LC-MS/MS Analysis of Protein Content from Extracellular Vesicles Enriched Samples from Osteoclasts Secretome
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Supplement 1 Microarray Studies
EASE Categories Significantly Enriched in vs MG vs vs MGC4-2 Pt1-C vs C4-2 Pt1-C UP-Regulated Genes MG System Gene Category EASE Global MGRWV Pt1-N RWV Pt1-N Score FDR GO Molecular Extracellular matrix cellular construction 0.0008 0 110 genes up- Function Interpro EGF-like domain 0.0009 0 regulated GO Molecular Oxidoreductase activity\ acting on single dono 0.0015 0 Function GO Molecular Calcium ion binding 0.0018 0 Function Interpro Laminin-G domain 0.0025 0 GO Biological Process Cell Adhesion 0.0045 0 Interpro Collagen Triple helix repeat 0.0047 0 KEGG pathway Complement and coagulation cascades 0.0053 0 KEGG pathway Immune System – Homo sapiens 0.0053 0 Interpro Fibrillar collagen C-terminal domain 0.0062 0 Interpro Calcium-binding EGF-like domain 0.0077 0 GO Molecular Cell adhesion molecule activity 0.0105 0 Function EASE Categories Significantly Enriched in Down-Regulated Genes System Gene Category EASE Global Score FDR GO Biological Process Copper ion homeostasis 2.5E-09 0 Interpro Metallothionein 6.1E-08 0 Interpro Vertebrate metallothionein, Family 1 6.1E-08 0 GO Biological Process Transition metal ion homeostasis 8.5E-08 0 GO Biological Process Heavy metal sensitivity/resistance 1.9E-07 0 GO Biological Process Di-, tri-valent inorganic cation homeostasis 6.3E-07 0 GO Biological Process Metal ion homeostasis 6.3E-07 0 GO Biological Process Cation homeostasis 2.1E-06 0 GO Biological Process Cell ion homeostasis 2.1E-06 0 GO Biological Process Ion homeostasis 2.1E-06 0 GO Molecular Helicase activity 2.3E-06 0 Function GO Biological -
Snps) Distant from Xenobiotic Response Elements Can Modulate Aryl Hydrocarbon Receptor Function: SNP-Dependent CYP1A1 Induction S
Supplemental material to this article can be found at: http://dmd.aspetjournals.org/content/suppl/2018/07/06/dmd.118.082164.DC1 1521-009X/46/9/1372–1381$35.00 https://doi.org/10.1124/dmd.118.082164 DRUG METABOLISM AND DISPOSITION Drug Metab Dispos 46:1372–1381, September 2018 Copyright ª 2018 by The American Society for Pharmacology and Experimental Therapeutics Single Nucleotide Polymorphisms (SNPs) Distant from Xenobiotic Response Elements Can Modulate Aryl Hydrocarbon Receptor Function: SNP-Dependent CYP1A1 Induction s Duan Liu, Sisi Qin, Balmiki Ray,1 Krishna R. Kalari, Liewei Wang, and Richard M. Weinshilboum Division of Clinical Pharmacology, Department of Molecular Pharmacology and Experimental Therapeutics (D.L., S.Q., B.R., L.W., R.M.W.) and Division of Biomedical Statistics and Informatics, Department of Health Sciences Research (K.R.K.), Mayo Clinic, Rochester, Minnesota Received April 22, 2018; accepted June 28, 2018 ABSTRACT Downloaded from CYP1A1 expression can be upregulated by the ligand-activated aryl fashion. LCLs with the AA genotype displayed significantly higher hydrocarbon receptor (AHR). Based on prior observations with AHR-XRE binding and CYP1A1 mRNA expression after 3MC estrogen receptors and estrogen response elements, we tested treatment than did those with the GG genotype. Electrophoretic the hypothesis that single-nucleotide polymorphisms (SNPs) map- mobility shift assay (EMSA) showed that oligonucleotides with the ping hundreds of base pairs (bp) from xenobiotic response elements AA genotype displayed higher LCL nuclear extract binding after (XREs) might influence AHR binding and subsequent gene expres- 3MC treatment than did those with the GG genotype, and mass dmd.aspetjournals.org sion. -
