Genotype and allele frequencies of SNPs within IFNG promoter in Brazilians from Rio de Janeiro.
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His previous appointment was as researcher in School of Civil & Environmental Engineering of Nanyang Technological University of Singapore where he studied for his PhD during 2008-2011. His research is predominantly focused on hydrological modeling and flood forecasting using artificial intelligence techniques. Most recently, he has been also involved in research projects dealing with sustainable urban water management. To date, he has published over 50 articles in reputable journals and international conference proceedings. He has supervised several PhD and Master students and won the Supervisor of the Year Award in Monash University Malaysia in 2017. 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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. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"43286",title:"Influence of the Interferon–Gamma (IFN–γ) and Tumor Necrosis Factor Alpha (TNF–α) Gene Polymorphisms in TB Occurrence and Clinical Spectrum",doi:"10.5772/55099",slug:"influence-of-the-interferon-gamma-ifn-and-tumor-necrosis-factor-alpha-tnf-gene-polymorphisms-in-tb-o",body:'Tuberculosis (TB) is a major public concern and is the most important single infectious cause of mortality and morbidity worldwide. According the World Health Organization (WHO) records, in 2009, there were an estimated 9.4 million new cases, 14 million prevalent cases, and approximately 1.7 million deaths by TB [1]. Additionally, approximately one third of the world’s population is infected with the causative bacterium, Mycobacterium tuberculosis (Mtb), and is at risk for developing active tuberculosis. Interestingly, while approximately 9 million people develop active TB each year, the majority remain asymptomatically (latently) infected with the pathogen presumably due to a protective immune response. Without intervention, approximately five to ten percent of those latently infected will develop overt disease and the potential to transmit Mtb to others [2].
Familial clustering data, twin studies and complex segregation analysis have all suggested a strong genetic component in the human susceptibility to the chronic mycobacterial diseases [3-7] but also a complex picture of geographic heterogeneity in genetic effects on the different mycobacterial infections is involved [8, 9]. Several non-HLA genes have been implicated in TB susceptibility. However, the discrepant data reported may be attributed to a number of different factors, such as the types of studies, ethnicity, genetic background, and clinical status of patients with tuberculosis that may be associated with a particular genetic profile. The interaction among lung cells with pro and anti-inflammatory mediators during the infection with Mtb have been deeply investigated [10]. Among involved cytokines, the key role of interferon-gamma (IFN-γ) and tumor necrosis factor (TNF-α) in eliciting an inflammatory response against Mtb have been emphasized [11-13].
In human studies, the crucial role of TNF-α in protective host immunity against reactivation of latent TB was highlighted by the observation that the relapse and severe course of TB is over-represented in rheumatoid arthritis patients following the use of anti-TNF-α antibodies [14]. Concerning the IFN-γ, it is well established that deficiency in IFN-γ gene expression is associated with severe impairment of resistance to infections, in particular those that are normally killed by activated macrophages [15, 16]. Low synthesis of this cytokine has been associated with active tuberculosis [17]. However, on the contrary of TNF-α, the Interferon gamma conding gene (IFNG) is highly conserved and few single nucleotide polymorphisms (SNPs) are found in the intragenic region. Several case-control studies to evaluate association of SNPs in these genes with TB have produced mixed results, with little consensus in most cases on whether any TNF polymorphisms are actually associated with active TB disease [18, 19].
In the present study we aimed to analyse the existing promoter variability of the IFNG and TNF-α genes by partial mapping of this region in samples from Brazilians, followed by an association study of the identified SNPs and TB outcome after infection with Mtb.
In a case-control design, five hundred consanguineously unrelated individuals admitted at the University Hospital Complex: Thoracic Institute/ClementinoFraga University Hospital from Federal University of Rio de Janeiro-UFRJ were enrolled in this study after signing informed consent approved by the local Ethics Committee of HUCFF-UFRJ.
Demographic, clinical, and microbiological data as well as the HIV status of the subjects (age > 18 years old) were collected. Active TB cases (n=265) were defined as those after a positive culture confirmation in clinical specimen or with clinical, radiographic and laboratory improvement according to the American Thoracic Statements.They comprised 265 TB patients to be used for the descriptive genetic analysis. For the association study, TB-HIV comorbity was considered as an exclusion criteria and sample size was reduced as follow: 140 TB patients, being 121 with pulmonary TB (PTB) and 19 extrapulmonary forms of TB (TBE). The mean age of TB patients was ± 51 years (range 18-84 years) including 73 males and 67 females.
For the control group, a complete questionnaire to document TB risk factors since baseline testing was used. Individuals were eligible as controls if they had no previous TB history, consanguinity and negative HIV status. In formations concerning Tuberculin Skin Test (TST) response were available for all controls. They comprised 235 individuals, to be used for descriptive genetic analysis. For the association study, after application of the exclusion criteria, 154 individuals were included in this group, of which, 96 were TST positive (TST+) and 58 TST negative (TST-). The mean age in this group was ± 50 (range 18 - 82 years) and included 55 males and 99 females.
Sample Collection and handling
A volume of 3 mL of venous blood was collected from each volunteer and stored at -20°C. Genomic DNA was isolated from 100 µL of frozen whole blood using the FlexiGene DNA Kit (Qiagen Inc., USA), according to the manufacturer’s specifications. After extraction, DNA samples were stored at -20°C.
Genotyping of the proximal portion of the promoter region in TNF-α and IFNG genes was achieved by direct sequencing of PCR products. Two sets of primers for PCR amplification and sequencing of IFNG, DNA fragment of 863bp, (IFN-EF: 5’ GGAACTCCCCCTGGGAATATTCT 3`, IFNER: 5´AGCTGATCAGGTCCAAAGGA3´, IFNIF: 5´CGAAGTGGGGAGGT ACAAAA 3´ and IFNIR: 5´ CCCAGGAAACTGCTACTCTG 3´), and TNF-α, DNA fragment of 855bp (TNFEF: 5´CAGGACCTCCAGGTATGGAA3´, TNFER: 5’ TAGCTGGTCCTCTGCTGTCC3’, TNFIF: 5´CCTGCATCCTGTCTGGAAGT 3´ and TNF-IR: 5’TTTCAACCCCTGTGTGTTCG 3’) were designed by using the Primer3 software [20].
For PCR-mediated DNA amplification of IFNG, 100 ng of genomic DNA were added to a 50µL reaction mixture containing 200ng of each primer (IFN-EF and IFN-ER), 0.2mM of each dNTPs, 2.0mM MgCl2 and 1U Taq DNA polymerase (Invitrogen by Life Technologies, USA) and submitted to an initial denaturation at 94°C for 5 min., followed by 35 cycles of 1 min. at 94°C, 1 min. at 65.3°C and 1 min at 72°C. Final extension was performed for 5 min. at 72°C. Likewise, for amplification of the 855pb TNF-α fragment, 100ng of genomic DNA were added to a 25µL reaction mixture containing 200ng of each primer (TNF-EF and TNF-ER), 2mM MgCl2, 0.2mM of each dNTPs and 0.5U of Taq gold DNA polymerase (PE Applied BioSystems) and submitted to initial denaturation at 94°C for 15 min, followed by 35 cycles of 1 min. at 94°C, 1 min. at 65.9°C and 1 min at 72°C with a final extension at 72°C for 5 min. Evaluation of PCR products was done by electrophoresis on 1.2% agarose gel followed by ethidium bromide staining.
For sequencing, PCR products were purified with ChargeSwitch Kit (Invitrogen Life Technologies), according to the manufacturer’s recommendations. Sequencing of the amplified fragments was performed in both DNA strands using a combination of the internal and external primers using ABI PRISM Big Dye Terminator v. 3.1 Kit (PE Applied BioSystems), according to the manufacturer’s recommendations, on an ABI PRISM 3730 DNA Analyser (PE Applied BioSystems). All singletons and even new/rare mutation identified were confirmed by re-amplification and re-sequencing.
The SNPs identification in each individual sample was achieved after alignment of the generated sequences with the GenBank reference sequences AF3757790 and AB088112 for IFNG and TNF-α respectively. Transcription starting site sequence definition adopted for both genes considered as starting point, the first nucleotide immediately preceding position (-1) out of mRNA. Sequence analysis was carried out through SeqScapev. 2.6 software (Applied Biosystem). Haplotype reconstruction was achieved through the use of PHASE Vs. 2.1.1 software [21, 22].
Pair-wise linkage disequilibrium was tested for the loci studies. The Hardy-Weinberg equilibrium using χ2 test. Statistics were performed in XLSTAT 2008.7 (Addinsoft Software Inc - New York USA). The magnitude of the associations was estimated by odds ratio values and the coefficient of associations. All tests were performed at the 0.05 level of significance by Epi Info version 3.5.1 2008 (Centers for Disease Control and Prevention, USA).
In this work, a partial mapping of the promoter regions of IFNG and TNF-α genes was performed by direct PCR sequencing approach in 265 TB patients and 235 healthy controls residents in Rio de Janeiro, Brazil. Sequencing approach allowed the identification of new SNPs and consequently new haplotypes for both genes. Expected genotype frequencies were calculated from respective single allele frequencies and were consistent with Hard Weinberg Equilibrium using χ2test.
Sequence analysis of the proximal portion of IFNG promoter region (863 bp upstream of the transcription starting site) revealed the presence of seven SNPs, of which, four were new, and located at positions (-787C>T, -599C>G, -517C>T, and -255A>G). The three remaining SNPs, already deposited in GenBank-Entrez SNP database, were located at positions (-785C>T, -200G>T, reported as (-183 and -179) and -172A>G (reported as -155). Table 1 show the allele and genotype frequencies of the identified SNPs in the whole studied population (500 samples). All SNPs were found in a very low frequency, sometimes as a singleton (-255A>G). In this case, the SNP was confirmed by new PCR amplification and re-sequencing. The two more frequent SNPs were the ones located at positions -599C>G and -200G>T, both with 1.4%. No homozygosity was identified in these positions.
