Research Article in Silico Prediction and in Vitro Characterization of Multifunctional Human Rnase3
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Seq2pathway Vignette
seq2pathway Vignette Bin Wang, Xinan Holly Yang, Arjun Kinstlick May 19, 2021 Contents 1 Abstract 1 2 Package Installation 2 3 runseq2pathway 2 4 Two main functions 3 4.1 seq2gene . .3 4.1.1 seq2gene flowchart . .3 4.1.2 runseq2gene inputs/parameters . .5 4.1.3 runseq2gene outputs . .8 4.2 gene2pathway . 10 4.2.1 gene2pathway flowchart . 11 4.2.2 gene2pathway test inputs/parameters . 11 4.2.3 gene2pathway test outputs . 12 5 Examples 13 5.1 ChIP-seq data analysis . 13 5.1.1 Map ChIP-seq enriched peaks to genes using runseq2gene .................... 13 5.1.2 Discover enriched GO terms using gene2pathway_test with gene scores . 15 5.1.3 Discover enriched GO terms using Fisher's Exact test without gene scores . 17 5.1.4 Add description for genes . 20 5.2 RNA-seq data analysis . 20 6 R environment session 23 1 Abstract Seq2pathway is a novel computational tool to analyze functional gene-sets (including signaling pathways) using variable next-generation sequencing data[1]. Integral to this tool are the \seq2gene" and \gene2pathway" components in series that infer a quantitative pathway-level profile for each sample. The seq2gene function assigns phenotype-associated significance of genomic regions to gene-level scores, where the significance could be p-values of SNPs or point mutations, protein-binding affinity, or transcriptional expression level. The seq2gene function has the feasibility to assign non-exon regions to a range of neighboring genes besides the nearest one, thus facilitating the study of functional non-coding elements[2]. Then the gene2pathway summarizes gene-level measurements to pathway-level scores, comparing the quantity of significance for gene members within a pathway with those outside a pathway. -
A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus
Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated. -
Figure S1. Representative Report Generated by the Ion Torrent System Server for Each of the KCC71 Panel Analysis and Pcafusion Analysis
Figure S1. Representative report generated by the Ion Torrent system server for each of the KCC71 panel analysis and PCaFusion analysis. (A) Details of the run summary report followed by the alignment summary report for the KCC71 panel analysis sequencing. (B) Details of the run summary report for the PCaFusion panel analysis. A Figure S1. Continued. Representative report generated by the Ion Torrent system server for each of the KCC71 panel analysis and PCaFusion analysis. (A) Details of the run summary report followed by the alignment summary report for the KCC71 panel analysis sequencing. (B) Details of the run summary report for the PCaFusion panel analysis. B Figure S2. Comparative analysis of the variant frequency found by the KCC71 panel and calculated from publicly available cBioPortal datasets. For each of the 71 genes in the KCC71 panel, the frequency of variants was calculated as the variant number found in the examined cases. Datasets marked with different colors and sample numbers of prostate cancer are presented in the upper right. *Significantly high in the present study. Figure S3. Seven subnetworks extracted from each of seven public prostate cancer gene networks in TCNG (Table SVI). Blue dots represent genes that include initial seed genes (parent nodes), and parent‑child and child‑grandchild genes in the network. Graphical representation of node‑to‑node associations and subnetwork structures that differed among and were unique to each of the seven subnetworks. TCNG, The Cancer Network Galaxy. Figure S4. REVIGO tree map showing the predicted biological processes of prostate cancer in the Japanese. Each rectangle represents a biological function in terms of a Gene Ontology (GO) term, with the size adjusted to represent the P‑value of the GO term in the underlying GO term database. -
Transcriptomic Signature and Metabolic Programming of Bovine Classical and Nonclassical Monocytes Indicate Distinct Functional Specializations
