Gene Expression Differences Associated with Human Papillomavirus Status in Head and Neck Squamous Cell Carcinoma Robbertj.C
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DEPs in osteosarcoma cells comparing to osteoblastic cells Biological Process Protein Percentage of Hits metabolic process (GO:0008152) 29.3 29.3% cellular process (GO:0009987) 20.2 20.2% localization (GO:0051179) 9.4 9.4% biological regulation (GO:0065007) 8 8.0% developmental process (GO:0032502) 7.8 7.8% response to stimulus (GO:0050896) 5.6 5.6% cellular component organization (GO:0071840) 5.6 5.6% multicellular organismal process (GO:0032501) 4.4 4.4% immune system process (GO:0002376) 4.2 4.2% biological adhesion (GO:0022610) 2.7 2.7% apoptotic process (GO:0006915) 1.6 1.6% reproduction (GO:0000003) 0.8 0.8% locomotion (GO:0040011) 0.4 0.4% cell killing (GO:0001906) 0.1 0.1% 100.1% Genes 2179Hits 3870 biological adhesion apoptotic process … reproduction (GO:0000003) , 0.8% (GO:0022610) , 2.7% locomotion (GO:0040011) ,… immune system process cell killing (GO:0001906) , 0.1% (GO:0002376) , 4.2% multicellular organismal process (GO:0032501) , metabolic process 4.4% (GO:0008152) , 29.3% cellular component organization (GO:0071840) , 5.6% response to stimulus (GO:0050896), 5.6% developmental process (GO:0032502) , 7.8% biological regulation (GO:0065007) , 8.0% cellular process (GO:0009987) , 20.2% localization (GO:0051179) , 9. -
Maria João Abordagem À Regulação Da Mobilidade Do Martinho De Freitas Espermatozoide Através Da Caracterização E Modulação Da Via De Sinalização GSK3/PPP1R2/PPP1
Universidade de Aveiro Departamento de Biologia Ano 2018 Maria João Abordagem à regulação da mobilidade do Martinho de Freitas espermatozoide através da caracterização e modulação da via de sinalização GSK3/PPP1R2/PPP1 Addressing sperm motility regulation through characterization and modulation of the GSK3/PPP1R2/PPP1 signaling pathway Universidade de Aveiro Departamento de Biologia Ano 2018 Maria João Abordagem à regulação da mobilidade do Martinho de Freitas espermatozoide através da caracterização e modulação da via de sinalização GSK3/PPP1R2/PPP1 Addressing sperm motility regulation through characterization and modulation of the GSK3/PPP1R2/PPP1 signaling pathway Tese apresentada à Universidade de Aveiro para cumprimento dos requisitos necessários à obtenção do grau de Doutor em Biologia, realizada sob a orientação científica da Doutora Margarida de Sâncio da Cruz Fardilha, Professora Professora Auxiliar do Departamento de Ciências Médicas da Universidade de Aveiro e do Doutor Srinivasan Vijayaraghavan, Professor Professor Auxiliar do Departamento de Ciências Biológicas da Kent State University. Este trabalho é financiado por Fundos FEDER através do Programa Operacional Fatores de Competitividade-COMPETE e por Fundos Nacionais através da FCT- Fundação para a Ciência e a Tecnologia no âmbito dos projetos «PTDC/DTP-PIC/0460/2012»; «PTDB/BBB-BQB/3804/2014»; «NIH R15 HD068971-01»; da bolsa individual «SFRH/BD/84876/2012»; e do instituto de Biomedicina- iBiMED «UID/BIM/04501/2013». o júri presidente Professor Doutor Amadeu Mortágua Velho -
A Network Propagation Approach to Prioritize Long Tail Genes in Cancer
bioRxiv preprint doi: https://doi.org/10.1101/2021.02.05.429983; this version posted February 8, 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-NC-ND 4.0 International license. A Network Propagation Approach to Prioritize Long Tail Genes in Cancer Hussein Mohsen1,*, Vignesh Gunasekharan2, Tao Qing2, Sahand Negahban3, Zoltan Szallasi4, Lajos Pusztai2,*, Mark B. Gerstein1,5,6,3,* 1 Computational Biology & Bioinformatics Program, Yale University, New Haven, CT 06511, USA 2 Breast Medical Oncology, Yale School of Medicine, New Haven, CT 06511, USA 3 Department of Statistics & Data Science, Yale University, New Haven, CT 06511, USA 4 Children’s Hospital Informatics Program, Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School, Boston, MA 02115, USA 5 Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06511, USA 6 Department of Computer Science, Yale University, New Haven, CT 06511, USA * Corresponding author Abstract Introduction. The diversity of genomic alterations in cancer pose challenges to fully understanding the etiologies of the disease. Recent interest in infrequent mutations, in genes that reside in the “long tail” of the mutational distribution, uncovered new genes with significant implication in cancer development. The study of these genes often requires integrative approaches with multiple types of biological data. Network propagation methods have demonstrated high efficacy in uncovering genomic patterns underlying cancer using biological interaction networks. Yet, the majority of these analyses have focused their assessment on detecting known cancer genes or identifying altered subnetworks. -
