vitro I-TSANG CHIANG1,2, WEI-SHU WANG3, HSIN-CHUNG LIU4, SU-TSO YANG5, NOU-YING TANG6 and JING-GUNG CHUNG4,7 1Department of Radiation Oncology, National Yang‑Ming University Hospital, Yilan 260; 2Department of Radiological Technology, Central Taiwan University of Science and Technology, Taichung 40601; 3Department of Internal Medicine, National Yang‑Ming University Hospital, Yilan 260; 4Department of Biological Science and Technology, China Medical University, Taichung 404; 5Department of Radiology, China Medical University Hospital, Taichung 404; 6Graduate Institute of Chinese Medicine, China Medical University, Taichung 404; 7Department of Biotechnology, Asia University, Taichung 404, Taiwan, R.O.C. Received March 31, 2015; Accepted June 26, 2015 DOI: 10.3892/or.2015.4159 Abstract. Lung cancer is the most common cause of cancer CARD6, ID1 and ID2 genes, associated with cell survival and mortality and new cases are on the increase worldwide. the BRMS1L, associated with cell migration and invasion. However, the treatment of lung cancer remains unsatisfactory. Additionally, 59 downregulated genes exhibited a >4-fold Curcumin has been shown to induce cell death in many human change, including the DDIT3 gene, associated with DNA cancer cells, including human lung cancer cells. However, the damage; while 97 genes had a >3- to 4-fold change including the effects of curcumin on genetic mechanisms associated with DDIT4 gene, associated with DNA damage; the CCPG1 gene, these actions remain unclear. Curcumin (2 µM) was added associated with cell cycle and 321 genes with a >2- to 3-fold to NCI-H460 human lung cancer cells and the cells were including the GADD45A and CGREF1 genes, associated with incubated for 24 h.
  • Imaging-Based Pooled CRISPR Screening Reveals Regulators of Lncrna Localization

    Imaging-Based Pooled CRISPR Screening Reveals Regulators of Lncrna Localization

    Imaging-based pooled CRISPR screening reveals regulators of lncRNA localization Chong Wanga,b,c,1, Tian Lua,b,c,1, George Emanuela,b,c, Hazen P. Babcockd, and Xiaowei Zhuanga,b,c,2 aHoward Hughes Medical Institute, Harvard University, Cambridge, MA 02138; bDepartment of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138; cDepartment of Physics, Harvard University, Cambridge, MA 02138; and dCenter for Advanced Imaging, Harvard University, Cambridge, MA 02138 Contributed by Xiaowei Zhuang, April 16, 2019 (sent for review March 5, 2019; reviewed by Joshua C. Vaughan and Jonathan S. Weissman) Pooled-library CRISPR screening provides a powerful means to determination by sequencing after physically isolating cells with discover genetic factors involved in cellular processes in a high- certain phenotypes (15, 16). However, determining the full genotype- throughput manner. However, the phenotypes accessible to pooled- phenotype correspondence requires an all-imaging–based pooled- library screening are limited. Complex phenotypes, such as cellular library screen approach in which both genotypes and phenotypes morphology and subcellular molecular organization, as well as their are imaged for individual cells in situ. dynamics, require imaging-based readout and are currently beyond In this work, we report an approach for all-imaging–based the reach of pooled-library CRISPR screening. Here we report an all pooled-library CRISPR screening in mammalian cells. This ap- imaging-based pooled-library CRISPR screening approach that com- proach allows both high-content phenotype imaging of multiple bines high-content phenotype imaging with high-throughput single molecular targets in individual cells and high-accuracy identifica- guide RNA (sgRNA) identification in individual cells.
  • Cerebral Small Vessel Disease Genomics and Its Implications Across the Lifespan Muralidharan Sargurupremraj, Hideaki Suzuki, Xueqiu Jian, Chloé Sarnowski, Tavia E