bioRxiv preprint doi: https://doi.org/10.1101/2020.10.30.362731; this version posted November 1, 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. Transcriptomic signature and metabolic programming of bovine classical and nonclassical monocytes indicate distinct functional specializations Stephanie C. Talker1,2, G. Tuba Barut1,2, Reto Rufener3, Lilly von Münchow4, Artur Summerfield1,2 1Institute of Virology and Immunology, Bern and Mittelhäusern, Switzerland 2Department of Infectious Diseases and Pathobiology, Vetsuisse Faculty, University of Bern, Bern, Switzerland 3Institute of Parasitology, Vetsuisse Faculty, University of Bern, Bern, Switzerland 4 Bucher Biotec AG, Basel, Switzerland *Correspondence: Corresponding Author [email protected] Keywords: monocyte subsets, transcriptome, metabolism, cattle Abstract Similar to human monocytes, bovine monocytes can be split into CD14+CD16- classical and CD14-CD16+ nonclassical monocytes (cM and ncM, respectively). Here, we present an in-depth analysis of their steady-state transcriptomes, highlighting pronounced functional specializations. Gene transcription indicates that pro-inflammatory and antibacterial processes are associated with cM, while ncM appear to be specialized in regulatory/anti-inflammatory functions and tissue repair, as well as antiviral responses and T-cell immunomodulation. In support of these functional differences, we found that oxidative phosphorylation prevails in ncM, whereas cM are clearly biased towards aerobic glycolysis. Furthermore, bovine monocyte subsets differed in their responsiveness to TLR ligands, supporting an antiviral role of ncM. Taken together, these data clearly indicate a variety of subset-specific functions in cM and ncM that are likely to be transferable to monocyte subsets of other species, including humans. -
In Silico Prediction and in Vitro Characterization of Multifunctional Human Rnase3
Hindawi Publishing Corporation BioMed Research International Volume 2013, Article ID 170398, 12 pages http://dx.doi.org/10.1155/2013/170398 Research Article In Silico Prediction and In Vitro Characterization of Multifunctional Human RNase3 Pei-Chun Lien,1 Ping-Hsueh Kuo,1 Chien-Jung Chen,1 Hsiu-Hui Chang,1 Shun-lung Fang,1 Wei-Shuo Wu,2 Yiu-Kay Lai,2 Tun-Wen Pai,3 and Margaret Dah-Tsyr Chang1, 4 1 InstituteofMolecularandCellularBiology,NationalTsingHuaUniversity,No.101,Section2,KuangFuRoad, Hsinchu 30013, Taiwan 2 Institute of Biotechnology, National Tsing Hua University, No. 101, Section 2, Kuang Fu Road, Hsinchu 30013, Taiwan 3 Department of Computer Science and Engineering, National Taiwan Ocean University, 2 Pei Ning Road, Keelung 20224, Taiwan 4 Department of Medical Science, National Tsing Hua University, No. 101, Section 2, Kuang Fu Road, Hsinchu 30013, Taiwan Correspondence should be addressed to Margaret Dah-Tsyr Chang; [email protected] Received 31 October 2012; Accepted 2 December 2012 Academic Editor: Hao-Teng Chang Copyright © 2013 Pei-Chun Lien et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Human ribonucleases A (hRNaseA) superfamily consists of thirteen members with high-structure similarities but exhibits divergent physiological functions other than RNase activity. Evolution of hRNaseA superfamily has gained novel functions which may be preserved in a unique region or domain to account for additional molecular interactions. hRNase3 has multiple functions including ribonucleolytic, heparan sulfate (HS) binding, cellular binding, endocytic, lipid destabilization, cytotoxic, and antimicrobial activities. -
(12) Patent Application Publication (10) Pub. No.: US 2003/0082511 A1 Brown Et Al
