\r\n\tRecent advances in gene-editing research and technologies have enabled the CRISPR Cas9 system to become a popular tool for sequence specific gene editing to correct and modify eukaryotic systems. The technology allows targeted knock-ins and knocks-outs of any gene within the genome. DNA mutations ranging from a single base pair to large deletions in both in vitro and in vivo model systems have been accomplished, thereby, revolutionizing the molecular biology world of science. \r\n\tCurrently, most research on genome editing is conducted to understand diseases using cells and animal models, making it a promising therapeutic tool to treat and prevent complex diseases, such as cancer, heart disease and neurological disorders. \r\n\tIn this book we will focus on the mechanisms, applications, regulations (their pros and cons); and various ways in which the CRIPSR technology has stimulated the genome editing revolution.
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Khan",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/7051.jpg",keywords:"Genome Editing, Knock-Out, Knock-in, CRISPR, Cas9 Nuclease , Cell Engineering, Genomic Deletion, Homology Directed Repair (HDR), Guide RNA, Tracer RNA, CRISPR RNA, PAM Motif , Transgenic Animals",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"April 11th 2018",dateEndSecondStepPublish:"May 2nd 2018",dateEndThirdStepPublish:"July 1st 2018",dateEndFourthStepPublish:"September 19th 2018",dateEndFifthStepPublish:"November 18th 2018",remainingDaysToSecondStep:"2 years",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,editors:[{id:"240724",title:"Dr.",name:"Aditi",middleName:null,surname:"Singh",slug:"aditi-singh",fullName:"Aditi Singh",profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:"Aditi Singh is a Research Enthusiast, working as an Application Scientist in a Biotechnology company. She has a PhD degree in Molecular-Cell Biology from University of Heidelberg in Germany; and worked as a Post-Doctoral Scholar at University of California San Diego, USA. Aditi has more than 10 years of experience in Molecular and Cell Biology area where she has been involved in various R&D projects and Technical Support. Her Research Interests are RNA Biology, Gene Editing, and Infectious Diseases.",institutionString:"Independent scientist",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"1",institution:null}],coeditorOne:{id:"243898",title:"Dr.",name:"Mohammad W.",middleName:null,surname:"Khan",slug:"mohammad-w.-khan",fullName:"Mohammad W. Khan",profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:"Mohammad Khan is a research enthusiast, working as an Application Scientist in a Biotechnology company. He has a Ph.D. degree from the University of Heidelberg in Germany and a DVM degree from India. He also worked as a Post-Doctoral scholar at San Diego State University , USA.\n\nDr. Khan has more than 15 years of experience in Molecular and Cell Biology area where he has been involved in various R&D projects and Technical Support. 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Chan and Manoj Kumar Tiwari",coverURL:"https://cdn.intechopen.com/books/images_new/3794.jpg",editedByType:"Edited by",editors:[{id:"252210",title:"Dr.",name:"Felix",surname:"Chan",slug:"felix-chan",fullName:"Felix Chan"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"60908",title:"Microarrays as Platform for Multiplex Assays in Biomarker and Drug Discovery",doi:"10.5772/intechopen.75614",slug:"microarrays-as-platform-for-multiplex-assays-in-biomarker-and-drug-discovery",body:'\n
\n
1. Introduction
\n
Despite the tremendous advances in the understanding of the molecular basis of diseases (such as cancer, Alzheimer, genetic diseases), there are some crucial gaps that difficult us to understand disease pathogenesis and develop effective strategies for early diagnosis and treatment [1].
\n
The current interest in protein microarrays due, mostly in part, to the prospects that proteomics assays, based on them, allow: deciphering the altered protein expression in different levels (tissue, cells, subcellular structures, body fluids, protein complexes,…); the development of novel biomarkers for diagnosis or early detection of diseases; the identification of new targets for therapeutics; and accelerating drug development through more effective strategies to evaluate therapeutic effect and toxicity [1].
\n
Mainly based on the proteome alterations in disease may occur in many different ways that are not predictable from genomics analysis, and it is clear that a better understanding from genomic analysis together with an substantial impact in biomedicine.
\n
Recently, there is a large amount of protein microarray approaches available for applications related to disease because the employment of these technologies is very useful for better diagnostics and to shorten the path for developing effective therapy. In general, protein microarrays allow to increase sensitivity, reduce sample requirement, increase high-throughput and identify protein alterations (quantitative and qualitative), such as post-translational modifications.
\n
If we want to quantify thousands of proteins in a small number of samples, a typical proteomics discovery experiment employs a non-targeted approach (shot-gun proteomics). The hundreds of proteins that result from the comparative analysis, are differentially expressed between healthy and diseased samples. After discovery phase, the potential biomarkers proteins are reduced by performing studies on additional patients or at more time points, and/or by using another technique. Then, these potential biomarkers are verified on a set of 10–50 clinical samples. In the final step, a small number of biomarkers is “validated” on 100–500 clinical samples. Bearing in mind this conventional biomarker workflow, high-throughput assays are required in order to overcome the gap between translational research and clinical needs (from bench to the bedside) [1] (Figure 1).
\n
Figure 1.
Stages in the development of a biomarker.
\n
Biomarkers or biological markers are defined as measurable characteristics that indicate the state of a biological process, differentiating if it is normal or pathological. They are all those molecules that are found in body fluids in low abundance and that are associated with specific health or disease processes [2]. The methodology to establish that a biomarker is good for clinical application is divided into three steps: discovery, verification and validation as it has been said before [3, 4].
\n
Proteomics, the scientific discipline that studies proteins, has identified several proteins that can act as biomarkers. There are several techniques that use biomarkers and that allow an early diagnosis of the disease. These include new generation sequencing, mass spectrometry and protein arrays [5, 6]. In this chapter we will focus on the study of arrays.
\n
Microarray technology is a term that refers to the miniaturization of thousands of assays in a single device, allowing the simultaneous and massive analysis of a large number of biomolecules in a single biological sample. They allow the study of a large number of parameters in a single test with a minimum requirement of sample and reagents, which is why they constitute a simple, fast and very sensitive sampling technique [7, 8].
\n
The microarrays present several innovative strategies for their applications such as the identification of biomarkers and the interactions between proteins that may help in the future to routine clinical analysis.
\n
There is a great variety of arrays which can be classified according to their content in protein microarrays, functional protein arrays and reverse phase arrays; according to their shape in planes and spheres; and also according to their detection method.
\n
In this chapter we will explain all these types of arrays, some of their applications in biomarker discovery as well as validation/verification.
\n
\n
1.1. Biomarkers discovery
\n
A biomarker is defined by the Food and Drug Administration (FDA) as “A characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacological responses to a therapeutic intervention.”
\n
It is possible to distinguished three categories depending on the application of the biomarker: diagnostic biomarkers used for disease detection, prognostic biomarkers used for predict the course of a particular disease such as, recurrence, progression, and survival and predictive biomarkers used for predict the response to treatment that could be subsequently applied in patient assessment [1].
\n
During last years, biomarker research and its translation into the clinics have been accelerated by protein microarrays due to the extraordinary capacity for identifying of valuable biomarkers in a short period of time without the requirement of any prior in-depth knowledge into the mechanism of disease progression.
\n
Moreover, protein microarrays are reliable for analyzing targeted/non-targeted biomarkers presented in mostly of human proximal body fluids (such as plasma, serum, synovial liquid,…) with a wide dynamic range. In contrast with other proteomic strategies, protein microarrays avoid the sample pre-fractionation. Thus, for example, serum, plasma, urine and tissue extracts which are complex and non-fractionated proteome mixtures, could be used for experimentation. For this reason, among others, protein microarrays offer a powerful technology for functional proteomics analysis in HT format.