\n\t\t\t\t\tLocus\n\t\t\t\t\t \n\t\t\t\t\t\n\t\t\t\t\t\tIFNG\n\t\t\t\t\t\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tGenotype\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tSubjects(n = 500)\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tAbsolute Frequency\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tAllele frequency\n\t\t\t\t | \n\t\t\t
\n\t\t\t | CC | \n\t\t\t495 | \n\t\t\t0.99 | \n\t\t\t\n\t\t |
-787* | \n\t\t\tTC | \n\t\t\t5 | \n\t\t\t.001 | \n\t\t\t0.05 | \n\t\t
\n\t\t\t | (f) T | \n\t\t\t5 | \n\t\t\t_ | \n\t\t\t\n\t\t |
\n\t\t\t | CC | \n\t\t\t495 | \n\t\t\t0.99 | \n\t\t\t\n\t\t |
-785 | \n\t\t\tCT | \n\t\t\t5 | \n\t\t\t0.01 | \n\t\t\t0.05 | \n\t\t
\n\t\t\t | (f) T | \n\t\t\t5 | \n\t\t\t_ | \n\t\t\t\n\t\t |
\n\t\t\t | CC | \n\t\t\t487 | \n\t\t\t0.974 | \n\t\t\t\n\t\t |
-599* | \n\t\t\tCG | \n\t\t\t12 | \n\t\t\t0.024 | \n\t\t\t\n\t\t |
\n\t\t\t | GG | \n\t\t\t1 | \n\t\t\t0.002 | \n\t\t\t0.014 | \n\t\t
\n\t\t\t | (f) G | \n\t\t\t2 | \n\t\t\t- | \n\t\t\t\n\t\t |
\n\t\t\t | CC | \n\t\t\t497 | \n\t\t\t0.994 | \n\t\t\t\n\t\t |
-517* | \n\t\t\tCT | \n\t\t\t3 | \n\t\t\t0.06 | \n\t\t\t0.003 | \n\t\t
\n\t\t\t | (f)T | \n\t\t\t3 | \n\t\t\t- | \n\t\t\t\n\t\t |
\n\t\t\t | AA | \n\t\t\t499 | \n\t\t\t0.998 | \n\t\t\t\n\t\t |
-255* | \n\t\t\tAG | \n\t\t\t1 | \n\t\t\t0.002 | \n\t\t\t0.001 | \n\t\t
\n\t\t\t | \n\t\t\t | 1 | \n\t\t\t- | \n\t\t\t\n\t\t |
\n\t\t\t | (f) G | \n\t\t\t\n\t\t\t | \n\t\t\t | \n\t\t |
\n\t\t\t | GG | \n\t\t\t486 | \n\t\t\t0.972 | \n\t\t\t\n\t\t |
-200 | \n\t\t\tGT | \n\t\t\t14 | \n\t\t\t0.028 | \n\t\t\t0.014 | \n\t\t
\n\t\t\t | (f) T | \n\t\t\t14 | \n\t\t\t- | \n\t\t\t\n\t\t |
\n\t\t\t | AA | \n\t\t\t498 | \n\t\t\t0.996 | \n\t\t\t\n\t\t |
-172 | \n\t\t\tAG | \n\t\t\t2 | \n\t\t\t0.004 | \n\t\t\t0.002 | \n\t\t
\n\t\t\t | (f) G | \n\t\t\t2 | \n\t\t\t\n\t\t\t | \n\t\t |
Genotype and allele frequencies of SNPs within IFNG promoter in Brazilians from Rio de Janeiro.
Haplotype reconstruction was achieved from genotyping data by using Phase Vs. 2.1.1 software. A total of eight different haplotypes were characterized with basis on the combination of the seven SNPs identified within the IFNG promoter. Table 2 shows the frequencies of the identified haplotypes in the total population. The haplotype 4 was the, more frequent among the whole samples analyzed.
\n\t\t\t\t\tHaplotypes\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t-787\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t-785\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t-599\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t-517\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t-255\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t-200\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t-172\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tFrequency\n\t\t\t\t | \n\t\t\t
1 | \n\t\t\tC | \n\t\t\tC | \n\t\t\tC | \n\t\t\tC | \n\t\t\tA | \n\t\t\tG | \n\t\t\tA | \n\t\t\t0.916 | \n\t\t
2 | \n\t\t\tC | \n\t\t\t\n\t\t\t\tT\n\t\t\t | \n\t\t\tC | \n\t\t\tC | \n\t\t\tA | \n\t\t\tG | \n\t\t\tA | \n\t\t\t0.010 | \n\t\t
3 | \n\t\t\tC | \n\t\t\tC | \n\t\t\tG | \n\t\t\tC | \n\t\t\tA | \n\t\t\tG | \n\t\t\tA | \n\t\t\t0.024 | \n\t\t
4 | \n\t\t\tC | \n\t\t\tC | \n\t\t\tC | \n\t\t\tC | \n\t\t\tA | \n\t\t\t\n\t\t\t\tT\n\t\t\t | \n\t\t\tA | \n\t\t\t0.028 | \n\t\t
5 | \n\t\t\t\n\t\t\t\tT\n\t\t\t | \n\t\t\tC | \n\t\t\tC | \n\t\t\tC | \n\t\t\tA | \n\t\t\tG | \n\t\t\tA | \n\t\t\t0.010 | \n\t\t
6 | \n\t\t\tC | \n\t\t\tC | \n\t\t\tC | \n\t\t\tC | \n\t\t\t\n\t\t\t\tG\n\t\t\t | \n\t\t\tG | \n\t\t\tA | \n\t\t\t0.002 | \n\t\t
7 | \n\t\t\tC | \n\t\t\tC | \n\t\t\tC | \n\t\t\tC | \n\t\t\tA | \n\t\t\tG | \n\t\t\t\n\t\t\t\tG\n\t\t\t | \n\t\t\t0.004 | \n\t\t
8 | \n\t\t\tC | \n\t\t\tC | \n\t\t\tC | \n\t\t\t\n\t\t\t\tT\n\t\t\t | \n\t\t\tA | \n\t\t\tG | \n\t\t\tA | \n\t\t\t0.006 | \n\t\t
Characterization of the identified haplotypes within IFNG proximal promoter region in Brazilians from Rio de Janeiro (n=500).
The partial mapping of the proximal portion (855 bp upstream of the transcription starting site) of TNF-α promoter was also performed by direct sequencing of PCR products. Upon analysis of the generated sequences seven SNPs, all described in the literature, and were identified in a total of 500 samples. Table 3 shows the allele and genotype frequencies. With the exception of the most studied SNPs (-238 -308, and -376) presenting frequencies higher than 3%, all others were present in less than one percent.
\n\t\t\t\t\tLocus\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tGenotype\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tSubjects(n=500)\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tAbsoluteFrequency\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tAlleleFrequency\n\t\t\t\t | \n\t\t\t|
\n\t\t\t | GG | \n\t\t\t495 | \n\t\t\t0.990 | \n\t\t\t- | \n\t\t|
-646 | \n\t\t\tGA | \n\t\t\t5 | \n\t\t\t0.010 | \n\t\t\t- | \n\t\t|
\n\t\t\t | A | \n\t\t\t5 | \n\t\t\t_ | \n\t\t\t0.005 | \n\t\t|
\n\t\t\t | AA | \n\t\t\t495 | \n\t\t\t0.990 | \n\t\t\t- | \n\t\t|
-572 | \n\t\t\tAC | \n\t\t\t5 | \n\t\t\t0.010 | \n\t\t\t- | \n\t\t|
\n\t\t\t | C | \n\t\t\t5 | \n\t\t\t_ | \n\t\t\t0.005 | \n\t\t|
\n\t\t\t | CC | \n\t\t\t499 | \n\t\t\t0.998 | \n\t\t\t_ | \n\t\t|
-422 | \n\t\t\tCT | \n\t\t\t1 | \n\t\t\t0.002 | \n\t\t\t_ | \n\t\t|
\n\t\t\t | T | \n\t\t\t1 | \n\t\t\t_ | \n\t\t\t0.001 | \n\t\t|
\n\t\t\t | GG | \n\t\t\t471 | \n\t\t\t0.942 | \n\t\t\t_ | \n\t\t|
-376 | \n\t\t\tGA | \n\t\t\t28 | \n\t\t\t0.056 | \n\t\t\t_ | \n\t\t|
\n\t\t\t | AA | \n\t\t\t1 | \n\t\t\t0.002 | \n\t\t\t_ | \n\t\t|
\n\t\t\t | A | \n\t\t\t30 | \n\t\t\t_ | \n\t\t\t0.030 | \n\t\t|
\n\t\t\t | GG | \n\t\t\t418 | \n\t\t\t0.836 | \n\t\t\t_ | \n\t\t|
-308 | \n\t\t\tGA | \n\t\t\t77 | \n\t\t\t0.154 | \n\t\t\t_ | \n\t\t|
\n\t\t\t | AA | \n\t\t\t5 | \n\t\t\t0.010 | \n\t\t\t_ | \n\t\t|
\n\t\t\t | A | \n\t\t\t87 | \n\t\t\t\n\t\t\t\t_\n\t\t\t | \n\t\t\t0.087 | \n\t\t|
\n\t\t\t | GG | \n\t\t\t489 | \n\t\t\t0.978 | \n\t\t\t_ | \n\t\t|
-244 | \n\t\t\tGA | \n\t\t\t11 | \n\t\t\t0.022 | \n\t\t\t_ | \n\t\t|
\n\t\t\t | A | \n\t\t\t11 | \n\t\t\t_ | \n\t\t\t0.011 | \n\t\t|
\n\t\t\t | GG | \n\t\t\t453 | \n\t\t\t0.906 | \n\t\t\t_ | \n\t\t|
-238 | \n\t\t\tGA | \n\t\t\t44 | \n\t\t\t0.088 | \n\t\t\t_ | \n\t\t|
\n\t\t\t | AA | \n\t\t\t3 | \n\t\t\t0.006 | \n\t\t\t_ | \n\t\t|
\n\t\t\t | A | \n\t\t\t50 | \n\t\t\t\n\t\t\t\t_\n\t\t\t | \n\t\t\t0.050 | \n\t\t
Genotype and allele frequencies of SNPs within TNF-α promoter in Brazilians from Rio de Janeiro.
