The characteristics of subjects.
\r\n\tIn this book the amperometry principles, instrumentation, cells (including flow cells), and functional materials used in amperometric sensors are presented together with the numerous applications of the amperometric (bio)sensors and the amperometric titrations in the environmental, food, and clinical analysis.
",isbn:null,printIsbn:null,pdfIsbn:null,doi:null,price:0,priceEur:null,priceUsd:null,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"502756538d952207e98c5b53b0f8c6ed",bookSignature:"Dr. Margarita Stoytcheva and Dr. Roumen Zlatev",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/8638.jpg",keywords:"Voltammetry, Direct Amperometry, Pulse Amperometry, Amperometric Sensors, Functional Materials, Amperometric Biosensors, Electrode Modification, Cells, Flow Cells, Amperometric titration, Amperometric Detection, Application",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"August 16th 2018",dateEndSecondStepPublish:"September 6th 2018",dateEndThirdStepPublish:"November 5th 2018",dateEndFourthStepPublish:"January 24th 2019",dateEndFifthStepPublish:"March 25th 2019",remainingDaysToSecondStep:"2 years",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:null,coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"170080",title:"Dr.",name:"Margarita",middleName:null,surname:"Stoytcheva",slug:"margarita-stoytcheva",fullName:"Margarita Stoytcheva",profilePictureURL:"https://mts.intechopen.com/storage/users/170080/images/system/170080.jpg",biography:"Prof. Margarita Stoytcheva has graduated from the University of Chemical Technologies and Metallurgy of Sofia, Bulgaria with titles of Chemical Engineer and Master of Electrochemical technologies. She has PhD and DSc degrees in Chemistry and Technical Sciences. \r\nShe has participated in research and teaching in several universities in Bulgaria, Algeria, and France. From 2006 to the present, she has participated in activities of scientific research, technological development, and teaching at the Autonomous University of Baja California at Mexicali, Mexico, as a full-time researcher. Since 2008, she has been a member of the National System of Researches of Mexico, and since 2011 she has been a regular member of the Mexican Academy of Sciences. \r\nHer interests and area of research are analytical electrochemistry and biotechnology.",institutionString:"Autonomous University of Baja California, Engineering Institute",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"5",totalChapterViews:"0",totalEditedBooks:"4",institution:{name:"Autonomous University of Baja California",institutionURL:null,country:{name:"Mexico"}}}],coeditorOne:{id:"128534",title:"Dr.",name:"Roumen",middleName:null,surname:"Zlatev",slug:"roumen-zlatev",fullName:"Roumen Zlatev",profilePictureURL:"https://mts.intechopen.com/storage/users/128534/images/system/128534.jpeg",biography:"Dr. Roumen Zlatev is a fulltime researcher in the Engineering Institute of the Autonomous University of Baja California (UABC) at Mexicali, Mexico. He obtained his Bachelor’s and Master’s degrees from the University of Chemical Technology and Metallurgy of Sofia, Bulgaria, and his Ph.D. degree from the National Polytechnic University of Grenoble, France. He was a fulltime researcher in the Bulgarian Academy of Sciences and a part-time professor at Sofia University. In 2005 he accepted the position of fulltime senior researcher in UABC.\nDr. R. Zlatev is a member of the Mexican National System of Researchers and a regular member of the Mexican Academy of Sciences. He participates in research projects in France, Germany, and Mexico. Dr. R. Zlatev is the author of more than 170 publications, book chapters, and reports in scientific congresses and holds 14 patents in the field of the electrochemical and spectroscopic methods of analysis, corrosion, and materials, electrochemical and analytical instrumentation.",institutionString:"Autonomous University of Baja California, Engineering Institute",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Autonomous University of Baja California",institutionURL:null,country:{name:"Mexico"}}},coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"8",title:"Chemistry",slug:"chemistry"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"220811",firstName:"Anita",lastName:"Condic",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/220811/images/6068_n.jpg",email:"anita.c@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. Whether that be identifying an exceptional author and proposing an editorship collaboration, or contacting researchers who would like the opportunity to work with IntechOpen, I establish and help manage author and editor acquisition and contact."}},relatedBooks:[{type:"book",id:"5182",title:"Lab-on-a-Chip Fabrication and Application",subtitle:null,isOpenForSubmission:!1,hash:"f4c8e226ea2612f5ecceb7e6311581d4",slug:"lab-on-a-chip-fabrication-and-application",bookSignature:"Margarita Stoytcheva and Roumen Zlatev",coverURL:"https://cdn.intechopen.com/books/images_new/5182.jpg",editedByType:"Edited by",editors:[{id:"170080",title:"Dr.",name:"Margarita",surname:"Stoytcheva",slug:"margarita-stoytcheva",fullName:"Margarita Stoytcheva"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"6690",title:"Arsenic",subtitle:"Analytical and Toxicological 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by"}},{type:"book",id:"1373",title:"Ionic Liquids",subtitle:"Applications and Perspectives",isOpenForSubmission:!1,hash:"5e9ae5ae9167cde4b344e499a792c41c",slug:"ionic-liquids-applications-and-perspectives",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/1373.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"57",title:"Physics and Applications of Graphene",subtitle:"Experiments",isOpenForSubmission:!1,hash:"0e6622a71cf4f02f45bfdd5691e1189a",slug:"physics-and-applications-of-graphene-experiments",bookSignature:"Sergey Mikhailov",coverURL:"https://cdn.intechopen.com/books/images_new/57.jpg",editedByType:"Edited by",editors:[{id:"16042",title:"Dr.",name:"Sergey",surname:"Mikhailov",slug:"sergey-mikhailov",fullName:"Sergey Mikhailov"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"69074",title:"X-Ray Repair Cross Complementing 4 (XRCC4) Genetic Single Nucleotide Polymorphisms and the Liver Toxicity of AFB1 in Hepatocellular Carcinoma",doi:"10.5772/intechopen.88752",slug:"x-ray-repair-cross-complementing-4-xrcc4-genetic-single-nucleotide-polymorphisms-and-the-liver-toxic",body:'Aflatoxin B1 (AFB1) is an important type I chemical toxicant mainly produced by the toxigenic strains of Aspergillus flavus (A. flavus) and Aspergillus parasiticus (A. parasiticus) [1, 2]. This carcinogen is often taken into human body via contaminating human foods such as nuts and cereals and displays its toxic effects, especially hepatic toxicity [1, 2, 3, 4, 5, 6, 7, 8]. AFB1-induced hepatic effects consist of acute toxic damages (such as severe DNA damage, severe liver degeneration and necrosis, and the failure of hepatic function) and chronic cumulative damages (such as a series of cumulative DNA damage, slight hepatocellular degeneration and necrosis, chronic inflammation, liver cirrhosis, and liver cancer) [3, 4, 5]. Increasing evidence has shown that under the same exposure of AFB1, some individuals feature severe hepatic damage; others have no noticeable damage [9, 10, 11, 12, 13, 14]. This suggests that different individuals have different responses to the toxic effects of AFB1 and genetic factors may play a central role in the AFB1-induced hepatic toxicity.
X-ray repair cross complementing 4 (XRCC4), an important DNA repair gene involved in nonhomologous end-joining (NHEJ) repair pathway, plays a scaffold function via stabilizing and localizing DNA repair enzymes LIG IV, Ku70/80 heterodimer, and the DNA-dependent protein kinase (DNA-PK) catalytic subunit (DNA-PKcs) in the ends of DNA double-stranded breaks (DSBs) during NHEJ [15, 16]. In the past decades, growing reports have exhibited that the abnormal structures and functions of XRCC4 may alter the capacity of DNA repair and ultimately result in human diseases [17, 18, 19, 20, 21, 22]. Several recent studies have also shown that the genetic alterations in the coding regions of XRCC4 can modify hepatocellular carcinoma (HCC) risk and prognosis [23, 24, 25, 26, 27]. However, the effects of this genetic alteration on the hepatic toxicity of AFB1 is unclear. Here, we conducted a clinical sample study exposure to explore whether the genetic single-nucleotide polymorphisms (GSNPs, a type of genetic alterations) in the coding regions of XRCC4 modified the effects of AFB1 on hepatic damage.
This was a hospital-based molecular epidemiological study conducted in high AFB1 exposure area, Guangxi Zhuang Region, China. All participants were newly diagnosed HCC cases and recruited from the Affiliated Hospitals of Youjiang Medical University for Nationalities (located at Bose region, a major AFB1 exposure area) between January 2010 and January 2013 inclusively. The inclusive criteria of cases consisted of (a) cases with ultimately histopathologically confirmed HCC; (b) cases without any evidence of hepatitis virus infection; (c) cases with the history of AFB1 exposure which was defined according to positive history of peripheral serum AFB1-albumin adducts [5, 24]; and (d) cases with available tumor tissue samples and clinicopathological data.
According to the criteria, a total of 380 cases with HCC were recruited in this study during the period. With informed consent, the tissue samples with HCC for all patients and clinicopathological data were collected. Additionally, survival follow-up information was also collected through cases themselves or their family contact. In this study, the last follow-up date was set on January 31, 2019. The protocol for clinical samples was approved by Youjiang Medical University for Nationalities Medical Ethics Committee.
Hepatic toxicity of AFB1 was evaluated using AFB1-DNA adducts in the tissue samples with HCC, and the amounts of AFB1-DNA adducts were tested by the previously described enzyme-linked immunosorbent assay (ELISA).
All GSNPs of XRCC4 gene were first screened from the SNPdatabase (
Genomic DNA in all tumor tissue samples with HCC was standard phenol-chloroform extraction binding with proteinase K. The GSNPs of XRCC4 were genotyped using SNaPshot method (Applied Biosystems [ABI], Foster City, CA) as previously described [28]. For quality control, all laboratory personnel were blind to the status of every sample with hepatocarcinoma, and controls were also included in each analysis.