\n
Microarray technologies, like DNA arrays, printed dense spots of capture ligands immobilized onto a solid support that are exposed to samples containing corresponding binding molecules (often called queries), allowing the simultaneous analysis of thousands of capture targets within the same assay. Ekins and collaborators described these binding events based on miniaturization as the key parameter. They predict that a system that uses small amounts of capture molecules and a small amount of sample could be more sensitive than a system using a hundred times more material. In fact, this is the case when K < 0.1 (where K is the affinity constant between ligand and target) [9].
\n
The capture ligand is presented in a confined area of array, reducing its diffusion. The specific binding event with its target takes place with the highest signal intensities and optimal signal-to-noise ratio could be achieve in these small spots [9] (Figure 2). An immunoassay in an array format displays sensitivities in the pM to fM range, enabling test low-abundant (pg/mL) analytes in crude proteomes with a small volume of sample. In many cases, protein microarrays show a relevant advantage in clinical applications because the samples to test are minimal [10].
\n
Figure 2.
Process of microarray technology.
\n
For that reason, protein array technology needs to use a multiplex and highly sensitive protein assay capable of handling and resolving complex proteomes with limited available sample [10].
\n
Recently, several types of protein microarrays have been developed and applied as multiplex throughput assay in several biological characterization. Here, it is described the principal features of protein microarrays.
\n
\n
\n
\n
2. Description of protein microarrays
\n
In general, protein microarrays are classified according to features such as content, format, detection method and according to final application such as analytical and/or functional. Here, it is described the main aspects of the different types of protein microarrays.
\n
\n
2.1. Arrays according to their nature of the capturing agent
\n
In general, there are three types of arrays according to their content:
\n
\n
2.1.1. Analytical protein microarrays or antibody microarrays
\n
In these arrays, antibodies are printed onto array surface and used multiplexed affinity reagents to detect and quantify proteins in complex biological samples [8, 11].
\n
It is based on the antibody antigen interaction. Antibodies have the ability to bind to a very specific protein, so particular or rare analytes can be detected in highly heterogeneous mixtures. They are usually used to identify biomarkers which predict a biological condition such as healthy or pathologic.
\n
So many studies have shown that only a fraction of antibodies may properly work and this may be due to the loss of antibody activity by degradation or denaturation on storage or during the printing process, or due to inappropriate antibody orientation onto the array surface [7].
\n
\n
\n
2.1.2. Functional protein arrays or recombinant protein arrays
\n
It is based in the identification of protein interactions with different molecules (proteins, DNA, lipids, drug, etc.), so recombinant proteins are printed onto array surface.
\n
Some examples are protein in situ array (PISA), printing arrays from DNA (DAPA) nucleic acid programmable protein array (NAPPA) and multiplexed nucleic acid programmable protein array (M-NAPPA). They are essential for pharmaceutical industry because they allow to know protein interactions [11].
\n
PISA is based on DNA amplified by PCR as a template. The DNA that encode the protein of interest contains a T7 promoter or another strong transcriptional promoter and an in-frame N-12 or C-terminal tag sequence for protein capture onto the surface. PISA offers the possibility to cell-free production of protein arrays. It means protein are produced using cell extracts directly on the surface os arrays. PISA demonstrated that multiple proteins could be produced without the need of using cells for expression followed by lysis and purification to make the proteins [11].
\n
DAPA is a technique derived from PISA, but DAPA allows use the same DNA template slide repeatedly for printing up to 20 copies of the same protein and also DNA could be reused after prolonged periods of time. DAPA takes a long time to express proteins due to the diffusion of proteins through the membrane. This technique starts by spotting the PCR amplified DNA fragments encoding the tagged protein on one slide. This slide is sandwiched with another Ni-NTA slide where a tag-capturing agent immobilizes the expressed protein. A permeable membrane with the cell-free lysate which allows coupled transcription and translation is places between the two slides. Then, the expressed proteins are captured to the surface on the other slide through the capture ligand. Overall, DAPPA requires long time to express proteins and this technique presents the protein diffusion as strong limitation, in particular with large proteins [7, 9, 11, 12].
\n
NAPPA uses cDNA templates cloned into expression plasmids which adds a transcriptional promoter and also an in-frame polypeptide capture tag. It has several advantages: (1) once the clone is produced as a glycerol stock it becomes an indefinitely renewable resource that could be shared with other labs; (2) if the clone is carefully sequence verified, then the resource will have long-term sequence fidelity; (3) the use of plasmids removes some of the length constraints on the epitope tags, so that functional protein tags can be used. There are many applications of NAPPA where the proteins are fused with glutathione-S-transferase (GST); nevertheless, other tags such as flag, HA, c-myc, and Halo tag have been used in specific applications. High quality supercoiled plasmid DNA is purified from bacteria cultures and printed onto an activated ester surface along with a homo-bifunctional crosslinker, bovine serum albumin (BSA) and anti-GST antibody. BSA efficiently increased the DNA binding and narrows down the unspecific interactions and anti-GST attaches the protein expressed. When cell-free expression system is added to the array, a coupled transcription/translation reaction is produced and the nascent protein is linked to the capture agent tag the C-terminal end assuring the complete translation of the protein [10, 11, 13].
\n
Puromycin capture protein arrays (PuCA) are cell-free expression protein arrays based on the affinity of puromycin by expressed peptide/protein. First, PCR DNA is transcribed to mRNA, and a single-stranded DNA oligonucleotide modified with biotin and puromycin on each end is then hybridized to the 3′-end of the mRNA. The mRNAs are placed on a slide and immobilized by the binding of biotin to streptavidin which is previously coated on the slide. Cell extract is then dispensed on the slide for in situ translation to take place. When the ribosome reaches the hybridized oligonucleotide, it stops and incorporates the puromycin molecule to the nascent polypeptide chain, thereby attaching the newly synthesized protein to the microarray through the DNA oligonucleotide [14].
\n
M-NAPPA is based on combining up to five different DNA plasmids at one point. This increases the number of proteins displayed by microarrays by five. It also reduces the cost and the time spent in work. M-NAPPA would be useful in unbiased HT screening studies, such as protein-protein interactions, protein-DNA interactions, discovery of drug binding target as well as (auto)antibody biomarkers for a variety of human diseases [15].
\n
\n
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2.1.3. Reverse phase protein arrays
\n
In these arrays, biological samples, tissues and cell lysates are printed onto array surface. The detection is accomplished by antibodies link with specific proteins, and then link with another antibody with fluorescence. They offers the potential for rapid comparison of the levels of such proteins present among a significant number of samples on a single array [7, 8].
\n
The problem is the interaction of the lysate matrix, therefore validated antibodies should demonstrate no cross-reactivity toward other biomolecules of the lysate (Figure 3).
\n
Figure 3.
Arrays according to their nature of the capturing agent.
\n
\n
\n
\n
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3. Arrays according to the format
\n
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3.1. Planar arrays
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Planar arrays are based on high-density microspots of ligand deposited onto a solid support and separated by a minimal distance. It is necessary to know some parameters that influence in the assay performance like spot size and morphology, ligand capacity, background signal, limit of detection and spot reproducibility [13].
\n
\n
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3.2. Microsphere arrays
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Microsphere arrays are based on the simultaneous use of different populations of microspheres labeled with several fluorochromes. Each one is covered by different antibodies in order to incubate a biological sample of interest [7].
\n
\n
\n
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4. Arrays according to their detection method
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Depending on the detection method it is classified in methods based on labels and free-label methods [7].
\n
\n
4.1. Methods based on labeling
\n
\n
4.1.1. Conventional fluorescent labels
\n
Among them are radioisotopes and conventional fluorochromes like fluorescein, BODIPY or cyanins. Cyanines are the most used in protein arrays due to its minimal interaction with other biomolecules and its high intensity. More than two fluorochromes can be combined to identify several biomarkers at the same time. It is used for cancer biomarkers.