Table 1. Swine Proteins Identified As Differentially Expressed at 24Dpi in OURT 88/3 Infected Animals
Table 1. swine proteins identified as differentially expressed at 24dpi in OURT 88/3 infected animals. Gene name Protein ID Protein Name -Log p-value control vs A_24DPI Difference control Vs A_24DPI F8 K7GL28 Coagulation factor VIII 2.123919902 5.42533493 PPBP F1RUL6 C-X-C motif chemokine 3.219079808 4.493174871 SDPR I3LDR9 Caveolae associated protein 2 2.191007299 4.085711161 IGHG L8B0X5 IgG heavy chain 2.084611488 -4.282530149 LOC100517145 F1S3H9 Complement C3 (LOC100517145) 3.885740476 -4.364484406 GOLM1 F1S4I1 Golgi membrane protein 1 1.746130664 -4.767168681 FCN2 I3L5W3 Ficolin-2 2.937884686 -6.029483795 Table 2. swine proteins identified as differentially expressed at 7dpi in Benin ΔMGF infected animals. Gene name Protein ID Protein Name -Log p-value control vs B_7DPI Difference control Vs B_7DPI A0A075B7I5 Ig-like domain-containing protein 1.765578164 -3.480728149 ATP5A1 F1RPS8_PIG ATP synthase subunit alpha 2.270386995 3.270935059 LOC100627396 F1RX35_PIG Fibrinogen C-terminal domain-containing protein 2.211242648 3.967363358 LOC100514666;LOC102158263 F1RX36_PIG Fibrinogen alpha chain 2.337934993 3.758180618 FGB F1RX37_PIG Fibrinogen beta chain 2.411948004 4.03753376 PSMA8 F1SBA5_PIG Proteasome subunit alpha type 1.473601007 -3.815182686 ACAN F1SKR0_PIG Aggrecan core protein 1.974489764 -3.726634026 TFG F1SL01_PIG PB1 domain-containing protein 1.809215274 -3.131304741 LOC100154408 F1SSL6_PIG Proteasome subunit alpha type 1.701949053 -3.944885254 PSMA4 F2Z528_PIG Proteasome subunit alpha type 2.045768185 -4.502977371 PSMA5 F2Z5K2_PIG -
Gene Set Size Count Z-Score P-Value Q-Value List of Genes
Supplementary Data 2-1 Results from competitive pathway enrichment analysis based on genome-wide summary-level data of hsGWAS. Gene Set Size Count z-score p-value q-value List of Genes KEGG_ALLOGRAFT_ 38 36 12.6476 0.00E+00 0.00E+00 HLA-DRB5(4.11028); IL2(3.62922); HLA-E(3.27708); HLA-G(3.2749); HLA- REJECTION DQA2(3.16347); HLA-DRA(3.14994); HLA-DOB(3.11438); HLA-DMB(3.09917); HLA-DMA(3.09615); TNF(3.09414); HLA-DPB1(3.03922); HLA-DPA1(3.0355); HLA-A(3.02312); HLA-C(2.98835); HLA-DQB1(2.97025); HLA-B(2.95626); HLA- DQA1(2.94591); HLA-F(2.83254); HLA-DRB1(2.76738); HLA-DOA(2.72508); IFNG(1.74523); PRF1(1.46483); FASLG(1.10083); FAS(0.923795); HLA- DRB3(0.760622); CD80(0.436654); IL4(0.324564); CD40(0.271072); HLA- DRB4(0.0708609); IL12A(0.0685942); IL5(0.0659333); CD86(0.049911); CD28(- 0.16103); IL10(-0.165903); IL12B(-0.245281); GZMB(-0.268581); KEGG_GRAFT_VERSUS_HOS 42 37 12.6129 0.00E+00 0.00E+00 HLA-DRB5(4.11028); IL2(3.62922); HLA-E(3.27708); HLA-G(3.2749); HLA- T_DISEASE DQA2(3.16347); HLA-DRA(3.14994); HLA-DOB(3.11438); HLA-DMB(3.09917); HLA-DMA(3.09615); TNF(3.09414); HLA-DPB1(3.03922); HLA-DPA1(3.0355); HLA-A(3.02312); HLA-C(2.98835); HLA-DQB1(2.97025); HLA-B(2.95626); HLA- DQA1(2.94591); HLA-F(2.83254); HLA-DRB1(2.76738); HLA-DOA(2.72508); IL6(1.94139); IFNG(1.74523); PRF1(1.46483); FASLG(1.10083); FAS(0.923795); HLA-DRB3(0.760622); CD80(0.436654); IL1A(0.402186); KLRD1(0.29064); KIR3DL1(0.157683); HLA-DRB4(0.0708609); CD86(0.049911); CD28(-0.16103); GZMB(-0.268581); IL1B(-0.388308); KLRC1(-0.466394); KIR3DL2(-0.786806); KEGG_TYPE_I_DIABETES_ME -
Supplementary Information