The test for genotypic distribution of XRCC4 GSNPs among HCC cases featuring different AFB1-DNA adducts was accomplished using student t-test or one-way analysis of variance (ANOVA) test. Multiple tests were adjusted using a Bonferroni correction, and the threshold for GSNP screening was defined as α = 3.53 × 10−4. Kaplan-Meier survival model with log-rank test and Cox regression model (the selection of significant varies based on forward-step method with likelihood ratio test) was used to analyze the association between XRCC4 GSNPs and HCC outcomes. Cumulative hazard value for the effects of XRCC4 GSNPs on the hepatic toxicity for AFB1 and corresponding 95% confidence interval (CI) were calculated using hazard ratio (HR) from significant multivariate Cox regression model (including all significant variates). All statistical analyses were performed with SPSS statistical package (Version 18, SPSS Institute, Chicago, IL, USA).
All subjects suffered from hepatic carcinoma, and Table 1 summarized their characteristics. The mean age of all participants was 50.74 ± 11.55 years, and more than 70% of them are male. For these cancer patients, 70.3% (267/380) and 26.3% (100/380) cases featured TNM II and III stages of tumor, and they also had an average AFB1 exposure value of 2.27 ± 1.09 μmol/mol DNA.
n | % | |
---|---|---|
Total | 380 | 100.0 |
Age (years) | ||
≤35 | 53 | 13.9 |
36–40 | 38 | 10.0 |
41–45 | 55 | 14.5 |
46–50 | 43 | 11.3 |
51–55 | 56 | 14.7 |
56–60 | 42 | 11.1 |
61–65 | 49 | 12.9 |
≥66 | 44 | 11.6 |
Gender | ||
Male | 271 | 71.3 |
Female | 109 | 28.7 |
Race | ||
Han | 221 | 58.2 |
Zhuang | 159 | 41.8 |
TNM stage | ||
I–II | 13 | 3.4 |
III | 267 | 70.3 |
IV | 100 | 26.3 |
The characteristics of subjects.
A total of 143 GSNPs in the coding regions of XRCC4 gene were selected in our final analyses, and Table 2 showed the genotypic distribution of all GSNPs. To evaluate the effects of these potential GSNPs on AFB1-DNA adducts, the role of each GSNP in the coding regions of XRCC4 gene was tested using Student t-test or ANOVA test with the adjustment of multiple test. Among these GSNPs, only rs1237462915 (cat#SNP016, at codon 38), rs28383151 (cat#SNP026, at codon 56), rs762419679 (cat#SNP069, at codon 127), rs766287987 (cat#SNP112, at codon 203), and rs3734091 (cat#SNP138, at codon 247) significantly affected the levels of AFB1-DNA adducts in the tumor tissues with HCC. The adduct amounts of their wild genotypes (defined as XX genotype) were 2.15 ± 0.97 μmol/mol DNA, 2.07 ± 0.99 μmol/mol DNA, 2.12 ± 0.86 μmol/mol DNA, 2.11 ± 0.89 μmol/mol DNA, and 2.09 ± 0.97 μmol/mol DNA, respectively. For their mutant heterozygotic genotypes (defined as XY genotype), the amounts of AFB-DNA adduct were from 2.64 to 4.33 μmol/mol DNA, whereas the adduct levels were from 3.04 to 5.78 for the mutant homozygotic genotypes (defined as YY genotype) (Table 2).
No. | SNP ID | Chr: bp | Alleles (x/y)a | Genotypes (xx/xy/yy)b | Amino acid position | Amino acid change | Nxx/xy/yyc | Adducts (mean ± SD) | Pd |
---|---|---|---|---|---|---|---|---|---|
SNP001 | rs142575170 | 5:83104923 | G/A | GG/GA/AA | 2 | E/K | 323/54/3 | 2.28 ± 1.09/2.21 ± 1.08/2.36 ± 1.05 | 0.23 |
SNP002 | rs1449631425 | 5:83104929 | A/G | AA/AG/GG | 4 | K/E | 377/3/0 | 2.27 ± 1.09/2.21 ± 0.54/− | 0.99 |
SNP003 | rs1166890864 | 5:83104930 | A/G | AA/AG/GG | 4 | K/R | 378/2/0 | 2.27 ± 1.09/1.57 ± 0.03/− | 0.99 |
SNP004 | rs1425642930 | 5:83104933 | T/G | TT/TG/GG | 5 | I/R | 376/4/0 | 2.27 ± 1.09/2.10 ± 0.61/− | 0.96 |
SNP005 | rs1252823908 | 5:83104946 | C/A | CC/CA/AA | 9 | H/Q | 376/4/0 | 2.27 ± 1.09/2.16 ± 0.56/− | 0.94 |
SNP006 | rs28383138 | 5:83104954 | C/G | CC//CG/GG | 12 | S/C | 319/47/14 | 2.26 ± 1.09/2.41 ± 1.06/2.00 ± 1.07 | 0.23 |
SNP007 | rs774071130 | 5:83104966 | T/C | TT/TC/CC | 16 | I/T | 370/10/0 | 2.28 ± 1.09/2.05 ± 0.94/− | 0.99 |
SNP008 | rs761695470 | 5:83104971 | C/T | CC/CT/TT | 18 | H/Y | 371/6/3 | 2.24 ± 1.03/2.87 ± 2.00/4.42 ± 2.93 | 0.04 |
SNP009 | rs755776572 | 5:83104983 | G/A | GG/GA/AA | 22 | V/I | 380/0/0 | 2.27 ± 1.09/−/− | — |
SNP010 | rs1271180927 | 5:83104986 | T/A | TT/TA/AA | 23 | S/T | 364/13/3 | 2.26 ± 1.09/2.52 ± 1.04/2.44 ± 0.77 | 0.89 |
SNP011 | rs1288681786 | 5:83104992 | G/A | GG/GA/AA | 25 | E/K | 354/26/0 | 2.28 ± 1.10/2.16 ± 0.96/− | 0.56 |
SNP012 | rs550773308 | 5:83104999 | C/T | CC/CT/TT | 27 | T/I | 367/13/0 | 2.26 ± 1.06/2.51 ± 1.80/− | 0.63 |
SNP013 | rs1484250582 | 5:83105006 | A/C | AA/AC/CC | 29 | E/D | 376/4/0 | 2.27 ± 1.09/2.18 ± 1.42/− | 0.87 |
SNP014 | rs1305679408 | 5:83105013 | T/A | TT/TA/AA | 32 | F/I | 350/30/0 | 2.27 ± 1.08/2.26 ± 1.14/− | 0.96 |
SNP015 | rs1232077487 | 5:83105022 | A/G | AA/AG/GG | 35 | T/A | 380/0/0 | 2.27 ± 1.09/−/− | — |
SNP016 | rs1237462915 | 5:83105031 | G/T | GG/GT/TT | 38 | D/Y | 352/19/9 | 2.15 ± 0.97/3.62 ± 1.40/4.21 ± 0.93 | 2.96 × 10−5 |
SNP017 | rs748540743 | 5:83105032 | A/T | AA/AT/TT | 38 | D/V | 380/0/0 | 2.27 ± 1.09/−/− | — |
SNP018 | rs758779099 | 5:83105039 | T/G | TT/TG/GG | 40 | H/Q | 353/25/2 | 2.23 ± 1.03/2.78 ± 1.61/2.63 ± 0.01 | 0.04 |
SNP019 | rs587779351 | 5:83105046 | T/C | TT/TC/CC | 43 | W/R | 380/0/0 | 2.27 ± 1.09/−/− | — |
SNP020 | rs771111816 | 5:83105050 | C/T | CC/CT/TT | 44 | T/I | 362/25/3 | 2.26 ± 1.08/2.33 ± 1.23/2.68 ± 0.07 | 0.78 |
SNP021 | rs1460004120 | 5:83105053 | G/C | GG/GC/CC | 45 | G/A | 375/5/0 | 2.27 ± 1.09/2.21 ± 0.82/− | 0.91 |
SNP022 | rs1248835327 | 5:83111031 | C/T | CC/CT/TT | 48 | S/F | 370/10/0 | 2.27 ± 1.09/2.21 ± 1.01/− | 0.87 |
SNP023 | rs1326359694 | 5:83111033 | G/A | GG/GA/AA | 49 | E/K | 355/25/0 | 2.28 ± 1.11/2.16 ± 0.76/− | 0.61 |
SNP024 | rs868207005 | 5:83111039 | G/A | GG/GA/AA | 51 | E/K | 380/0/0 | 2.27 ± 1.09/−/− | — |
SNP025 | rs945155269 | 5:83111049 | A/C | AA/AC/CC | 54 | Q/P | 364/26/0 | 2.29 ± 1.07/2.02 ± 1.04/− | 0.24 |
SNP026 | rs28383151 | 5:83111054 | G/A | GG/GA/AA | 56 | A/T | 277/63/40 | 2.07 ± 0.99/2.64 ± 1.33/3.04 ± 0.78 | 2.83 × 10−55 |
SNP027 | rs1245127767 | 5:83111063 | A/G | AA/AG/GG | 59 | M/V | 354/25/1 | 2.27 ± 1.10/2.18 ± 0.92/2.78* | 0.67* |
SNP028 | rs748124509 | 5:83111066 | G/T | GG/GT/TT | 60 | A/S | 354/23/3 | 2.25 ± 1.11/2.36 ± 0.74/2.78 ± 1.01 | 0.66 |
SNP029 | rs760546308 | 5:83111070 | T/C | TT/TC/CC | 61 | M/T | 352/28/0 | 2.27 ± 1.04/2.25 ± 1.56/− | 0.91 |
SNP030 | rs1369565470 | 5:83111071 | G/C | GG/GC/CC | 61 | M/I | 380/0/0 | 2.27 ± 1.09/−/− | — |