\n
\n
\n
4.1.2. Flow cytometry sphere arrays
\n
Flow cytometry can be used to detect soluble proteins present in body fluids or in cell lysates using microspheres. The CF has two lasers for the analysis quantitative and qualitative that allows to know the binding of target proteins with the microspheres. Comparing conventional fluorescent techniques, flow cytometry allows rapid and accurate quantitative-qualitative evaluation of a high number of proteins, with low cost and high sensitivity.
\n
\n
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4.1.3. Magnetic spheres
\n
These systems are an union of magnetic spheres with antibodies that allow the detection of proteins. They capture soluble proteins with high reproducibility and sensitivity, associated with low background noise, a wide dynamic range and low cost.
\n
\n
\n
4.1.4. Quantum dots as fluorescence labels
\n
The “Quantum dots” (QDs) are nano-crystals, formed by a core of semiconductor and fluorescent material coated with another semiconductor, which have very stable optical properties and a large gap between the wavelengths of emission and excitation of the complement. These nanometric particles could work like labels joining with peptides or antibodies for the recognition of cellular components. They have a high fluorescence, greater photo-stability, multicolored excitation, an adjustable and narrow emission spectrum, and their higher quantum yield, compared with organic fluorochromes. They also have several disadvantages like its high susceptibility to oxidation and photolysis as well as some risks for human health and the environment. They are used to localize tumor biomarkers.
\n
\n
\n
4.1.5. Metal NPs as a label
\n
Gold nanoparticles are used in protein arrays due to their optical properties, quantum efficiency and compatibility with a wide range of wavelengths.
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\n
\n
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4.2. Free-label methods
\n
\n
4.2.1. Surface plasmon resonance (SPR)
\n
This technology is based on the generation of plasmons on a surface, which are oscillations of free electrons propagated parallel to the metal/medium interface, which allows to measure the changes in the refractive index of the sensor surface. The intensity variation of the reflected light and/or the angle of incidence tell us if there are molecular associations or dissociations, allowing to determine the relationship between the association and dissociation of biomolecules with each other. In addition, the SPR technology also allows evaluate the affinity and specificity of these interactions, facilitating the measurement of biomolecular interactions in real time with high sensitivity.
\n
\n
\n
4.2.2. Microcantilevers
\n
Microcantilevers are thin sheets of silicon coated with a gold surface associated with nanomechanical systems of biomolecular recognition. For this reason, the antibodies or proteins are immobilized on said sheets, so that when there is an interaction between these molecules and their possible ligands we can measure the variation in the position of the sheet using different optical or electronic systems.
\n
\n
\n
4.2.3. Atomic force microscopy (AFM)
\n
It is a microscopic analysis observing only the topological variations of the surface of an object. It has been used for the evaluation of the immobilization of biomolecules with a micrometric resolution, and for the detection of protein interactions on the arrays surface.
\n
\n
\n
\n
\n
5. Arrays according to the applications
\n
Bearing in mind the final application, protein microarrays are classified into two categories: Analytical arrays and functional arrays.
\n
\n
5.1. Analytical arrays
\n
These arrays are normally used to quantitatively identify the presence of multiples protein in one single assay. Commonly, the main application is the detection of differently expressed proteins and their abundance in different samples. Biomarkers are determined by this type of arrays. They are biometric measurements, including molecular signatures, that predict a biological or clinical condition for example, normal or pathologic, often with potential diagnostic or prognostic value [9, 10, 16].
\n
\n
\n
5.2. Functional protein arrays
\n
These arrays are mainly used for the characterization and identification the specific function of proteins, as well as their interactions with other molecules (including proteins, peptides, small molecules/drug, enzyme/substrates or nucleic acids,…) [12, 17]. Moreover, functional protein arrays also allow the detection and identification of post-translational modifications (PTMs), such as glycosylation, phosphorylation, acetylation,… which typically modulate the proteins’ function, regulation and/or turnover.
\n
Refers to research, functional protein arrays present the detection of multiple protein interactions with low reagent consumption in a fast and low cost fashion. On the translational side, the discovery of these interactions will promote the progress of new pharmaceutical targets, diagnostics and therapeutics. As a consequence, this technology is very interesting in the pharmaceutical industry [10] (Table 1).
\n
Table 1.
Advantages and limitations of protein microarrays in biomedicine.
\n
\n
\n
\n
6. Multiplex biomarker detection by protein microarray assays
\n
During last decade, protein microarrays have been successfully employed in multiplex detection for biomarker discovery; here, it is remarked a few of these studies in order to illustrate the utility of these approaches in the field.
\n
\n
6.1. Oncology diseases-specific biomarkers
\n
Haab et al. defined a panel of five serum proteins significantly expressed in serum between prostate cancer patients (33) and 20 controls [18]. In addition, this team has also identified 84 proteins with differentially relative abundance between diagnosed lung cancer patients and healthy controls [19].
\n
In a similar approach, Wittekind and colleagues reported a set of proteins as biomarker candidates associated with hepatocellular carcinoma [20]. Nowadays, several studies have been performed and focused on biomarker identification in several oncological pathologies; for example: In ovarian cancer, 11 proteins have been identified by Amonkar et al. [21]; Sreekumar et al. identified a panel of proteins as biomarker candidates in colon carcinoma cells [22]; Díez et al. has identified differentially expressed proteins in B-cell chronic lymphocytic leukemia which are related with target therapeutics [23]. Previously, Below and coworkers have developed an antibody microarray to immunophenotype 1100 leukemia and lymphomas according to the abundance of a panel of 82 antigens or cluster of differentiation (CD) characterized at the surfaces of lymphocytes [24].
\n
\n
\n
6.2. Vaccine candidates
\n
In 2014, the firstly reported study about pathogen-host protein interactions by CID and workers. In this work, they studied the presence of post-transcriptional modifications in effector proteins, T3SS proteins, from different mutants of Salmonella typhimurium when they infected in vitro Hela. Lysate collection representing all infection conditions are printed and using several validated antibodies, they show a comparative results among the different assays according to abundance proteins or post-transcriptional modifications [25].
\n
Another similar study, performed by Li and coworkers, has been published about identification of 149 antigens from Yersinia pestis presented in the serum from EV76 rabbit and to test the immunogenicity of several viral proteins [26].
\n
In 2009 Thanawastein et al. developed an approached called Expressed protein screen for immune activators (EPSIA) which were successfully applied in the identification of novel bacterial immunostimulatory proteins from Vibrio cholerae [27].
\n
Recently, Manzano et al. reported a set of novel vaccine candidates for O. moubata based on a systematic characterization of >2000 host-pathogen interactions, which were evaluated with self-assembled protein microarrays based on a cDNA library encoding >400 recombinant proteins of O. moubata [28].
\n
In a most recent study, Montor et al. describe a work using NAPPA arrays to evaluate candidate membrane antigens in P. aeruginosa which could help to track the immune responses of patients infected with P. aeruginosa and healthy ones. In this work, 12 proteins have been identified being mostly of them related with adaptive immune response in infected patients [29].
\n
\n
\n
6.3. Auto-immune diseases
\n
In response to many pathological processes, the humoral immune system generates antibodies to self-proteins (“auto-antibodies”). These auto-antibodies are generated due to antigen over-expression, mutation, altered post-transcriptional modifications of altered degradation released from damaged tissue which lead to their recognition by the immune system. Auto-antibodies have several benefits which make them as suitable source of biomarkers: (1) They have been discovered before the appearance of clinical symptoms; (2) They are simple to identify even at low levels once their target antigen is known; (3) they are easy to reunite from blood; and (4) they could be show in higher levels and with a longer half-life than their target antigens, which may only be present in transiently in blood [10].
