Potential gene network for the health effect of exposure to pcbfs on human diffuse large cell lymphoma

THAI NGUYEN UNIVERSITY UNIVERSITY OF AGRICULTURE AND FORESTRY NATIONAL TSING HUA UNIVERSITY THAVISACK MIVONGSACK TITLE: POTENTIAL GENE-NETWORK FOR THE HEALTH EFFECT OF EXPOSURE TO PCB/FS ON HUMAN DIFFUSE LARGE CELL LYMPHOMA BACHELOR THESIS Study Mode:Full-time Major: Environmental science and management Faculty:International Programs Office Batch: 2013-2017 Thai Nguyen, 21/12/2017 DOCUMENTATION PAGE WITH ABSTRACT Thai Nguyen University of Agriculture and Forestry Degree Program Bachelor of Environmental Science and Management Student name THAVISACK MIVONGSACK Student ID DNT 1253110103 Thesis Title Potential gene-network for the health effect of exposure to pcb/fs on human diffuse large cell lymphoma Supervisor (s) Prof Chun-Yu Chuang, Associate Prof DRTran Van Dien Abstract: The thesis describes the Lymphoma is the most top cancers in the worldwide, and the incidence rises strikingly since the last half of 20thcentury Lymphoma is a cancer affecting the immune system; the major risk factor is associated with exposure to occupational or environmental chemicals Polychlorinated biphenyls (PCBS) are a class of organic chemicals, known as congeners that have been used in a variety of commercial products PCBs were used in caulking, electronics, fluorescent light ballasts and other building materials from the 1950s to the late 1970s Buildings built or renovated during that time may contain PCBs in caulking and other materials PCBare very stable mixtures that are resistant to extreme temperature and pressure PCBS were used widely in electrical equipment like capacitors and transformers They also were used in hydraulic fluids, heat transfer fluids, lubricants, and plasticizers i PCBs have been released into the environment through spills, leaks from electrical and other equipment, and improper disposal and storage It is estimated that more than half of the PCBS produced have been released into the environment Once in the environment, PCBS can be transported long distances and they bind strongly to soil and sediment so they tend to be persistent in the environment They have been found in air, water,soil, and sediments throughoutthe world.PCBs can enter the body through inhalation, ingestion, and dermal routes of exposure They are readily absorbed but are slowly metabolized and excreted In particular, PCBs initially distribute to the liver and muscle tissues, but eventually accumulate in lipid-rich tissues This leads to greater concentrations of PCBS in adipose tissue, breast milk, the liver, and skin The data analysis was subsequently performed using Network Analyst, a standard web browser for network analysis and interactive exploration Keywords TCDD, Furans, DBLCL, bioinformatics, GEO, Array Express Number of pages 59 Date of submission October,2017 Supervisor’s signature ii ACKNOWLEDGEMENT First of all, we know that knowledge is just only can be proved by our works, and internship is one of the best opportunity for a student whose can their first project before they find their jobs to enroll in the future Besides that, we are not only improving ourselves by knowledge in company environment, institute or laboratory but also making more friends whose are having many experiences in environment, and it will help us in the near future From my perspective, this internship is absolutely needed, helpful and important Because of that, and be assigned by the International Programs Office and also the allowed of Department of Biomedical Engineering and Environmental Sciences (National TsingHua University, Taiwan) To well done this thesis, I want to express profound gratitude to Advanced Education Program, the school administrators, the staffs in Department of Biomedical Engineering