US 20030082511A1 (19) United States (12) Patent Application Publication (10) Pub. No.: US 2003/0082511 A1 Brown et al. (43) Pub. Date: May 1, 2003 (54) IDENTIFICATION OF MODULATORY Publication Classification MOLECULES USING INDUCIBLE PROMOTERS (51) Int. Cl." ............................... C12O 1/00; C12O 1/68 (52) U.S. Cl. ..................................................... 435/4; 435/6 (76) Inventors: Steven J. Brown, San Diego, CA (US); Damien J. Dunnington, San Diego, CA (US); Imran Clark, San Diego, CA (57) ABSTRACT (US) Correspondence Address: Methods for identifying an ion channel modulator, a target David B. Waller & Associates membrane receptor modulator molecule, and other modula 5677 Oberlin Drive tory molecules are disclosed, as well as cells and vectors for Suit 214 use in those methods. A polynucleotide encoding target is San Diego, CA 92121 (US) provided in a cell under control of an inducible promoter, and candidate modulatory molecules are contacted with the (21) Appl. No.: 09/965,201 cell after induction of the promoter to ascertain whether a change in a measurable physiological parameter occurs as a (22) Filed: Sep. 25, 2001 result of the candidate modulatory molecule. Patent Application Publication May 1, 2003 Sheet 1 of 8 US 2003/0082511 A1 KCNC1 cDNA F.G. 1 Patent Application Publication May 1, 2003 Sheet 2 of 8 US 2003/0082511 A1 49 - -9 G C EH H EH N t R M h so as se W M M MP N FIG.2 Patent Application Publication May 1, 2003 Sheet 3 of 8 US 2003/0082511 A1 FG. 3 Patent Application Publication May 1, 2003 Sheet 4 of 8 US 2003/0082511 A1 KCNC1 ITREXCHO KC 150 mM KC 2000000 so 100 mM induced Uninduced Steady state O 100 200 300 400 500 600 700 Time (seconds) FIG. -
Anti-RNASE1 (GW10801F)
Anti-RNASE1 antibody produced in chicken, affinity isolated antibody Catalog Number GW10801F Formerly listed as GenWay Catalog Number 15-288- 10801F, Ribonuclease pancreatic Antibody. Storage Temperature –20 °C EC 3.1.27.5 Precautions and Disclaimer Synonyms: Pancreatic ribonuclease; RNase 1; This product is for R&D use only, not for drug, RNase A; RNase UpI-1; RIB-1; HP-RNase household, or other uses. Due to the sodium azide content a material safety data sheet (MSDS) for this Product Description product has been sent to the attention of the safety Endonuclease that catalyzes the cleavage of RNA on officer of your institution. Please consult the Material the 3¢ side of pyrimidine nucleotides. Acts on single Safety Data Sheet for information regarding hazards stranded and double stranded RNA. and safe handling practices. NCBI Accession number: NP_002924.1 Storage/Stability Swiss Prot Accession number: P07998 For continuous use, store at 2–8 °C for up to one week. For extended storage, store in –20 °C freezer in Gene Information: Human .. RNASE1 (6035) working aliquots. Repeated freezing and thawing, or storage in “frostfree” freezers, is not recommended. If Immunogen: Recombinant protein Pancreatic slight turbidity occurs upon prolonged storage, clarify ribonuclease the solution by centrifugation before use. Working dilution samples should be discarded if not used within Immunogen Sequence: Gi # 4506547, sequence 1–156 12 hours. The product is a clear, colorless solution in phosphate TD,LPG,MAM 04/09-1 buffered saline, pH 7.2, containing 0.02% sodium azide. Species Reactivity: Human Tested Applications: WB Recommended Dilutions: Recommended starting dilution for Western blot analysis is 1:500 for tissue or cell staining 1:200. -
Analysis of Dynamic Molecular Networks for Pancreatic Ductal
Pan et al. Cancer Cell Int (2018) 18:214 https://doi.org/10.1186/s12935-018-0718-5 Cancer Cell International PRIMARY RESEARCH Open Access Analysis of dynamic molecular networks for pancreatic ductal adenocarcinoma progression Zongfu Pan1†, Lu Li2†, Qilu Fang1, Yiwen Zhang1, Xiaoping Hu1, Yangyang Qian3 and Ping Huang1* Abstract Background: Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest solid tumors. The rapid progression of PDAC results in an advanced stage of patients when diagnosed. However, the dynamic molecular mechanism underlying PDAC progression remains far from clear. Methods: The microarray GSE62165 containing PDAC staging samples was obtained from Gene Expression Omnibus and the diferentially expressed genes (DEGs) between normal tissue and PDAC of diferent stages were profled using R software, respectively. The software program Short Time-series Expression Miner was applied to cluster, compare, and visualize gene expression diferences between PDAC stages. Then, function annotation and pathway enrichment