\n
For example, NAPPA arrays were used for serological screening for the first time in 2007 by Anderson et al. They investigated the presence of antibodies against tumor antigens in breast cancer. The tumor-suppressor p53 is well-characterized in several solid tumors and the presence of antibodies against p53 is mainly due to mutations in its gene which lead to alterations in its half-life. By this approach, the authors presented that p53-specific antibody levels were significantly lower in healthy donors than in breast cancer patients and the response to p53 antigen was detected in Stage II disease. Also they studied that the antigen sites of p53 with several antibodies which recognized distinct epitopes of the protein to confirm that many regions of the protein expressed in NAPPA were accessible to antibodies in serum detected to them [10, 30].
\n
In a follow-up work, also this group performed a wide screen for new auto-antibodies in breast cancer. They designed and developed 4988 candidate antigens to detect their auto-antibodies in serum samples from breast cancer patients with stage I–III disease. This screening was performed in three stage design that entailed comparing cases and controls and eliminating uninformative antigens at each stage. At the final phase, slightly more than 100 antigens were tested and 28 auto-antibodies were identified that distinguished benign breast disease from invasive cancer under blinded conditions [10, 30].
\n
With a similar workflow, Labaer et al. developed a pilot NAPPA to assess auto-antibodies present in juvenile idiopathic arthritis (JIA) which is a disease characterized by chronic joint inflammation in children [31].
\n
Recently, Fuentes’s lab has performed a screening of auto-antibodies in osteoarthritis and arthritis rheumatoid by using NAPPA arrays and validated with other protein arrays technologies [32].
\n
\n
\n
6.4. Functional biomarkers as drug targets
\n
A decade ago, Labaer and colleagues evaluated functional properties of the proteins IVTT expressed onto the array by performing protein-protein interactions in high-throughput format. In this report, they printed an array expressing 647 unique genes in duplicate and tested for several well characterized interacting pairs including Jun-Fos and p53-MDM2 [33].
\n
A more recent study, Manzano et al. published a work where they applied NAPPA to study protein interactions. In this study, a novel interactor partners were identified for P-selectin and phospholipase A2 and further validated [34].
\n
Recently, a novel functional array, designed by Pascal Braun, identified novel cell signaling pathways in Arabidopsis thaliana by evaluating protein interactions onto this NAPPA technology [35].
\n
In addition, a further study has just been published where evaluated multi-protein complex in NAPPA format. In this study, four novel tuberculosis-related antigens where identified in guinea pigs vaccinated with Bacillus Calmette-Guerin (BCG) and also validated with ELISA [36].
\n
\n
\n
\n
7. Conclusions and further directions
\n
Here, we have briefly reviewed protein microarray field as suitable platform for multiplex assays in high-throughput format. Thus, the focus was on two main perspectives: (i) Key technological aspects, (ii) Biological Applications.
\n
However, as we described previously, despite the fundamental advances in protein microarrays, allowing characterization of whole human proteome is still remaining as a challenge. Then, the information provides light on the functions of proteins and genes whose functions are currently unknown.
\n
Overall, protein arrays may provide relevant information about the biological function of gene products. Although, it is still necessary to develop and optimize some key aspects of protein microarray in the future, other proteomics approaches could provide complimentary results.
\n
\n
Acknowledgments
\n
We gratefully acknowledge financial support from the Spanish Health Institute Carlos III (ISCIII) for the grants: FIS PI17/01930 and CB16/12/00400. Fundación Solórzano FS/38-2017. The Proteomics Unit belongs to ProteoRed, PRB3-ISCIII, supported by grant PT17/0019/0023, of the PE I + D + I 2017-2020, funded by ISCIII and FEDER.
\n
Conflict of interest
The authors do not declare any conflict of interest.
\n',keywords:"biomarker discovery, multiplex detection, protein microarrays",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/60908.pdf",chapterXML:"https://mts.intechopen.com/source/xml/60908.xml",downloadPdfUrl:"/chapter/pdf-download/60908",previewPdfUrl:"/chapter/pdf-preview/60908",totalDownloads:278,totalViews:209,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,dateSubmitted:"November 8th 2017",dateReviewed:"February 19th 2018",datePrePublished:null,datePublished:"September 26th 2018",readingETA:"0",abstract:"Despite the tremendous advances in the understanding of the molecular mechanisms and the complexity of the diseases is one of the present challenges for the scientific community; then, novel strategies are required to be designed and developed for effective strategies for early diagnosis and treatment. As many cellular alterations are observed at protein level, high-throughput assays are dramatically needed for biomarker discovery. Herein, we describe advantages and limitations of protein microarrays, as proteomics strategy useful for multiplex and high-throughput protein characterization in clinical samples. Finally, a few examples are discussed; mostly of them related to currently disease biomarkers already identified in proximal fluids by protein arrays are discussed.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/60908",risUrl:"/chapter/ris/60908",book:{slug:"rapid-test-advances-in-design-format-and-diagnostic-applications"},signatures:"Pablo Juanes-Velasco, Javier Carabias-Sanchez, Rodrigo Garcia-\nValiente, Jonatan Fernandez-García, Rafael Gongora, Maria\nGonzalez-Gonzalez and Manuel Fuentes",authors:[{id:"173804",title:"Dr.",name:"Manuel",middleName:null,surname:"Fuentes",fullName:"Manuel Fuentes",slug:"manuel-fuentes",email:"mfuentes@usal.es",position:null,institution:{name:"University of Salamanca",institutionURL:null,country:{name:"Spain"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_1_2",title:"1.1. Biomarkers discovery",level:"2"},{id:"sec_3",title:"2. Description of protein microarrays",level:"1"},{id:"sec_3_2",title:"2.1. Arrays according to their nature of the capturing agent",level:"2"},{id:"sec_3_3",title:"2.1.1. Analytical protein microarrays or antibody microarrays",level:"3"},{id:"sec_4_3",title:"2.1.2. Functional protein arrays or recombinant protein arrays",level:"3"},{id:"sec_5_3",title:"2.1.3. Reverse phase protein arrays",level:"3"},{id:"sec_8",title:"3. Arrays according to the format",level:"1"},{id:"sec_8_2",title:"3.1. Planar arrays",level:"2"},{id:"sec_9_2",title:"3.2. Microsphere arrays",level:"2"},{id:"sec_11",title:"4. Arrays according to their detection method",level:"1"},{id:"sec_11_2",title:"4.1. Methods based on labeling",level:"2"},{id:"sec_11_3",title:"4.1.1. Conventional fluorescent labels",level:"3"},{id:"sec_12_3",title:"4.1.2. Flow cytometry sphere arrays",level:"3"},{id:"sec_13_3",title:"4.1.3. Magnetic spheres",level:"3"},{id:"sec_14_3",title:"4.1.4. Quantum dots as fluorescence labels",level:"3"},{id:"sec_15_3",title:"4.1.5. Metal NPs as a label",level:"3"},{id:"sec_17_2",title:"4.2. Free-label methods",level:"2"},{id:"sec_17_3",title:"4.2.1. Surface plasmon resonance (SPR)",level:"3"},{id:"sec_18_3",title:"4.2.2. Microcantilevers",level:"3"},{id:"sec_19_3",title:"4.2.3. Atomic force microscopy (AFM)",level:"3"},{id:"sec_22",title:"5. Arrays according to the applications",level:"1"},{id:"sec_22_2",title:"5.1. Analytical arrays",level:"2"},{id:"sec_23_2",title:"5.2. Functional protein arrays",level:"2"},{id:"sec_25",title:"6. Multiplex biomarker detection by protein microarray assays",level:"1"},{id:"sec_25_2",title:"6.1. Oncology diseases-specific biomarkers",level:"2"},{id:"sec_26_2",title:"6.2. Vaccine candidates",level:"2"},{id:"sec_27_2",title:"6.3. Auto-immune diseases",level:"2"},{id:"sec_28_2",title:"6.4. Functional biomarkers as drug targets",level:"2"},{id:"sec_30",title:"7. Conclusions and further directions",level:"1"},{id:"sec_31",title:"Acknowledgments",level:"1"},{id:"sec_34",title:"Conflict of interest",level:"1"}],chapterReferences:[{id:"B1",body:'Díez P, Dégano RM, Ibarrola N, Casado‐Vela J, Fuentes M. Genomics and proteomics for biomarker validation. In: Seitz H, Schumacher S, editors. Biomarker Validation. Technological, Clinical and Commercial Aspects. 