Differential Expression Profile Prioritization of Positional Candidate Glaucoma Genes the GLC1C Locus
LABORATORY SCIENCES Differential Expression Profile Prioritization of Positional Candidate Glaucoma Genes The GLC1C Locus Frank W. Rozsa, PhD; Kathleen M. Scott, BS; Hemant Pawar, PhD; John R. Samples, MD; Mary K. Wirtz, PhD; Julia E. Richards, PhD Objectives: To develop and apply a model for priori- est because of moderate expression and changes in tization of candidate glaucoma genes. expression. Transcription factor ZBTB38 emerges as an interesting candidate gene because of the overall expres- Methods: This Affymetrix GeneChip (Affymetrix, Santa sion level, differential expression, and function. Clara, Calif) study of gene expression in primary cul- ture human trabecular meshwork cells uses a positional Conclusions: Only1geneintheGLC1C interval fits our differential expression profile model for prioritization of model for differential expression under multiple glau- candidate genes within the GLC1C genetic inclusion in- coma risk conditions. The use of multiple prioritization terval. models resulted in filtering 7 candidate genes of higher interest out of the 41 known genes in the region. Results: Sixteen genes were expressed under all condi- tions within the GLC1C interval. TMEM22 was the only Clinical Relevance: This study identified a small sub- gene within the interval with differential expression in set of genes that are most likely to harbor mutations that the same direction under both conditions tested. Two cause glaucoma linked to GLC1C. genes, ATP1B3 and COPB2, are of interest in the con- text of a protein-misfolding model for candidate selec- tion. SLC25A36, PCCB, and FNDC6 are of lesser inter- Arch Ophthalmol. 2007;125:117-127 IGH PREVALENCE AND PO- identification of additional GLC1C fami- tential for severe out- lies7,18-20 who provide optimal samples for come combine to make screening candidate genes for muta- adult-onset primary tions.7,18,20 The existence of 2 distinct open-angle glaucoma GLC1C haplotypes suggests that muta- (POAG) a significant public health prob- tions will not be limited to rare descen- H1 lem. -
Predicting Gene Ontology Biological Process from Temporal Gene Expression Patterns Astrid Lægreid,1,4 Torgeir R
Methods Predicting Gene Ontology Biological Process From Temporal Gene Expression Patterns Astrid Lægreid,1,4 Torgeir R. Hvidsten,2 Herman Midelfart,2 Jan Komorowski,2,3,4 and Arne K. Sandvik1 1Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology, N-7489 Trondheim, Norway; 2Department of Information and Computer Science, Norwegian University of Science and Technology, N-7491 Trondheim, Norway; 3The Linnaeus Centre for Bioinformatics, Uppsala University, SE-751 24 Uppsala, Sweden The aim of the present study was to generate hypotheses on the involvement of uncharacterized genes in biological processes. To this end,supervised learning was used to analyz e microarray-derived time-series gene expression data. Our method was objectively evaluated on known genes using cross-validation and provided high-precision Gene Ontology biological process classifications for 211 of the 213 uncharacterized genes in the data set used. In addition,new roles in biological process were hypothesi zed for known genes. Our method uses biological knowledge expressed by Gene Ontology and generates a rule model associating this knowledge with minimal characteristic features of temporal gene expression profiles. This model allows learning and classification of multiple biological process roles for each gene and can predict participation of genes in a biological process even though the genes of this class exhibit a wide variety of gene expression profiles including inverse coregulation. A considerable number of the hypothesized new roles for known genes were confirmed by literature search. In addition,many biological process roles hypothesi zed for uncharacterized genes were found to agree with assumptions based on homology information. -