of DEGs were conducted by Database for Annotation Visualization and Integrated Discovery. Further, the Cytoscape plugin DyNetViewer was applied to construct the dynamic protein–protein interaction networks and to analyze dif- ferent topological variation of nodes and clusters over time. The phosphosite markers of stage-specifc protein kinases were predicted by PhosphoSitePlus database. Moreover, survival analysis of candidate genes and pathways was per- formed by Kaplan–Meier plotter. Finally, candidate genes were validated by immunohistochemistry in PDAC tissues. Results: Compared with normal tissues, the total DEGs number for each PDAC stage were 994 (stage I), 967 (stage IIa), 965 (stage IIb), 1027 (stage III), 925 (stage IV), respectively. The stage-course gene expression analysis showed that 30 distinct expressional models were clustered. -
Title:Ribosome Clearance During RNA Interference
Downloaded from rnajournal.cshlp.org on October 9, 2021 - Published by Cold Spring Harbor Laboratory Press Pule, 1 Title: Ribosome clearance during RNA interference 1 1 2 1 Authors: Makena N. Pule , Marissa L. Glover , Andrew Z. Fire , Joshua A. Arribere Short Title: Ribosome clearance during RNA interference Keywords: SKI, PELOTA, RNAi, C. elegans, ribosome, nonstop 1 Department of MCD Biology, UC Santa Cruz 2 Departments of Pathology and Genetics, Stanford University School of Medicine Downloaded from rnajournal.cshlp.org on October 9, 2021 - Published by Cold Spring Harbor Laboratory Press Pule, 2 ABSTRACT In the course of identifying and cleaving RNA, the RNAi machinery must encounter and contend with the megadalton-sized ribosomes that carry out translation. We investigated this interface by examining the fate of actively translated mRNAs subjected to RNAi in C. elegans. Quantifying RNA levels (RNA-seq) and ongoing translation (Ribo-seq), we found there is a greater fold repression of ongoing translation than expected from loss of RNA alone, observing stronger translation repression relative to RNA repression for multiple, independent double-stranded RNA triggers, and for multiple genes. In animals that lack the RNA helicase SKI complex and the ribosome rescue factor PELOTA, ribosomes stall on the 3’ edges of mRNAs at and upstream of the RNAi trigger. One model to explain these observations is that ribosomes are actively cleared from mRNAs by SKI and PELO during or following mRNA cleavage. Our results expand prior studies that show a role for the SKI RNA helicase complex in removing RNA targets following RNAi in flies and plants, illuminating the widespread role of the nonstop translation surveillance in RNA silencing during RNAi. -
Relationship Between Rnase1, Ang and Rnase6 Gene Polymorphism and the Values of Blood Indices in Suckling Piglets
Acta Veterinaria Hungarica 67 (3), pp. 385–400 (2019) DOI: 10.1556/004.2019.039 RELATIONSHIP BETWEEN RNASE1, ANG AND RNASE6 GENE POLYMORPHISM AND THE VALUES OF BLOOD INDICES IN SUCKLING PIGLETS 1* 2 2 Hanna SZYMAŃSKA , Krystyna ŻYCZKO and Tadeusz ZABOLEWICZ 1Department of Medical Biology, University of Warmia and Mazury, Żołnierska 14C, 10-561 Olsztyn, Poland; 2Department of Animal Genetics, University of Warmia and Mazury, Olsztyn, Poland (Received 6 July 2018; accepted 2 May 2019) The relationship between PCR-restriction fragment length polymorphism in RNASE1 (296 A/G), ANG (149 G/T) and RNASE6 (389 C/T) genes and the values of haematological and biochemical blood indices was analysed in cross- bred suckling piglets (n = 473), aged 21 ± 3 days (younger, n = 274) and 35 ± 3 days (older, n = 199), descending from Polish Large White × Polish Landrace sows and Duroc × Pietrain boars. The observed distribution of all genotypes was consistent with the Hardy–Weinberg equilibrium. Anaemia was more common in younger piglets with RNASE1 GA genotype but in the blood of older GA piglets a higher count and percentage of granulocytes were noted. This could be related to the destruction of erythrocytes in younger piglets and enhanced host defence in older ones. ANG gene polymorphism was associated with the severity of iron de- ficiency in younger piglets. This is supposed to be linked with the different ability to protect immune cells against suppression and degradation during iron deficien- cy. In older piglets, this mutation differentiated the reactivity of the immune sys- tem. Varying levels of iron status and red blood cell indices in RNASE6 genotypes presumably resulted from the coupling of genes involved in iron metabolism and expressed in an age-dependent manner. -