and Environmental Sciences, the staffs of YC laboratory, and particularly my supervisor,Associate Prof DRTran Van Dien and Prof Chun-Yu Chuang whose were always supporting me every single time I got troubles I would like to send both of supervisors a warmly thanks for the supporting me, and for their sacrifice for education, as same as environmental issues in Taiwan and Vietnam as all countries in the world Finally, I would like to say that I had tried my best to finish this thesis in the best way, I guess However, to be honest, I partly believe that my thesis still have some problems because of the limitation of knowledge and reality experiences, especially in our environmental circumstances these days It is totally happy if I can iii get feedbacks and comments from you, my Teachers, Professors, and Supervisors, to finish my thesis in a fantastic way, to get the best results Sincerely, Thai Nguyen October, 2017 THAVISACK MIVONGSACK iv TABLE OF CONTENTS DOCUMENTATION PAGE WITH ABSTRACT i ACKNOWLEDGEMENT iii TABLE OF CONTENTS v LIST OF FIGURES vii ABBRIVIATION ix PART I: INTRODUCTION 1.1 Rationale 1.2 Objectives PART II:LITERATURE REVIEW 2.1 Polychlorinated biphenyls (PCBs) 2.1.1 Polychlorinated Biphenyls (PCBs) Toxicity 2.1.2 The industrial production of PCB 2.1.3 PCBs and Environment 2.1.4 Health Effects of PCBs 2.2 Lymphoma 12 2.3 Biological pathway 13 2.3.1 Diffuse large B cell lymphoma (DLBCL) 15 2.4 Gene-network components .17 2.4.1 Gene-network database:Array Express and (GEO) 18 2.4.2 Statistical analysis .19 3.2 Gene network analysis and Cytoscape for gene-network analysis 24 v PART IV: RESULTS AND DISCUSSIONS 29 4.1 Genetic datasets 29 4.1.1 Differentially expressed genes .32 4.1.2 Gene-network construction of DLBCL and PCBs 35 4.2 Discussion .38 PART V: CONCLUSION AND RECOMMENDATION 41 5.1 Conclusion 41 5.2 Recommendation 42 REFERENCES 43 vi LIST OF FIGURES Figure 2.1: Diagram of Polychlorinated Biphenyls (PCBs)(Shoemaker, 2005) .6 Figure2.2: Polychlorinated biphenyls – levels in foods 11 Figure 2.3: Diffuse large Bcell lymphoma 16 Figure 3.1: The flowchart of methodology 24 Figure 3.2: Cytoscape home page(Shannon P, 2003) 26 Figure 4.1: Diagram of Gene-network construction of DLBCL & PBCs Sources: (Rosenwald,2002) 37 Figure 4.2: The potential regulatory pathway of NHL progression in response to PCB exposure Source: (Miller, 2001) 38 vii LIST OF TABLES Table 4.1: Genetics Datasets of DLBCL 30 Table 4.2: Datasets on Array Express used for PCB analysis 31 Table 4.3: Differentially expressed genes, including –up and down – regulate genes in Diffuse Large B cell lymphoma compared to normal cells 33 Table 4.4: Differentially expressed genes, including up-and down – regulated genes activated by PCB compared to control group 35 viii ABBRIVIATION ABC Activated B cell AML Acute myeloid leukemia ALL Acute lymphoblastic leukemia B-Cells B-lymphocytes B-NHL Non Hodgkin lymphoma DLBCL Diffuse large B cell lymphoma DMSO Dimethyl sulfoxide DEGS Differentially expressed genes analysis FL Follicular lymphoma FDR False discovery rate GEO (NCBI) Gene expression omnibus National center for Biotechnology information GO Gene Ontology HL Hodgkin lymphoma HIV Human immunodeficiency virus ID Identifier IARC International agency for research on cancer MIAME Minimum information about microarray experiment MAGE-ML Microarray and Gene Expression Markup Language NPL N-acetylneuraminate pyruvate lyase NHL Non Hodgkin lymphoma PCB Polychlorinated biphenyls RS Reed-Sternberg SNPs Single Nocleotide Polymorphisms T-Cells T-lymphocytes ix