Growth and Molecular Profile of Lung Cancer Cells Expressing Ectopic LKB1: Down-Regulation of the Phosphatidylinositol 3-Phosphate Kinase/PTEN Pathway1
[CANCER RESEARCH 63, 1382–1388, March 15, 2003] Growth and Molecular Profile of Lung Cancer Cells Expressing Ectopic LKB1: Down-Regulation of the Phosphatidylinositol 3-Phosphate Kinase/PTEN Pathway1 Ana I. Jimenez, Paloma Fernandez, Orlando Dominguez, Ana Dopazo, and Montserrat Sanchez-Cespedes2 Molecular Pathology Program [A. I. J., P. F., M. S-C.], Genomics Unit [O. D.], and Microarray Analysis Unit [A. D.], Spanish National Cancer Center, 28029 Madrid, Spain ABSTRACT the cell cycle in G1 (8, 9). However, the intrinsic mechanism by which LKB1 activity is regulated in cells and how it leads to the suppression Germ-line mutations in LKB1 gene cause the Peutz-Jeghers syndrome of cell growth is still unknown. It has been proposed that growth (PJS), a genetic disease with increased risk of malignancies. Recently, suppression by LKB1 is mediated through p21 in a p53-dependent LKB1-inactivating mutations have been identified in one-third of sporadic lung adenocarcinomas, indicating that LKB1 gene inactivation is critical in mechanism (7). In addition, it has been observed that LKB1 binds to tumors other than those of the PJS syndrome. However, the in vivo brahma-related gene 1 protein (BRG1) and this interaction is required substrates of LKB1 and its role in cancer development have not been for BRG1-induced growth arrest (10). Similar to what happens in the completely elucidated. Here we show that overexpression of wild-type PJS, Lkb1 heterozygous knockout mice show gastrointestinal hamar- LKB1 protein in A549 lung adenocarcinomas cells leads to cell-growth tomatous polyposis and frequent hepatocellular carcinomas (11, 12). suppression. To examine changes in gene expression profiles subsequent to Interestingly, the hamartomas, but not the malignant tumors, arising in exogenous wild-type LKB1 in A549 cells, we used cDNA microarrays. -
The Middle Temporal Gyrus Is Transcriptionally Altered in Patients with Alzheimer’S Disease
1 The middle temporal gyrus is transcriptionally altered in patients with Alzheimer’s Disease. 2 1 3 Shahan Mamoor 1Thomas Jefferson School of Law 4 East Islip, NY 11730 [email protected] 5 6 We sought to understand, at the systems level and in an unbiased fashion, how gene 7 expression was most different in the brains of patients with Alzheimer’s Disease (AD) by mining published microarray datasets (1, 2). Comparing global gene expression profiles between 8 patient and control revealed that a set of 84 genes were expressed at significantly different levels in the middle temporal gyrus (MTG) of patients with Alzheimer’s Disease (1, 2). We used 9 computational analyses to classify these genes into known pathways and existing gene sets, 10 and to describe the major differences in the epigenetic marks at the genomic loci of these genes. While a portion of these genes is computationally cognizable as part of a set of genes 11 up-regulated in the brains of patients with AD (3), many other genes in the gene set identified here have not previously been studied in association with AD. Transcriptional repression, both 12 pre- and post-transcription appears to be affected; nearly 40% of these genes are transcriptional 13 targets of MicroRNA-19A/B (miR-19A/B), the zinc finger protein 10 (ZNF10), or of the AP-1 repressor jun dimerization protein 2 (JDP2). 14 15 16 17 18 19 20 21 22 23 24 25 26 Keywords: Alzheimer’s Disease, systems biology of Alzheimer’s Disease, differential gene 27 expression, middle temporal gyrus. -
Original Article Bioinformatic Analysis of Gene Expression Profile in Prostate Epithelial Cells Exposed to Low-Dose Cadmium