2015:231-242\n'},{id:"B2",body:'Strimbu K, Tavel JA. What are biomarkers? Current Opinion in HIV and AIDS. 2010;5(6):463\n'},{id:"B3",body:'Dasilva N, Díez P, Matarraz S, González-González M, Paradinas S, Orfao A, Fuentes M. Biomarker discovery by novel sensors based on nanoproteomics approaches. Sensors. 2012;12(2):2284-2308\n'},{id:"B4",body:'Frantzi M, Bhat A, Latosinska A. Clinical proteomic biomarkers: Relevant issues on study design & technical considerations in biomarker development. Clinical and Translational Medicine. 2014;3(1):7\n'},{id:"B5",body:'Ray S, Reddy PJ, Choudhary S, Raghu D, Srivastava S. Emerging nanoproteomics approaches for disease biomarker detection: A current perspective. Journal of Proteomics. 2011;74(12):2660-2681\n'},{id:"B6",body:'Ramachandran N, Srivastava S, LaBaer J. Applications of protein microarrays for biomarker discovery. Proteomics-Clinical Applications. 2008;2(10-11):1444-1459\n'},{id:"B7",body:'Gonzalez-Gonzalez M, Jara-Acevedo R, Matarraz S, Jara-Acevedo M, Paradinas S, Sayagües JM, et al. Nanotechniques in proteomics: Protein microarrays and novel detection platforms. European Journal of Pharmaceutical Sciences. 2012;45(4):499-506\n'},{id:"B8",body:'Matarraz S, González-González M, Jara M, Orfao A, Fuentes M. New technologies in cancer. Protein microarrays for biomarker discovery. Clinical and Translational Oncology. 2011;13(3):156\n'},{id:"B9",body:'Casado-Vela J, Fuentes M, Franco-Zorrilla JM. Screening of protein–protein and protein–DNA interactions using microarrays: Applications in biomedicine. In: Advances in Protein Chemistry and Structural Biology. Academic Press. 2014;95:231-281\n'},{id:"B10",body:'Lourido L, Diez P, Dasilva N, Gonzalez-Gonzalez M, Ruiz-Romero C, Blanco F, et al. Protein microarrays: Overview, applications and challenges. In: Genomics and Proteomics for Clinical Discovery and Development. Dordrecht: Springer; 2014. pp. 147-173\n'},{id:"B11",body:'Fernández-García J, García-Valiente R, Carabias-Sánchez J, Landeira-Viñuela A, Góngora R, Gonzalez-Gonzalez M, Fuentes M. A systematic workflow for design and computational analysis of protein microarrays. 2014\n'},{id:"B12",body:'Dasilva N, Díez P, González-González M, Matarraz S, Sayagués JM, Orfao A, Fuentes M. Protein microarrays: Technological aspects, applications and intellectual property. Recent Patents on Biotechnology. 2013 Aug;7(2):142-152. Review\n'},{id:"B13",body:'Díez P, González-González M, Lourido L, Dégano RM, Ibarrola N, Casado-Vela J, LaBaer J, Fuentes M. NAPPA as a real new method for protein microarray generation. Microarrays (Basel). 2015 Apr 24;4(2):214-227. DOI: 10.3390/microarrays4020214. Review. PMID: 27600221\n'},{id:"B14",body:'Tao SC, Zhu H. Protein chip fabrication by capture of nascent polypeptides. Nature Biotechnology. 2006;24(10):1253-1254\n'},{id:"B15",body:'Yu X, Song L, Petritis B, Bian X, Wang H, Viloria J, et al. Multiplexed nucleic acid programmable protein arrays. Theranostics. 2017;7(16):4057\n'},{id:"B16",body:'González-González M, Bartolome R, Jara-Acevedo R, Casado-Vela J, Dasilva N, Matarraz S, García J, Alcazar JA, Sayagues JM, Orfao A, Fuentes M. Evaluation of homo- and hetero-functionally activated glass surfaces for optimized antibody arrays. Analytical Biochemistry. 2014 Apr 1;450:37-45. DOI: 10.1016/j.ab.2014.01.002. Epub 2014 Jan 15\n'},{id:"B17",body:'Fuentes M. Protein microarrays overview. Recent Patents on Biotechnology. 2013 Aug;7(2):83. No abstract available. PMID: 24001089\n'},{id:"B18",body:'Miller JC, Butler EB, Teh BS, Haab BB. The application of protein microarrays to serum diagnostics: Prostate cancer as a test case. Disease Markers. 2001;17(4):225-234\n'},{id:"B19",body:'Gao WM, Kuick R, Orchekowski RP, Misek DE, Qiu J, Greenberg AK, et al. Distinctive serum protein profiles involving abundant proteins in lung cancer patients based upon antibody microarray analysis. BMC Cancer. 2005;5(1):110\n'},{id:"B20",body:'Tannapfel A, Anhalt K, Häusermann P, Sommerer F, Benicke M, Uhlmann D, et al. Identification of novel proteins associated with hepatocellular carcinomas using protein microarrays. The Journal of Pathology. 2003;201(2):238-249\n'},{id:"B21",body:'Seshaiah P, Bertenshaw GP, Chen TH, Bergstrom KJ, Zhao J, Mapes JP, et al. Validation of a Multivariate Serum Profile for Epithelial Ovarian Cancer Using a Prospective Multi-Site Collection; 2010\n'},{id:"B22",body:'Sreekumar A, Nyati MK, Varambally S, Barrette TR, Ghosh D, Lawrence TS, Chinnaiyan AM. Profiling of cancer cells using protein microarrays: Discovery of novel radiation-regulated proteins. Cancer Research. 2001;61(20):7585-7593\n'},{id:"B23",body:'Díez P, Lorenzo S, Dégano RM, Ibarrola N, González-González M, Nieto W, Almeida J, González M, Orfao A, Fuentes M. Multipronged functional proteomics approaches for global identification of altered cell signalling pathways in B-cell chronic lymphocytic leukaemia. Proteomics. 2016 Apr;16(8):1193-1203. DOI: 10.1002/pmic.201500372. Epub 2016 Apr 4. PMID: 26910488\n'},{id:"B24",body:'Belov L, Huang P, Barber N, Mulligan SP, Christopherson RI. Identification of repertoires of surface antigens on leukemias using an antibody microarray. Proteomics. 2003;3(11):2147-2154\n'},{id:"B25",body:'Molero C, Rodríguez-Escudero I, Aleman A, Rotger R, Molina M, Cid VJ. Addressing the effects of salmonella internalization in host cell signaling on a reverse-phase protein array. Proteomics. 2009;9(14):3652-3665\n'},{id:"B26",body:'Li B, Jiang L, Song Q, Yang J, Chen Z, Guo Z, et al. Protein microarray for profiling antibody responses to Yersinia pestis live vaccine. Infection and Immunity. 2005;73(6):3734-3739\n'},{id:"B27",body:'Thanawastien A, Montor WR, LaBaer J, Mekalanos JJ, Yoon SS. Vibrio cholerae proteome-wide screen for immunostimulatory proteins identifies phosphatidylserine decarboxylase as a novel toll-like receptor 4 agonist. PLoS Pathogens. 2009;5(8):e1000556\n'},{id:"B28",body:'Manzano-Román R, Díaz-Martín V, González-González M, Matarraz S, Álvarez-Prado AF, LaBaer J, Orfao A, Pérez-Sánchez R, Fuentes M. Self-assembled protein arrays from an Ornithodoros moubata salivary gland expression library. Journal of Proteome Research. 2012 Dec 7;11(12):5972-5982. DOI: 10.1021/pr300696h. Epub 2012 Nov 20\n'},{id:"B29",body:'Montor WR, Huang J, Hu Y, Hainsworth E, Lynch S, Kronish JW, et al. Genome-wide study of Pseudomonas aeruginosa outer membrane protein immunogenicity using self-assembling protein microarrays. Infection and Immunity. 