PSMD10, DNTTIP2, OAT, NSMCE1, TBCB, C14orf119, ACP5, PPM1G, POLR2K, TSG101, PEA15, MRPL49, NIT2, ATIC, PPP2CB, NCBP2, RABAC1, DRG1, NUP107, TCF4, SLC25A19, UFC1, CIB1, BIRC2, NDUFB10, RBBP8, SNX3, SMNDC1, HDHD2, ETF1, RAD23A, MYBL2, SRRM1, TIMMDC1, COX5B, LYRM1, IL18, ARHGAP17, IRF2BPL, NONO, TM2D2, MFAP1, ITGA3, KCTD12, NUPR1, HAT1, AP3S1, MANF,, TMEM14B, CPSF4, PPIH, MIEN1, MTIF2, FAM50A, LRRC47, PAPSS1, GLO1, CCNG1, RPIA, ASNSD1, LYPLA1, WDR83OS, CUTA, DAZAP1, AP1S2, BTBD1, VPS25, BCL11A, MT1E, ZNHIT3, EIF3I, RPL11, S100A8, ANXA2, PPIL3, GLRX, ENOPH1, IER5, CISD1, HAUS1, DRAM1, DDX21, SNRPD3, UBE2L6, TMEM138, RPF2, DUT, GTF3C6, TSPAN13, ITM2A, PPP1R7,PIH1D1, GTF2B, CDK5RAP3, TMEM208, DBF4, GTF3A, RFC4, IER3, YTHDF2, FIBP, TIMM8B, MPLKIP, VPS28, LAGE3, CLIC1, HARS, IMP3, CS, CEBPZ, RFX5, DNAJB1, MRPL16, CSRP1, ORMDL2, PIGP, CDKN1A, NMI, FAM35A, TNFAIP3, PCMT1, EBPL,TUBB6, GBP1, PLOD1, TUBA1C, REEP5, EIF2S1, MRPL1, IMP4, SNRPA, MARCKSL1, DYNLT3, UBE2E2, SCAMP3, POLR3GL, CUEDC2 Down- DUSP6, CYTH4, LCP2, SIRPB1, ITGB2, CORO1A, RAB7A, COX7A2L, MEFV, regulated ANPEP, C5AR1, ZYX, DOCK5, STEAP4, GRK6, MSL2, PLXNC1, STK17B, PYGL, genes CD3E, KCNJ15, SCIMP, CAPNS1, GLIPR1, CPPED1, IST1, LILRA1, PRKAR1A, (172) ARRB2, WDR1, ARHGAP26, DUSP1, WIPF1, MXD1, BSG, CELF2, GNAQ, ZFAND5, MBOAT7, GABARAP, MBNL1, AOAH, CTSS, DOK3, HIST1H1E, CYP4F3, PTBP3, NCF2, RNASET2, TCP11L2, MAPK1, PIP4K2A, STAT3, DOCK8, TLN1, TGFBR2, SELPLG, PGK1, FPR1, SDHA, SMCHD1, MOB3A, DDX17, TUBB1, GUK1, LYN, CD37, ETS1, CCNI, STK38, ATP6V1B2, CAP1, PDZK1IP1, HBB, EPB41, TREM1, PTAFR, GNAS, FFAR2, RPL18, IL7R, EIF4EBP2, SLC44A2, HLA-DPA1 LITAF ITM2B CXCR2, CYBB, CFL1, LCP1, ALAS2, PTPRC, CSF3R, ARHGDIB, AQP9, DAZAP2, SLC6A6, B2M, SMAP2, BCL2L1, SORL1, RAC2, FBXO7, PSAP, FCN1, ND5, SLC25A37, TNFRSF10C, TMBIM6, CD74, HLA-E, SLC25A39, DCAF12, CX3CR1, RHOA, CD53, XPO6, TAGLN2, FCGR2A, MSN, LYZ, LAPTM5, MALAT1, TXNIP, ACTB 34 Table 4.4: Differentially expressed genes, including up-and down – regulated genes activated by PCB compared to control group PCBs(59 DEGs) Down- UBB, ND4,COX1,HBB,SLC25A39, IFITM2, HLA-A, HBM, OAZ1, regulated genes R3HDM4, FTL,COX2,UBA52,TXNIP,S100A9,RPL32,IFITM3, IFITM1 STRADB, HLA-C, RPS4X, RPS12, GYPC, HLA-B, SELL, RPL37A, ACTB, TMSB4X, MNDA, B2M, BLVRB, FCGR3B, UBC, USMG5, HSPE1, PPP1CC, HIF1A, ERH, RPL39, PTGES3, RPL27, HSP90AB1, SRP9, IFNG, H2AFZ, RPL11, NACAP1, RPL24, RPL35, LDHA, RPS17, RPL6, HSP90AA1, RPL3, GZMB, RPL7, RPS7, RPL34, RPL9 4.1.2 Gene-network construction of DLBCL and PCBs Molecular biologists have collected considerable data regarding the involvement of genes and microRNAs (miRNAs) in cancer However the underlying mechanisms of cancer with regard to genes and miRNAs remain unclear The first differential expression network that is presented is an experimentally validated network of miRNAs and genes This network presents known biological regulatory associations among miRNAs and genes in the human body The second network is a DLBCL differential expression network Differentially expressed gene and miRNA data regarding DLBCL were collected and, based on the first network and the differentially expressed data, the second network was inferred, which demonstrates the 35 irregular regulatory associations that may lead to the occurrence of DLBCL The third network is a DLBCL-associated network This network is comprised of nondifferentially expressed genes and miRNAs that contribute to numerous DLBCL processes The similarities and differences among the three networks were extracted and compared to distinguish key regulatory associations; furthermore, important signaling pathways in DLBCL were identified The present study partially clarified the pathogenesis of DLBCL and provided an improved understanding of the underlying molecular mechanisms, as well as a potential treatment for DLBCL About half of patients with diffuse large B-cell lymphoma (DLBCL) not respond to or relapse soon after the standard chemotherapy, indicating a critical need to better understand the specific pathways perturbed in DLBCL for developing effective