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Screening and Identification of Key Biomarkers in Clear Cell Renal Cell Carcinoma Based on Bioinformatics Analysis
bioRxiv preprint doi: https://doi.org/10.1101/2020.12.21.423889; this version posted December 23, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Screening and identification of key biomarkers in clear cell renal cell carcinoma based on bioinformatics analysis Basavaraj Vastrad1, Chanabasayya Vastrad*2 , Iranna Kotturshetti 1. Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, Karnataka 582103, India. 2. Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karanataka, India. 3. Department of Ayurveda, Rajiv Gandhi Education Society`s Ayurvedic Medical College, Ron, Karnataka 562209, India. * Chanabasayya Vastrad [email protected] Ph: +919480073398 Chanabasava Nilaya, Bharthinagar, Dharwad 580001 , Karanataka, India bioRxiv preprint doi: https://doi.org/10.1101/2020.12.21.423889; this version posted December 23, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Abstract Clear cell renal cell carcinoma (ccRCC) is one of the most common types of malignancy of the urinary system. The pathogenesis and effective diagnosis of ccRCC have become popular topics for research in the previous decade. In the current study, an integrated bioinformatics analysis was performed to identify core genes associated in ccRCC. An expression dataset (GSE105261) was downloaded from the Gene Expression Omnibus database, and included 26 ccRCC and 9 normal kideny samples. Assessment of the microarray dataset led to the recognition of differentially expressed genes (DEGs), which was subsequently used for pathway and gene ontology (GO) enrichment analysis. -
C19) United States C12) Patent Application Publication C10) Pub
1111111111111111 IIIIII IIIII 1111111111 11111 11111 111111111111111 1111111111 1111111111 11111111 US 20200081016Al c19) United States c12) Patent Application Publication c10) Pub. No.: US 2020/0081016 Al Talaat et al. (43) Pub. Date: Mar. 12, 2020 (54) BIOMARKERS FOR EARLY DIAGNOSIS Publication Classification AND DIFFERENTIATION OF (51) Int. Cl. MYCOBACTERIAL INFECTION GOIN 33/68 (2006.01) C12Q 116851 (2006.01) (71) Applicant: Wisconsin Alumni Research GOIN 33/569 (2006.01) Foundation, Madison, WI (US) (52) U.S. Cl. (72) Inventors: Adel Mohamed Talaat, Madison, WI CPC ......... GOIN 33/6854 (2013.01); GOIN 33/68 (US); Chia-wei Wu, Madison, WI (US) (2013.01); GOIN 2800/50 (2013.01); GOIN 33/5695 (2013.01); GOIN 2800/26 (2013.01); (21) Appl. No.: 16/555,819 C12Q 116851 (2013.01) (22) Filed: Aug. 29, 2019 (57) ABSTRACT Mycobacterial-specific biomarkers and methods of using Related U.S. Application Data such biomarkers for diagnosis of mycobacterial infection in (60) Provisional application No. 62/728,387, filed on Sep. a mammal are disclosed. 7, 2018. Specification includes a Sequence Listing. Patent Application Publication Mar. 12, 2020 Sheet 1 of 10 US 2020/0081016 Al FIG. 1 ·~{:: -{t i * !lpNbiNi$ 1 !lpN p~ra 111:111111 llillllll: 111!11,111llltllllll~ 11111 ■111 ~; C,,Nmnsus KR.IGINMTKX L.lC(X.AXXXXG AXXXXMPXTX RXO-GXVXXVG VKVXPWIPTX ® • ® l I I iipN lK>V(S ~Hl!lli!Wiofflij 1!11.llofJiillj mllB~lijftlt flol=fiolill ••t-il-~MM ~9 llpN p~ra HfHJoffit:torti ilffllGNillm miJllt~ttiollf ~•01:101111 llm:l:l1IA@~ iOO C,,nstmsus XXRXLXXGRS Vt IOGNT.LDP i LOt.MLSXXR XXGXOG.I...XVO ODXXXSR:AXM t2:;: i-/4~~ ! l 1 I~~~~b;:: llllil~l:1:1 llil 111111:1~:111~ 1111111::;1 1lllilllll: ~:~ C,,nimnsus XXXXXXXPGP QtHVDVXXI...X XPGPAGXIPA RHYRPXGGXX QXPt.l...VFYHG Consl:lrvat,ofl -:§;::. -