SNP031 | rs867574505 | 5:83111072 | G/A | GG/GA/AA | 62 | E/K | 380/0/0 | 2.27 ± 1.09/−/− | — |
SNP032 | rs1285557699 | 5:83111073 | A/G | AA/AG/GG | 62 | E/G | 380/0/0 | 2.27 ± 1.09/−/− | — |
SNP033 | rs1248297453 | 5:83111085 | A/G | AA/AG/GG | 66 | Y/C | 351/29/0 | 2.26 ± 1.07/2.36 ± 1.25/− | 0.63 |
SNP034 | rs776362814 | 5:83111087 | G/A | GG/GA/AA | 67 | V/I | 350/30/0 | 2.26 ± 1.11/2.31 ± 0.84/− | 0.83 |
SNP035 | rs1423928075 | 5:83111090 | G/A | GG/GA/AA | 68 | G/S | 380/0/0 | 2.27 ± 1.09/−/− | — |
SNP036 | rs1478505961 | 5:83111093 | G/A | GG/GA/AA | 69 | E/K | 250/28/2 | 2.27 ± 1.07/2.24 ± 1.28/2.69 ± 1.16 | 0.86 |
SNP037 | rs759064378 | 5:83111097 | T/C | TT/TC/CC | 70 | L/P | 354/26/0 | 2.28 ± 1.09/2.15 ± 1.01/− | 0.58 |
SNP038 | rs371307071 | 5:83111100 | G/C | GG/GC/CC | 71 | R/T | 380/0/0 | 2.27 ± 1.09/−/− | — |
SNP039 | rs61762970 | 5:83111112 | T/C | TT/TC/CC | 75 | L/S | 356/24/0 | 2.29 ± 1.10/1.99 ± 0.89/− | 0.20 |
SNP040 | rs763501478 | 5:83111117 | G/A | GG/GA/AA | 77 | G/R | 361/29/0 | 2.27 ± 1.09/−/− | — |
SNP041 | rs764572419 | 5:83111120 | G/T | GG/GT/TT | 78 | A/S | 349/31/0 | 2.26 ± 1.09/2.39 ± 1.09/− | 0.52 |
SNP042 | rs528259464 | 5:83111121 | C/T | CC/CT/TT | 78 | A/V | 376/4/0 | 2.26 ± 1.09/2.77 ± 0.44/− | 0.36 |
SNP043 | rs757644947 | 5:83111126 | C/T | CC/CT/TT | 80 | P/S | 369/11/0 | 2.26 ± 1.07/2.51 ± 1.51/− | 0.46 |
SNP044 | rs572613361 | 5:83111138 | T/C | TT/TC/CC | 84 | Y/H | 351/27/2 | 2.27 ± /2.24 ± 1.02/3.09 ± 0.47 | 0.57 |
SNP045 | rs1398927737 | 5:83111141 | A/C | AA/AC/CC | 85 | T/P | 380/0/0 | 2.27 ± 1.09/−/− | — |
SNP046 | rs756247552 | 5:83111142 | C/T | CC/CT/TT | 85 | T/M | 374/6/0 | 2.26 ± 1.08/2.56 ± 1.35/− | 0.50 |
SNP047 | rs149355996 | 5:83111147 | A/T | AA/AT/TT | 87 | N/Y | 371/9/0 | 2.27 ± 1.10/2.36 ± 0.70/− | 0.81 |
SNP048 | rs1049631686 | 5:83111162 | T/A | TT/TA/AA | 92 | S/T | 364/26/0 | 2.27 ± 1.09/−/− | — |
SNP049 | rs772190420 | 5:83111169 | A/G | AA/AG/GG | 94 | Y/C | 354/26/0 | 2.28 ± 1.08/2.08 ± 1.23/− | 0.36 |
SNP050 | rs1239084198 | 5:83111171 | T/G | TT/TG/GG | 95 | F/V | 356/24/0 | 2.26 ± 1.07/2.36 ± 1.34/− | 0.68 |
SNP051 | rs1184763400 | 5:83111174 | T/A | TT/TA/AA | 96 | F/I | 380/0/0 | 2.27 ± 1.09/−/− | — |
SNP052 | rs1236326383 | 5:83111177 | T/C | TT/TC/CC | 97 | F/L | 366/14/0 | 2.28 ± 1.09/1.92 ± 0.92/− | 0.24 |
SNP053 | rs1472843465 | 5:83111180 | G/A | GG/GA/AA | 98 | E/K | 377/3/0 | 2.27 ± 1.09/2.58 ± 0.84/− | 0.63 |
SNP054 | rs144653114 | 5:83111195 | G/A | GG/GA/AA | 103 | N/D | 240/127/13 | 2.17 ± 1.09/2.48 ± 1.08/2.11 ± 0.84 | 0.03 |
SNP055 | rs79561451 | 5:83195782 | T/C | TT/TC/CC | 110 | S/P | 352/25/3 | 2.28 ± 1.11/2.20 ± 0.84/1.82 ± 0.37 | 0.74 |
SNP056 | rs757113391 | 5:83195787 | C/G | CC//CG/GG | 111 | F/L | 380/0/0 | 2.27 ± 1.09/−/− | — |
SNP057 | rs1378785946 | 5:83195794 | G/C | GG/GC/CC | 114 | E/Q | 360/20/0 | 2.277 ± 1.10/2.227 ± 0.84/− | 0.83 |
SNP058 | rs1240771489 | 5:83195795 | A/G | AA/AG/GG | 114 | E/G | 281/80/19 | 2.297 ± 1.09/2.197 ± 1.06/− | 0.75 |
SNP059 | rs769556494 | 5:83195797 | A/G | AA/AG/GG | 115 | K/E | 340/40/0 | 2.277 ± 1.09/2.277 ± 1.04/− | 0.99 |
SNP060 | rs1056939125 | 5:83195798 | A/C | AA/AC/CC | 115 | K/T | 380/0/0 | 2.27 ± 1.09/−/− | — |
SNP061 | rs1301137729 | 5:83195800 | G/C | GG/GC/CC | 116 | V/L | 359/21/0 | 2.27 ± 1.09/2.19 ± 0.99/− | 0.74 |
SNP062 | rs550178738 | 5:83195807 | A/T | AA/AT/TT | 118 | N/I | 341/39/0 | 2.27 ± 1.10/2.28 ± 1.00/− | 0.93 |
SNP063 | rs375157105 | 5:83195809 | C/A | CC/CA/AA | 119 | P/T | 352/28/0 | 2.29 ± 1.102.06 ± 0.83/− | 0.28 |
SNP064 | rs768175717 | 5:83195810 | C/T | CC/CT/TT | 119 | P/L | 380/0/0 | 2.27 ± 1.09/−/− | — |
SNP065 | rs369641536 | 5:83195816 | A/G | AA/AG/GG | 121 | E/G | 380/0/0 | 2.27 ± 1.09/−/− | — |
SNP066 | rs1395194011 | 5:83195819 | T/A | TT/TA/AA | 122 | V/D | 289/81/10 | 2.24 ± 1.07/2.62 ± 1.22/− | 0.05 |
SNP067 | rs1198491910 | 5:83195821 | A/G | AA/AG/GG | 123 | I/V | 348/32/0 | 2.27 ± 1.09/2.25 ± 1.13/− | 0.95 |
SNP068 | rs1159852376 | 5:83195824 | A/G | AA/AG/GG | 124 | R/G | 369/11/0 | 2.26 ± 1.10/2.40 ± 0.88/− | 0.64 |
SNP069 | rs762419679 | 5:83195834 | T/C | TT/TC/CC | 127 | I/T | 359/15/6 | 2.12 ± 0.86/4.33 ± 1.99/5.99 ± 1.12 | 1.17 × 10−43 |
SNP070 | rs1412506484 | 5:83195840 | A/G | AA/AG/GG | 129 | Y/C | 349/31/0 | 2.27 ± 1.10/2.26 ± 1.32/− | 0.96 |
SNP071 | rs1178870682 | 5:83195848 | G/C | GG/GC/CC | 132 | D/H | 365/15/0 | 2.27 ± 1.10/2.26 ± 0.78/− | 0.98 |
SNP072 | rs1484047716 | 5:83195852 | C/G | CC//CG/GG | 133 | T/S | 374/6/0 | 2.26 ± 1.07/2.71 ± 1.90/− | 0.32 |
SNP073 | rs28360135 | 5:83195855 | T/C | TT/TC/CC | 134 | I/T | 304/64/12 | 2.31 ± 1.13/2.12 ± 0.90/2.12 ± 0.69 | 0.39 |
SNP074 | rs1335801774 | 5:83195856 | T/G | TT/TG/GG | 134 | I/M | 380/0/0 | 2.27 ± 1.09/−/− | — |
SNP075 | rs372774793 | 5:83195857 | G/C | GG/GC/CC | 135 | A/P | 358/22/0 | 2.27 ± 1.06/2.25 ± 1.46/− | 0.92 |
SNP076 | rs1384832919 | 5:83195863 | A/G | AA/AG/GG | 137 | N/D | 368/12/0 | 2.27 ± 1.10/2.00 ± 0.59/− | 0.38 |
SNP077 | rs56334522 | 5:83195865 | T/G | TT/TG/GG | 137 | N/K | 366/13/1 | 2.26 ± 1.10/2.53 ± 0.80/1.34b | 0.89 |
SNP078 | rs370037164 | 5:83195869 | G/A | GG/GA/AA | 139 | A/T | 292/84/4 | 2.28 ± 1.06/2.23 ± 1.18/2.49 ± 0.83 | 0.89 |
SNP079 | rs374892515 | 5:83195876 | A/G | AA/AG/GG | 141 | N/S | 380/0/0 | 2.27 ± 1.09/−/− | — |
SNP080 | rs28360136 | 5:83195878 | G/C | GG/GC/CC | 142 | E/Q | 254/111/15 | 2.33 ± 1.10/2.12 ± 1.02/2.35 ± 1.26 | 0.21 |
SNP081 | rs553111266 | 5:83195883 | C/A | CC/CA/AA | 143 | H/Q | 373/7/0 | 2.26 ± 1.09/2.75 ± 1.07/− | 0.24 |
SNP082 | rs1022211508 | 5:83195897 | A/G | AA/AG/GG | 148 | N/S | 342/38/0 | 2.30 ± 1.11/2.03 ± 0.79/− | 0.15 |
SNP083 | rs757278630 | 5:83195903 | G/A | GG/GA/AA | 150 | R/K | 378/12/0 | 2.27 ± 1.09/1.99 ± 1.01/− | 0.36 |
SNP084 | rs1375107569 | 5:83195905 | C/T | CC/CT/TT | 151 | L/F | 380/0/0 | 2.27 ± 1.09/−/− | — |
SNP085 | rs1277864722 | 5:83195912 | G/A | GG/GA/AA | 153 | R/K | 355/25/0 | 2.29 ± 1.01/1.93 ± 0.72/− | 0.02 |
SNP086 | rs745751715 | 5:83195922 | T/G | TT/TG/GG | 156 | N/K | 289/89/2 | 2.28 ± 1.04/2.25 ± 1.23/2.12 ± 1.83 | 0.97 |
SNP087 | rs1201811742 | 5:83195924 | A/C | AA/AC/CC | 157 | D/A | 353/26/1 | 2.24 ± 1.07/2.67 ± 1.22/2.72e | 0.13 |
SNP088 | rs369499884 | 5:83195930 | A/G | AA/AG/GG | 159 | Q/R | 367/13/0 | 2.28 ± 1.09/2.03 ± 1.08/− | 0.42 |
SNP089 | rs372493882 | 5:83195933 | G/T | GG/GT/TT | 160 | G/V | 250/30/0 | 2.29 ± 1.10/2.07 ± 0.97/− | 0.30 |