A total of fourteen different haplotypes were characterized. Except for the wild-type, haplotype 1, the higher frequent was the haplotype 3, presenting a mutant variation only at -308 position. As expected, the rare combination presenting polymorphisms only at positions -238 and -308 was present in the sample studied although in a low frequency (Table 4).
\n\t\t\t\t\tHaplotype\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t-646\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t-572\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t-422\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t-376\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t-308\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t-238\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t-244\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tFrequency\n\t\t\t\t | \n\t\t\t
1 | \n\t\t\tG | \n\t\t\tA | \n\t\t\tC | \n\t\t\tG | \n\t\t\tG | \n\t\t\tG | \n\t\t\tG | \n\t\t\t0.710 | \n\t\t
2 | \n\t\t\tG | \n\t\t\tA | \n\t\t\tC | \n\t\t\t\n\t\t\t\tA\n\t\t\t | \n\t\t\tG | \n\t\t\tG | \n\t\t\tG | \n\t\t\t0.006 | \n\t\t
3 | \n\t\t\tG | \n\t\t\tA | \n\t\t\tC | \n\t\t\tG | \n\t\t\t\n\t\t\t\tA\n\t\t\t | \n\t\t\tG | \n\t\t\tG | \n\t\t\t0.146 | \n\t\t
4 | \n\t\t\tG | \n\t\t\t\n\t\t\t\tC\n\t\t\t | \n\t\t\tC | \n\t\t\tG | \n\t\t\tG | \n\t\t\tG | \n\t\t\tG | \n\t\t\t0.006 | \n\t\t
5 | \n\t\t\tG | \n\t\t\tA | \n\t\t\tC | \n\t\t\tG | \n\t\t\tG | \n\t\t\tG | \n\t\t\t\n\t\t\t\tA\n\t\t\t | \n\t\t\t0.020 | \n\t\t
6 | \n\t\t\tG | \n\t\t\tA | \n\t\t\tC | \n\t\t\tG | \n\t\t\tG | \n\t\t\t\n\t\t\t\tA\n\t\t\t | \n\t\t\tG | \n\t\t\t0.040 | \n\t\t
7 | \n\t\t\t\n\t\t\t\tA\n\t\t\t | \n\t\t\tA | \n\t\t\tC | \n\t\t\tG | \n\t\t\tG | \n\t\t\tG | \n\t\t\tG | \n\t\t\t0.008 | \n\t\t
8 | \n\t\t\tG | \n\t\t\tA | \n\t\t\t\n\t\t\t\tT\n\t\t\t | \n\t\t\tG | \n\t\t\tG | \n\t\t\tG | \n\t\t\tG | \n\t\t\t0.002 | \n\t\t
9 | \n\t\t\tG | \n\t\t\tA | \n\t\t\tC | \n\t\t\t\n\t\t\t\tA\n\t\t\t | \n\t\t\tG | \n\t\t\t\n\t\t\t\tA\n\t\t\t | \n\t\t\tG | \n\t\t\t0.044 | \n\t\t
10 | \n\t\t\tG | \n\t\t\tA | \n\t\t\tC | \n\t\t\t\n\t\t\t\tA\n\t\t\t | \n\t\t\t\n\t\t\t\tA\n\t\t\t | \n\t\t\t\n\t\t\t\tA\n\t\t\t | \n\t\t\tG | \n\t\t\t0.060 | \n\t\t
11 | \n\t\t\tG | \n\t\t\tA | \n\t\t\tC | \n\t\t\t\n\t\t\t\tA\n\t\t\t | \n\t\t\t\n\t\t\t\tA\n\t\t\t | \n\t\t\tG | \n\t\t\tG | \n\t\t\t0.002 | \n\t\t
12 | \n\t\t\tG | \n\t\t\tA | \n\t\t\tC | \n\t\t\tG | \n\t\t\t\n\t\t\t\tA\n\t\t\t | \n\t\t\tG | \n\t\t\t\n\t\t\t\tA\n\t\t\t | \n\t\t\t0.002 | \n\t\t
13 | \n\t\t\tG | \n\t\t\tA | \n\t\t\tC | \n\t\t\tG | \n\t\t\t\n\t\t\t\tA\n\t\t\t | \n\t\t\t\n\t\t\t\tA\n\t\t\t | \n\t\t\tG | \n\t\t\t0.004 | \n\t\t
14 | \n\t\t\tG | \n\t\t\t\n\t\t\t\tC\n\t\t\t | \n\t\t\tC | \n\t\t\tG | \n\t\t\t\n\t\t\t\tA\n\t\t\t | \n\t\t\tG | \n\t\t\tG | \n\t\t\t0.004 | \n\t\t
Haplotypes description and frequencies within TNF-α promoter in Brazilians from Rio de Janeiro.
Association of the identified SNPs variations within the analyzed region ofIFNG with different TB outcomes (susceptibility per se, protection, severity and susceptibility to latent M. tuberculosis infection) was assessed based in the comparison of allele, genotype and haplotype frequencies between the stratified groups. The groups used for each evaluation were as follow: a) susceptibility per se to TB (TB patients versus TST+ controls), b) disease severity (PTB versus TBE) and c) susceptibility to the latent infection (healthy controls TST+ versus TST-).
As previously stated, for this analysis, the sample size was reduced in groups, (patients and controls) because of the exclusion criteria of TB-HIV co-infection and consanguinity. After exclusions, because of the very low frequency of the -255 A>G and -172 A>G these SNPs were also excluded.
Results of the association study upon comparison of genotype frequencies of the five remaining SNPs between TB patients (TBP/EPTB) versus TST+ controls are shown in Table 5. Only the SNP -200G>T presented a significantly higher frequency of the GT genotype in the control group indicating an association of this genotype with protection to the occurrence of active TB (χ2 = 3.86, p = 0.033, OR = 0.18 CI = 0.03 -1.00). Evaluation of the identified SNPs with the other outcomes did not show any association (data not shown).
\n\t\t\t\t\tLoci\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tGenotype\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tPatientes(N=140)\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tControls TST+* (N= 96)\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tχ2\n\t\t\t\t\t\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\tp-value\n\t\t\t\t\t\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tOR\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tIC\n\t\t\t\t | \n\t\t\t
-787 | \n\t\t\tCC | \n\t\t\t138 | \n\t\t\t96 | \n\t\t\t1.383 | \n\t\t\t0.515 | \n\t\t\t# | \n\t\t\t# | \n\t\t
CT | \n\t\t\t2 | \n\t\t\t0 | \n\t\t|||||
-785 | \n\t\t\tCC | \n\t\t\t140 | \n\t\t\t94 | \n\t\t\t2.942 | \n\t\t\t0.16 | \n\t\t\t# | \n\t\t\t0.00<2.79 | \n\t\t
CT | \n\t\t\t0 | \n\t\t\t2 | \n\t\t|||||
-599 | \n\t\t\tCC | \n\t\t\t135 | \n\t\t\t93 | \n\t\t\t0.035 | \n\t\t\tNS | \n\t\t\t1.15 | \n\t\t\t0.23<6.23 | \n\t\t
CG | \n\t\t\t5 | \n\t\t\t3 | \n\t\t|||||
-517 | \n\t\t\tCC | \n\t\t\t140 | \n\t\t\t93 | \n\t\t\t2.29 \n\t\t\t | \n\t\t\t0.066 | \n\t\t\t# | \n\t\t\t0.00<1.52 | \n\t\t
CG | \n\t\t\t0 | \n\t\t\t3 | \n\t\t|||||
-200 | \n\t\t\tGG | \n\t\t\t138 | \n\t\t\t89 | \n\t\t\t3.86 | \n\t\t\t0.033 | \n\t\t\t0.18 | \n\t\t\t0.03<1.00 | \n\t\t
GT | \n\t\t\t2 | \n\t\t\t7 | \n\t\t
Genotype distribution of the IFNG SNPs among TB patients and healthy controls (TST+).
Given that the SNP -200 IFNG was the only one that was associated with any of the studied outcomes at genotype level, allele frequency was also tested for the same outcomes. Table 6 shows the comparison of the -200T variant between the stratified groups. The results confirm the association with protection to the occurrence of active TB and, additionally to TBP. Association of the -200T variant was also seen to occurrence of latent infection (p=0.035).
\n\t\t\t\t\tDifferent outcomes\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tGroups\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tSNP IFNG -200\n\t\t\t\t | \n\t\t\t|||
\n\t\t\t\t | \n\t\t\t\t | \n\t\t\t\t\t\n\t\t\t\t\t\tP-valor*\n\t\t\t\t\t\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tOR\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tIC\n\t\t\t\t | \n\t\t\t|
Occurrence of TBactive | \n\t\t\tPacientes 0.0071 | \n\t\t\tTST+ 0.036 | \n\t\t\t0033 | \n\t\t\t0.19 | \n\t\t\t0.03<1.01 | \n\t\t
Occurrence pulmonary TB | \n\t\t\tTBP 0.0082 | \n\t\t\tTST+ 0.036 | \n\t\t\t0.043 | \n\t\t\t0.22 | \n\t\t\t0.033<1.17 | \n\t\t
disease severity | \n\t\t\tTBP 0.0082 | \n\t\t\tTBE 0.00 | \n\t\t\t1.00 | \n\t\t\t# | \n\t\t\t# | \n\t\t
latent infection | \n\t\t\tTST+ 0.036 | \n\t\t\tTST- 0.000 | \n\t\t\t0.035 | \n\t\t\t# | \n\t\t\t# | \n\t\t
Distribution of allele frequencies of-200T variant mutant groups according to the different outcomes.