PAPC Couples the Segmentation Clock to Somite Morphogenesis by Regulating N-Cadherin-Dependent Adhesion
© 2017. Published by The Company of Biologists Ltd | Development (2017) 144, 664-676 doi:10.1242/dev.143974 RESEARCH ARTICLE PAPC couples the segmentation clock to somite morphogenesis by regulating N-cadherin-dependent adhesion Jérome Chal1,2,3,4,5,*, Charlenè Guillot3,4,* and Olivier Pourquié1,2,3,4,5,6,7,‡ ABSTRACT specific level of the PSM called the determination front. The Vertebrate segmentation is characterized by the periodic formation of determination front is defined as a signaling threshold epithelial somites from the mesenchymal presomitic mesoderm implemented by posterior gradients of Wnt and FGF (Aulehla (PSM). How the rhythmic signaling pulse delivered by the et al., 2003; Diez del Corral and Storey, 2004; Dubrulle et al., segmentation clock is translated into the periodic morphogenesis of 2001; Hubaud and Pourquie, 2014; Sawada et al., 2001). Cells of somites remains poorly understood. Here, we focused on the role of the posterior PSM exhibit mesenchymal characteristics and paraxial protocadherin (PAPC/Pcdh8) in this process. We showed express Snail-related transcription factors (Dale et al., 2006; that in chicken and mouse embryos, PAPC expression is tightly Nieto, 2002). In the anterior PSM, cells downregulate snail/slug regulated by the clock and wavefront system in the posterior PSM. We expression and upregulate epithelialization-promoting factors such observed that PAPC exhibits a striking complementary pattern to N- as paraxis (Barnes et al., 1997; Sosic et al., 1997). This molecular cadherin (CDH2), marking the interface of the future somite boundary transition correlates with the anterior PSM cells progressively in the anterior PSM. Gain and loss of function of PAPC in chicken acquiring epithelial characteristics (Duband et al., 1987; Martins embryos disrupted somite segmentation by altering the CDH2- et al., 2009). -
ACE2 Interaction Networks in COVID-19: a Physiological Framework for Prediction of Outcome in Patients with Cardiovascular Risk Factors
Journal of Clinical Medicine Article ACE2 Interaction Networks in COVID-19: A Physiological Framework for Prediction of Outcome in Patients with Cardiovascular Risk Factors Zofia Wicik 1,2 , Ceren Eyileten 2, Daniel Jakubik 2,Sérgio N. Simões 3, David C. Martins Jr. 1, Rodrigo Pavão 1, Jolanta M. Siller-Matula 2,4,* and Marek Postula 2 1 Centro de Matemática, Computação e Cognição, Universidade Federal do ABC, Santo Andre 09606-045, Brazil; zofi[email protected] (Z.W.); [email protected] (D.C.M.J.); [email protected] (R.P.) 2 Department of Experimental and Clinical Pharmacology, Medical University of Warsaw, Center for Preclinical Research and Technology CEPT, 02-091 Warsaw, Poland; [email protected] (C.E.); [email protected] (D.J.); [email protected] (M.P.) 3 Federal Institute of Education, Science and Technology of Espírito Santo, Serra, Espírito Santo 29056-264, Brazil; [email protected] 4 Department of Internal Medicine II, Division of Cardiology, Medical University of Vienna, 1090 Vienna, Austria * Correspondence: [email protected]; Tel.: +43-1-40400-46140; Fax: +43-1-40400-42160 Received: 9 October 2020; Accepted: 17 November 2020; Published: 21 November 2020 Abstract: Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (coronavirus disease 2019; COVID-19) is associated with adverse outcomes in patients with cardiovascular disease (CVD). The aim of the study was to characterize the interaction between SARS-CoV-2 and Angiotensin-Converting Enzyme 2 (ACE2) functional networks with a focus on CVD. Methods: Using the network medicine approach and publicly available datasets, we investigated ACE2 tissue expression and described ACE2 interaction networks that could be affected by SARS-CoV-2 infection in the heart, lungs and nervous system. -