SNP090 | rs1156689163 | 5:83203553 | T/G | TT/TG/GG | 162 | F/V | 380/0/0 | 2.27 ± 1.09/−/− | — |
SNP091 | rs1325151692 | 5:83203554 | T/A | TT/TA/AA | 162 | F/Y | 345/95/0 | 2.26 ± 1.04/2.39 ± 1.46/− | 0.49 |
SNP092 | rs375731584 | 5:83203566 | T/G | TT/TG/GG | 166 | V/G | 355/25/0 | 2.25 ± 1.09/2.48 ± 0.97/− | 0.32 |
SNP093 | rs1359488275 | 5:83203568 | A/T | AA/AT/TT | 167 | S/C | 380/0/0 | 2.27 ± 1.09/−/− | — |
SNP094 | rs753605351 | 5:83203575 | A/C | AA/AC/CC | 169 | K/T | 385/15/0 | 2.26 ± 1.10/2.40 ± 0.89/− | 0.64 |
SNP095 | rs1376295451 | 5:83203587 | A/T | AA/AT/TT | 173 | E/V | 340/40/0 | 2.28 ± 1.10/2.18 ± 0.98/− | 0.58 |
SNP096 | rs778422015 | 5:83203595 | C/T | CC/CT/TT | 176 | L/F | 380/0/0 | 2.27 ± 1.09/−/− | — |
SNP097 | rs777300742 | 5:83203599 | A/G | AA/AG/GG | 177 | Y/C | 354/26/0 | 2.26 ± 1.08/2.39 ± 1.24/− | 0.57 |
SNP098 | rs140143447 | 5:83203604 | C/T | CC/CT/TT | 179 | R/W | 372/8/0 | 2.28 ± 1.09/1.69 ± 0.56/− | 0.13 |
SNP099 | rs771544881 | 5:83203605 | G/A | GG/GA/AA | 179 | R/Q | 380/0/0 | 2.27 ± 1.09/−/− | — |
SNP100 | rs777195630 | 5:83203607 | T/C | TT/TC/CC | 180 | F/L | 354/26/0 | 2.22 ± 1.04/2.89 ± 1.51/− | 0.04 |
SNP101 | rs1379408635 | 5:83203610 | A/T | AA/AT/TT | 181 | I/F | 338/41/1 | 2.26 ± 1.07/2.37 ± 1.22/2.98e | 0.67 |
SNP102 | rs1199884817 | 5:83203611 | T/C | TT/TC/CC | 181 | I/T | 345/35/− | 2.27 ± 1.08/2.26 ± 1.20/− | 0.96 |
SNP103 | rs1018879495 | 5:83203623 | A/T | AA/AT/TT | 185 | N/I | 366/14/0 | 2.27 ± 1.09/2.18 ± 0.94/− | 0.76 |
SNP104 | rs1482288279 | 5:83203635 | C/T | CC/CT/TT | 189 | T/I | 380/0/0 | 2.27 ± 1.09/−/− | — |
SNP105 | rs770217916 | 5:83203641 | T/A | TT/TA/AA | 191 | I/N | 341/38/1 | 2.28 ± 1.10/2.10 ± 0.90/4.31b | 0.11 |
SNP106 | rs775587299 | 5:83203644 | G/T | GG/GT/TT | 192 | R/I | 351/24/5 | 2.28 ± 1.09/2.24 ± 1.16/1.94 ± 0.64 | 0.79 |
SNP107 | rs763186148 | 5:83203652 | C/T | CC/CT/TT | 195 | H/Y | 328/50/2 | 2.28 ± 1.11/2.23 ± 0.97/2.18 ± 0.47 | 0.95 |
SNP108 | rs1173748737 | 5:83203653 | A/G | AA/AG/GG | 195 | H/R | 380/0/0 | 2.27 ± 1.09/−/− | — |
SNP109 | rs764109844 | 5:83203654 | T/G | TT/TG/GG | 195 | H/Q | 380/0/0 | 2.27 ± 1.09/−/− | — |
SNP110 | rs1458486332 | 5:83203659 | A/G | AA/AG/GG | 197 | K/R | 380/0/0 | 2.27 ± 1.09/−/− | — |
SNP111 | rs1263079073 | 5:83203676 | C/G | CC//CG/GG | 203 | Q/E | 355/25/− | 2.28 ± 1.09/2.18 ± 1.09/− | 0.69 |
SNP112 | rs766287987 | 5:83203678 | A/T | AA/AT/TT | 203 | Q/H | 357/16/7 | 2.11 ± 0.89/4.15 ± 0.77/5.78 ± 1.17 | 1.05 × 10−59 |
SNP113 | rs778723397 | 5:83203683 | G/A | GG/GA/AA | 205 | R/Q | 365/15/0 | 2.29 ± 1.09/1.87 ± 0.97/− | 0.15 |
SNP114 | rs777199609 | 5:83203691 | G/C | GG/GC/CC | 208 | D/H | 292/86/2 | 2.22 ± 1.05/2.43 ± 1.18/2.86 ± 1.40 | 0.20 |
SNP115 | rs1224705261 | 5:83203694 | A/G | AA/AG/GG | 209 | I/V | 380/0/0 | 2.27 ± 1.09/−/− | — |
SNP116 | rs201604424 | 5:83203698 | A/G | AA/AG/GG | 210 | K/R | 300/72/8 | 2.29 ± 1.11/2.15 ± 0.97/2.77 ± 1.03 | 0.26 |
SNP117 | rs1276157833 | 5:83203701 | A/G | AA/AG/GG | 211 | Q/R | 371/9/0 | 2.28 ± 1.08/1.82 ± 1.17/− | 0.22 |
SNP118 | rs1298401873 | 5:83203706 | G/A | GG/GA/AA | 213 | G/R | 359/21/0 | 2.29 ± 1.10/1.93 ± 0.87/− | 0.14 |
SNP119 | rs746407658 | 5:83204816 | G/A | GG/GA/AA | 214 | E/K | 291/79/10 | 2.30 ± 1.10/2.22 ± 1.09/1.72 ± 072 | 0.23 |
SNP120 | rs969467594 | 5:83204820 | C/G | CC//CG/GG | 215 | T/S | 380/0/0 | 2.27 ± 1.09/−/− | — |
SNP121 | rs780330653 | 5:83204823 | C/A | CC/CA/AA | 216 | A/E | 307/61/12 | 2.24 ± 1.08/2.46 ± 1.19/2.06 ± 0.72 | 0.28 |
SNP122 | rs1348464342 | 5:83204825 | A/G | AA/AG/GG | 217 | I/V | 375/5/0 | 2.27 ± 1.09/2.46 ± 0.94/− | 0.69 |
SNP123 | rs749647860 | 5:83204828 | T/G | TT/TG/GG | 218 | C/G | 358/22/0 | 2.27 ± 1.08/2.22 ± 1.23/− | 0.84 |
SNP124 | rs908326126 | 5:83204840 | A/G | AA/AG/GG | 222 | T/A | 380/0/0 | 2.27 ± 1.09/−/− | — |
SNP125 | rs1347664669 | 5:83204847 | A/G | AA/AG/GG | 224 | D/G | 354/26/0 | 2.26 ± 1.09/2.38 ± 1.12/− | 0.58 |
SNP126 | rs866477694 | 5:83204848 | C/A | CC/CA/AA | 224 | D/E | 380/0/0 | 2.27 ± 1.09/−/− | — |
SNP127 | rs774555675 | 5:83204850 | G/A | GG/GA/AA | 225 | R/Q | 348/32/0 | 2.28 ± 1.10/2.11 ± 0.97/− | 0.39 |
SNP128 | rs748307585 | 5:83204852 | G/A | GG/GA/AA | 226 | D/N | 285/86/9 | 2.29 ± 1.09/2.20 ± 1.09/2.37 ± 1.12 | 0.77 |
SNP129 | rs368106955 | 5:83204867 | G/A | GG/GA/AA | 231 | E/K | 380/0/0 | 2.27 ± 1.09/−/− | — |
SNP130 | rs140579916 | 5:83204874 | C/A | CC/CA/AA | 233 | T/N | 342/38/0 | 2.26 ± 1.07/2.39 ± 1.23/2.27 ± 1.09 | 0.46 |
SNP131 | rs762812825 | 5:83204882 | G/A | GG/GA/AA | 236 | E/K | 357/21/2 | 2.26 ± 1.08/2.43 ± 1.33/2.23 ± 0.26 | 0.78 |
SNP132 | rs574436773 | 5:83204897 | A/T | AA/AT/TT | 241 | T/S | 376/4/0 | 2.27 ± 1.09/1.96 ± 1.19/− | 0.57 |
SNP133 | rs542187236 | 5:83204901 | A/G | AA/AG/GG | 242 | D/G | 380/0/0 | 2.27 ± 1.09/−/− | — |
SNP134 | rs1013137284 | 5:83204904 | T/A | TT/TA/AA | 243 | L/H | 374/6/0 | 2.26 ± 1.09/3.00 ± 0.56/− | 0.10 |
SNP135 | rs1261641487 | 5:83204906 | T/C | TT/TC/CC | 244 | S/P | 380/0/0 | 2.27 ± 1.09/−/− | — |
SNP136 | rs767176080 | 5:83204907 | C/T | CC/CT/TT | 244 | S/F | 376/6/0 | 2.27 ± 1.09/1.71 ± 0.34/− | 0.31 |
SNP137 | rs371824973 | 5:83204910 | G/A | GG/GA/AA | 245 | G/E | 380/0/0 | 2.27 ± 1.09/−/− | — |
SNP138 | rs3734091 | 5:83204915 | G/T | GG/GT/TT | 247 | A/S | 296/46/38 | 2.09 ± 0.97/2.79 ± 1.49/3.08 ± 0.77 | 1.20 × 10−70 |
SNP139 | rs141122119 | 5:83258556 | A/G | AA/AG/GG | 258 | I/V | 363/17/0 | 2.28 ± 1.10/2.04 ± 0.82/− | 0.37 |
SNP140 | rs138837678 | 5:83258665 | A/C | AA/AC/CC | 294 | Q/P | 372/8/0 | 2.28 ± 1.09/1.98 ± 0.79/− | 0.45 |
SNP141 | rs61749611 | 5:83353207 | A/C | AA/AC/CC | 324 | N/H | 348/30/2 | 2.27 ± 1.09/2.21 ± 1.14/2.53 ± 0.16 | 0.90 |
SNP142 | rs148273490 | 5:83353231 | G/C | GG/GC/CC | 332 | D/H | 356/24/0 | 2.29 ± 1.10/2.02 ± 0.86/− | 0.25 |
SNP143 | rs141304949 | 5:83353238 | T/C | TT/TC/CC | 334 | I/T | 344/35/1 | 2.26 ± 1.07/2.39 ± 1.26/1.37e | 0.58 |
The association between SNPs in the coding region of XRCC4 and AFB1-DNA adducts in tissues with hepatocellular carcinoma.
x/y represents wild-type allele/ variant type allele.
xx/xy/yy represents wild-type homozygote/heterozygote/variant-type homozygote.
Nxx/xy/yy represents the number of subjects with xx genotype, the number of subjects with xy genotype, and the number of subjects with yy genotype.
P values are calculated using t-test or one-way analysis of variance.