Supplementary information (a) (b) Figure S1. Resistant (a) and sensitive (b) gene scores plotted against subsystems involved in cell regulation. The small circles represent the individual hits and the large circles represent the mean of each subsystem. Each individual score signifies the mean of 12 trials – three biological and four technical. The p-value was calculated as a two-tailed t-test and significance was determined using the Benjamini-Hochberg procedure; false discovery rate was selected to be 0.1. Plots constructed using Pathway Tools, Omics Dashboard. Figure S2. Connectivity map displaying the predicted functional associations between the silver-resistant gene hits; disconnected gene hits not shown. The thicknesses of the lines indicate the degree of confidence prediction for the given interaction, based on fusion, co-occurrence, experimental and co-expression data. Figure produced using STRING (version 10.5) and a medium confidence score (approximate probability) of 0.4. Figure S3. Connectivity map displaying the predicted functional associations between the silver-sensitive gene hits; disconnected gene hits not shown. The thicknesses of the lines indicate the degree of confidence prediction for the given interaction, based on fusion, co-occurrence, experimental and co-expression data. Figure produced using STRING (version 10.5) and a medium confidence score (approximate probability) of 0.4. Figure S4. Metabolic overview of the pathways in Escherichia coli. The pathways involved in silver-resistance are coloured according to respective normalized score. Each individual score represents the mean of 12 trials – three biological and four technical. Amino acid – upward pointing triangle, carbohydrate – square, proteins – diamond, purines – vertical ellipse, cofactor – downward pointing triangle, tRNA – tee, and other – circle. -
Polyubiquitin Gene Ubb Is Required for Upregulation of Piwi Protein Level During Mouse Testis Development
www.nature.com/cddiscovery ARTICLE OPEN Polyubiquitin gene Ubb is required for upregulation of Piwi protein level during mouse testis development 1,4 2,4 2 1 1 2 ✉ Bitnara Han , Byung-Kwon✉ Jung , So-Hyun Park , Kyu Jin Song , Muhammad Ayaz Anwar , Kwon-Yul Ryu and Kwang Pyo Kim 1,3 © The Author(s) 2021 Testis development, including early embryonic gonad formation and late postnatal spermatogenesis, is essential for the reproduction of higher metazoans to generate fertile gametes, called sperm. We have previously reported that the polyubiquitin gene Ubb is required for fertility in both male and female mice. In particular, the Ubb-null male mice showed an azoospermia phenotype due to arrest of spermatogenesis at the pachytene stage. Here, we analyzed the whole testis proteome at postnatal day 20 to define the molecular mediators of the male-infertility phenotype caused by Ubb knockout. From the identified proteome, 564 proteins were significantly and differentially expressed in Ubb-knockout testes and, among these, 36 downregulated proteins were involved at different stages of spermatogenesis. We also found that levels of piRNA metabolic process-related proteins, including Piwil2 and Tdrd1, were downregulated in Ubb-null testes through functional gene ontology analysis. Further, protein–protein interaction mapping revealed that 24 testis development-related proteins, including Hsp90aa1, Eef1a1, and Pabpc1, were directly influenced by the depletion of ubiquitin. In addition, the reduced mRNA levels of these proteins were observed in Ubb-knockout testes, which closely resembled the global downregulation of piRNA-metabolic gene expression at the transcriptional and post- transcriptional levels. Together with proteomic and transcriptional analyses, our data suggest that Ubb expression is essential for the maintenance of testicular RNA-binding regulators and piRNA-metabolic proteins to complete spermatogenesis in mice. -
VIEW Open Access the Role of Ubiquitination and Deubiquitination in Cancer Metabolism Tianshui Sun1, Zhuonan Liu2 and Qing Yang1*