The Role of the Mtor Pathway in Developmental Reprogramming Of
THE ROLE OF THE MTOR PATHWAY IN DEVELOPMENTAL REPROGRAMMING OF HEPATIC LIPID METABOLISM AND THE HEPATIC TRANSCRIPTOME AFTER EXPOSURE TO 2,2',4,4'- TETRABROMODIPHENYL ETHER (BDE-47) An Honors Thesis Presented By JOSEPH PAUL MCGAUNN Approved as to style and content by: ________________________________________________________** Alexander Suvorov 05/18/20 10:40 ** Chair ________________________________________________________** Laura V Danai 05/18/20 10:51 ** Committee Member ________________________________________________________** Scott C Garman 05/18/20 10:57 ** Honors Program Director ABSTRACT An emerging hypothesis links the epidemic of metabolic diseases, such as non-alcoholic fatty liver disease (NAFLD) and diabetes with chemical exposures during development. Evidence from our lab and others suggests that developmental exposure to environmentally prevalent flame-retardant BDE47 may permanently reprogram hepatic lipid metabolism, resulting in an NAFLD-like phenotype. Additionally, we have demonstrated that BDE-47 alters the activity of both mTOR complexes (mTORC1 and 2) in hepatocytes. The mTOR pathway integrates environmental information from different signaling pathways, and regulates key cellular functions such as lipid metabolism, innate immunity, and ribosome biogenesis. Thus, we hypothesized that the developmental effects of BDE-47 on liver lipid metabolism are mTOR-dependent. To assess this, we generated mice with liver-specific deletions of mTORC1 or mTORC2 and exposed these mice and their respective controls perinatally to -
The Role and Mechanisms of Action of Micrornas in Cancer Drug Resistance Wengong Si1,2,3, Jiaying Shen4, Huilin Zheng1,5 and Weimin Fan1,6*
Si et al. Clinical Epigenetics (2019) 11:25 https://doi.org/10.1186/s13148-018-0587-8 REVIEW Open Access The role and mechanisms of action of microRNAs in cancer drug resistance Wengong Si1,2,3, Jiaying Shen4, Huilin Zheng1,5 and Weimin Fan1,6* Abstract MicroRNAs (miRNAs) are small non-coding RNAs with a length of about 19–25 nt, which can regulate various target genes and are thus involved in the regulation of a variety of biological and pathological processes, including the formation and development of cancer. Drug resistance in cancer chemotherapy is one of the main obstacles to curing this malignant disease. Statistical data indicate that over 90% of the mortality of patients with cancer is related to drug resistance. Drug resistance of cancer chemotherapy can be caused by many mechanisms, such as decreased antitumor drug uptake, modified drug targets, altered cell cycle checkpoints, or increased DNA damage repair, among others. In recent years, many studies have shown that miRNAs are involved in the drug resistance of tumor cells by targeting drug-resistance-related genes or influencing genes related to cell proliferation, cell cycle, and apoptosis. A single miRNA often targets a number of genes, and its regulatory