Finally, the more prevalent IFNG polymorphisms (-599C>G and-200G>T) were tested against demographic variables, such as, gender and age. No significant association was found after stratified analysis at allele, genotype or haplotype levels (data not shown).
Table 7 summarizes the distribution and comparison of genotype frequencies of each individual SNP among TB patients and TST+ controls. No significant difference was observed. The evaluation of the possible association of different genotypes of TNF-α gene with susceptibility to the occurrence of TBP or TBE was also carried out separately, however, no association was found (data not shown).
\n\t\t\t\t\tLocus\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tGenotype\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tPatients(N=140)\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tControlsTST+ (N= 96)\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\tp-valor\n\t\t\t\t\t\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tOR\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tIC\n\t\t\t\t | \n\t\t\t
-646 | \n\t\t\tGG | \n\t\t\t139 | \n\t\t\t95 | \n\t\t\t\n\t\t\t | \n\t\t\t | \n\t\t |
GA | \n\t\t\t1 | \n\t\t\t1 | \n\t\t\t1.00 | \n\t\t\t0.68 | \n\t\t\t0.02<25.33 | \n\t\t|
-572 | \n\t\t\tAA | \n\t\t\t138 | \n\t\t\t95 | \n\t\t\t\n\t\t\t | \n\t\t\t | \n\t\t |
CA | \n\t\t\t2 | \n\t\t\t1 | \n\t\t\t1.00 | \n\t\t\t1.38 | \n\t\t\t0.10<38.92 | \n\t\t|
-422 | \n\t\t\tCC | \n\t\t\t140 | \n\t\t\t96 | \n\t\t\t\n\t\t\t | \n\t\t\t | \n\t\t |
CT | \n\t\t\t0 | \n\t\t\t0 | \n\t\t\t- | \n\t\t\t- | \n\t\t\t- | \n\t\t|
-376 | \n\t\t\tGG | \n\t\t\t127 | \n\t\t\t93 | \n\t\t\t\n\t\t\t | \n\t\t\t | \n\t\t |
GA | \n\t\t\t11 | \n\t\t\t3 | \n\t\t\t0.11 | \n\t\t\t3.17 | \n\t\t\t0.81<14.46 | \n\t\t|
AA | \n\t\t\t2 | \n\t\t\t0 | \n\t\t\t\n\t\t\t | \n\t\t\t | \n\t\t | |
-308 | \n\t\t\tGG | \n\t\t\t118 | \n\t\t\t83 | \n\t\t\t\n\t\t\t | \n\t\t\t | \n\t\t |
GA | \n\t\t\t19 | \n\t\t\t11 | \n\t\t\t0.78 | \n\t\t\t1.19 | \n\t\t\t0.54<2.67 | \n\t\t|
AA | \n\t\t\t3 | \n\t\t\t2 | \n\t\t\t\n\t\t\t | \n\t\t\t | \n\t\t | |
-244 | \n\t\t\tGG | \n\t\t\t136 | \n\t\t\t92 | \n\t\t\t\n\t\t\t | \n\t\t\t | \n\t\t |
GA | \n\t\t\t4 | \n\t\t\t4 | \n\t\t\t0.71 | \n\t\t\t0.68 | \n\t\t\t0.14<3.31 | \n\t\t|
-238 | \n\t\t\tGG | \n\t\t\t123 | \n\t\t\t88 | \n\t\t\t\n\t\t\t | \n\t\t\t | \n\t\t |
GA | \n\t\t\t15 | \n\t\t\t8 | \n\t\t\t0.47 | \n\t\t\t1.50 | \n\t\t\t0.58<3.97 | \n\t\t|
AA | \n\t\t\t2 | \n\t\t\t0 | \n\t\t\t\n\t\t\t | \n\t\t\t | \n\t\t |
Genotype distribution of the TNF-α SNPs among PTB patients and healthy controls (TST+).
Comparison of the TNF-α SNPs frequencies between TBP and TBEis shown in (Table 8). Only the -572A>C (CA genotype) presented a significant difference between these groups, being absent among the 121 TBP subjects, (RR = 8.12, CI = 5.20 <12.67 and p-value = 0.0175). The results indicate a risk for disease severity. This association was confirmed upon allele frequency evaluation (RR = 7.72, CI = 5.69 <10.47 and p = 0.0179) data not shown.
\n\t\t\t\t\tLocus\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tGenotype\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tPTBN=121\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tTBEN=19\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\tp-value\n\t\t\t\t\t\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tRR\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tIC\n\t\t\t\t | \n\t\t\t
-646 | \n\t\t\tGG | \n\t\t\t120 | \n\t\t\t19 | \n\t\t\t1.00 | \n\t\t\t0.00 | \n\t\t\t0,00<115.2 | \n\t\t
GA | \n\t\t\t1 | \n\t\t\t0 | \n\t\t||||
-572 | \n\t\t\tAA | \n\t\t\t121 | \n\t\t\t17 | \n\t\t\t0.0175 | \n\t\t\t8.12 | \n\t\t\t5.20<12.67 | \n\t\t
AC | \n\t\t\t0 | \n\t\t\t2 | \n\t\t||||
-422 | \n\t\t\tCC | \n\t\t\t121 | \n\t\t\t19 | \n\t\t\t- | \n\t\t\t- | \n\t\t\t- | \n\t\t
CT | \n\t\t\t0 | \n\t\t\t0 | \n\t\t||||
-376 | \n\t\t\tGG | \n\t\t\t111 | \n\t\t\t17 | \n\t\t\t0.506 | \n\t\t\t1.25 | \n\t\t\t0.33<4.79 | \n\t\t
GA | \n\t\t\t10 | \n\t\t\t1 | \n\t\t||||
AA | \n\t\t\t0 | \n\t\t\t1 | \n\t\t||||
-308 | \n\t\t\tGG | \n\t\t\t101 | \n\t\t\t17 | \n\t\t\t0.392 | \n\t\t\t0.63 | \n\t\t\t0.16<2.54 | \n\t\t
GA | \n\t\t\t18 | \n\t\t\t1 | \n\t\t||||
AA | \n\t\t\t2 | \n\t\t\t1 | \n\t\t||||
-244 | \n\t\t\tGG | \n\t\t\t117 | \n\t\t\t19 | \n\t\t\t0.554 | \n\t\t\t# | \n\t\t\t# | \n\t\t
GA | \n\t\t\t4 | \n\t\t\t0 | \n\t\t||||
-238 | \n\t\t\tGG | \n\t\t\t106 | \n\t\t\t17 | \n\t\t\t0.585 | \n\t\t\t0.85 | \n\t\t\t0.22<3.37 | \n\t\t
GA | \n\t\t\t14 | \n\t\t\t1 | \n\t\t||||
AA | \n\t\t\t1 | \n\t\t\t1 | \n\t\t
Genotypes distribution of the TNF-α SNPs among TBP and TBE.
The association between the TNF-α genotypes with latent infection was also evaluated. No significant difference was found (data not shown).
The final evaluation of independent SNPs with the different TB outcomes was performed based in the allele frequencies comparison for the most common TNF-α SNPs (-376G>A, -308G>A, -244G>A and -238G>A). The SNP -376G>A, allele variant -376A, showed a significant association with susceptibility to the occurrence of active TB (p = 0.035, OR = 3.57, CI = 0.95 < 15.72) and severity (p = 0.038 and RR = 2.68) (Table 9). All other outcomes showed no significant association with any of the variants tested, (data not shown).
\n\t\t\t\t\tDifferent outcomes\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tStudy Group\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tSNP TNF-α -376A\n\t\t\t\t | \n\t\t\t|||
\n\t\t\t\t | \n\t\t\t\t | \n\t\t\t\t\t\n\t\t\t\t\t\tp-valor\n\t\t\t\t\t\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tOR\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tIC\n\t\t\t\t | \n\t\t\t|
Occurrence of activeTB | \n\t\t\tPatients (fa) 0.054 | \n\t\t\tTST+ 0.016 | \n\t\t\t0.035 | \n\t\t\t3.57 | \n\t\t\t0.95<15.72 | \n\t\t
Occurrence of PTB | \n\t\t\tPTB (fa) 0.041 | \n\t\t\tTST+ 0.016 | \n\t\t\t0.201 | \n\t\t\t2.72 | \n\t\t\t0,68<12.62 | \n\t\t
Severity of disease | \n\t\t\tPTB (fa) 0.041 | \n\t\t\tTBE 0.052 | \n\t\t\t0.038 | \n\t\t\t2.68* | \n\t\t\t1.22<5.86 | \n\t\t
Latent infection | \n\t\t\tTST+ (fa) 0.016 | \n\t\t\tTST- 0.017 | \n\t\t\t0.90 | \n\t\t\t0.623 | \n\t\t\t0.12<7.86 | \n\t\t
Distribution of allele frequency of the TNF-α -376A variant and association analysis with different outcomes studied.
Table 10 shows the distribution of the 14 identified haplotypes in the different groups used for the association study. No significant difference was observed in the haplotypes frequencies between groups (data not shown) and their distribution was quite homogeneous.