Sun et al. Molecular Cancer (2020) 19:146 https://doi.org/10.1186/s12943-020-01262-x REVIEW Open Access The role of ubiquitination and deubiquitination in cancer metabolism Tianshui Sun1, Zhuonan Liu2 and Qing Yang1* Abstract Metabolic reprogramming, including enhanced biosynthesis of macromolecules, altered energy metabolism, and maintenance of redox homeostasis, is considered a hallmark of cancer, sustaining cancer cell growth. Multiple signaling pathways, transcription factors and metabolic enzymes participate in the modulation of cancer metabolism and thus, metabolic reprogramming is a highly complex process. Recent studies have observed that ubiquitination and deubiquitination are involved in the regulation of metabolic reprogramming in cancer cells. As one of the most important type of post-translational modifications, ubiquitination is a multistep enzymatic process, involved in diverse cellular biological activities. Dysregulation of ubiquitination and deubiquitination contributes to various disease, including cancer. Here, we discuss the role of ubiquitination and deubiquitination in the regulation of cancer metabolism, which is aimed at highlighting the importance of this post-translational modification in metabolic reprogramming and supporting the development of new therapeutic approaches for cancer treatment. Keywords: Ubiquitination, Deubiquitination, Cancer, Metabolic reprogramming Background cells have aroused increasing attention and interest [3]. Metabolic pathways are of vital importance in proliferat- Because of the generality of metabolic alterations in can- ing cells to meet their demands of various macromole- cer cells, metabolic reprogramming is thought as hall- cules and energy [1]. Compared with normal cells, mark of cancer, providing basis for tumor diagnosis and cancer cells own malignant properties, such as increased treatment [1]. For instance, the application of 18F- proliferation rate, and reside in environments short of deoxyglucose positron emission tomography is based on oxygen and nutrient. -
A Genomic Analysis of Rat Proteases and Protease Inhibitors
A genomic analysis of rat proteases and protease inhibitors Xose S. Puente and Carlos López-Otín Departamento de Bioquímica y Biología Molecular, Facultad de Medicina, Instituto Universitario de Oncología, Universidad de Oviedo, 33006-Oviedo, Spain Send correspondence to: Carlos López-Otín Departamento de Bioquímica y Biología Molecular Facultad de Medicina, Universidad de Oviedo 33006 Oviedo-SPAIN Tel. 34-985-104201; Fax: 34-985-103564 E-mail: [email protected] Proteases perform fundamental roles in multiple biological processes and are associated with a growing number of pathological conditions that involve abnormal or deficient functions of these enzymes. The availability of the rat genome sequence has opened the possibility to perform a global analysis of the complete protease repertoire or degradome of this model organism. The rat degradome consists of at least 626 proteases and homologs, which are distributed into five catalytic classes: 24 aspartic, 160 cysteine, 192 metallo, 221 serine, and 29 threonine proteases. Overall, this distribution is similar to that of the mouse degradome, but significatively more complex than that corresponding to the human degradome composed of 561 proteases and homologs. This increased complexity of the rat protease complement mainly derives from the expansion of several gene families including placental cathepsins, testases, kallikreins and hematopoietic serine proteases, involved in reproductive or immunological functions. These protease families have also evolved differently in the rat and mouse genomes and may contribute to explain some functional differences between these two closely related species. Likewise, genomic analysis of rat protease inhibitors has shown some differences with the mouse protease inhibitor complement and the marked expansion of families of cysteine and serine protease inhibitors in rat and mouse with respect to human. -