effect is tissue-specific. In this review, we emphasize the miRNAs that are involved in the regulation of drug resistance among different cancers and probe the mechanisms of the deregulated expression of miRNAs. The molecular targets of miRNAs and their underlying signaling pathways are also explored comprehensively. A holistic understanding of the functions of miRNAs in drug resistance will help us develop better strategies to regulate them efficiently and will finally pave the way toward better translation of miRNAs into clinics, developing them into a promising approach in cancer therapy. -
Supporting Online Material
1 2 3 4 5 6 7 Supplementary Information for 8 9 Fractalkine-induced microglial vasoregulation occurs within the retina and is altered early in diabetic 10 retinopathy 11 12 *Samuel A. Mills, *Andrew I. Jobling, *Michael A. Dixon, Bang V. Bui, Kirstan A. Vessey, Joanna A. Phipps, 13 Ursula Greferath, Gene Venables, Vickie H.Y. Wong, Connie H.Y. Wong, Zheng He, Flora Hui, James C. 14 Young, Josh Tonc, Elena Ivanova, Botir T. Sagdullaev, Erica L. Fletcher 15 * Joint first authors 16 17 Corresponding author: 18 Prof. Erica L. Fletcher. Department of Anatomy & Neuroscience. The University of Melbourne, Grattan St, 19 Parkville 3010, Victoria, Australia. 20 Email: [email protected] ; Tel: +61-3-8344-3218; Fax: +61-3-9347-5219 21 22 This PDF file includes: 23 24 Supplementary text 25 Figures S1 to S10 26 Tables S1 to S7 27 Legends for Movies S1 to S2 28 SI References 29 30 Other supplementary materials for this manuscript include the following: 31 32 Movies S1 to S2 33 34 35 36 1 1 Supplementary Information Text 2 Materials and Methods 3 Microglial process movement on retinal vessels 4 Dark agouti rats were anaesthetized, injected intraperitoneally with rhodamine B (Sigma-Aldrich) to label blood 5 vessels and retinal explants established as described in the main text. Retinal microglia were labelled with Iba-1 6 and imaging performed on an inverted confocal microscope (Leica SP5). Baseline images were taken for 10 7 minutes, followed by the addition of PBS (10 minutes) and then either fractalkine or fractalkine + candesartan 8 (10 minutes) using concentrations outlined in the main text. -
Kinetic Properties of Collagen Receptors on Human Keratinocytes 2337
Journal of Cell Science 112, 2335-2345 (1999) 2335 Printed in Great Britain © The Company of Biologists Limited 1999 JCS9937 Integrin α and β subunit contribution to the kinetic properties of α2β1 collagen receptors on human keratinocytes analyzed under hydrodynamic conditions Bénédicte Masson-Gadais, Anne Pierres, Anne-Marie Benoliel, Pierre Bongrand* and Jean-Claude Lissitzky Laboratoire d’Immunologie, INSERM U 387, Hôpital de Sainte-Marguerite, BP 29, 13274 Marseille Cedex 09, France *Author for correspondence (e-mail: [email protected]) Accepted 10 May; published on WWW 24 June 1999 SUMMARY The adhesion of keratinocytes to type I collagen or with ligand recognition and also with the ligand-β1 chain laminin 5 was studied in a laminar flow chamber. These interactions responsible for bond stabilization. The latter experiments provided an insight into the binding kinetics hypothesis was supported by the finding that the partial of integrins in their natural environment and the effects of alteration of α2 chain function by inhibiting antibodies was monoclonal antibodies specific for α and β chains. Cells corrected by anti-β1 chain antibody TS2/16. These results driven