Novogene Research Collection
Leading Edge Genomic Services & Solutions Leading Edge Genomic Services & Solutions Contents 1 De novo Sequencing Services The Apostasia genome and the evolution of orchids ………………………………………………………………………………………01 Scallop genome reveals molecular adaptations to semi-sessile life and neurotoxins ………………………………………………02 The Aegilops tauschii genome reveals multiple impacts of transposons ………………………………………………………………03 Comprehensive variation discovery and recovery of missing sequence in the pig genome using multiple de novo assemblies …………04 Scallop genome provides insights into evolution of bilaterian karyotype and development ………………………………………05 Sequencing of allotetraploid cotton (Gossypium hirsutum L. acc. TM-1) provides a resource for fiber improvement …………06 Whole-genome sequencing of the snub-nosed monkey provides insights into folivory and evolutionary history ………………07 De novo assembly of soybean wild relatives for pan-genome analysis of diversity and agronomic traits ………………………08 Genomic analyses identify distinct patterns of selection in domesticated pigs and Tibetan wild boars …………………………09 2 Re-sequencing Services 2.1 Human Whole Genome Sequencing (WGS) and Whole Exome Sequencing (WES) Genetic alterations in esophageal tissues from squamous dysplasia to carcinoma 10 ………………………………… Probing meiotic recombination and aneuploidy of single sperm cells by whole-genome sequencing 11 ………………… Simultaneous evolutionary expansion and constraint of genomic heterogeneity in multifocal lung cancer 12 …………… Single-cell exome sequencing identifies mutations -
Bioinformatic Analysis Reveals the Importance of Epithelial-Mesenchymal Transition in the Development of Endometriosis
www.nature.com/scientificreports OPEN Bioinformatic analysis reveals the importance of epithelial- mesenchymal transition in the development of endometriosis Meihong Chen1,6, Yilu Zhou2,3,6, Hong Xu4, Charlotte Hill2, Rob M. Ewing2,3, Deming He1, Xiaoling Zhang1 ✉ & Yihua Wang2,3,5 ✉ Background: Endometriosis is a frequently occurring disease in women, which seriously afects their quality of life. However, its etiology and pathogenesis are still unclear. Methods: To identify key genes/ pathways involved in the pathogenesis of endometriosis, we recruited 3 raw microarray datasets (GSE11691, GSE7305, and GSE12768) from Gene Expression Omnibus database (GEO), which contain endometriosis tissues and normal endometrial tissues. We then performed in-depth bioinformatic analysis to determine diferentially expressed genes (DEGs), followed by gene ontology (GO), Hallmark pathway enrichment and protein-protein interaction (PPI) network analysis. The fndings were further validated by immunohistochemistry (IHC) staining in endometrial tissues from endometriosis or control patients. Results: We identifed 186 DEGs, of which 118 were up-regulated and 68 were down-regulated. The most enriched DEGs in GO functional analysis were mainly associated with cell adhesion, infammatory response, and extracellular exosome. We found that epithelial-mesenchymal transition (EMT) ranked frst in the Hallmark pathway enrichment. EMT may potentially be induced by infammatory cytokines such as CXCL12. IHC confrmed the down-regulation of E-cadherin (CDH1) and up-regulation of CXCL12 in endometriosis tissues. Conclusions: Utilizing bioinformatics and patient samples, we provide evidence of EMT in endometriosis. Elucidating the role of EMT will improve the understanding of the molecular mechanisms involved in the development of endometriosis. Endometriosis is a frequently occurring gynaecological disease characterised by chronic pelvic pain, dysmenor- rhea and infertility1.