therapeutic approaches Mice deficient in the E3 ubiquitin ligase Smurf2 spontaneously develop B-cell lymphomas that resemble human DLBCL with molecular features of germinal centre or post-germinal centre B cells Here we show that Smurf mediates ubiquitination and degradation of YY1, a key germinal centre transcription factor Smurf2 deficiency enhances YY1-mediated transactivation of cMyc and B-cell proliferation Furthermore, Smurf2 expression is significantly decreased in primary human DLBCL samples, and low levels of Smurf expression correlate with inferior survival in DLBCL patients The Smurf2-YY1-c-Myc regulatory axis represents a novel pathway perturbed in DLBCL that suppresses B-cell proliferation and lymphomagenesis, suggesting pharmaceutical targeting of Smurf2 as a new therapeutic paradigm for DLBCL 36 Figure 4.1: Diagram of Gene-network construction of DLBCL & PBCs Sources: (Rosenwald,2002) 4.1.3 Sub- network and potential pathways for DLBCL and PCB exposure To identify the gene regulatory sub-network between DLBCL and PCB modules, the gene ontological networks via the analysis of ClueGo plug-in were further conducted into Cluepedia plug-in The above two ontological networks, NHL and PCB exposure, were plugged into clupedia These two modules were integrated to perform a union gene-gene regulatory network of PCB exposure relevant to NHL Gene in this integrated regulatory network was selected with highly gene connectivity and the significance within each network Red lines represented the regulatory pathway of PCB exposure only Yellow lines displayed the regulatory pathway of NHL; while black lines performed the colarbory pathway of PCB exposure and NHL NFKB1(purple circle) was the only one gene that interacted with red, blue and 37 blacklines This study chose NFKB1 as a middle point to explore its Up-stream (PCB only) and down-stream (intersection of PCB and NHL) genes Of NFKB1, CTNNB1 and NFKB1 were considered as initiator genes of PCB exposure Down- stream genes of NFKB1, AR, IGF1 and TWIST1 were regarded as carcinogenic genes of NHL progression after PCB exposure The final carcinogenic pathway related to NHL progression corresponding to PCB exposure was shown in figure… Eventually, this study focused on the CTNNB1-NFKB1-AR-IGF1-TWIST1 pathway as a potential pathway of NHL progression in response to PCB exposure Figure 4.2: The potential regulatory pathway of NHL progression in response to PCB exposure Source: (Miller, 2001) 4.2 Discussion The impact of chemical on human health, especially PCB leading to DLBCL in this study was examined based on gene-network construction In fact, gene network was created by the interaction of most obvious differentially expressed genes, which 38 were activated by any kinds of chemical or human disease In the study, the most different expressed genes of PCB/Furan and DLBCL compared to control and normal groups respectively were selected in order to construct gene network that can stimulate the biological pathway including these kind of gene According to the result of this study, the potential pathway underlying cause of PCB/ Furan to DLBCL disease can be easily to find out, especially through protein interaction network The main biological pathways involved in this merge network including cell proliferation, DNA and histone modification, cell response to hypoxia, angiogenesis, xenobiotic stimulus, tumor necrosis factor production and NIK/NF-kB signaling might be generally relate to carcinogenesis (Hu et at., 2006) Figure also indicated that PCB mainly effects human body by response to hypoxia (green zone) while Furans mainly cause the alteration of DNA, histone modification and cell division (blue zone) 4.3 Inhibition of cancer cell apoptosis and tumorigenesis factor inDLBCL TWIST1isawell–knownproteinassociatedwithtumorgenesis,angiogenesis,cell