by a force too low to alter the natural lifetime of a could not be ascribed to allosteric changes of the functional single bond displayed multiple arrests. Studying the region of β1 integrin subunits regulated by TS2/16 since frequency and duration of these arrests yielded fairly direct there was no competition between the binding of TS2/16 information on the rate of bond formation (on-rate) and and anti-α2 chain antibodies. dissociation (off-rate). Off-rate values obtained on collagen Interpreted within the framework of current concepts of or laminin 5 (0.06 seconds−1) were tenfold lower than values integrin-ligand binding topology, these data suggest that determined on selectins. -
Supplementary Table 3 Complete List of RNA-Sequencing Analysis of Gene Expression Changed by ≥ Tenfold Between Xenograft and Cells Cultured in 10%O2
Supplementary Table 3 Complete list of RNA-Sequencing analysis of gene expression changed by ≥ tenfold between xenograft and cells cultured in 10%O2 Expr Log2 Ratio Symbol Entrez Gene Name (culture/xenograft) -7.182 PGM5 phosphoglucomutase 5 -6.883 GPBAR1 G protein-coupled bile acid receptor 1 -6.683 CPVL carboxypeptidase, vitellogenic like -6.398 MTMR9LP myotubularin related protein 9-like, pseudogene -6.131 SCN7A sodium voltage-gated channel alpha subunit 7 -6.115 POPDC2 popeye domain containing 2 -6.014 LGI1 leucine rich glioma inactivated 1 -5.86 SCN1A sodium voltage-gated channel alpha subunit 1 -5.713 C6 complement C6 -5.365 ANGPTL1 angiopoietin like 1 -5.327 TNN tenascin N -5.228 DHRS2 dehydrogenase/reductase 2 leucine rich repeat and fibronectin type III domain -5.115 LRFN2 containing 2 -5.076 FOXO6 forkhead box O6 -5.035 ETNPPL ethanolamine-phosphate phospho-lyase -4.993 MYO15A myosin XVA -4.972 IGF1 insulin like growth factor 1 -4.956 DLG2 discs large MAGUK scaffold protein 2 -4.86 SCML4 sex comb on midleg like 4 (Drosophila) Src homology 2 domain containing transforming -4.816 SHD protein D -4.764 PLP1 proteolipid protein 1 -4.764 TSPAN32 tetraspanin 32 -4.713 N4BP3 NEDD4 binding protein 3 -4.705 MYOC myocilin -4.646 CLEC3B C-type lectin domain family 3 member B -4.646 C7 complement C7 -4.62 TGM2 transglutaminase 2 -4.562 COL9A1 collagen type IX alpha 1 chain -4.55 SOSTDC1 sclerostin domain containing 1 -4.55 OGN osteoglycin -4.505 DAPL1 death associated protein like 1 -4.491 C10orf105 chromosome 10 open reading frame 105 -4.491 -
The 'C3ar Antagonist' SB290157 Is a Partial C5ar2 Agonist
bioRxiv preprint doi: https://doi.org/10.1101/2020.08.01.232090; this version posted August 3, 2020. 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. The ‘C3aR antagonist’ SB290157 is a partial C5aR2 agonist Xaria X. Li1, Vinod Kumar1, John D. Lee1, Trent M. Woodruff1* 1School of Biomedical Sciences, The University of Queensland, St Lucia, 4072 Australia. * Correspondence: Prof. Trent M. Woodruff School of Biomedical Sciences, The University of Queensland, St Lucia, 4072 Australia. Ph: +61 7 3365 2924; Fax: +61 7 3365 1766; E-mail: [email protected] Keywords: Complement C3a, C3aR, SB290157, C5aR1, C5aR2 1 bioRxiv preprint doi: https://doi.org/10.1101/2020.08.01.232090; this version posted August 3, 2020. 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. Abbreviations used in this article: BRET, bioluminescence resonance energy transfer; BSA, bovine serum albumin; C3aR, C3a receptor C5aR1, C5a receptor 1; CHO-C3aR, Chinese hamster ovary cells stably expressing C3aR; CHO-C5aR1, Chinese hamster ovary cells stably expressing C5aR1; DMEM, Dulbecco's Modified Eagle's Medium; ERK1/2, extracellular signal-regulated kinase 1/2; FBS, foetal bovine serum; HEK293, human embryonic kidney 293 cells; HMDM, human monocyte-derived macrophage; i.p., intraperitoneal; i.v., intravenous; rhC5a, recombinant human C5a; RT, room temperature; S.E.M. -
