Journal of Proteomics & Computational Biology
Proteomic Translation of Chronic Granulomatous Disease (CGD)
Hiba Siddig Ibrahim*
- National Ribat University, Sudan
*Address for Correspondence:Hiba Siddig Ibrahim, National Ribat University, Sudan; E-mail: email@example.com
Citation: Ibrahim HS. Proteomic Translation of Chronic Granulomatous Disease (CGD). J Proteomics Computational Biol. 2017;3(1): 12.
Copyright: © 2017 Ibrahim HS, et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Journal of Proteomics & ComputationalBiology | ISSN: 2572-8679 | Volume: 3, Issue: 1
Submission: 27 February 2017| Accepted: 10 April 2017 | Published: 21 April 2017
Submission: 27 February 2017| Accepted: 10 April 2017 | Published: 21 April 2017
Chronic granulomatous disease considered as one of the congenital hereditary disease that present due to mutation in one of these following genes; CYBA, CYBB, NCF1, NCF2, or NCF4 gene in X chromosome, that lead to lack a body defense mechanisms against infections specially bacterial & fungal infections due to absence of NADPH oxidase productions in phagocytic cells; lungs is the most common site of infections. Sometimes the causes of CGD is unknown & we did not have a scientific explanation for this; the main aim of this study is to identify the CYBB gene SNPs change in a way to predict mutation effects of this gene at the proteomic level; through in silicotools by using sift, polyphen-2, I mutant suite-3, SNPs & GO software prediction programs for SNPs detections. A according to these .predictions tools & their confirmations tools I found that CYBB gene SNPs mutation showed damaging predictions which was considered as clinical manifestation of this study beside this; a lots of those SNPs ..illustrate decreasing in protein functionality even those that were predicted benign by polyphen-2.
CGD; NADPH oxidase; Mulch pneumonitis
CGD is a congenital immune deficiency disease that is geneticallyinherited in an X-linked manner; these means only men can beinfected; also both sexes can be infected in case of autosomal recessiveforms. CGD manifested by recurrent severe infections including;pneumonia, lymphadenitis, skin and hepatic abscesses, osteomyelitisand septicemia; inflammation of these tissue areas in various organs(granulomas) can result on tissue damaging. Usually infectionsbecome apparent during the first year of life; in this disease phagocyticneutrophils are unable to produce a bactericidal respiratory burstdue to a deficiency of one of the proteins component of the NADPHoxidase complex [1-8].
The features of chronic granulomatous disease usually firstappear in childhood, although some individuals do not showsymptoms until later on in their life; they may have at least oneserious bacterial or fungal infections every 3 to 4 years, especially inthe lungs (pneumonia) or fungal pneumonia (mulch pneumonitis;which causes fever and shortness of breath after exposure to decayingorganic materials such as mulch, hay, or dead leaves). Other commonareas of infection include; the skin, liver, and lymph nodes; so themost common area of inflammation are gastrointestinal tract; (in many cases the intestinal wall is inflamed, causing a form ofinflammatory bowel disease that varies in severity but can lead tostomach pain, diarrhea, bloody stool, nausea, and vomiting) and thegenitourinary tract, in addition to the stomach, colon, and rectum, aswell as the mouth, throat, and skin inflammations; also, inflammationin the stomach can prevent food from passing through esophagus tothe intestines (gastric outlet obstruction), leading to an inability todigest food, vomiting after eating and weight loss. In the genitourinarytract, inflammation can occur in the kidneys and bladder .
Inflammation of the lymph nodes (lymphadenitis) and bone marrow (osteomyelitis), which both produce immune cells, can lead to further impairment of the immune system; rarely those people with chronic granulomatous disease develop autoimmune disorders. Repeated episodes of infection and inflammation reduce the life expectancy of individuals with chronic granulomatous disease; however, with the treatment, they can live until mid to late adult hood. The disease can occur in 1 in 200,000 to 250,000 people worldwide,due to mutation in the CYBA, CYBB, NCF1, NCF2, or NCF4 gene, which leads to the presence of five types of this condition. The proteins which produced from those affected genes are parts (subunits) of an enzyme complex called NADPH oxidase, that plays an essential role in the immune system, specifically in phagocytes; by production of superoxide that is used to generate other toxic substances, which play a role in killing foreign invaders and preventing them from reproducing in the body and causing illness. NADPH oxidase is also thought to regulate the activity of neutrophils, which play a critical role in adjusting the inflammatory response to optimize healing and reduce injury to the body. Beside the above, mutation in those genescan lead to production of proteins with little or no function or the productions of no protein at all [1-8].
Chronic granulomatous disease that caused by mutations in the CYBB gene is inherited in an X-linked recessive pattern. The CYBB gene is located on the X chromosome, which is one of the two sex chromosomes. When chronic granulomatous disease is caused by CYBA, NCF1, NCF2, or NCF4 gene mutations, the condition is inherited in an autosomal recessive pattern, which means both copies of the gene in each cell have mutations .