2009;77(11):4877-4886\n'},{id:"B30",body:'Anderson KS, Ramachandran N, Wong J, Raphael JV, Hainsworth E, Demirkan G, et al. Application of protein microarrays for multiplexed detection of antibodies to tumor antigens in breast cancer. Journal of Proteome Research. 2008;7(4):1490-1499\n'},{id:"B31",body:'Gibson DS, Qiu J, Mendoza EA, Barker K, Rooney ME, LaBaer J. Circulating and synovial antibody profiling of juvenile arthritis patients by nucleic acid programmable protein arrays. Arthritis Research & Therapy. 2012;14(2):R77\n'},{id:"B32",body:'Henjes F, Lourido L, Ruiz-Romero C, Fernández-Tajes J, Schwenk JM, Gonzalez-Gonzalez M, Blanco FJ, Nilsson P, Fuentes M. Analysis of autoantibody profiles in osteoarthritis using comprehensive protein array concepts. Journal of Proteome Research. 2014 Nov 7;13(11):5218-5229. DOI: 10.1021/pr500775a. Epub 2014 Oct 9. PMID: 25227461\n'},{id:"B33",body:'Ramachandran N, Raphael JV, Hainsworth E, Demirkan G, Fuentes MG, Rolfs A, et al. Next-generation high-density self-assembling functional protein arrays. Nature Methods. 2008;5(6):535\n'},{id:"B34",body:'Manzano-Román R, Dasilva N, Díez P, Díaz-Martín V, Pérez-Sánchez R, Orfao A, Fuentes M. Protein arrays as tool for studies at the host-pathogen interface. Journal of Proteomics. 2013 Dec 6;94:387-400. DOI: 10.1016/j.jprot.2013.10.010. Epub 2013 Oct 16. Review\n'},{id:"B35",body:'Yazaki J, Galli M, Kim AY, Nito K, Aleman F, Chang KN, Carvunis AR, Quan R, Nguyen H, Song L, Alvarez JM, Huang SS, Chen H, Ramachandran N, Altmann S, Gutiérrez RA, Hill DE, Schroeder JI, Chory J, LaBaer J, Vidal M, Braun P, Ecker JR. Mapping transcription factor interactome networks using HaloTag protein arrays. Proceedings of the National Academy of Sciences of the United States of America. 2016 Jul 19;113(29):E4238-E4247. DOI: 10.1073/pnas.1603229113. Epub 2016 Jun 29. PMID: 27357687\n'},{id:"B36",body:'Yu X, Song L, Petritis B, Bian X, Wang H, Viloria J, Park J, Bui H, Li H, Wang J, Liu L, Yang L, Duan H, McMurray DN, Achkar JM, Magee M, Qiu J, LaBaer J. Multiplexed nucleic acid programmable protein arrays. Theranostics. 2017 Sep 20;7(16):4057-4070. DOI: 10.7150/thno.20151. eCollection 2017. PMID: 29109798\n'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"Pablo Juanes-Velasco",address:null,affiliation:'
Department of Medicine, General Cytometry Service-Nucleus, Cancer Research Center (IBMCC/CSIC/USAL/IBSAL), University of Salamanca, CSIC, IBSAL, Spain
Proteomics Unit, Cancer Research Center, IBSAL, University of Salamanca, CSIC, Spain
Department of Medicine, General Cytometry Service-Nucleus, Cancer Research Center (IBMCC/CSIC/USAL/IBSAL), University of Salamanca, CSIC, IBSAL, Spain
Proteomics Unit, Cancer Research Center, IBSAL, University of Salamanca, CSIC, Spain
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1. Introduction
Genetics terms returns to origin from Greek genetikos meaning “genitive,” which in turn derives from genesis meaning “origin” [1, 2]. Genetics in general is a branch of biology related to survey of genes, genetic variation, and even heredity in living systems [3, 4, 5]. The study of inheritance pattern that influences genes on human nature and occurs in human beings is called human genetics. Human genetics represents an original aspect that encompasses a variety of overlapping fields like the structure of gene and organization; the study of mutation detection; genetic mapping and linkage analysis; molecular diagnostics; gene expression; cytogenetics assessment; biomedical genetics; disease association studies; tumorigenesis of molecular levels; developmental genetics; and genetic epidemiology, in addition to genetics of complex disease [6, 7]. In general, genes can be considered as a key of most human inherited lesions. So, the benefit study of human genetics can be helpful to answer many questions concerning human diseases and invent effective drugs [8]. The passing of a certain genes from parents to offspring by biological process is called heredity. Each baby carries genes from their biological parents and some of these genes express particular trait or lesion [9]. Various traits may be physical like color of eyes, hair, skin, and other phenotypic matter. In other direction, some genes may play a key role in the risk of certain disorders and increase incidence of disease, or prophylaxis from it [10]. Several disorders occur and arise from multiple factors such as genetic, lifestyle, and environmental [11]. Many previous studies revealed facts for the hereditability of main neuropsychiatric disorders, for example, depression, bipolar disorder, and schizophrenia [12, 13]. In all these behavioral disorders, a specific genetic fault is transported from parents to children and will enhance a progeny susceptibility risk of inheriting a specific disorder [11, 12, 14, 15].
Neurological and mental diseases cannot be related to genetics science alone, so it is significant to seek implicated one’s genetic composition material that possibly affects various direction of human behavior [16]. The relationship between genes and behavior leads to development of highly prevalent responses and disorders throughout a new biological factors. About 30–50% of the risk for anxiety and depression is genetic, while the other 50–70% of the risk may be attributed to environmental factors, such as substance use, stress, diet, and childhood experiences [17]. A comparison between genetic and environmental factors, demonstrated about 30–50% of the risk for anxiety and depression and 50–70% of substance uses, stress, and diet, respectively [17, 18].
2. Genes in the cell
Not each living cell of the human body have nucleus. Skin, hair, and red blood cells contain no nucleus [19, 20]. Nucleus contains a genetic material that is responsible for information. Half of these information of the genetic materials come from each parent [20].
2.1 Chromosome
All nuclei of human cell contain 23 pairs of small thread-like structures called chromosomes. Genes are localized within these 23 pairs chromosomes. About 23 out of 46 chromosomes come from the father and others like them come from the mother [21]. These chromosomes contain genes and some of them carry thousands of important genes while some carry only a few [22]. In addition to these, genes are made up of a chemical substance named deoxyribonucleic acid (DNA). The chromosomes are very long thread strands of DNA, coiled up tightly and compacted [23].
Along every chromosome, there is a constriction point called centromere, the numbering and divided packages of chromosome started from it [24]. The centromere separates the chromosome into two arms: long called “q arm” and short called “p arm” as in Figure 1. Chromosomes are numbered from 1 to 22 in both sexes and called autosomes, while the last one remaining take letters X and Y which are responsible for the gender. In female, X chromosome is duplicated, whereas Y chromosome is combined with X chromosome in male [24, 25] (Figures 2 and 3).
Figure 1.
Autosomes and sex chromosomes of Homo sapiens from US National Library of Medicine, National Institutes of Health, Department of Health & Human Services. June 4, 2012.
Figure 2.
Chromosome structure explained long and short arms [26].
Figure 3.
Distribution of gene in the chromosome within human cell from U S National Library of Medicine, National Institutes of Health, Department of Health & Human Services. June 4, 2012.