Supplementary Table 1: Genes Located on Chromosome 18P11-18Q23, an Area Significantly Linked to TMPRSS2-ERG Fusion
Supplementary Table 1: Genes located on Chromosome 18p11-18q23, an area significantly linked to TMPRSS2-ERG fusion Symbol Cytoband Description LOC260334 18p11 HSA18p11 beta-tubulin 4Q pseudogene IL9RP4 18p11.3 interleukin 9 receptor pseudogene 4 LOC100132166 18p11.32 hypothetical LOC100132166 similar to Rho-associated protein kinase 1 (Rho- associated, coiled-coil-containing protein kinase 1) (p160 LOC727758 18p11.32 ROCK-1) (p160ROCK) (NY-REN-35 antigen) ubiquitin specific peptidase 14 (tRNA-guanine USP14 18p11.32 transglycosylase) THOC1 18p11.32 THO complex 1 COLEC12 18pter-p11.3 collectin sub-family member 12 CETN1 18p11.32 centrin, EF-hand protein, 1 CLUL1 18p11.32 clusterin-like 1 (retinal) C18orf56 18p11.32 chromosome 18 open reading frame 56 TYMS 18p11.32 thymidylate synthetase ENOSF1 18p11.32 enolase superfamily member 1 YES1 18p11.31-p11.21 v-yes-1 Yamaguchi sarcoma viral oncogene homolog 1 LOC645053 18p11.32 similar to BolA-like protein 2 isoform a similar to 26S proteasome non-ATPase regulatory LOC441806 18p11.32 subunit 8 (26S proteasome regulatory subunit S14) (p31) ADCYAP1 18p11 adenylate cyclase activating polypeptide 1 (pituitary) LOC100130247 18p11.32 similar to cytochrome c oxidase subunit VIc LOC100129774 18p11.32 hypothetical LOC100129774 LOC100128360 18p11.32 hypothetical LOC100128360 METTL4 18p11.32 methyltransferase like 4 LOC100128926 18p11.32 hypothetical LOC100128926 NDC80 homolog, kinetochore complex component (S. NDC80 18p11.32 cerevisiae) LOC100130608 18p11.32 hypothetical LOC100130608 structural maintenance -
Isolation and Identification of Proteins from Swine Sperm Chromatin and Nuclear Matrix
DOI: 10.21451/1984-3143-AR816 Anim. Reprod., v.14, n.2, p.418-428, Apr./Jun. 2017 Isolation and identification of proteins from swine sperm chromatin and nuclear matrix Guilherme Arantes Mendonça1,3, Romualdo Morandi Filho2, Elisson Terêncio Souza2, Thais Schwarz Gaggini1, Marina Cruvinel Assunção Silva-Mendonça1, Robson Carlos Antunes1, Marcelo Emílio Beletti1,2 1Post-graduation Program in Veterinary Science, Federal University of Uberlandia, Uberlandia, MG, Brazil. 2Post-graduation Program in Cellular and Molecular Biology, Federal University of Uberlandia, Uberlandia, MG, Brazil. Abstract (Yamauchi et al., 2011). According to the same authors, these active sperm chromatin sites in protamine toroids The aim of this study was to perform a may contain important epigenetic information for the proteomic analysis to isolate and identify proteins from developing embryo. the swine sperm nuclear matrix to contribute to a The isolated use of genomic and transcriptomic database of swine sperm nuclear proteins. We used pre- information may be insufficient to fully understand a chilled diluted semen from seven boars (19 to 24 week- complex organism because proteomics and old) from the commercial line Landrace x Large White transcriptomics can be discordant and DNA-RNA x Pietran. The semen was processed to separate the relationships cannot be fully correlated. Thus, sperm heads and extract the chromatin and nuclear measurements of other metabolic levels should also be matrix for protein quantification and analysis by mass obtained, such as the study of proteins (Wright et al., spectrometry, by LTQ Orbitrap ELITE mass 2012). According to these same authors, large-scale spectrometer (Thermo-Finnigan) coupled to a nanoflow protein research in organisms (i.e., the proteome-protein chromatography system (LC-MS/MS). -