SD is not determined.* SD for genotype yy is not determined and P-value is used for genotypes xx and xy.
Additionally, mutant genotypes of several other GSNPs, including rs761695470 (SNP008, at codon 18), rs758779099 (SNP018, at codon 40), rs144653114 (SNP054, at codon 103), rs1277864722 (SNP085, at codon 153), and rs777195630 (SNP100, at codon 180), also increased the amounts of AFB1-DNA adducts; however, they had no statistical significance according to screening threshold value.
Because the poor prognosis of patients with HCC has been associated with the toxicity of AFB1, we followed up the survival information of all patients and explored whether positive GSNPs of XRCC4 modified HCC outcomes, including overall survival (OS) and disease recurrence-free survival (RFS) (Figures 1 and 2). Results from Kaplan-Meier survival model (based on the cumulative risk models) and Cox regression model analyses showed that compared with their wild genotypes (XX genotypes), the mutant genotypes (including XY and YY genotypes) of rs28383151, rs766287987, and rs3734091 polymorphisms increased cumulative hazard for OS [HR = 1.31 (1.04–1.67), 1.72 (1.09–2.71), and 1.42 (1.10–1.82), respectively] (Figure 1). For RFS, the corresponding hazard values were 3.31 (2.33–4.69) for rs28383151, 1.85 (1.03–3.67) for rs766287987, and 4.45 (3.13–6.34) for rs3734091, respectively (Figure 2).
XRCC4 GSNPs significantly correlating with the overall survival (OS) of hepatocellular carcinoma (HCC). Cumulative hazard function was plotted by Kaplan-Meier methodology, and P value was calculated with two-sided log-rank tests. The relative hazard ratio (HR) values for genotypes were calculated using multivariable Cox regression models (with all significant variables) based on forward-step method with likelihood ratio test.
XRCC4 GSNPs significantly correlating with the disease recurrence-free survival (RFS) of hepatocellular carcinoma (HCC). Cumulative hazard function was plotted by Kaplan-Meier methodology, and P value was calculated with two-sided log-rank tests. The relative hazard ratio (HR) values for genotypes were calculated using multivariable Cox regression models (with all significant variables) based on forward-step method with likelihood ratio test.
In this study, we investigated the association between the GSNPs in the coding regions of XRCC4 gene and the toxic effects of AFB1 on the liver. We found that five XRCC4 GSNPs, including rs1237462915 (at codon 38), rs28383151 (at codon 56), rs762419679 (at codon 127), rs766287987 (at codon 203), and rs3734091 (at codon 247), significantly increased the amount of AFB1-DNA adducts in tissues with HCC (2.07–2.15 μmol/mol DNA for XY genotypes and 2.64–4.33 μmol/mol DNA for YY genotypes, respectively) and progressed the cumulative hazard of AFB1 hepatic toxicity.
AFB1 acts as a type of human chemical toxicant, and the toxic effects of this toxicant are characterized by organophilism (mainly causing hepatic damage), genic toxicity (mainly inducing DNA damages such as hotspot mutation at codon 249 of TP53 gene, AFB1-DNA adduct formation, and so on), and carcinogenicity (mainly resulting in HCC) [6, 7, 8]. Among the hepatic toxicity of AFB1, the formation of AFB1-DNA adducts in hepatic cells is a key step during the metabolism of this toxicant [9, 10, 11, 12, 13, 14]. Evidence from molecular epidemiological studies and clinical studies has proved that the levels of AFB1-DNA adducts in the hepatic tissues are positively associated with the levels and time of AFB1 exposure [3, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44]. This is indicative of AFB1-DNA adduct acting as the biomarker for AFB1’s toxic capacity in the liver. In this study, AFB1-DNA adduct in the tumor tissues with HCC was used to evaluate hepatic toxicity related to AFB1, mainly because normal liver tissue samples cannot be obtained. Our results exhibited HCC tumor samples from high AFB1 exposure areas have an average adduct amount of 2.27 ± 1.09 μmol/mol DNA. Supporting our findings, several studies from high AFB1 exposure areas Nanning and Tiandong, China, have also shown the similar level of DNA adducts [4, 5, 26, 27, 37, 39, 45]. Taken together, the amount of AFB1-DNA adducts should be able to reflect the hepatic toxic potential of AFB1.
XRCC4, a key gene in the V(D)J recombination repair pathway, is located at 5q14.2 and consists of 13 exons (PubMed). Normally, XRCC4 is mainly expressed in genital meatus, alimentary tract, and lymphoid tissue; however, its expression will noticeably increase in other tissues such as the skin and liver under the condition of in vitro and in vivo injuries. This gene’s encoding protein plays a vital role in both NHEJ and the completion of V(D)J recombination via acting as a scaffold protein for DNA ligase IV and DNA-PK in the repair of DNA DSBs [15, 19]. Mutations in XRCC4, including GSNPs and other non-GSNPs variants, can cause endocrine dysfunction, microcephaly, short stature, and diseases [16, 21]. With the development of human Geno projects, more than 1000 GSNPs are identified. Among these GSNPs, we focused on genetic alterations in the coding regions of XRCC4, mainly because they will result in missense mutations and ultimately cause the structure damage and function deficiency of XRCC4 protein. Molecular epidemiological studies have displayed that the GSNPs in the XRCC4 genes can increase DNA repair capacity and increase the risk of some tumors such as lung cancer, colon cancer, HCC, and so on [21, 46, 47, 48, 49, 50, 51]. Evidence from in vitro and in vivo studies has also proved that XRCC4 GSNPs increase the amount of DNA damage and induce more gene mutations [23, 24, 26, 27]. In our study, we tested the genotypic distributions of all known GSNPs in the coding region of XRCC4 in liver tumor tissues. Five positive GSNPs were identified, and they result in the change of amino acid D to Y at codon 38 for rs1237462915, A to T at codon 56 for rs28383151, I to T at codon 127 for rs762419679, Q to H at codon 203 for rs766287987, and A to S at codon 247 for rs3734091, respectively. Although evidence that several other GSNPs, including rs761695470, rs758779099, rs144653114, rs1277864722, and rs777195630, increased the amounts of AFB1-DNA adducts was not statistically significant according to our defined threshold value, their effects should not be neglected because small-size samples may underestimate values.