2.2 Nitrogenous bases
The nitrogenous bases are organic molecule with a nitrogen atom that bears chemical prosperities of a base and involves four letters in addition to fifth letter used in ribonucleic acid (RNA), and classified into two main compounds: pyrimidine cytosine (C), thymine (T), and uracil (U); and purine guanine (G) and adenine (A), respectively [27]. They were arranged in unique position in genes, which makes up combinations with permutations and combinations. It is worth mentioning (AT and GC) that bases pair is always found together, and there are different sequences of bases pairs in DNA coded messages [28]. These bases are part of DNA, and constitute a language when arranged together acting as a guide telling cells what need to do such as growing, division, maturity, and variety functions [29].
2.3 Gender
Both gender have 46 paired chromosomes (44 autosomes), numbered from 1 to 22 chromosome pairs according to size, chromosome number 1 being the biggest one. In addition to autosomes, there are other two copies of sex chromosomes X and Y responsible for determination of gender. In female, two copies of X chromosome are present, while in male, one X copy and one Y copy in their body cells. In female, 22 autosomes paired chromosomes in addition to X sex chromosome present in the egg cells, while in male, the same number of chromosomes are present and the difference only X or Y chromosome in their sperm cells. The combination egg with sperm gives 46 chromosomes regarding the sex chromosome (XY in male and XX in female babies) [30, 31].
2.4 Human genes and genetics
The whole DNA in the cell makes a genome which contains both the exon (coding regions) and intron (non-coding region) that represent large sequences that do not encode any protein and their function is exactly not known yet [32, 33]. In genome, the gene is a basic biological functional unit of heredity that contributes to phenotype/function. A segment of DNA that encodes instruction is needed for a certain protein or enzyme. In other side, a lot of genes do not encode any protein. Only a fraction of DNA of the gene in the cell is expressed through transcription process that involves copy of chemical bases into messenger RNA (mRNA) in order to produce protein according to central dogma [33]. After that it will be translated by using ribosomes organelles, and mRNA migrates toward cytoplasm from nucleus to create polypeptide that folds in a certain configuration to make the protein. A range of human genes are between hundred to more than two millions of nitrogen bases. About 20,000–25,000 genes are located on the 23 pairs of chromosomes within nucleus according to human genome project [34]. The human genome project completed officially in April 2003, and only 12,800 genes and numerous other genes have been well mapped to loci on each of the chromosomes. On the other hand, the correct number of human gene is still unknown [35, 36].
In human being, any individual has two copies from each gene, one copy comes from father and second copy comes from mother [32]. In all people, most genes are similar, excluding small number of genes that are little a bit diverse between people (<1%). The alternative form of a gene that occurs at the same locus on homologous chromosomes called allele bears a small variation in their DNA sequences and participates to every person’s single physical features. A single allele for each gene is inherited from each parent (e.g., at a locus for hair or eye color and blood type). About 2% only of genome represent the DNA in genes and full information is stored in a database that is publicly accessible [37].
2.5 Inherit characteristics
Genes are considered as building blocks of inheritance [38]. The traits pass from parents to their offspring and are controlled by some genes; these are carried out by either sexual or asexual reproduction [39]. The genetic information and characteristics are acquired in the progeny cells from their natural parents. However, most of them are affected by mixing the environmental effect and genes. Many traits are observed simply like tongue rolling, dimples, freckles, hands clasping, etc. [40].
2.6 Dominant, recessive, and co-dominant genes
The cell works via coded messages that send from both alleles of genes that involved in every set of chromosomes. Some of these genes appear dominant more than other in works [41]. The dominant occurs when one allele of gene is dominant, while the recessive appears in opposite to dominant within the pair. However, there is present other situation neither dominant nor recessive called homozygous [32]. Homozygous is an equal weight carrier combination of each allele in the gene pair and demonstrates phenotypic and physical characteristics between them [42].
2.7 The genetic keys
The genetic information that endures on the genes order to produce specific protein will be converted to “switched on” position in a few specific specialized cells, and at the same time, other genes may be “switched off” position [43]. Cells differ in differentiation, so genes “switched on” in liver cells are completely different to those that at same position in brain cells [44].
Numerous cases are born with a defect in particular gene which related to a specific illness, and this does not mean you are more susceptible to it. At the same time, this raises the risk of appearing of the disease. So, the predisposition genetic effects such as occurrence of many types of cancer may be need to be triggered by environmental factors, and to reducing the risk achieved through decreasing or avoiding such triggers [45, 46].
In each gene, non-coding regions (introns) account more than 98% and as suggested previously, they do not have any function “junk,” and do not involve any information of gene output in cells. The previous opinion about non-coding DNA regions is rejected despite that role is still unknown and appears to have very important roles to do in through gene expression and regulation in each cell [47].
2.8 Genes, mutation, and single nucleotide polymorphism
As mentioned previously, about 23,000 genes in the human cell act as leader in growth and general health, which are responsible for everything in human life; in which the genetic code is a set of rules used by human being and every living system to translate the information encoded within DNA or RNA sequence to protein. Every three nucleotides called codon encode a certain amino acid in protein [48].
In this direction, any changes in the genetic code can lead to each person is exceptional in his behavior and health, that is to say, the alteration in the sequences of nucleotide in DNA can give a uniqueness characteristic for person. Mainly these changes are risk free, while others may have embroiled in proteins production either not properly, wrong sequence, or not produced totally [49]. Hence, the changes in genetic material cause inactive or disturbed gene called mutation. These mutations occur in DNA sequence either by mistake during copy process or by environmental effectors. Sometimes mutations affect individuals directly or indirectly and are prone more susceptible to certain disease circumstances [50].
3. Polymorphism
A brief glance across a time: the beginning of the human genetic polymorphism was belonging to the b globin gene in 1978, which utilized to recognize a heredity disease. After 2 years, in 1980, short distinctions in DNA discovered were spread over the whole human genome. It was described by utilized restriction fragment length polymorphisms (RFLPs) method. Further complicated interesting information of DNA polymorphisms was reported in 1985. They were named minisatellites. The empirical arguments about DNA fingerprinting remained to the 1990s. With the trial of OJ Simpson in the USA in 1995, the DNA proofs play a very important role in forensic medicine history presented by the prosecution; OJ Simpson was acquitted. This event call attention to the proofs of DNA has great significance [51].
When we see the great diversity of human ethnicities, really we find it shocking that all of these different ethnicities share a genetically identical sequence at 99%. The range of their variances is only within limits 0.1% of sequence genetic that differs between double chromosomal threads, Figure 4, [52, 53]. It is a small ratio of variances (1%) indeed, but it is accountable for the multiplicity in person’s phenotypes and receptiveness of them to ecological contacts [53, 54].
Figure 4.
Human genetic polymorphism is identical in sequences at 99% and variation sequences only about 1% [52].
Polymorphism at the DNA grade contains a broad domain of variations from single base pair alteration, numerous unite pairs, and frequent sequences [55]. One of the most famous types of genetic variations is the genetic mutation. Genetic mutation can be definite as order variants which happen in a smaller than 1% of the populace, whereas the extra prevalent variants are identified as polymorphisms. The greatest public hereditary variants than 1% are single nucleotide polymorphisms (SNPs) [53, 54].
Generally, genetic polymorphism can be available in numerous designs, comprising: single nucleotide polymorphisms (SNPs), tandem repeat polymorphisms which include a variable number of tandem repeats (VNTRs) and short tandem repeats (STRs), insertion/deletion polymorphisms, transposable elements (TE) or Alu repeats also known as “jumping genes,” structural alterations, and copy number variations (CNV) [55].
For the studying diverse kinds of DNA polymorphisms, different techniques can be utilized, such as restriction fragment length polymorphisms (RFLPs) accompanied by southern blots, polymerase chain reactions (PCRs), hybridization methods (southern and northern blotting) utilizing DNA microarray chips, and whole genome sequencing (WGS) [55]. The following is an illustration of the most famous polymorphism (Figure 5).