Genome-Scale Metabolic Network Analysis and Drug Targeting of Multi-Drug Resistant Pathogen Acinetobacter Baumannii AYE
Electronic Supplementary Material (ESI) for Molecular BioSystems. This journal is © The Royal Society of Chemistry 2017 Electronic Supplementary Information (ESI) for Molecular BioSystems Genome-scale metabolic network analysis and drug targeting of multi-drug resistant pathogen Acinetobacter baumannii AYE Hyun Uk Kim, Tae Yong Kim and Sang Yup Lee* E-mail: [email protected] Supplementary Table 1. Metabolic reactions of AbyMBEL891 with information on their genes and enzymes. Supplementary Table 2. Metabolites participating in reactions of AbyMBEL891. Supplementary Table 3. Biomass composition of Acinetobacter baumannii. Supplementary Table 4. List of 246 essential reactions predicted under minimal medium with succinate as a sole carbon source. Supplementary Table 5. List of 681 reactions considered for comparison of their essentiality in AbyMBEL891 with those from Acinetobacter baylyi ADP1. Supplementary Table 6. List of 162 essential reactions predicted under arbitrary complex medium. Supplementary Table 7. List of 211 essential metabolites predicted under arbitrary complex medium. AbyMBEL891.sbml Genome-scale metabolic model of Acinetobacter baumannii AYE, AbyMBEL891, is available as a separate file in the format of Systems Biology Markup Language (SBML) version 2. Supplementary Table 1. Metabolic reactions of AbyMBEL891 with information on their genes and enzymes. Highlighed (yellow) reactions indicate that they are not assigned with genes. No. Metabolism EC Number ORF Reaction Enzyme R001 Glycolysis/ Gluconeogenesis 5.1.3.3 ABAYE2829 -
Table S1. the Statistical Metrics for Key Differentially Expressed Genes (Degs)
Table S1. The statistical metrics for key differentially expressed genes (DEGs) Gene Agilent Id Symbol logFC pValue FDR tvalue Regulation Gene Name oxidized low density lipoprotein A_24_P124624 OLR1 2.458429 1.19E-13 7.25E-10 24.04241 Up receptor 1 A_23_P90273 CHST8 2.622464 3.85E-12 6.96E-09 19.05867 Up carbohydrate sulfotransferase 8 A_23_P217528 KLF8 2.109007 4.85E-12 7.64E-09 18.76234 Up Kruppel like factor 8 A_23_P114740 CFH 2.651636 1.85E-11 1.79E-08 17.13652 Up complement factor H A_23_P34031 XAGE2 2.000935 2.04E-11 1.81E-08 17.02457 Up X antigen family member 2 A_23_P27332 TCF4 1.613097 2.32E-11 1.87E-08 16.87275 Up transcription factor 4 histone cluster 1 H1 family A_23_P250385 HIST1H1B 2.298658 2.47E-11 1.87E-08 16.80362 Up member b abnormal spindle microtubule A_33_P3288159 ASPM 2.162032 2.79E-11 2.01E-08 16.66292 Up assembly H19, imprinted maternally expressed transcript (non-protein A_24_P52697 H19 1.499364 4.09E-11 2.76E-08 16.23387 Up coding) potassium voltage-gated channel A_24_P31627 KCNB1 2.289689 6.65E-11 3.97E-08 15.70253 Up subfamily B member 1 A_23_P214168 COL12A1 2.155835 7.59E-11 4.15E-08 15.56005 Up collagen type XII alpha 1 chain A_33_P3271341 LOC388282 2.859496 7.61E-11 4.15E-08 15.55704 Up uncharacterized LOC388282 A_32_P150891 DIAPH3 2.2068 7.83E-11 4.22E-08 15.5268 Up diaphanous related formin 3 zinc finger protein 185 with LIM A_23_P11025 ZNF185 1.385721 8.74E-11 4.59E-08 15.41041 Up domain heat shock protein family B A_23_P96872 HSPB11 1.887166 8.94E-11 4.64E-08 15.38599 Up (small) member 11 A_23_P107454