Int J Clin Exp Med 2018;11(3):1669-1678 www.ijcem.com /ISSN:1940-5901/IJCEM0062792 Original Article Bioinformatic analysis of gene expression profile in prostate epithelial cells exposed to low-dose cadmium Qiling Liu1,2*, Rongqiang Zhang2*, Xiang Wang1, Peili Wang2, Xiaomei Ren2, Na Sun2, Xiangwen Li2, Xinhui Li2, Chunxu Hai1 1Department of Toxicology, The Ministry of Education Key Lab of Hazard Assessment and Control in Special Op- erational Environment, Shaanxi Provincial Key Lab of Free Radical Biology and Medicine, School of Public Health, Medical University of The Air Force, Xi’an, Shaanxi 710032, China; 2Department of Epidemic and Health Statis- tics, The College of Public Health for The Shaanxi University of Chinese Medicine, Shaanxi 712046, China. *Equal contributors. Received July 25, 2017; Accepted February 5, 2018; Epub March 15, 2018; Published March 30, 2018 Abstract: Objective: This study was to identify key genes and biological pathways involved in responses of prostate epithelial cells after low-dose Cd exposure by using bioinformatic analysis. Methods: The gene chip data of prostate epithelial cells after low-dose Cd exposure were collected from public databases Gene Expression Omnibus. After identification of differentially expressed genes (DEGs), data were input into Qlucore Omics Explorer, Network Analyst, String, and Genclip for further analysis of gene expression profiles, protein-protein interactions (PPI) and protein- chemicals interactions, and critical molecular pathways. Results: A total of 384 DEGs were identified in Cd treated group compared with control group. The number of DEGs gradually decreased over time, with the largest number at 0 h. Furthermore, NDUFB5 (A, S), CYC1, UQCRB, ETFA (B), SNRPD2, and LSM3 (5, 6) were the hub proteins in the PPI network. -
Low Abundance of the Matrix Arm of Complex I in Mitochondria Predicts Longevity in Mice
ARTICLE Received 24 Jan 2014 | Accepted 9 Apr 2014 | Published 12 May 2014 DOI: 10.1038/ncomms4837 OPEN Low abundance of the matrix arm of complex I in mitochondria predicts longevity in mice Satomi Miwa1, Howsun Jow2, Karen Baty3, Amy Johnson1, Rafal Czapiewski1, Gabriele Saretzki1, Achim Treumann3 & Thomas von Zglinicki1 Mitochondrial function is an important determinant of the ageing process; however, the mitochondrial properties that enable longevity are not well understood. Here we show that optimal assembly of mitochondrial complex I predicts longevity in mice. Using an unbiased high-coverage high-confidence approach, we demonstrate that electron transport chain proteins, especially the matrix arm subunits of complex I, are decreased in young long-living mice, which is associated with improved complex I assembly, higher complex I-linked state 3 oxygen consumption rates and decreased superoxide production, whereas the opposite is seen in old mice. Disruption of complex I assembly reduces oxidative metabolism with concomitant increase in mitochondrial superoxide production. This is rescued by knockdown of the mitochondrial chaperone, prohibitin. Disrupted complex I assembly causes premature senescence in primary cells. We propose that lower abundance of free catalytic complex I components supports complex I assembly, efficacy of substrate utilization and minimal ROS production, enabling enhanced longevity. 1 Institute for Ageing and Health, Newcastle University, Newcastle upon Tyne NE4 5PL, UK. 2 Centre for Integrated Systems Biology of Ageing and Nutrition, Newcastle University, Newcastle upon Tyne NE4 5PL, UK. 3 Newcastle University Protein and Proteome Analysis, Devonshire Building, Devonshire Terrace, Newcastle upon Tyne NE1 7RU, UK. Correspondence and requests for materials should be addressed to T.v.Z. -
Identification of the Key Genes and Pathways in Prostate Cancer
ONCOLOGY LETTERS 16: 6663-6669, 2018 Identification of the key genes and pathways in prostate cancer SHUTONG FAN1*, ZUMU LIANG1*, ZHIQIN GAO1, ZHIWEI PAN2, SHAOJIE HAN3, XIAOYING LIU1, CHUNLING ZHAO1, WEIWEI YANG1, ZHIFANG PAN1 and WEIGUO FENG1 1College of Bioscience and Technology, Weifang Medical University, Weifang, Shandong 261053; 2Department of Internal Medicine, Laizhou Development Zone Hospital, Yantai, Shandong 261400; 3Animal Epidemic Prevention and Epidemic Control Center, Changle County Bureau of Animal Health and Production, Weifang, Shandong 262400, P.R. China Received March 5, 2018; Accepted September 17, 2018 DOI: 10.3892/ol.2018.9491 Abstract. Prostate cancer (PCa) is one of the most common Introduction malignancies in men globally. The aim of the present study was to identify the key genes and pathways involved in the Prostate cancer (PCa) is one of the most common malignancies occurrence of PCa. Gene expression profile (GSE55945) in men globally and the second leading cause of cancer was downloaded from Gene Expression Omnibus, and associated mortality