Figure 5.
Single nucleotide polymorphism for two alleles.
3.1 Single-nucleotide polymorphism (SNP)
Single nucleotide polymorphisms (SNPs) (pronounced: snip) are an alteration in a lone DNA order structure building block unit: (A, T, C, or G) which termed a nucleotide, Figure 2 [56]. It is the simplest formula of genetic difference among persons. SNPs are the most frequent occurrence from all genetic variants, which happen usually in a person’s DNA. It is a ratio of occurrence near 90% of human genomic variants [57, 58].
They may be occurring one time in each 300 nucleotides on usual, that is, average is about 10 million SNPs in the individual’s genome. Greatest frequently, those SNPs are set between genes or within genes. They may perform as living signs and/or hereditary indicators, aiding experts find sequence, which are linked with disease. As soon as SNPs happen inside a gene or in an adjusting area nearby a gene, they might show an additional strong impact in disease via stirring the gene’s role. However, the SNPs generally have no influence on the general state of health. Moreover, investigators have instituted that SNPs might assist and guess a person’s reaction to definite medications. Additionally, they are utilized for a pathway of genetic factors of malady inside relatives [59, 60, 61, 62].
3.2 Polymorphic repetitive sequences
The extension of the human genome threads that include gene sequences or intergenic and include retro (pseudo) genes and transposons are composed of small sequences of nitrogen bases that have repeated in tandem. It can consist of more two-thirds of human DNA. The number of units of these tandems in a specified site is extremely variable between separated persons. Tandem repeat polymorphisms include a variable number of tandem repeats (VNTRs) minisatellites and short tandem repeats (STRs) microsatellites. Both of VNTRs and STRs are the same in the total grounds. The difference between different alleles is consequence to a difference in the number of repeat bases that exist in alleles that are of various lengths, and later, tandem repeat polymorphisms have been identified as length polymorphisms. So, widely distinguished types from mini- and microsatellites depend on the distance of the repeated blocks. In microsatellites, the order repeat base composes between 2 and 9 units; while mini-satellites composes between 9 and 100 units [63, 64, 65].
3.2.1 Variable number of tandem repeats (VNTRs)
VNTR is among the earliest DND markers in the application. It is a kind of tandem repetitions in which a small order of bases (10–60 base pairs) are frequented changeable times in a certain position. Therefore, VNTR is additionally familiar as minisatellites. Minisatellites are scattered everywhere in the humane DNA. Usually, the number of repeated bases in minisatellites differs among persons. Hence, the array extension shaped by VNTRs as well differs among persons. Accordingly, the variant number of chromosomes is familial from parents, so they can be applied in parental or individual identification. The techniques that use to determine this type are: routines PCR, gel electrophoresis, and amplicons of band designs by southern blotting. The utilization of VNTRs was, nevertheless, restricted by the kind of specimen that could give good results for the reason that a big quantity of DNA was needed. In addition, understanding VNTR profiles might be a difficulty. Their utilization in forensic genomics has been replaced at the present time by short tandem repeats (STRs) [66].
3.2.2 Short tandem repeats (STRs)
Short tandem repeats (STRs) give an extremely good method because of their great grade of polymorphism and a comparatively small length. Additionally, STRs are typical methods for genotyping in the identity of one’s parents check and forensic identity check. A category of tandem repeats depended on presents a small order of bases (2–6 base pairs) are frequented a variable number of times in a certain site. STRs are a type of microsatellites, and they are furthermore recognized as short sequence repeats (SSRs) in plant DNA. The repeating bases consist of a single nucleotide that is familiar as a single nucleotide polymorphism (SNP) [66].
3.3 Insertion/deletion polymorphisms
It is a type of genomic difference in which a particular base order of different sizes ranging from one base to several 100 units is inserted or deleted. Indels are very extending across the DNA. Several writers consider one base pair as SNPs or frequent insertion/deletion as indels [55].
4. Genome-wide association studies (GWAS)
Genome-wide association studies (GWAS) are a new technique used worldwide in genetics research to identify inherited genetic risk variants linked with risk of prevalent disease. GWAS are the most inclusive way of genetic variation study. In general, this approach deals with full scan of genome for identifying genetic markers frequently are polymorphisms (SNPs) that appear more in patients relative to healthy individuals, and also understanding the contribution of genes in the diseases and developing better prevention and treatment approaches.
GWAS are the greatest complete way of research and contain skimming the whole genome of research members for polymorphisms and anomalies related with the sickness of attention. GWAS have the benefit of supplying a complete investigation of related genetical anomalies inside the genome, as the term “genome-wide” shows, in spite of that, GWAS are timing spender, costly, and yields a massive quantity of data that can/cannot actually be related to the illness of study. As of that, they are best beneficial as an initial mark pretty than a way for measuring accurate connotation [67]. In additional, GWAS are case-control study setup concentrating on obviously distinct participators geneticists who assume to find only proportionally brief extends of participate chromosomes Figure 6 [67]. It includes comparison between two groups of individuals, one healthy and second patients or affected group with disease. Therefore, GWAS purpose is to recognize hereditary variations that convert danger of public maladies or touch additional phenotypes. The simple thought of it is: check hereditary variations (frequently SNPs), and phenotypes in haphazardly-tested persons, and view which SNPs are connected with phenotypes, while infrequent variations are inferiorly taken by GWAS method [68, 69, 70].
Figure 6.
Genome-wide association studies [71].
5. Discussion
All humans have variation in genetic material, even identical twins by the time of born, and this variation give us the uniqueness. We inherit our genes from our parents, so the members of the same family share majority of their genetic material involving its variations. The variation in DNA that cause wrong in required genetic code translates into a specific protein called pathogenic variant or mutation when linked to phenotypic particularly if they occur within the protein coding sequence of the gene. Variations are caused by the environment and genetic factors. Several disorders arise from multiple effectors like environmental, lifestyle, and genetic factors. From these diseases is behavioral genetics neuropsychiatric, such as schizophrenia, depression in which a genetic fault transferred from parents to sons through familial genes causing elevated the risk of a particular disease. SNPs can affect more than 90% of genetic variation and are responsible for the occurrence of differences between the humans. Despite the presence of SNPs, there is no relation to modify or change cellular function that is to say have no effects, and at the same time, many SNPs were found to participate in the initiation of disease like cancer or act as prophylaxis against a certain disease or even impact the responses to medications. Depending on the SNPs’ position in the genome, we can classify them into that happen with exome (coding region), intron (non-coding region), and between adjacent genes (intergenic region). Because there is much of non-coding DNA (99%) that lead to harboring majority of SNPs occurring in these segments. However, only small part (1%) of genome is considered important to represent exome that can contribute existing mutations, and SNPs that have a big outcome on several diseases. Non-coding genome includes many categories of regulatory factors such as promoters, enhancers, silencers, and insulators. Each one of them provides binding sites for proteins, carries out transcription, activates transcription, suppresses transcription, and controls transcription process, respectively.
SNPs are very important and used in various studies such as estimating the tendency to disease and predicting genetic lesion; and are also used as biomarkers since they can occur near disease genes for complicated diseases, but not always. In complex disease, which means a pathological circumstance of the body due to a defect in a number of genetic and environmental aspect, SNP can affect that person has toward a specific disease. Nowadays, various methods have been established and assembled to identify known or unknown SNPs through two categories which are genotyping and scanning sequence, respectively.
Acknowledgments
I am grateful to my family, father, mother, sisters, and brothers, for always being the person I could turn to during those dark and desperate years. They sustained me in ways that I never knew that I needed.
I have to start by thanking my wife, and my daughter awesome, from reading early drafts to giving me the support. Thank you so much. “Thanks to everyone in my publishing team.”
Conflict of interest
We would like to declare there is no conflict for this work.
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