\n\t\t\t\t\tHaplotype\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tGeneral TBn= 140\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tPTBn=121\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tEPTBn=19\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tControls PPD+n=96\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tControls PPD-n=58\n\t\t\t\t | \n\t\t\t
1 | \n\t\t\t9567.9%)\n\t\t\t | \n\t\t\t82(67.8%)\n\t\t\t | \n\t\t\t0 | \n\t\t\t70(72.9%)\n\t\t\t | \n\t\t\t40(69%)\n\t\t\t | \n\t\t
2 | \n\t\t\t2(1.4%)\n\t\t\t | \n\t\t\t1(0.8%)\n\t\t\t | \n\t\t\t0 | \n\t\t\t0 | \n\t\t\t0 | \n\t\t
3 | \n\t\t\t19(13.6%)\n\t\t\t | \n\t\t\t17(14.1%)\n\t\t\t | \n\t\t\t2(10.5%)\n\t\t\t | \n\t\t\t11(11.5%)\n\t\t\t | \n\t\t\t7(12.1%)\n\t\t\t | \n\t\t
4 | \n\t\t\t1(0.7%)\n\t\t\t | \n\t\t\t0 | \n\t\t\t1(5.26%)\n\t\t\t | \n\t\t\t0 | \n\t\t\t1(1.72%)\n\t\t\t | \n\t\t
5 | \n\t\t\t3(2.1%)\n\t\t\t | \n\t\t\t3(2.5%)\n\t\t\t | \n\t\t\t0 | \n\t\t\t4(4.2%)\n\t\t\t | \n\t\t\t2(3.4%)\n\t\t\t | \n\t\t
6 | \n\t\t\t7(5 %)\n\t\t\t | \n\t\t\t6(5%)\n\t\t\t | \n\t\t\t1(5.3%)\n\t\t\t | \n\t\t\t5(5.2%)\n\t\t\t | \n\t\t\t5(8.6%)\n\t\t\t | \n\t\t
7 | \n\t\t\t1(0.7%)\n\t\t\t | \n\t\t\t1(0.8%)\n\t\t\t | \n\t\t\t0 | \n\t\t\t1(1.1%)\n\t\t\t | \n\t\t\t1(1.7%)\n\t\t\t | \n\t\t
9 | \n\t\t\t9(6%)\n\t\t\t | \n\t\t\t8(6.6%)\n\t\t\t | \n\t\t\t1(5.3%)\n\t\t\t | \n\t\t\t3(3.1%)\n\t\t\t | \n\t\t\t2(3.4%)\n\t\t\t | \n\t\t
11 | \n\t\t\t1(0.7%)\n\t\t\t | \n\t\t\t1(0.8%)\n\t\t\t | \n\t\t\t0 | \n\t\t\t1(1.1%)\n\t\t\t | \n\t\t\t0 | \n\t\t
12 | \n\t\t\t1(0.7%)\n\t\t\t | \n\t\t\t1(0.8%)\n\t\t\t | \n\t\t\t0 | \n\t\t\t0 | \n\t\t\t0 | \n\t\t
13 | \n\t\t\t1(0.7%)\n\t\t\t | \n\t\t\t1(0.8%)\n\t\t\t | \n\t\t\t0 | \n\t\t\t0 | \n\t\t\t0 | \n\t\t
14 | \n\t\t\t1(0.71%)\n\t\t\t | \n\t\t\t0 | \n\t\t\t1(5.26%)\n\t\t\t | \n\t\t\t1(1.04%)\n\t\t\t | \n\t\t\t0 | \n\t\t
Frequency of TNF-α haplotypes in the different groups studied
After the genotyping of all samples and evaluation of the possible association with the different TB outcomes, the most frequent polymorphisms (-376G>A; -308G>A; -244G>A and -238G>A) were tested in a stratified analysis against the demographic variables gender and age. No significant differences were found for gender or age (data not shown).
It is well known that to M. tuberculosis, ethiologic agent of human TB can cause a broad spectrum of effects ranging from no infection to different clinical disease phenotypes [2, 16, 23-25]. However, the reasons for individual or ethnic differences in acquiring infection, active disease, disease severity, and different clinical outcomes have not been completely clarified. It has long been realized that many human diseases arise from the complex interplay between environmental exposures and host genetics susceptibilities [26]. In addition, several genetic factors have also been associated with different outcomes: host susceptibility per se the occurrence of active TB, disease severity and / or protection for the occurrence of active disease [27-33].
The establishment of an efficient immune response involves many different molecules, among which, cytokines and their receptors play an extremely important role. Thus, any genetic alteration leading to changes in the regulation of gene expression may reflect this response. It is known that the interindividual variation in the production of these molecules is directly related to the genetic "background". Literature data have clearly demonstrated that genetic variability of the genes encoding these molecules can affect the regulation of gene expression positively or negatively influencing the final yield of the molecule in question. In the last decade, several single nucleotide polymorphisms (SNPs) in the regulatory region of different cytokine genes have been described and associated with susceptibility, severity or protection for a growing number of diseases of different etiologies including tuberculosis [7, 34-35].
Among the possible genetic variations associated with an increased risk of developing TB, there are several polymorphisms, mainly SNPs, in genes coding for cytokines, cytokine receptors and several other molecules such as vitamin D receptor, NRAMP1 (SLC11A1), HLA genes, etc.
The immune defense against M. tuberculosis is complex and involves the interaction between T CD4+, T CD8+ lymphocytes, macrophages, and monocytes along with the production of cytokines, such as interferon-γ (IFN-γ) and tumor necrosis factor-α (TNF-α) [36].
Convincing evidence indicating the importance of IFN-γ in particular, in the control of mycobacterial infections has been found in both experimental and clinical studies [37-38].
Among the mainly important cytokines involved in TB progress after infection with M. tuberculosis, TNF-α plays a key role. It is also a potent proinflammatory cytokine acting in protection against intracellular pathogens [39-40].
The genetic variability of TNF-α and IFNG has been described in the last decade [41-46] including association studies with tuberculosis. However, the frequencies of the polymorphisms already described varies according to the ethnicity of the population studied, hampering the better interpretation of the value of association studies. Unfortunately, most of these are performed in ethnically homogeneous populations, and therefore, many of the associations described for a particular allelic variant in a certain gene may not represent genetic risk factor in other populations. In Brazil, a country characterized by ethnically mixed population, there are few data regarding the frequency of single nucleotide polymorphisms in these genes (IFNG,TNF-α) and the few existing studies refers to one or two SNPs only. In view of the importance of the promoter region with respect to regulation of gene expression, the major goal of this work was to proceed a partial mapping of the promoter region of IFNG and TNF-α genes (approximately, 800bp upstream of the transcription starting site) through PCR-sequencing approach in samples from TB patients and healthy controls from Rio de Janeiro, Brazil. Subsequently, based on frequencies of the different SNPs found individually for each gene, we performed an association study with different TB outcomes.
Characterization of the important portion within the IFNGwas firstly identified two decades ago bydeletion analysisstudies [47-48]. According to authors, it comprises a highly conserved region from positions -117 to -47 and contains two sub regions that can be complexes with proteins. The sub-proximal region (-90 to -65) shows strong homology to the IL-2 promoter [49]. Several transcription factors activate transcription of IFNG by binding to this region. Conversely, several others inhibit factors binds in other regions affecting transcription. Hence, the interest in investigating the polymorphic sites within IFNG gene promoter, particularly considering the importance of this cytokine in eliciting the immune response.
Here, analysis of the generated sequences identified seven polymorphic sites, four of which were new. The transition C →T, was identified at position-787 from the transcription starting site in five subjects, all heterozygous. The second C → T transition, previously described in the data base of SNPs at position-785 was also found in five individuals, all heterozygous, however, no reference to this SNP was found in the literature. The other three SNPs not yet described, were C to G transition at position -599; C to T at position -517 and A to G at position -255. Finally, two additional SNPs, transition from G to T at position -200 and A to G at position -172, already described and well characterized [45] were found in our population.
One of the main problem found during this mapping was the confirmation of the identified SNPs based on literature data and from different SNPs data bases available online because of the lack of standardization regarding to the reference nucleotide to define the promoter region (transcription starting site nt +1). Many authors describe the SNPs identified in relation to the site of translation or use reference sequences containing sequencing errors leading to misclassification of SNPs (eg SNP-200G>T, originally described as -183 [45], later called as -179 [50] and finally, confirmed in this study as -200). The current name, confirmed in this study is based on the correction of the reference sequence used in previous studies and now available online. These types of errors greatly hampered the beginning of the sequence analysis regarding the identification of novel SNPs.
The frequency of each polymorphism was determined in the study population. As noted in Table 1, the allele frequencies for all the identified SNPs were less than one percent, except for the variants -599G and -200T, both in a frequency of 1.4%.
Functionally, it is known that polymorphisms (-200 and -172) can affect transcription of the IFNG. The region from -213 to -200 induces transcription factor through (AP-1) [51]. A polymorphism at this site (position -200, for example) must change the connection of AP-1 and the promoter activity in T cells. The polymorphism -172 is near to the nuclear factors-activated T-cells site (NFAT site) (-186TAAAGGAAA-178) and should affect the stability of this region [50].
The variant IFNG -200T is highly inducible by TNF-α and binds constitutively to nuclear extracts obtained from T cells, whereas the allele -200G does not respond to TNF-α [50; 52]. The induction of transcriptional activity, when the T allele is present, increases protection against tuberculosis. Our results corroborate these data, since the IFNG -200T variant showed to be associated with protection the occurrence of active TB in our study group (P = 0.033, OR= 0.18, CI= 0.03 to 1.00).
According Bream,JH et al., 2002 [50], the promoter region of IFNG is highly conserved, suggesting that these cytokine production variations are probably due to difference in binding to regulatory factors instead of polymorphisms in the gene, which is consistent with our results. Only seven SNPs were found in our population, five of which were at a low frequency. The polymorphisms found with a higher frequency were the -200G>T and -599C>G, the latter being located between two putative binding sites of transcription factors. As this is not yet a SNP described in the literature, functional studies are needed to better understand their functional role. The SNP -200 is of great interest for association studies. However, this polymorphism was not found in Caucasians or Indian populations, suggesting that different selective forces may be operating in different ethnic or racial groups. These data corroborate the evidence that IFN-γ is very important in the immune response and that mutations that interfere with their production may influence the outcome of active tuberculosis as shown by authors [28,31,53-54], and therefore, a selective force lead the gene to be so conserved.