proliferation and cell differentiation and an important target for cancer treatment Twist1 is a member of Twist protein group, and a previous study has revealed that the expression of TWIST1 protein is higher than B-NHL tissues and it can be connected with B-NHL progression (Jiaet al., 2014) Twist can promote tumor cell growth throughexpression and hence both of them can induce tumor progression, cell growth and oncogenesis in many cases of cancer (Shiotaet al., 2008) In addition, up – regulated protein TWIST1 has ability to activate lymph angiogenesis, which has a general role in tumorsdevelopment, invasionandmetastatic.Infact,somelymphomastudieshaveshown that several markers of 39 angiogenesis have a correlation with outcome oflymphoma development and progression (Ganjooet al., 2007) Persistent activation of NF-KB has been reported to play an essential role in the growth and survival of specific cancer cell types, including adult T-cell leukemia, lymphoma, melanoma, and prostate cancer cells The NF-KB family of transcription factors, NFKB1 and NFKB2, are compelling mediators of MYC’s response in B cells, which are key regulators of B cell developments 40 PART V: CONCLUSION AND RECOMMENDATION 5.1 Conclusion Firstly, by summarizing and analyzing data sets, There are some differentially expressed genes were found and carried out in gene functional annotation in order to construct individual gene-network of DLBCL and some dioxin-compounds for further analytical steps Secondly, the merge gene-network of DLBCL, PCB and Furans was constructed in order to present biological processes generated by these DE genes; for instance, cell response to xenobiotic stimulus, regulation of lymphocyte proliferation, regulation of intrinsicapoptoticsignalingpathway,regulationofangiogenesis, NIF/NFkappasignaling, regulationoffibroblastproliferation,regulationoftransitionofmitoticcellcycle, B cell proliferation, intracellular signaling pathway, tumor necrosis factor production, cell death in response to hydrogen peroxide DNA modification and regulation of histone modification Thirdly, the pathway of dioxins entering human body via the receptor In genenetwork analysis, this study identified five target genes of PCB exposure (CTNNB1, NFKB1, AR, IGF1 and TWIST1) underlying NHL progression (Figure 7), in this pathway CTNNB1 is a downstream effectors of canonical wnt signaling with the ability to induce cell-cell adhesion, cell adhesion, cell differentiation and cell growth 41 Thereforeconcludedthatallresearchobjectiveshavebeenachievedgiventhese results, and the research has provided the information on the effect of exposure onPCBs/ Furans in DLBCL development This research, however, still has some limitations, for instance,thelackofknowledgeongeneticandpracticalexperienceinthisfieldareneeded to be fulfilled in order to complete the research since it was only carried out during the period of three months for a preliminaryresearch Itishencesuggestedthatfurtherstudiescanconcentratedonexploringtheeffectof chemicals on human body by the advanced application of bioinformatics, especially the effect of dioxins coming from various sources Apart from contribution to technical development in lymphoma treatment, the results of this study could be implemented for further studies or research relating to exposure dioxins in order to explore the long-term impact of this chemical remaining from the American War in Vietnam in 20thcentury 5.2 Recommendation We continue to survey the synthesis of a gene-network analysis to identify potential pathway and target genes of NHL carcinogenesis in response to PCB exposure with different conditions to induce CTNNB1 expression to increase downstream genes NFKB1, AR, IGF1 and TWIST1 expression to initiate NHL progressionfor powerful and useful processing