Because the toxic effects of AFB1 also modify the prognosis of patients with HCC [26, 27, 33, 52, 53], we accomplished patients’ survival analyses on the basis of the cumulative risk models and found only rs28383151, rs766287987, and rs3734091 polymorphisms shortened HCC cases’ OS and RFS. Supporting our findings, several previous reports have proved that XRCC4 GSNPs can alter the levels of XRCC4 mRNA and protein expression and dysregulation of XRCC4 expression increasing the amount of AFB1-DNA adducts and mutative risk of TP53 gene [23, 24, 26, 27].
To conclude, this study is the first report investigating the modified function of XRCC4 GSNPs on AFB1’s hepatic toxicity. Our findings suggest that the GSNPs in the coding regions of XRCC4 gene, like rs1237462915, rs28383151, rs762419679, rs766287987, and rs3734091, may alter the DNA repair capacity of DNA damage induced by AFB1. If these individuals with mutant genotypes of these GSNPs decrease their exposure to AFB1, they will be free from toxic effects of AFB1 on hepatic damage. Several limitations should be focused for our study. First, relatively small-size samples may underestimate the effects of XRCC4 GSNPs on AFB1 hepatic toxicity. Second, the hospital-based design may result in selective bias. Third, we only accomplished the cumulative risk analyses but not the cumulative survival analyses. Finally, we did not finish functional and mechanical analyses. Thus, XRCC4 GSNPs may be valuable biomarkers for predicting the toxic effects of AFB1 on the liver once the present findings were proved by larger samples and toxic function analyses.
We thank Dr. Qiu-Xiang Liang, Dr. Yun Yi, Dr. Yun Xia, Dr. Yong-Zhi Huang, and Dr. Yuan-Feng Zhou for sample collection and management and Dr. Hua Huang for molecular biochemical technique. We also thank all members of the Department of Medical Test and Infective Control, Affiliated Hospital of Youjiang Medical University for Nationalities for their help.
The authors declare no competing financial interests. This study was supported in part by the National Natural Science Foundation of China (Nos. 81860489, 81760502, 81572353, and 81660495), the Natural Science Foundation of Guangxi (Nos. 2018GXNSFAA281043, 2017GXNSFAA198002, and 2017GXNSFGA198002), Research Program of Guangxi “Zhouyue Scholar” (No. 2017-38), Research Program of Guangxi Specially Invited Expert (No. 2017-6th), “12th Five-Year” Planning Program of Guangxi Education Science (No. 2015C397), Innovative Program of Guangxi Graduate Education (No. JGY2015139), Research Program of Guangxi Clinic Research Center of Hepatobiliary Diseases (No. AD17129025), and Open Research Program from Molecular Immunity Study Room Involving in Acute and Severe Diseases in Guangxi Colleges and Universities (Nos. kfkt20160062 and kfkt20160063).
AFB1 | aflatoxin B1 |
CI | confidence interval |
GSNPs | the genetic single-nucleotide polymorphisms |
HCC | hepatocellular carcinoma |
HR | hazard ratio |
OS | overall survival |
RFS | disease recurrence-free survival |
XRCC4 | X-ray repair cross complementing 4 |
Disintegration of the soil are impelled by the effect of rain drops on plain or almost plain soils, which detaches and splash soil particles and transports them downslope as a feature of surface flow. The net disintegration rate (sediments mass/unit zone) is an element of both rain sprinkle and surface flow. Runoff from earth surface conveys with it the most erodible sediment and fine sand particles from the dirt surface as the water streams downhill. At that point, rills are shaped; they start little channels, inevitably framing gaps, which can bring about enormous soil losses [1]. These processes are regularly studied in the field with normal precipitation which may be moderated by uncontrolled factors such as irregularity of the precipitation events. This paper therefore reviewed rainfall simulator as a potential tool for hydrological research.
Rainfall simulators (RS) are device designed to model the characteristics of natural rainfall to the nearest possible. It can be used to determine inter-rill erosion rates and their dependent rainfall and soil parameters [1]. It has been a tool for agricultural research and has been used for different studies ranging from determination of soil characteristic, such as infiltration rate, surface runoff, storage or erosion process studies [2]. Yakubu and Yusop [3] pointed out two most important aspects to note while using rainfall simulator; the method used to simulating rainfall and runoff from plot. Consideration was not given to infiltration.
There are three classification of rainfall simulator: drip, pressurised nozzle (PN) [3, 4, 5] and hybrid [6] rainfall simulator.
Drip simulator: also known as drop former (DF) [4] uses hanging yarn or hypodermic needles to produce drops of necessary size at zero velocity. Its impact velocity is attained by free fall which made others defined it as non-pressurised simulator [7]. The drilled holes and drop height determines the diameter of the raindrop and kinetic energy respectively [3] (see Figure 1).
Drop formers simulator [11].
It is capable of producing drops which ranges from 3 to 6 mm depending on the diameter holes [8]. Main advantage of the drip simulator is that it has the ability to produce relatively large drops at low application rate [5, 9]. It has the following disadvantages [10]:
It is impractical for field since it requires huge distance of at least 10 m height to attain terminal velocity.
Another constraint of this simulator is that simulation is only carried out on a limited plot depending on the size of the hanging yarn.
It does not produce distribution drops unless a variety of drop forming sized tubes are used.
Pressurised Simulator (PN): produces drop distribution that includes both small and large range of drop sizes with nonzero initial velocity and an impact velocity similar to terminal velocity of raindrops. In order to obtain drops of suitable sizes while upholding high velocity, high discharge nozzles are required [7]. The application rates are reduced by means of an intermittent moving object which intercepts the rainfall. An example of this type of simulator was developed by [12]. The authors found out that it utilises the best nozzle known as “yet-for-rain simulation” (spraying system 80,100-veejet nozzle). But problem with the 80,100-veejet nozzles was that it did not simulate rainfall energy characteristic and is still better than other nozzles. This type of rainfall simulator provides about 80% of the required kinetic energy per volume of natural rain [5, 12, 13]. This nonzero pressurised nozzle has the following advantages over the hanging yarn simulator [10] as presented by Figure 2:
They can be used in the field and their intensities can be varied more than the drop forming type of simulator.
Since the nozzle simulator has an initial velocity greater zero, it requires shorter height to reproduce the terminal velocity obtained from natural rain.
According to Home, (2017), this simulator is often portable compared to drop former.
Pressurised nozzle simulator [14].
Hybrid type simulator: uses the principles of pressurised and drip former techniques of simulation incorporated together. It was first developed by [15] to reduce the kinetic energy impact of the rainfall, but the research indicated that the technique reduced the kinetic energy at the detriment of the rain uniformity [3]. Wildhaber et al. used a similar method by placing mesh 0.5 m of aperture 2 mm × 1.7 mm under a spraying nozzle. The obtained result was not far from the non-pressurised simulator type. Carvalho et al. also designed a pressurised nozzle simulator with mesh placed 2.35 m below the nozzle to change rainfall characteristics (see Figure 3), and varying the nozzles and mesh types. The results varied based on the aperture of the meshes employed. Conclusively, the hybrid simulator was noted as suitable tool to assessing erodibility of different types of soil.
Hybrid simulator [16].
According to its transportability, rainfall simulators are classified as indoor and outdoor [7].
Indoor rainfall simulator: this rainfall simulator is used for modelling precipitation in a controlled environment. It is also known as Laboratory scale model. This simulator reduces lot of disadvantages incurred by the transportable type of rainfall simulator [7]. For example, Darboux et al. designed an indoor rainfall simulator system that simulated infiltration, run-off and erosion (see Figure 4), and the output was effective but the system was constrained with lack of non-recycle of the water system as well as not portable.
Rainfall simulation building [17].
Outdoor rainfall simulator: could be portable or large depending on the projected purpose. Many of these types of simulator have been used to relate soil surface characteristics and controlling to runoff, infiltration and erosion as influenced by different parameters [18, 19, 20, 21, 22].
A research carried out at Duke University Durham, using large transportable rainfall simulator of area 15.12 m2 (Figure 5). The system was tested with common pressure washing nozzles which produced rainfall intensity of 62.43 mm/h and 32 mm h−1 with a corresponding uniformity coefficient (C.U) of 76.65 and 62% [13]. [23] developed a portable field simulator for use in hillside and obtained a consistent raindrop size of 2.58 mm with an intensities of 20 to 90 mm h−1 and C.U of 91.7% at an intensity of 60 mmh−1. In a similar event Abudi et al. [24] also designed and constructed a portable rainfall simulator for field investigation of runoff, the drop size obtained was 1.5 mm with a ground hitting velocity near that of natural rainfall and energy flux 76% of the natural rainfall. All the simulators offered good performance. The merit of these simulators is that it can be used to study field parameters required for hydrologic modelling on any surface including the ones covered with vegetation. But they were limited by problem of natural rainfall which resulted to dismantling the setup when experiment schedule was not over and water was not recycled.
Outdoor rainfall simulator [13].