TNF-α is a proinflammatory and immunoregulaty cytokine which plays a key role in the initiation, regulation and perpetuation of host defense against infections, but is fatal in excess. As this molecule plays an important role against a variety of pathogens involving different patterns of risks and benefits, it is expected that several genetic elements are involved in its control and production.
The levels of circulating TNF-α are regulated at transcriptional and post-transcriptional levels and several polymorphisms within the promoter region of TNF-α have been associated with altered circulating levels of this cytokine.
In humans, the TNF-α gene is located within the complex involving the human leukocyte antigens (HLA), a highly polymorphic region on chromosome 6p21.3 and hence, many of TNF-α polymorphisms are in linkage disequilibrium with the HLA genes. Because of differences in the distribution of HLA alleles we might expect variations in associations between polymorphisms of TNF-α and various conditions in different geographical areas.
The human genome analysis showed that the level of variations in the genome is approximately one SNP/1.71Kb [55]. However, the TNF-α promoter has higher density of SNPs. Despite this level of variation, the regions involved in gene regulation are highly conserved in humans [56-58].
In our work, we perform the mapping of the first 800pb promoter region, through direct sequencing of the amplified PCR product in 500 DNA samples from individuals living in the metropolitan area of Rio de Janeiro, Brazil and found seven polymorphisms previously described in the literature, most of which well characterized. However, according allele frequencies, only the variants -376A, -308A, -238A and -244A were present in more than one percent (0.030, 0.087, 0.011 and 0.050) respectively. The influence of these SNPs in binding of transcription factors have not been fully explored, most of the studies are focused on the association of one or two SNPs with different diseases. The SNPs -376, -308 and -238 have been the most studied but, the results of functional studies performed so far for SNPs -308 and -238 are controversial. It is believed that the variant -308A is associated with an increased transcription rate, leading to an increased production of TNF-α [59] and the variant-238A with a decreased rate of transcription. Regarding the SNP-376G>A different studies show that this polymorphism is located in a region of multiple interactions between proteins and DNA, and that the minor allele acts in the recruitment of proteins OCT1 for this region. According Knight et al (1999) [60] there is a significant interaction between variant -376A and the OCT-1 protein, this variant binds the proteins while the variant -376G does not. The authors report also by tests with the reporter gene system, that this mutant variant moderately increases the basal levels of TNF-α and associate the same with a relative risk of 4 to cerebral malaria. The problem is that the linkage disequilibrium is strong in this area and it is difficult to study the function of an isolated SNP. In some Caucasian populations -376A allele variant is liked to -308G and -238A [60-61], what is not observed in the African Gambia. Thus, association between the linked allelic variants on TNF-α production and diseases has been studied. According, Hajeer& Hutchinson (2001), the combined allele variants -238G, -308A and -376G are associated with high TNF-α levels [62].
A large number of studies have investigated the association between polymorphisms in the promoter region of the gene for TNF-α and tuberculosis. Results vary according to the different populations studied, finding no association [63-72] or a positive association [26, 29, 73-75]. In our analysis of the single SNP association, TB was associated with the -376G>A. In this case, we observed an association of the minor allele -376A with the outcome of susceptibility per se the occurrence of active TB (p= 0.035, OR 3.57, IC 0.95 <15.72) and an increased relative risk for the occurrence of extrapulmonary TB (p= 0.038, OR 2.68, IC 1.22 < 5.86). The association of this allele variant with the occurrence of TB and an increased relative risk for the development of extrapulmonary TB is intriguing. Given the influence of this allele with increased expression of TNF-α, one would expect an association with the protection. One possible explanation for this observation may be the small sample size in the stratified groups. The large confidence intervals (CI) for both outcomes could be a reflection of the small sample size.
The PCR-sequencing approach (gold standard) used for the mapping these genes practically discard the possibility of genotyping errors and all mutants found for all SNPs evaluated were confirmed twice by new PCR and resequencing. Another possibility would be due to the strong linkage disequilibrium observed in this region of the gene promoter of TNF-α. It is possible that other allelic variant (eg, 238A), as opposed to the functional role of variant -376A is canceling the same level of control of gene expression.
An important aspect of this study relates to the ethnic characteristics of the studied population. Brazilian population is characterized by mixture of ethnicities and the results obtained here contribute for a global understanding of the influence of genetic factors in TB outcomes. Usually, most of the studies on this field are made with ethnically homogeneous populations. A study conducted by Baena et al., 2002 [76] clearly shows the importance of ethnic difference in the association study of SNPs in TNF-α promoter with disease. According authors, the -857 SNP is a marker for Amerindians. In that study, SNPs in TNF-α promoter were also used to identify markers of ancestry, understanding that this region was well characterized previously with primates and humans. Several studies [77-81] have shown that some polymorphisms as -238; - 244 and -308 are in association with the HLA genes and in addition, the SNPs -308 [77]; -863 and -857 [81], are markers of Caucasians. The -238 SNP was found in three populations studied, although it has also been found in Caucasians [78] and Asian [80]. Instead, the -376 SNP was not detected in any of the non-African analyzed and were found with the -238.
These data demonstrate the importance of taking into account the "background" of the frequencies of SNPs of TNF-α in studies of gene-disease association.
Association studies of genetic factors with infectious diseases are difficult to conduct because of the multi factorial nature of these diseases that includes host, pathogen and environmental variables in different proportions for each disease. This multi factorial nature of TB stresses the importance to look for haplotypes in the association studies. The fact that our population is so mixed allowed us to find mutations that do not exist in other populations, such as the -308, for example, which is relatively rare in Asians and American Indians. Data from the genotyping of a large number of SNPs for different samples revealed that the human genome has a block structure haplotype [82-83] and configuration of a haplotype sometimes is more important than a single SNP genotype to determine phenotype [84]. Moreover, the construction of a haplotype block is useful for identifying SNPs that isolates would not influence the phenotype [85].
In conclusion, this study showed that the proximal part of the promoter region of IFNG is highly conserved, as seen in previous publications and the identified SNPs were in very low frequency. The -200T allele variant was associated with protection occurring active TB, and pulmonary TB. In addition, this variant was also associated with latent infection. Concerning TNF-α, the high genetic variability was confirmed, but only the -376G>A SNP showed an association with susceptibility per se to TB occurrence and increased risk for the occurrence of extrapulmonary TB.
The data presented here shows the reality of a population with characteristics of high ethnic miscegenation, provides the different SNPs identified enabling the realization of real sample calculation for any association studies that may be idealized with these targets and other conditions for this population and finally, provides haplotype that can be used in other studies of association with other diseases.
We thank all subjects involved in the study and the Platform-genome DNA sequencing RPT01A PDTIS/FIOCRUZ.
This work was supported by FAPERJ/Pronex:Proc: E-26/170.0003/2008 and FAPERJ Pensa Rio: E-26/110.288/2007.
Today, information has become the main component of what we produce, do, buy, and consume. Having an economic value in almost all products and services that meet the needs of today’s societies, it has been now obligatory for individuals and organizations to obtain information technologies and to actively use them in both work and social life domains. Hence, in the current information age, where information is seen as power, this situation has made it imperative for organizations to become increasingly information-based and to benefit from information technologies in many processes and activities.
The intensive use of information technologies in many functions and processes has also required some changes in organizations [1]. This is due to the fact that information technologies, unlike traditional technologies, do not only change the technical fields but also affect the communication channels, decision-making functions and mechanisms, control, etc. [2]. Consequently, one of the most striking developments is on organizational structures that are becoming increasingly flattened and horizontal. Relatedly, information technologies have begun to take over the role of middle management, which supports decision-making processes of senior management and has reduced the importance of this level [3, 4, 5]. Similarly, while information technologies enable managers to obtain faster, more accurate, and more information [6, 7, 8], it also provides lower-level managers with more information about the general situation of the organization, the nature of current problems, and important organizational matters [9, 10, 11, 12].
Moreover, information technologies also have an important potential in determining whether organizations have a mechanical or an organic structure [13]. Within the mechanical organizational structures, people do not have much autonomy, and behaviors expected from employees are being careful and obedience to upper authority and respect for traditions. In such organizations, predictability, consistency, and stability are desirable phenomena. In contrast, people in organic structures have more freedom in shaping and controlling their activities, and being enthusiastic, creative, and taking risks have important places among the desired behaviors [14].
Accordingly, information technologies begin to influence the cultural values of the organization over time, through these transformations they create on organizational structures, processes, and operations. In other words, the fact that organizational structures are mechanical or organic causes the formation of diverse cultural values in organizations [15]. Therefore, the desired cultural values in mechanical organizations are quite different from those in organic structures [1, 16, 17]. In this context, this chapter deals with the influences of information technologies on cultural characteristics of organizations along with the reflections of the use of these technologies on organizational structures and their functioning.
When we look at studies on the relations between organizational culture and information technologies, we generally see the studies on the effects of culture on technology adaptation or use [18, 19, 20, 21], as well as on the effects of certain specific information technologies and applications (e.g., e-mail use, group support practices, etc.) on some aspects of any organizational culture [22, 23, 24, 25, 26, 27, 28, 29, 30, 31]. However, the number of studies that consider the use of information technologies as a “whole” and that address “why” and “how” its effects on organizational culture occurred is still limited. And so, this chapter aims to examine and discuss the overall effects of the usage and intensity of information technologies established in organizations on the cultural life within.