Supplementary Materials
1 Supplementary Materials: Supplemental Figure 1. Gene expression profiles of kidneys in the Fcgr2b-/- and Fcgr2b-/-. Stinggt/gt mice. (A) A heat map of microarray data show the genes that significantly changed up to 2 fold compared between Fcgr2b-/- and Fcgr2b-/-. Stinggt/gt mice (N=4 mice per group; p<0.05). Data show in log2 (sample/wild-type). 2 Supplemental Figure 2. Sting signaling is essential for immuno-phenotypes of the Fcgr2b-/-lupus mice. (A-C) Flow cytometry analysis of splenocytes isolated from wild-type, Fcgr2b-/- and Fcgr2b-/-. Stinggt/gt mice at the age of 6-7 months (N= 13-14 per group). Data shown in the percentage of (A) CD4+ ICOS+ cells, (B) B220+ I-Ab+ cells and (C) CD138+ cells. Data show as mean ± SEM (*p < 0.05, **p<0.01 and ***p<0.001). 3 Supplemental Figure 3. Phenotypes of Sting activated dendritic cells. (A) Representative of western blot analysis from immunoprecipitation with Sting of Fcgr2b-/- mice (N= 4). The band was shown in STING protein of activated BMDC with DMXAA at 0, 3 and 6 hr. and phosphorylation of STING at Ser357. (B) Mass spectra of phosphorylation of STING at Ser357 of activated BMDC from Fcgr2b-/- mice after stimulated with DMXAA for 3 hour and followed by immunoprecipitation with STING. (C) Sting-activated BMDC were co-cultured with LYN inhibitor PP2 and analyzed by flow cytometry, which showed the mean fluorescence intensity (MFI) of IAb expressing DC (N = 3 mice per group). 4 Supplemental Table 1. Lists of up and down of regulated proteins Accession No. -
BD Biosciences New RUO Reagents - November 2020
BD Biosciences New RUO reagents - November 2020 Reactivity Description Format Clone Size Cat. number Hu CD133 FITC W6B3C1 100µg 567029 Hu CD133 FITC W6B3C1 25µg 567033 Hu CD39 PE A1/CD39 100Tst 567156 Hu CD39 PE A1/CD39 25Tst 567157 Hu KIR2DL1/S1/S3/S5 PE HP-MA4 100Tst 567158 Hu KIR2DL1/S1/S3/S5 PE HP-MA4 25Tst 567159 Hu IL-22 Alexa Fluor® 647 MH22B2 100µg 567160 Hu IL-22 Alexa Fluor® 647 MH22B2 25µg 567161 Hu CD99 R718 TU12 50µg 751651 Hu CD161 R718 DX12 50µg 751652 Hu CD116 R718 HGMCSFR-M1 50µg 751653 Hu HLA-G R718 87G 50µg 751670 Hu CD27 R718 O323 50µg 751686 Hu CD80 (B7-1) R718 2D10.4 50µg 751737 Hu Integrin αvβ5 R718 ALULA 50µg 751738 Hu CD266 (Tweak-R) R718 ITEM-4 50µg 751739 Hu ErbB3 (HER-3) R718 SGP1 50µg 751799 Hu TCR Vβ5.1 R718 LC4 50µg 751816 Hu CD123 (IL-3Ra) R718 6H6 50µg 751844 Hu CD1a R718 SK9 50µg 751847 Hu CD20 R718 L27 50µg 751849 Hu Disial GD2 R718 14.G2A 50µg 751851 Reactivity Description Format Clone Size Cat. number Hu CD71 R718 L01.1 50µg 751853 Hu CD278 (ICOS) R718 DX29 50µg 751854 Hu B7-H4 R718 MIH43 50µg 751857 Hu CD53 R718 HI29 50µg 751858 Hu CD197 (CCR7) R718 2-L1-A 50µg 751859 Hu CD197 (CCR7) R718 3D12 50µg 751861 Hu CD31 R718 L133.1 50µg 751863 Hu EGF Receptor R718 EMAB-134 50µg 751864 Hu CD8b R718 2ST8.5H7 50µg 751867 Hu CD31 R718 MBC 78.2 50µg 751869 Hu CD162 R718 KPL-1 50µg 751873 Hu CD24 R718 ML5 50µg 751874 Hu CD159C (NKG2C) R718 134591 50µg 751876 Hu CD169 (Siglec-1) R718 7-239 50µg 751877 Hu CD16b R718 CLB-GRAN11.5 50µg 751880 Hu IgM R718 UCH-B1 50µg 751881 Hu CD275 R718 2D3/B7-H2 50µg 751883 Hu CD307e