    Cerebral Small Vessel Disease Genomics and Its Implications Across the Lifespan Muralidharan Sargurupremraj, Hideaki Suzuki, Xueqiu Jian, Chloé Sarnowski, Tavia E

    Cerebral small vessel disease genomics and its implications across the lifespan Muralidharan Sargurupremraj, Hideaki Suzuki, Xueqiu Jian, Chloé Sarnowski, Tavia E. Evans, Joshua C Bis, Gudny Eiriksdottir, Saori Sakaue, Natalie Terzikhan, Mohamad Habes, et al. To cite this version: Muralidharan Sargurupremraj, Hideaki Suzuki, Xueqiu Jian, Chloé Sarnowski, Tavia E. Evans, et al.. Cerebral small vessel disease genomics and its implications across the lifespan. Nature Communica- tions, Nature Publishing Group, 2020, 11, 10.1038/s41467-020-19111-2. hal-03121357 HAL Id: hal-03121357 https://hal.archives-ouvertes.fr/hal-03121357 Submitted on 26 Jan 2021 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Distributed under a Creative Commons Attribution| 4.0 International License ARTICLE https://doi.org/10.1038/s41467-020-19111-2 OPEN Cerebral small vessel disease genomics and its implications across the lifespan Muralidharan Sargurupremraj et al.# White matter hyperintensities (WMH) are the most common brain-imaging feature of cer- ebral small vessel disease (SVD), hypertension being the main known risk factor. Here, we identify 27 genome-wide loci for WMH-volume in a cohort of 50,970 older individuals, fi 1234567890():,; accounting for modi cation/confounding by hypertension.
  • Biomedical Robots. Application to Translational Medicine

    Biomedical Robots. Application to Translational Medicine

    Biomedical robots. Application to translational medicine. Enrique J. deAndrés-Galiana Supervisors: Prof. Juan Luis Fernández-Martínez & Prof. Oscar Luaces This dissertation is submitted under the PhD program of Mathematics and Statistics May 2016 RESUMEN DEL CONTENIDO DE TESIS DOCTORAL 1.- Título de la Tesis Español/Otro Idioma: Inglés: Diseño de robots biomédicos. Aplicaciones en Biomedical robots. Application to translational medicina traslacional. medicine. 2.- Autor Nombre: Enrique Juan de Andrés Galiana DNI/Pasaporte/NIE: Programa de Doctorado: Matemáticas y Estadística. Órgano responsable: Departamento de Matemáticas. RESUMEN (en español) Esta tesis trata sobre el análisis y diseño de robots biomédicos y su aplicación a la medicina traslacional. Se define un robot biomédico como el conjunto de técnicas provenientes de la matemática aplicada, estadística y ciencias de la computación capaces de analizar datos biomédicos de alta dimensionalidad, aprender dinámicamente de dichos datos, extraer nuevo BIS - conocimiento e hipótesis de trabajo, y finalmente realizar predicciones con su incertidumbre asociada, cara a la toma de decisiones biomédicas. Se diseñan y analizan diferentes algorit- 010 - mos de aprendizaje, de reducción de la dimensión y selección de atributos, así como técnicas de optimización global, técnicas de agrupamiento no supervisado, clasificación y análisis de VOA incertidumbre. Dichas metodologías se aplican a datos a pie de hospital y de expresión génica - en predicción de fenotipos para optimización del diagnóstico, pronóstico, tratamiento y análisis de toxicidades. MAT - Se muestra que es posible establecer de modo sencillo el poder discriminatorio de las variables FOR pronóstico, y que dichos problemas de clasificación se aproximan a un comportamiento linealmente separable cuando se reduce la dimensión al conjunto de variables principales que definen el alfabeto del problema biomédico y están por tanto relacionadas con su génesis.
  • Assessment of Network Module Identification Across Complex Diseases