in the future The research experimental is very important to explore new scientific ideas for improve and develop the previous ideas as very necessary to identify potential pathway and target genes 42 REFERENCES 1) AidongZang (2006) Theadvance analysis of gene expression microarray data, world scientific connecting great minds, J.Neurooncol, doi: 10: 1007/s 11060017-2680-9 2) Jennifer Amengual, MD (2017)Lymphoma research fundation Diffuse Large BCell Lymphoma J Clin Oncol, 10; 35 (20): 2260-2267 3) The Non-Hodgkin's Lymphoma Classification Project National Cancer Institute Sponsored Study of Classifications of non-Hodgkin's Lymphomas Summary and description of Working Formulation for Clinical usage Cancer 1982; 49: 2112 4) Ofo matah AC, (2017) Agency for Toxic Substances and Disease Registry (ATSDR) 2000 Toxicological Profile for Polychlorinated Biphenyls (PCBs) Atlanta, GA: U.S Department of Health and Human Services, Public Health Service Environ Moint Asses, 9: 189 (12); 623 5) Arcaro, K F (1999) Antiestrogenicity of environmental polycyclic aromatic hydrocarbons in human breast cancer cells Toxicology , 133: 115-127 6) Bethesda (2017) mining tens of millions of expression profiles, National Center for Biotechnology Information, U.S National Library of Medicine, Sci Data; 19; 4: 170181 7) Buford Hwy Ne; Atlanta (2016) Agency for Toxic Substances & Disease Registry: Environmental Health and Medicine Education.? 8) Carpenter, D O (1998) Polychlorinated Biphenyls and Human Health International Journal of Occupational Medicine and Environmental Health , 11(4): 291-303 43 9) Chapman & Hall (2009) Nov Mathematical & Computational Biology'',) Hinrich Göhlmann, Willem Talloen-Gene expression studies using affymetrix microarrays-CRC Press Hypertension, 2010 Jan; 55 (1): 61-8 10) Consortium, G (2001) Creating the gene ontology resource: Design and implementation Genome Res , 11: 1425-1433 11) Consortium, G (2001) Creating the gene ontology resource: Design and implementation Genome Res , 11: 1425-1433 12) Crago et al, 2003 Psychological, neuropsychological, and electrocortical effects of mixed mold exposure: Archives of Environmental Health: 58(8):452-63 13) Deker, T O (2002) Discovering regulatory and signalling circuits in molecular interaction networks Bioinformatics , 18: S233-S240 14) Gateway Pr Baltimore (2016), Jan Genetic associations at 53 Loci highlight cell types and biological Pathways relevant for kidney function Nat Commun, 21; 7: 10023 15) DeRisi, J I.-6 (1997) Exploring the metabolic and genetic control of gene expression on a genomic scale Science 278: 680-686 16) Eades, P (1984) A heuristic for graph drawing Congressus Numerantium , 42: 142-160 17) Endy, D a ( 2001) Modelling cellular behaviour Nature , 409: 391-395 18) Etzel, Ruth A(2006), Sep What the primary care pediatrician should know about syndromes associated with exposures to mycotoxins; etzel 2006, 36 (8): 282-305 44 19) Español, E (August 27, 2015) National human Genome Research institute Advancing human health through genomics research, Ann Oncol, 2014, Jan; 25(1): 283-90 20) Ferlay, J (2013) Cancer incidence and mortality patterns in Europe: Estimates for 40 countries in 2012 European journal of cancer , 49(6): 1374-1403 21) Freeman, AS; Aster, JC (2012) "Epidemiology, clinical manifestations, pathologic features, and diagnosis of diffuse large B cell lymphoma,Leukemia 2013 Jun; 27(6): 1437-40 22) Fisher SG, Fisher RI The epidemiology of non-Hodgkin's lymphoma Oncogene 2004 Aug 23;23(38):6524–6534 23) Gilman, A a (2002) GENETIC “CODE”: Representations and dynamical models of genetic components and networks Annu Rev Genomics Hum Genet , 3: 341-369 24) Griffin, J M.-2 (2001) Choline containing metabolites during cell transfection: An insight into magnetic resonance spectroscopy detectable changes FEBS Lett , 509: 263-266 25) Gygi, S R.