In this context, the chapter plan is as follows: Firstly, the basic concepts related to information and information technologies are included. Emphasis is placed on the meaning differences between knowledge and information, and their connections to information technologies are tried to be explained briefly. Secondly, the effects of information technologies on organizational structure are given particular attention. The reason for this is that as a system of values, beliefs, assumptions, and practices [32], organizational culture encompasses many features closely related to structures of organizations. Thirdly, possible links between organizational structure and organizational culture are included. Fourthly, important theoretical approaches and studies on the relationships between information technologies and organizational culture are provided. Finally, by deepening a bit more and by emphasizing key points, some important arguments are discussed.
In the literature, the concepts of information and knowledge are sometimes expressed by a single term, “information.” However, although the concepts of knowledge and information are intertwined, they are two different concepts that have different meanings and describe different phenomena. The reason for this is that knowledge is also included in the concept of information as it is transformed into a commodity when it begins to be processed, stored, and shared by information technologies.
Becoming the basic elements of today’s economic, social, and cultural systems, information is obtained in a certain hierarchy. The images are at the beginning of the process, and the process is completed with a hierarchical staging in the form of data, information, and knowledge, respectively [33]. Image is located in the first step of the process. Humans copy the picture of any object and event they previously perceived by sensory organs. When faced with a similar phenomenon in the later stages of life, these pictures in the mind are redesigned. We call these pictures of realities occurring in the human mind as images [33]. The next stage, the data, contains symbols that represent events and their properties. For this reason, data are expressed as figures and/or facts without content and interpretation [34]. Information that constitutes the next stage of the process and is mixed with knowledge and used interchangeably is expressed as a reporting of one system’s own status to another system [33]. In information, associated data are combined for a specific purpose. Therefore, we can explain information as meaningful data [35]. Knowledge, on the other hand, is defined as personalized information that allows people to fully and accurately grasp what is happening around them and manifests itself in the form of thoughts, insights, intuition, ideas, lessons learned, practices, and experiences [36]. According to Kautz and Thaysen [37] who stated that knowledge is found only in the people’s minds, knowledge is, therefore, a subjective formation. In other words, knowledge is the form of information enriched with interpretation, analysis, and context [38]. However, here, it should be emphasized again by highlighting a very important issue that knowledge is also accepted as information when this knowledge begins to be processed, stored, shared, and used over information technologies. Therefore, after this, when talking about information, one should consider not only the information created by the data brought together in a meaningful way but also the knowledge shared and used over information technologies.
On the other hand, information technologies, used as the most important tool of generating value today, are defined as the technologies that enable processes such as recording and storing data, producing information through certain operational processes, and accessing, storing, and transmitting this produced information effectively and efficiently [39, 40, 41, 42, 43, 44, 45, 46]. The term information technologies is used to cover computer and electronic communication technologies, as they are now inseparably intertwined in literature and everyday use and are generally used in this way [47]. In this context, data processing systems, management information systems (MIS), office automation systems, executive support systems, expert systems, intranet and extranet, electronic mail (e-mail), group applications (groupware), database management systems, decision support systems, artificial intelligence, and telecommunication systems can be given as examples of information technologies [33, 48, 49].
Towards the end of the twentieth century, the rapid changes with the impact of developments in information technologies led to the emergence of customer satisfaction-based, learning, knowledge-based, and constantly changing organizations [50]. The fact that organizations have become considerably information-based and benefit from information technologies intensively in their activities and processes has made also the changes in their organizational structures mandatory [1]. Accordingly, the effects of information technologies on organizational structure will be summarized under the subtitles of differentiation, centralization, and standardization/formalization, which are the three main components of organizational structure [15].
Differentiation within an organization occurs in three ways: Specialization/division of labor, horizontal and vertical differentiation, and hierarchy and size [15]. Specialization refers to the amount of different expertise or types of work [51, 52]. Specialization generally increases the number of subunits and makes it harder to understand the larger structure that people contribute to with their skills and expertise [53]. Information technologies have the potential to reduce this tendency by providing more access to information and experts at this point. In this way, access to information resources provides synergy [54].
Vertical and horizontal differentiation refers to the amount of hierarchical levels in an organization [55]. Information technologies, with the support of problem solving and decision-making, lead to the emergence of more flattened organizational structures as they require fewer levels within the hierarchy [56]. Since information technologies give employees in lower positions more autonomy to harmonize their activities, this can allow them to find and try better methods while performing their work. In this context, we can increasingly see that organizational structures have become horizontal and strengthened and that virtual organizations have begun to emerge as the most cost-effective structure [17].
In terms of hierarchy and size, Heinze and Stuart [4] argue that the mid-level management staff is unnecessary, increases bureaucracy, reduces efficiency, and has no function in organizations any more. Since most of the tasks performed by mid-level executives can be fulfilled by computers, both less costly and faster, information technology has begun to take over the role of mid-level management, which supports the decision-making process of senior management [5]. Sharing the same opinion, Fulk and DeSanctis [57] also stated that the largely witnessed situation in modern organizational designs is the reduction of intermediate-level managers and administrative support.
Centralization points to the extent to which decision-making power within an organization is scattered or centered [58]. Due to increasing local and global competition, many companies have started to leave their strategic decision-making task further down the organization to benefit from the expert people with more precise and timely local knowledge [10]. Information technologies affect these efforts directly in two ways. Firstly, information technologies increase local knowledge by contributing to obtaining closer information about market trends, opportunities, and customers. Secondly, information technologies can create synergies for organizations because, thanks to information technologies, communication and coordination between distributed decision makers, central planners, and senior managers can be realized more effectively and efficiently [59].
However, whether information technologies will lead to centralization or decentralization is a very controversial question. Regarding centralization, it enables managers to acquire faster, more accurate, and more information, reduces uncertainty, and allows them to make decisions that they cannot make before [6, 7, 8]. Conversely, by the use of other forms of information technologies (e.g., electronic bulletin boards), decentralization provides more information to lower- and mid-level managers about the general situation of the organization and the nature of current matters and problems [9, 10, 11, 12]. Raymond et al. [60] argued that because information technologies facilitate the use and transmission of information by all levels and units in the organization, it enables top management, which is the decision authority, to be disabled in certain areas and the decentralization of control. Thach and Woodman [61] maintained that this is due to the fact that as a result of sharing information at lower levels with the help of information technologies, this power of senior management has decreased to a certain extent, and the knowledge and participation of the staff in organizational matters have increased.
The literature shows that information technologies allow both centralization and decentralization. Researchers are in the agreement that information technologies make it possible for organizational managers to leave their decision-making power to a large part of the hierarchical levels without compromising the quality and timeliness of the decision [62, 63]. Keen [64] combined the concepts of centralization and decentralization and used the term “federated organization” in which organizations do not have to choose either because information technologies simultaneously allow centralization-decentralization [64, 65].
Formalization is the process of detailing how activities are coordinated for organizational purposes in order for employees and organizational units to respond routinely to recurring situations [51, 66]. Formalization involves rules, instructions, shared values, and norms [67]. In fact, formalization is based on the objective of more efficiency and less uncertainty [13].
Information technologies provide the ability to reduce the negative effects of formalization by facilitating the documenting and retrieving of information on organizational occurrences and endeavors that make behaviors and processes more consistent through formalization [63]. The more information technologies assist in reducing search times and preventing downtime, the more the administrative cost of formalization decreases and the productivity increases, which ultimately benefits the path to innovation [68].
Different organizational structures lead to the development of different cultural values [15]. The fact that the structure which an organization has established to control its activities and is defined as a formal system consisting of duties and authority relations is mechanical or organic causes the emergence of completely different cultural values, rules, and norms [69]. While mechanical structures are vertical, highly centralized, and almost everything in them are standardized, organic structures are horizontal, decentralized, and based on mutual adaptation [14]. People feel relatively less autonomous in vertical and centralized organizations, and being careful, obeying the upper authority, and respecting traditions are among the desired behaviors. Therefore, in a mechanical organizational structure, there are cultural values where predictability and stability are important [69]. In contrast, in horizontal and decentralized organizations, people can freely choose their own activities and control them. Creativity, courage, and risk-taking are given importance as desired behaviors. Therefore, organic structures contribute to the formation of cultures that value innovation and flexibility [15].
Organizational structure is also important for the development of cultural values that support integration and coordination. In a structure with stable task and role relations, sharing of rules and norms is more since there will be no communication problems and the information flow will be fast [70]. In organizations where the sharing of cultural values, norms, and rules is at a high level, the level of performance also increases [15]. Particularly in team or matrix structures where face-to-face communication is intense, the sharing of these cultural values and common reactions to the problems develop more rapidly [9].
Whether an organization is centralized or not causes different cultural values to emerge. In decentralized structures, authority is divided into subordinate levels, and an environment is created for the formation of cultural values in which creativity and innovation are rewarded [13]. Employees are allowed to use the organization’s resources and work in projects that they want, by spending some of their time in these projects, thus contributing to the production of innovative and creative products and services [15]. The structures of such organizations constitute the cultural values that give their employees the message “as long as it is in the interest of the organization, it is okay to do things in an innovative and the way you want.”
Conversely, in some organizations, it may be more important for employees not to decide on their own and all activities to be followed and controlled by their superiors. In such cases, a centralized structure is preferred to create cultural values that will ensure accountability and obedience [71]. Through norms and rules, all employees are expected to behave honestly and consistently and inform their superiors about wrongs or mistakes, because this is the only acceptable form of behavior within these structures [72].
Since working on the factors that determine the consequences of the adoption and use of information technologies, researchers have focused on people’s beliefs, values, assumptions, and codes of conduct. As a result, they have given names to this research field such as “socio-technical systems,” “social system,” “social structure,” and most recently “culture” [73]. For example, Markus and Robey [23] using “social elements” and Barley [26] using “social system” or “social structure” tried to explain this phenomenon. When examined more closely, it is seen that the details that these authors emphasize while depicting the case are the assumptions, beliefs, and values that exist in common among the group members, and this corresponds to the definition of organizational culture.