Therapeutic options for CGD included prophylactic antibiotics and antifungal medications, interferon-gamma injections, and aggressive management of acute infections. Bone marrow transplantation can cure CGD, however this therapy is complex and transplant candidates and donors must be carefully selected, weighing the risks and benefits carefully. Researchers are investigating other approaches including gene therapy as a future option [1-8].
In this study I used different computational methods to identify the CYBB gene SNPs to predict mutation effects at the proteomic level.
Chronic granulomatous disease sequence (CGD) was retrieved from NCBI https://www.ncbi.nlm.nih.gov/projects/SNP/snp_ref. cgi?geneId=1536; rs141756032 [Homo sapiens], in chromosome X: 37804069; Gene: CYBB.
(SIFT - Predict effects of non synonmous /missense variants) (http://sift.bii.a-star.edu.sg/) SIFT dbSNP 138 was selected from batch tools from SIFT Sorting Intolerant From Tolerant software to predict whether an amino acid substitution affects protein function, based on the sequence homology and the physical properties of amino acids. SIFT can be applied to naturally occurring non synonymous polymorphisms and laboratory-induced missense mutations.
PolyPhen-2 (Polymorphism phenotyping v2)
PolyPhen-2 prediction of functional effects of human nsSNPs (http://genetics.bwh.harvard.edu/pph2/index.shtml) was used to predict the impact of an amino acid substitution on the structure and function of a human protein using straight forward physical and comparative consideration [9-12].
I mutant.3 (http://gpcr2.biocomp.unibo.it/cgi/predictors/ IMutant3.0/I-Mutant3.0.cgi) was used to predict the effect of single point protein mutation with disease association from Protein Sequence [13-15].
SNPs and GO
SNPs & GO (http://snps.biofold.org/snps-and-go/snps-and-go. html) was used to predicting a disease associated variations by GO terms through SVM-based classifier to confirm SNPs results, by putting protein sequence, profile and functional information to give output inform of disease/neutral with RI & scores [16-21].
Results and Discussions
141 out of 150 showed probably damaging by polyphen-2, while 3 showed possibly damaging and 6 were benign; that is why considered out of this study, all of them were considered deleterious by sift prediction including those showed benign predictions.
All those SNPs below showed deleterious, probably damaging with score predictions equal or slightly less than 1 according to sift & polyphen-2 prediction sequential (Table 1) rs137854585, rs137854586, rs137854587, rs137854589, rs137854591, rs137854593, rs137854594, rs137854595, rs137854596, rs139670417, rs141798777, rs146275471, rs151344453, rs151344454, rs151344456, rs151344457, rs151344458, rs151344459, rs151344460, rs151344462, rs151344465, rs151344466, rs151344467, rs151344468, rs151344469, rs151344470, rs151344471, rs151344472, rs151344473, rs151344474, rs151344475, rs151344477, rs151344478, rs151344479, rs151344480, rs151344481, rs151344482, rs151344484, rs151344485, rs151344486, rs151344487, rs151344488, rs151344489, rs151344490, rs151344491, rs151344492, rs151344493, rs151344495, rs151344496, rs151344497, rs151344498, rs200614534, rs267606451; except rs140677309 SNPs that showed both probably & possibly damaging predictions.
I mutant-3 prediction
The total number of 150 SNPs showed 21 with increased protein activity while the remaining 129 showed decreased in protein activity, also the same SNPs had the same wide types but different mutant types, instead of these some of them shared the same mutant types; as illustrated in Table 2 below.
SNPs & GO predictions
The total numbers of 126 SNPs were showed disease prediction by both PhD-SNP prediction and SNPS & GO prediction they are: rs137854585, rs137854586, rs137854587, rs137854589, rs137854591, rs137854593, rs137854594, rs137854595, rs137854596, rs139670417, rs141798777, rs146275471, rs151344453, rs151344454, rs151344456, rs151344457, rs151344458, rs151344459, rs151344460, rs151344462, rs151344465, rs151344466, rs151344467, rs151344468, rs151344469, rs151344470, rs151344471, rs151344472, rs151344473, rs151344474,rs151344475, rs151344477, rs151344478, rs151344479, rs151344480, rs151344481, rs151344482, rs151344484, rs151344485, rs151344486, rs151344487, rs151344488, rs151344489, rs151344490, rs151344491, rs151344492, rs151344493, rs151344495, rs151344496, rs151344497, rs151344498, rs200614534, rs267606451, except 13 SNPs they were showed both diseases & neutrals according to PhDSNP prediction and SNPS & GO prediction sequential; they are: rs139670417, rs151344468, rs151344470, rs151344489, rs151344498while 5 SNPs were showed neutral for both PhD-SNP prediction and SNPS & GO prediction they are; rs140677309 and rs141798777 (Table 3).
The output of this study were explained and confirmed the damaging effect of those selected CGD SNPs. Although due to some protein ID problems in ENSP00000441958 &ENSP00000441896; I used UPI00020654B0 & UPI00020654AF instead of them; which were suggested by uniprot web site to complete polyphen-2 prediction, also I faced error prediction with some sequences in meta-snps prediction, that is explains why I exclude them from this study, so on I suggest to check this program again to know where is the problem.
The author would like to thanks Allah, her family for always supporting her, Bio-Nile center members.
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