in developed countries (1,2). Like other the differentially expressed genes (DEGs) were identified. cancers, PCa is considered to be a disease which caused by Subsequently, Gene ontology analysis, KEGG pathway age, diet and gene aberrations (3). Accumulating evidences analysis and protein-protein interaction (PPI) analysis of have demonstrated that a series of genes and pathways involved DEGs were performed. Finally, the identified key genes were in the occurrence, progression and metastasis of PCa (4). At confirmed by immunohistochemistry. The GO analysis results present, the underlying mechanism of PCa occurrence is still showed that the DEGs were mainly participated in cell cycle, unclear, which limits the diagnosis and therapy. -
Protein Purification Protein Localization in Vivo Fluorescent Imaging Protein Arrays Real Time Imaging Protein Interactions Protein Trafficking Protein Turnover
Overcoming Challenges of Protein Analysis in Mammalian Systems Danette L. Daniels, Ph.D. Current Technologies for Protein Analysis Biochemical/ In Vivo Proteomic Cell Based Animal Analysis Analysis Models Fluorescent proteins Affinity tags Antibodies How about a system applicable to the all approaches that also addresses limitations of current methods? • Minimal interference with protein of interest • Efficient capture/isolation • Detection/real-time imaging • Differential labeling • High Signal/background HaloTag Platform Biochemical/ In Vivo Proteomic Cell Based Animal Analysis Analysis Models Protein purification Protein localization In vivo fluorescent imaging Protein arrays Real time imaging Protein interactions Protein trafficking Protein turnover HaloTag® HaloCHIP™ HaloLink™ HaloTag® Fluorescent Purification Protein:DNA Protein Arrays Pull-Down Ligands HaloTag is a Genetically Engineered Protein Fusion Tag O Functional Protein of Cl O Interest HT + group Protein of Functional HT O O Interest group . A monomeric , 34 kDa, modified bacterial dehalogenase genetically engineered to covalently bind specific, synthetic HaloTag® ligands . Irreversible, covalent attachment of chemical functionalities . Suitable as either N- or C- terminal fusion Mutagenized HaloTag® Protein Enables Covalent HaloTag®-Ligand Complex Hydrolase (DhaA) HaloTag® Catalytic process Facilitated bond formation T r p 1 0 7 T r p 1 0 7 HaloTag®: • 34kDa protein • Monomeric N N H N 4 1 H N A s n - H H • Single change: C l 4 1 A s n C l 2 1 His272Phe for covalent O R O O - C C bond. 3 R O 1 0 6 A s p 1 0 6 A s p O H O H Covalent bond: H H O O • Stable after N C G l u 1 3 0 C G l u 1 3 0 N - O denaturation. -
(BPA) Exposure Biomarkers in Ovarian Cancer
Journal of Clinical Medicine Article Identification of Potential Bisphenol A (BPA) Exposure Biomarkers in Ovarian Cancer Aeman Zahra 1, Qiduo Dong 1, Marcia Hall 1,2 , Jeyarooban Jeyaneethi 1, Elisabete Silva 1, Emmanouil Karteris 1,* and Cristina Sisu 1,* 1 Biosciences, College of Health, Medicine and Life Sciences, Brunel University London, Uxbridge UB8 3PH, UK; [email protected] (A.Z.); [email protected] (Q.D.); [email protected] (M.H.); [email protected] (J.J.); [email protected] (E.S.) 2 Mount Vernon Cancer Centre, Northwood HA6 2RN, UK * Correspondence: [email protected] (E.K.); [email protected] (C.S.) Abstract: Endocrine-disrupting chemicals (EDCs) can exert multiple deleterious effects and have been implicated in carcinogenesis. The xenoestrogen Bisphenol A (BPA) that is found in various consumer products has been involved in the dysregulation of numerous signalling pathways. In this paper, we present the analysis of a set of 94 genes that have been shown to be dysregulated in presence of BPA in ovarian cancer cell lines since we hypothesised that these genes might be of biomarker potential. This study sought to identify biomarkers of disease and biomarkers of disease- associated exposure. In silico analyses took place using gene expression data extracted from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) databases. Differential expression was further validated at protein level using immunohistochemistry on an ovarian cancer tissue microarray. We found that 14 out of 94 genes are solely dysregulated in the presence of BPA, while the remaining 80 genes are already dysregulated (p-value < 0.05) in their expression pattern Citation: Zahra, A.; Dong, Q.; Hall, as a consequence of the disease.