Research examining the relationships between information technologies and values, beliefs, and norms belonging to a particular group has gone through certain stages and used rich and complex research models to explain the relationships in each of these stages [74]. In the first studies on information technology applications, it has been suggested that information technologies cause changes in various organizational phenomena including structural features and thus have certain effects on organizations [74]. For instance, in some studies on adoption of groupware software, several researchers have used this deterministic approach to describe how groupware use affects communication and collaboration among employees and their productivity [27, 28]. These studies assume that certain results will certainly emerge after the adoption of information technologies, without considering the motives or activities that shape the use of information technologies by managers and employees. Like much more deterministic studies, these authors often assumed that information technologies would have predetermined influences on the adoption of information technologies, regardless of the environment in which information technologies were applied, how they were applied, and the users’ specific behaviors and particular purposes.
The second group of views concerning the relationships between organizational culture and information technologies includes the fact that information technologies are seen as a tool that can be used for any change that managers desire to make in organizational practices [22]. In studies in this approach, researchers believe that there is a wide range of possibilities to identify changes in organizational culture, structure, processes, and performance [22, 75]. Researchers from this tradition presume that with the right choice of information technologies and appropriate system design, managers can achieve whatever goals they desire.
These works were mostly adopted in the 1980s and reflect a perspective that managers think can manipulate organizational culture in the way they want. Often called “management and control,” “a functional or instrumental approach” to organizational culture, this methodology has caused serious debate in the literature [76]. This approach attributes great powers to the management level in this regard, which conflicts with anthropologists’ views that culture cannot be consciously controlled and goes much deeper to understand it [76]. Robey and Azevido [77] also do not accept the rational thought on the assumption that culture can be manipulated directly in this way.
Studies with this rational perspective in the information technology literature assume that managers can use information technologies as a leverage to make changes in the norms of behavior, strategy, structure, and performance among members within the organization. For example, in studies on group support systems (GSS), we find managers’ beliefs that they can use collaborative technologies to create a more cooperative organizational culture. This perspective was not accepted by Karsten [78] and some experimental research on GSS [30, 79]. Organizational necessity is no longer accepted, as it is viewed by information technology researchers as an overly simple approach [23, 80].
Researchers who take another approach suggest that information technologies and organizational culture can interact with each other to produce various results [22, 23]. These results can be in the form of adoption and effective use of information technologies (if there is a harmony between organizational culture and information technologies) or user reluctance, refusal, or sabotage (if no fit). Researchers who have been working on information systems since the 1980s have focused on understanding information technology features and functionality that cause effective or problematic information technology applications and the interaction between users’ values, assumptions, and other elements of organizational culture. In this regard, Romm et al. [81] argued that many forms of information technologies comprise cultural assumptions embedded within themselves and these assumptions may conflict with existing values of a particular organization. The authors argued that these embedded assumptions present information technologies as a “cultural boundary” and that a cultural analysis should be made to predict compliance or incompatibility. The authors in this approach warn managers to think of organizational culture as a binding limitation in information technology applications. In a warning by Pliskin et al. [76], managers are advised not to try to change the culture of the organization. Regarding this issue, Orlikowski [30] cites Lotus Notes (a group software) application at Alpha Corporation, a consultancy company. In this example, this system, which was established by the CEO of the company only with the benefits to be obtained, did not create the expected effects, became unsuccessful, and disappointed due to reasons such as no cultural analysis and inadequate training. Employees responded to the use of Notes with resistance and refrained from using it. The reason for this was that the employees in this organization, which had a competitive culture where information was seen as a power, avoided sharing information with others. As a result, this incompatibility between the collaborative culture that Notes had in itself and the competitive culture of the organization in question had failed this application of information technologies.
In a different approach, it is stated that information technologies and culture are not fixed and they are more flexible in terms of change [23, 75]. Managers in this approach may set specific goals for the use of information technologies, but actual results of the use of information technologies are not deterministic, and results cannot be predicted or controlled even under the best conditions [23]. The effects of information technologies are not deterministic because technology has interpretable flexibility considering that it can have different meanings for different employees. Similar technology can be interpreted in a different way by distinct people, based on certain assumptions, beliefs, and values. Robey and coauthors [24, 25], for instance, showed that it would be an empty attempt for organizational managers to try to intentionally manipulate the effects of these technologies, since there are many ways that diverse employees can configure a particular technology in different social environments.
Gopal and Prasad [31] also achieved similar results in their work on group support system (GSS), claiming that for researchers seeking fixed laws or regulations on how information technologies affect user behaviors, this would be an impossible goal to pursue. Conversely, the results of using information technologies depend on the symbolic meanings that information technologies have for a particular user. This work of Gopal and Prasad [31] expresses similar results with the work of Barley [26] and Robey and Sahay [25]. The authors stated that the symbolic meanings of certain technologies for users affect their perceptions of information technologies and their specific behaviors.
In the light of the above-mentioned approaches, arguments, and important studies in the literature, it will be useful to discuss some important points by deepening a little more and by emphasizing the key features related to the concepts of information, information technologies, and organizational culture.
First, organizational culture is a complex phenomenon that develops and changes in a historical process [32, 82, 83]. Thus, although it might seem like a plain and simple concept, organizational culture includes many subdimensions and processes. When considered as a complex pattern of these interactions of many factors with each other, it is also a difficult process to identify the direct and indirect effects of information technologies on organizational culture within this cluster of relationships and interactions. Moreover, culture is not a phenomenon that changes and develops in a short time and is therefore open to manipulations of managers. On the contrary, from this point of view, it is not possible to easily achieve control over cultural changes, and it is necessary to go much deeper [76]. So, it is not rational to expect that the rapid developments and changes in information technologies will cause changes in cultural characteristics at the same speed. In this sense, it could be inaccurate to seek direct relationships between two phenomena in question, whose rates of change are quite different.
Second, for cultural changes, there must also be changes in the basic assumptions, beliefs, and values on which the culture is built [84]. It would be misleading to expect little or intensive use of information technologies to cause changes in these rooted assumptions. For the desired changes in these basic assumptions, beliefs, and values, it is necessary to design the structure accordingly, to recruit employees who are qualified for the targeted culture, and to set ethical values and property rights to employees in accordance with this culture [15]. In this sense, information technologies may only catalyze the contribution of organizational structure to organizational culture.
Third, there are many and different types of hardware and software that fall under the scope of information technologies. It is not logical to accept all of them as homogeneous technologies in all aspects (with the same functions and features, similar usage areas, standard conditions they are applied, similar intentions, and behaviors of all users), and it can be, therefore, misleading to carry out research under a single “IT” concept from this perspective. The reason for this is that, as stated in the sections above, cultural features of each information technology application or product embedded in it might be different. The interactions between the cultural characteristics of the environment in which information technologies are applied and the unique cultural contents of information technologies may cause different results on the culture of the organization.
Fourth, contrary to what is believed, some of cultural features that we anticipate to support information technology applications and products may be interpreted otherwise by diverse people contingent on different assumptions, beliefs, and values. In fact, Robey et al. [24, 25] showed that managers cannot control the effects of these technologies, since different users can configure a particular technology in numerous ways in different social environments. Also, Gopal and Prasad [31] argued that this would be an impossible achievement for researchers looking for fixed laws or regulations on how information technologies affect user behaviors.
Fifth, information technologies were defined above as technologies that enable processing, storage, and sharing of information. The key concept in this definition is “knowledge-based” information and not the technology itself. Therefore, what makes information technologies essential and important is the information itself. According to the definition of knowledge, the most significant characteristic that differentiates it from information is its being a product of the human mind [37]. Because knowledge is the interpretation of information and expresses the value produced from it, qualifying information technologies as good-bad, useful-useless, and necessary-unnecessary can be a meaningless evaluation. So, the basic thing that creates value-added for organizations is not the technology used but the information itself, which is processed, stored, and shared on this technology. In this context, even if it is the latest, most advanced, and most expensive technology in the world, if the organization does not have a qualified human resource capable of producing knowledge that will create value-added, an appropriate organizational structure and culture that will activate this creative potential, and a management approach, all investments in these technologies will also be wasted.
This chapter has aimed to examine the impacts of information technologies on organizations’ cultures, and for this purpose, a special emphasis is given to the concept of “organizational structure” within the theoretical framework presented above. The most important reason for this is that relevant literature shows that organizational culture and organizational structure are in a very close relationship. Indeed, when the question items in the Denison organizational culture scale [85], which is the most frequently used in the literature, are examined, it is possible to see that most of these items point to many features of organizational structure concerning centralization, formalization, and differentiation dimensions. Therefore, it is a very rational approach to expect that information technologies can have direct and indirect effects on organizational cultures based on the influences of information technologies on structures of organizations. However, it should be underlined that different and controversial approaches and findings in the literature mentioned above on the relations between information technologies and organizational culture generate question marks in the minds as well.
In this regard, it is already quite difficult to draw a clear picture of the impacts of information technologies on cultural characteristics of organizations. The number of studies on the subject in the literature is still very limited. Accordingly, it is necessary to underline the great need for interdisciplinary studies in this field. But still, this study argues that the main factor that determines the actual impact and value of information technologies, which have become an integral part of human life in today’s world, is the information itself rather than technology, and it should be kept in mind that information technologies can only function as a means or tool in this knowledge-based social, economic, and cultural life. In other words, the determinant of the benefits, meaning, and importance of information technologies might be the conditions created by organizational factors such as cultural environment and organizational structure where knowledge is created, developed, and used and human resources have become the most important capital element and source of wealth.
The author declares no conflict of interest.
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\n\nPolicy last updated: 2018-09-11
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