From the numerous studies carried out on the simulation of rainfall both in field and laboratory experiment, two merits of rainfall simulator in a research carried out in 2010 using laboratory simulator [25] were pointed out as:
It is faster in data collection without waiting for the natural rain.
With rainfall simulator, you can work with controlled rain, thereby, eliminating the erratic and unpredictable changeability of natural rainfall.
The characteristics of raindrop is also important for storm-water management purpose particularly in relation to understanding runoff process [26, 27]. Rainfall simulation should exemplify the following fundamental characteristic of the natural rain [7, 28].
Drop size distribution
Terminal velocity
Distribution uniformity
The rainfall intensity and
Kinetic energy
One of the basic natural rainfall characteristics that are considered is it drop size which ranges between 0.5 and 5 mm [3]. The measurement of rain droplets sizes has been studied using various approaches [28], but there is no established standard for obtaining raindrop diameter size [3]. Basically, there are two methods used for determining drop size; manual and automatic raindrop measurement [26].
The manual measurement techniques of drop size distribution includes; stain, flour pellet, oil immersion and photographic methods while automated raindrop measurement techniques include; impact disdrometers (acoustic and displacement); optical disdrometer (optical image and optical scattering). Figure 6 presents a tree of the drop size distribution classification. Drop size can be determined using Eq. (1):
Classification of drop size distribution methods.
where W is the weight of the formed.
A natural raindrop from greater height tends to reach terminal velocity before impact. This impact produces several effects on soil disintegration and infiltration. This is important particularly for studying soil erosion challenges where drops should reach their terminal velocity before impact [28]. This rainfall characteristic highly depends on the height of the simulator [3]. When the downward gravitational forces acting on the rainfall are cancelled out by the drag acting on the drop, the terminal velocity is achieved. Terminal velocity have been measured by many researchers using electronic devices to estimate the time for drops to pass consecutive point via photograph during fall [1, 7, 22, 28, 29] by stopwatch, timing the individual fall from a known height or simple computation [28, 30]. Computation of velocity of drop reaching the ground at an angle in natural precipitation (storm) with wind conditions 3. Simulated rainfall if well done can attain up to 94% of the terminal velocity of the natural rainfall [3, 31].
One of the major ways to assess rainfall simulators is by the simulated rain intensity which the means by which other rainfall characteristics are defined, especially the rain impact kinetic energy [3]. Another characteristic that correlated with intensity is the drop size distribution [11]. The method to control rain intensity varies in rainfall simulator. But it is quite a difficult task most especially using drop forming simulator because it involved the manual movement of the frame [3, 11, 32]. In the case of pressurised nozzle simulators, intensity and drop diameter are control the varying the pressure [3] or by introducing a body in a swinging or rotating motion under the nozzle [6, 14].
Kinetic energy of rainfall is the degree by which the energy of the rain is measured. It is the major factor in soil detachment process. The energy of the rain is relational to its “erosivity” [1], and it is expressed in Jm−2 mm−1. The technique of varying kinetic energy differs among rainfall simulators and the purpose for which a research is carried out [3]. Obtaining higher kinetic energy with drop forming simulator is an indication of the non-portability of the simulator because it requires higher height get such KE value. Aksoy et al. [33] in an investigation obtained kinetic energy of 21 Jm−2 mm−1, using pressurised nozzle simulator at lower rainfall intensity of 45 mm/h and a height of 2.4 m. By varying the drop diameter from 2.7 to 5.1 mm and height of fall from 0.17 to 2.5 m, similar result was obtained [34].
The kinetic energy of rainfall is depending on two factors; terminal velocity at impact and the spraying nozzle which give intensity. Therefore when a simulator is designed for investigation of potential erosion by simulated rainfall, these aforementioned two factors should be taken note of [29]. This can however, be determined by using Eq. (2) [35].
where KE is the kinetic energy of the rainfall in (MJha−1 mm−1) and I is the rainfall intensity in (mm/h).
In simulated rainfall on a plot, uniformity is one the most important measure of determining how spatially distributed the rainfall is on a plot to avoid ponding and over saturation on one side [3]. It therefore measures the equal catches of simulation of rainfall [28]. There are factors that sometimes affect uniformity: this includes; wind, slope and altitude [1]. The degree of uniformity is dependent of the rainfall type. It is estimated using the Christiansen uniformity coefficient (Cu) equation as presented in Eq. (3) [3]
where Cu is the Christiansen uniformity coefficient; SD is the standard deviation of simulated rain over the plot; Im is the mean simulated rain intensity.
Eq. (2) can further be expressed as in Eq. (4)
where Xi is the individual rain gauge, Xm is the mean gauge of the rainfall and n is total number of rain gauges.
Spray patterns of different types are obtained from different nozzles. In rainfall simulators, there are two different types of nozzles that are often used based on their mould. Namely; flat and cone spray nozzles. From each of these nozzles there tends to be decrease in uniformity from centre to outward of the sprayed plot [3, 24]. The challenge of rainfall uniformity reducing from centre to outward of the plot can be mitigated by using network of nozzles, taking into consideration the wetted perimeter of individual nozzles. The wetted perimeter depends on the distance of the simulator from the plot for nozzle that produces cone spray, operating pressure [36] in drop forming simulators (DFs) whereas in pressurised simulator (PN) Cu is increased based on increase in pressure and intensity [3]. Many researches have been carried out to estimate uniform application of depths as was used by Christensen to investigate the factors affecting water distribution from group of sprinklers [28, 29, 37, 38, 39], but this has been recently criticised based on the fact that is less significant and that size of rain gauge for uniformity and intensity affects the results [14], yet it often used as guide in rainfall simulation. Uniformity of can be more than 90% [31] contrary to sprinkler uniformity standard bench mark of 85% [10].
The methods employed to measure coefficient of uniformity plays a significant role in achieving correct simulated rainfall data [3]. It is therefore difficult to compare the uniformity results of simulated rainfall from different report [31]. In a review, [5] pointed out that drop forming simulators produces higher rainfall uniformity than pressurised nozzle simulator at lower rain intensity. Generally speaking without considering rainfall simulator type, investigator achieved average rain uniformity of 83% within the intensity range of 10 mm/h and 182 mm/h [1, 3, 31].
To successfully achieve afore listed natural rainfall characteristics, a designer of a rainfall simulator should take into considerations the following phonotypical features; pump pressure, simulators height, plot size and nozzle spacing. Each these physical features have impact on the purpose for which the rainfall simulator is designed.
In pressurised nozzle simulator the choice of pressure is a determining factor to mimic the natural rainfall to the nearest possible outcome [40]. The basis for selecting pressure should be such that balance is stroked among rain intensity, uniformity, rain drop size and kinetic energy, but different researchers are embedded with different approach toward pressure [3]. For example, in an investigation carried out by Cerda et al. indicated that uniformity was obtained at pressure 152 kPa using HARDI-1553-10 single nozzle and anything above this settings resulted to higher rain concentration at the plot boarder and below resulted to concentration of rain at the centre of the plot. The researcher therefore noted that increase in pressure has a maximum limit when targeting at rain uniformity above which decreases the uniformity [3]. In similar research by Sousa-Junior & Siqueira [31], similar trend of results were observed. Simulator under rainfall intensity of 3.1 mm/min, produced uniformity coefficient of 85% at 40 kPa [36]. Comparing the result of [35] with [41] investigation of rainfall intensity at 20 kPa and achieving 1.42–1.58 mm/min with an average rain uniformity of 60%, therefore, the effect of pressure cannot be over emphasised.
In Aksoy et al. [33] investigation, the orifice size was appreciated on examining the effects of pressure on 4-Veejet 8030, 4-Veejet 8050, 5-Veejet 8060 and 5-Veejet 8070 nozzles of different orifices, except for 5-Veejet 8060 nozzle which gave rain uniformity of 83.6% at 33 kPa pressure otherwise the others mimicked uniformity of 82.1, 86, and 88.8% at 40, 42 and 48 kPa respectively. Larger orifice resulted to increase in uniformity though with increase in pressure. According to [14], study on development and calibration of pressurised nozzle simulator observed that uniformity and intensity of modelled rainfall are affected by nozzle pressure disc angular velocity and angle of aperture.
Nozzle spacing in rainfall simulation is a very vital parameter to be considered in the study of the rain uniformity. Where there is overlapping during spray from two or more nozzles results to higher intensity and uniformity. But report discussion on this has always been mute in literatures [3]. An average CU of 80% was obtained with the use of 4 fixed Veejet nozzles spaced between 2 and 4 m, but when the spacing was reduced to 1, 2 m greater uniformity >86% was achieved [42]. Gabric et al. [34] design Veejet 80,100 nozzle and spaced 100 cm apart to assess intensity and uniformity of simulated rainfall, he achieved a uniformity of 86% at pressures of 40 kPa. Aksoy et al. [32] also studied rain uniformity using a similar nozzle Veejet 8030 and varied nozzle space between 1.45 and 1.25 m at 40 kPa and they achieved CU of 82.1%. A similar trend of results was obtained by [31] using 2-FullJet1/2 SSHH40 nozzle with 1.06 m spacing and varied pressure between 50 and 170 kPa. This shows that the smaller the nozzle spacing, the less pressure required and the larger the spacing the more will be required to mimic good rain uniformity.