-c.-9 (1999) Quantitative analysis of complex protein mixtures using isotope-coded affinity tags Nat Biotechnol , 17: 994-999 26) Glossary of Environment Statistics, Studies in Methods, Series F, No 67, United Nations, New York, 1997 27) Gray, Michael R.; Thrasher, Jack D.; Crago, Robert; Madison, Roberta A.; Arnold, Linda; Campbell, Andrew W.; Vojdani, Aristo 2017 Mixed mold mycotoxicosis: immunological changes in humans following exposure in 45 water-damaged buildings: Archives of Environmental Health, Int J Equity Health, (2017) Nov 28; 16(1): 206 28) Hadson (2016) PCBs and Human Health, New York ILAR J 2016; 46(4): 364-9 29) Hinrich Gohlmann, Willem Talloen.; (2009) Principle Scientist and Seenior Biostatistician:Gene Expression Studies Using Affymetrix Microarrays,195 30) Hucka, M F, Finney A, Sauro HM, Bolouri H, Doyle J, Kitano H (2002) The ERATO Systems Biology Workbench: Enabling interaction and exchange between software tools for computational biology, Pac Symp Biocomput 2002: 450-61 Hucka, M., Finney, A., Sauro, H.M., Bolouri, H., Doyle, J., and Kitano, H 2002 The ERATO Systems Biology Workbench: Enab Pac Symp Biocomput , 450-461 31) Ito, T C (2001) Exploring the protein interactome using comprehensive twohybrid projects Trends Biotechnol , 19: S23-S27 32) Jeni (2017) Polychlorinated Biphenyls (PCBs), Sci Total Envirin 2017 Dec 16; 624: 377-384 33) Kumar, V., Abbas, A., Fausto, N., & Aster ( 28 May 2009)) Robbins & Cotran Pathologic Basis of Disease Elsevier Health Sciences , 607 34) Lee, T R.-J (2002) Lee, T.I., Rinaldi, N.J., Robert, F., Odom, D.T., Bar-Joseph, Z., Gerber, G.K., Hannett, N.M., Harbison, CTranscriptional regulatory networks in Saccharomyces cerevisiae Science 298: 799-804 35) Loew, L a.-4 ( 2001) The Virtual Cell: A software environment for computational cell biology Trends Biotechnol , 19: 401-406 46 36) Mendes, P ( 1997) Biochemistry by numbers: Simulation of biochemical pathways with Gepasi Trends Biochem Sci , 22: 361-363 37) Morris, Chapman (22-07-2-17) PCBs Polychlorinated biphenyls Facts on health and environment, Mol Med Red 2017 Feb; 15 (2): 511-533 38) Morton, L., Wang, S., Devesa, S., Hartge, P., Weisenburger, D., & Linet, M ( (1992-2001) Lymphoma incidence patterns byWHO subtype in the United States blood , 107 (1): 265–76 39) Shannon P, M A (2003) Cytoscape a software environment for integrated models of biomolecular interaction networks , 13 (11): 2498–504 40) 42 Rosenwald A, Wright G, Chan WC, et al The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma N Engl J Med 2002 Jun 20;346(25):1937–1947 41) Smith, A., Howell, D., Patmore, R., Jack, A., & Roman, E (2011) Incidence of haematological malignancy by sub-type: A report from the Haematological Malignancy Research Network British Journal of Cancer , 105 (11): 1684–92 42) Smith, A., Roman, E., Howell, D., Jones, R., Patmore, R., & Jack, A (2010) The Haematological Malignancy Research Network (HMRN): a new information strategy for population based epidemiology and health service research British Journal of Haematology , 148 (5): 739–53 43) Tomita, M H (1999) Software environment for whole-cell simulation Tomita, M., Hashimoto, K., Takahashi, K., Shimizu, T.S., Matsuzaki, Y., Miyoshi, F., Saito Bioinformatics , 15: 72-84 47 44) Tong, A E (2001) Systematic genetic analysis with ordered arrays of yeast deletion mutants Science 294: 2364-2368 45) Ward M H, C J (2009) Residential Exposure to Polychlorinated Biphenyls and Organochlorine Pesticides and Risk of Childhood Leukemia Environ Environ Health Perspect , 117(6):1007-13 46) Xenarios, I a (2001) Protein interaction databases Curr Opin Biotechnol , 12: 334-339 47) Zhou, H W (2001) A systematic approach to the analysis of protein phosphorylation Nat Biotechno , 19: 375-378 48