    Assessment of Network Module Identification Across Complex Diseases

    ANALYSIS https://doi.org/10.1038/s41592-019-0509-5 Assessment of network module identification across complex diseases Sarvenaz Choobdar1,2,20, Mehmet E. Ahsen3,117, Jake Crawford4,117, Mattia Tomasoni 1,2, Tao Fang5, David Lamparter1,2,6, Junyuan Lin7, Benjamin Hescott8, Xiaozhe Hu7, Johnathan Mercer9,10, Ted Natoli11, Rajiv Narayan11, The DREAM Module Identification Challenge Consortium12, Aravind Subramanian11, Jitao D. Zhang 5, Gustavo Stolovitzky 3,13, Zoltán Kutalik2,14, Kasper Lage 9,10,15, Donna K. Slonim 4,16, Julio Saez-Rodriguez 17,18, Lenore J. Cowen4,7, Sven Bergmann 1,2,19,21* and Daniel Marbach 1,2,5,21* Many bioinformatics methods have been proposed for reducing the complexity of large gene or protein networks into relevant subnetworks or modules. Yet, how such methods compare to each other in terms of their ability to identify disease-relevant modules in different types of network remains poorly understood. We launched the ‘Disease Module Identification DREAM Challenge’, an open competition to comprehensively assess module identification methods across diverse protein–protein interaction, signaling, gene co-expression, homology and cancer-gene networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies. Our robust assessment of 75 module identification methods reveals top-performing algorithms, which recover complementary trait- associated modules. We find that most of these modules correspond to core disease-relevant pathways, which often com- prise therapeutic targets. This community challenge establishes biologically interpretable benchmarks, tools and guidelines for molecular network analysis to study human disease biology. omplex diseases involve many genes and molecules that inter- assessed on in silico generated benchmark graphs11.
  • DEAH)/RNA Helicase a Helicases Sense Microbial DNA in Human Plasmacytoid Dendritic Cells

    DEAH)/RNA Helicase a Helicases Sense Microbial DNA in Human Plasmacytoid Dendritic Cells

    Aspartate-glutamate-alanine-histidine box motif (DEAH)/RNA helicase A helicases sense microbial DNA in human plasmacytoid dendritic cells Taeil Kima, Shwetha Pazhoora, Musheng Baoa, Zhiqiang Zhanga, Shino Hanabuchia, Valeria Facchinettia, Laura Bovera, Joel Plumasb, Laurence Chaperotb, Jun Qinc, and Yong-Jun Liua,1 aDepartment of Immunology, Center for Cancer Immunology Research, University of Texas M. D. Anderson Cancer Center, Houston, TX 77030; bDepartment of Research and Development, Etablissement Français du Sang Rhône-Alpes Grenoble, 38701 La Tronche, France; and cDepartment of Biochemistry, Baylor College of Medicine, Houston, TX 77030 Edited by Ralph M. Steinman, The Rockefeller University, New York, NY, and approved July 14, 2010 (received for review May 10, 2010) Toll-like receptor 9 (TLR9) senses microbial DNA and triggers type I Microbial nucleic acids, including their genomic DNA/RNA IFN responses in plasmacytoid dendritic cells (pDCs). Previous and replicating intermediates, work as strong PAMPs (13), so studies suggest the presence of myeloid differentiation primary finding PRR-sensing pathogenic nucleic acids and investigating response gene 88 (MyD88)-dependent DNA sensors other than their signaling pathway is of general interest. Cytosolic RNA is TLR9 in pDCs. Using MS, we investigated C-phosphate-G (CpG)- recognized by RLRs, including RIG-I, melanoma differentiation- binding proteins from human pDCs, pDC-cell lines, and interferon associated gene 5 (MDA5), and laboratory of genetics and physi- regulatory factor 7 (IRF7)-expressing B-cell lines. CpG-A selectively ology 2 (LGP2). RIG-I senses 5′-triphosphate dsRNA and ssRNA bound the aspartate-glutamate-any amino acid-aspartate/histi- or short dsRNA with blunt ends.