The size of a plot is very crucial in the simulation of rainfall most especially in the determination of uniformity. The plot is therefore the predefined seclusion upon which parameter are examined for the purpose of research using simulated rain. It determines the size of the rainfall simulator [3]. Previous research showed that plot area varied from 0.24 [38] to 99 m2 [43]. Many investigators’ results showed that the smaller the plot size for rainfall simulation the higher the uniformity [3]. An example is the result obtained by Sanguesa et al. as cited by [3] with one nozzle used on 1 m × 1 m and 2 m × 2 m plot size they achieved a CU of 91 and 86% respectively. The results gotten when four nozzles arranged in strength line on a plot size of approximately 4.0625 m2 was 90% [3]. To explicate more on the effect of plot size on uniformity, 4flood jet nozzle was used on two different plot sizes of 3.56 m2 [10] and 8.84 m2 [33] and they obtained a corresponding uniformity coefficient of >90% and an average of 85.1%. The aforementioned result confirms that the plot size of a rainfall simulator affects the rain uniformity thus; increase in rainfall simulator plot size will decrease the uniformity. Sometimes the size of plot for rainfall simulation depends on the purpose for which the simulator is designed for. For example [38] selected plot size larger than the simulator top while [5] in a review pointed out some researchers makes use of smaller to obtain good uniformity. In nutshell, the factor determines selection of plot size in rainfall simulator is size of the simulator and the parameter under investigation [23].
Based on the simulator type, drop forming simulators are generally small in area (0.98 ± 68 m2) which can cover plot size of area 1.07 ± 0.12 m2 while in the case of pressurised nozzle type of simulator except for those using single; it can be as large as 5.12 ± 1.58 m2 [3]. Larger plot size in pressurised nozzle requires high pressure at higher height to attain good rain uniformity on the plot. For example, with plot size of 2.8 m2, rainfall intensity of 1.43–1.58 mm/h and rain uniformity of only 60% was achieved with operating pressure of 20 kPa [41]. These results were not encouraging at all but when pressure of 41 kPa was used on similar plot size of 2 m × 1.5 m (3 m2) a rainfall uniformity of 95% was achieved as cited by [5].
Kinetic energy of simulated rain is being influenced by two major factors; height of simulator and surface of plot, most especially in drop former (DF) simulators [3] requires huge range of height from 7 m [6] and 10 m [44] to reach the terminal velocity. In similar research [45] developed a laboratory rainfall DF simulator, they would achieve the desired kinetic, the dripper was placed at 14 m above the plot. Examining the above results shows that the height of a simulator has significant influence on terminal velocity and kinetic energy. For example low kinetic energy of 5.8 Jm−2 mm−1 was achieved in a research due to low height of 2 m was used for their simulator [46]. This was also confirmed by when [34] used portable rainfall simulator to control rainfall, some of the rainfall parameters like KE was mimicked at 5 m above the plot to achieve the KE similar to natural rain.
One of the underlined differences between drop former (DF) simulator and pressurised nozzle (PN) is height of the simulator. The pressurised due to the pressure achieves kinetic energy (25 Jm−2 mm−1) and D50 of 2.19 mm at the height of as low as 2.4 m above the plot as indicated by [24, 33]. According to [5] comparing the results of drop former simulator and pressurised nozzle both positioned at downward spray, pressurised nozzles overestimated the kinetic energy while drip former underestimated the kinetic energy.
After close analysis of the relationships of rainfall simulator components interdependently, [5] further observed that increased in pressure increases the intensity, rain uniformity and kinetic energy. Differences in plot size do not relate any other parameter apart from uniformity. Nozzle spray angle of aperture impacts the nozzle spacing. The research further recommended that any rain simulator designer should take into consideration intensity, kinetic energy and uniformity when designing a rain simulator. Table 1 showed the results gotten by different researchers using different types of the rainfall simulators.
Type/class of rainfall simulator | Drop size (mm) | Area (m2) | Terminal velocity (m/s) | Uniformity coefficient (%) | Rainfall intensity (mm/h) | Height (m) | Ref. |
---|---|---|---|---|---|---|---|
Drip former RS | 4.5 | 1 | 0.08 | 75 | 0.25–160 (Avg = 80.13) | 14 | Regmi and Thomson [47] |
Drip former RS | 3–6 (Avg = 4.5) | 0.36 | 0.023 | 50–70 (Avg = 60) | 78 | 10–13 (Avg. = 11.5) | Law [11] |
Average | 4.5 | 0.05 | 67.5 | 79.06 | 12.75 | ||
Pressurised RS | 0.9–2 | 0.95 | 0.63–0.86 (Avg = 0.75) | 81.2–88.5 (Avg = 84.85) | 55–88 (Avg = 71.5) | 1.5–2.5 (Avg = 2) | Ngasoh [48] |
Pressurised | 2.35–2.55 | 1.2 | 1.01 | 85.7–87.5 (Avg = 86.6) | 50.8–152.4 (Avg = 101.6) | 4.27 | Rick et al. [49] |
Average | 2.45 | 0.88 | 85.72 | 86.55 | 3.14 | ||
Hybrid RS | 2.2–8 (Avg = 5.1) | 1 | 0.35 | 96.5–98.7 (Avg = 97.6) | 65–70 (Avg. = 67.2) | 2.35 | Carvalho et al. [16] |
Hybrid RS | 2.3 | 2.5–8 | 0.123 | 93 | 15–120 (Avg. = 67.5) | 3.4 | Bowyer-Bower and Bur [4] |
Average | 3.7 | 0.23 | 95.3 | 67.35 | 2.88 | ||
Natural rain | 0.125–1 (Avg = 0.56) | 0.85–5 | ≥85 | 15–160 (Avg = 87.5) | Liu et al., [50] |
Results of test from different rainfall simulator compared to natural rainfall.
The average results from the various test indicated that Drop former produces higher rainfall drop sizes followed by hybrid while with pressurised rainfall simulator, an average of 2.5 mm rainfall drop size is produced. That is, among the different types of rainfall simulators, the pressurised rainfall simulator produces small varieties of drop sizes close to that of the natural rain. However, on the terminal velocity, the natural rainfall attains it before reaching it is fall from an infinity distance compared to the on obtainable from simulated rainfall (see Figure 7).
Representation of rainfall drop size and terminal velocity of different rainfall simulator compared to natural rainfall.
Figure 8 compares the uniformity and rainfall intensity of different types of rainfall simulators to the one obtainable from natural rainfall. The findings indicate that Drop former and hybrid rainfall simulator produces higher uniformity coefficient compared to what is obtainable from natural rainfall. While, intensity of a rainfall from pressurised rainfall simulator is similar to the ones obtainable from natural rainfall.
Representation of rainfall intensity and uniformity coefficient of different rainfall simulator compared to natural rainfall.
For a rainfall simulator to be used to study either on erodible or non-erodible surface, it needs to achieve rainfall characteristics close to those of natural rainfall, it needs to be portable and easy to control [3].
Furthermost of the research on erodible surface have involved erosion, infiltration and tillage studies [24, 51]. In disparity, the process concerning urban wet weather studies involved non-erodible surface and were defined based on pollutant volume and the corresponding discharge volume [3, 52]. In run off and sediment yield studied by [53] from an erodible watershed and non-erodible watershed using 10 modelled precipitation event, they achieved a runoff volume and sediment load of 5.5 ± 2.7 and 5.5 ± 2.3 respectively, and the proportion of precipitation to runoff volume was on the average 14.5%. The simulated result was greater than when it was done on non-erodible soil surface. A conclusion was also made by [51, 54] that drop size and the fall velocity are given basic attention in the study of erosion and infiltration model involving erodible surfaces and [53] also noted that simulation on non-erodible surface increased runoff volumes linearly and peak flow rate exponentially and served as means of control of sediment load and flow rate by its spatial characteristics.
First of all, the method employed to accumulating runoff on non-erodible and erodible surface not the same. Simulating precipitation and collection of runoff on non-erodible surface is more challenging because non-erodible surface are mostly tiled surfaces where excavation is controlled. Recovering of the runoff from non-erodible surface is the priority of researchers but the task is difficult. To overcome the difficulties in regenerating the runoff from an urban non-erodible surface.
Secondly, take note of the length and slope of the study area in the study of erosion and infiltration as they are important requirement in simulation.
Thirdly, pressurised nozzle simulator will be suitable for simulating reasonable intensity, runoff and rain depth most especially for nonpoint source study on non-erodible surfaces because the controlling intensity will be limited using drop former simulator.
Fourthly, in the simulation of drop size and distribution, water quality should be taken note of. Though it may not be significant in the simulation of infiltration and soil erosion, but in urban water quality simulation which deals with measurement of pollutant level it is a very important factor to consider. In an investigation carried out in Malaysia [55], water quality presented a challenge in simulating intensity drop size, drop size distribution and uniformity using drop former simulator. As water is stored and kept for long period of time algae and other micro-organism may develop in it or around the dripper. This challenge is predominant in drop forming simulators and less in pressurised nozzle simulator because the pressure applied at the nozzle orifice reduces the risk of clogging. To minimise the challenge of clogging of dripper and nozzle orifices, screen should be provided at suction point or water source.
Duration of experiment on non-erodible surface using rain simulator is an important requirement. To overcome the delay in runoff generation on studying runoff on non-erodible surface which is predominant in drop former simulator, pressurised nozzles are preferable because it offers reasonable amount of runoff with short time. In contrast, on erodible surface, drop former simulators are preferred especially in the study of infiltration.
Rainfall uniformity is achieved higher in drop forming and hybrid simulators which is a good requirement for erodible surface that include infiltration studies where the interest is on measuring downward filtered water on the plot. Simulating on erodible surface, saturation of the plot surface is slower than simulation on non-erodible surface. On the non-erodible surface the study interest is runoff collection. The researcher further recommended that mounting and dismounting of rainfall simulator should be flexible.
Finally, to achieve a good rainfall distribution uniformity using rainfall simulator, the plot must be smaller than the wetted perimeter of the simulator most especially for outdoor simulator. In the case of indoor rainfall simulator, the plot can be larger than the wetted perimeter but consideration can only be given to collectors around the wetted perimeter.
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