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Complementarity Determining Regions (CDRs)

Complementarity Determining Regions (CDRs)


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Complementarity determining regions (CDRs) are part of the variable domains in immunoglobulins (antibodies) and T cell receptors, generated by B-cells and T-cells respectively, where these molecules bind to their specific antigen. (source: WIKI, with a minor change)

Now does this mean that this CDR is the paratope? As it has the same functionality as paratope, to bind to epitope of the antigen. So is CDR a type of paratope?

(I have not studied biology since last 8 years and now I am going through it because I need it for my research. So if someone can describe it in simple language it would be very helpful)


The paratope is the part of an antibody that binds the epitope on the antigen. The CDRs (heavy chain CDRs shown below) are part of the structure of the variable domain, and contain the hypervariable regions that bind to the epitope.

From Wikimedia

The actual paratope is within the hypervariable regions, which are within the CDRs - the paratope is not necessarily made up of the entire CDR region, and in fact may only be composed of amino acids contained within one or two of the three CDRs on each antibody chain (heavy and light).

If you're in the market for a good introductory immunology text, I highly recommend Janeway's Immunobiology. An earlier version is also available on the NCBI Bookshelf.


A germline knowledge based computational approach for determining antibody complementarity determining regions

Determination of framework regions (FRs) and complementarity determining regions (CDRs) in an antibody is essential for understanding the underlying biology as well as antibody engineering and optimization. However, there are no computational algorithms available to delimit an antibody sequence or a library of sequences into FRs and CDRs in a coherent and automatic fashion. Based upon the mapping relationships among mature antibody sequences and their corresponding germline gene segments, a novel computational algorithm has been developed for automatic determination of CDRs. Even though a human can make more than 10 12 different antibody molecules in its preimmune repertoire to fight off invading pathogens, these antibodies are generated from rearrangements of a very limited number of germline variable (V) gene, diversity (D) gene and joining (J) gene segments followed by somatic hypermutation. The framework regions FR1, FR2 and FR3 in mature antibodies are encoded by germline V gene segments, while FR4 is encoded by J gene segments. Since there are only a limited number of germline gene segments, these genes can be pre-delimited to generate a knowledge base of FRs and CDRs. Then for a given antibody sequence, the algorithm scans each pre-delimited gene in knowledge base, finds the best matching V and J segments, and accordingly, identifies the FRs and CDRs.

The described algorithm is stringently tested using nearly 25,000 human antibody sequences from NCBI, and it is proven to be very robust. Over 99.7% of antibody sequences can be delimited computationally. Of those delimited sequences, only 0.28% of them have somatic insertions and deletions in FRs, and their corresponding delimited results need manual checking. Another feature of the algorithm is that it is CDR definition independent, and can be easily extended to other CDR definitions besides the most widely used Kabat, Chothia and IMGT definitions. In addition to delimitation of antibody sequences into FRs and CDRs, the described algorithm is good for sequence annotation and sequence quality control by detecting unusual sequence patterns and features. Furthermore, it has been suggested that the algorithm may easily be embedded into other applications, such as to create a gene family specific PSSM (Position Specific Scoring Matrix) for antibody engineering, and to automatically number an antibody sequence.


A germline knowledge based computational approach for determining antibody complementarity determining regions

Determination of framework regions (FRs) and complementarity determining regions (CDRs) in an antibody is essential for understanding the underlying biology as well as antibody engineering and optimization. However, there are no computational algorithms available to delimit an antibody sequence or a library of sequences into FRs and CDRs in a coherent and automatic fashion. Based upon the mapping relationships among mature antibody sequences and their corresponding germline gene segments, a novel computational algorithm has been developed for automatic determination of CDRs. Even though a human can make more than 10 12 different antibody molecules in its preimmune repertoire to fight off invading pathogens, these antibodies are generated from rearrangements of a very limited number of germline variable (V) gene, diversity (D) gene and joining (J) gene segments followed by somatic hypermutation. The framework regions FR1, FR2 and FR3 in mature antibodies are encoded by germline V gene segments, while FR4 is encoded by J gene segments. Since there are only a limited number of germline gene segments, these genes can be pre-delimited to generate a knowledge base of FRs and CDRs. Then for a given antibody sequence, the algorithm scans each pre-delimited gene in knowledge base, finds the best matching V and J segments, and accordingly, identifies the FRs and CDRs.

The described algorithm is stringently tested using nearly 25,000 human antibody sequences from NCBI, and it is proven to be very robust. Over 99.7% of antibody sequences can be delimited computationally. Of those delimited sequences, only 0.28% of them have somatic insertions and deletions in FRs, and their corresponding delimited results need manual checking. Another feature of the algorithm is that it is CDR definition independent, and can be easily extended to other CDR definitions besides the most widely used Kabat, Chothia and IMGT definitions. In addition to delimitation of antibody sequences into FRs and CDRs, the described algorithm is good for sequence annotation and sequence quality control by detecting unusual sequence patterns and features. Furthermore, it has been suggested that the algorithm may easily be embedded into other applications, such as to create a gene family specific PSSM (Position Specific Scoring Matrix) for antibody engineering, and to automatically number an antibody sequence.


Complementarity Determining Regions (CDRs) are the binding sites for the specific antigens in immunoglobulins and T cell receptors. These are generated by B cells and T cells respectively. A set of CDRs makes up a paratope. CDRs are in fact parts of the variable chains in immunoglobulin proteins.

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Faculty Profile

Assistant Professor, Computational Biology
Department of Biology and Biochemistry

Office: Science & Engineering Research Center (SERC), 3007
Contact: [email protected]

Education: D.S., Federal University of Rio Grande do Sul (UFRGS, Brazil) M.S., UFRGS B.S, UFRGS

Fig 1. Structural representation of a TCR/peptide-HLA complex. The T-cell receptor (TCR) is depicted in shades of blue (cartoon representation), each one representing one of the two variable domains. The most variable region within these domains correspond to the complementarity determining regions (CDRs). These are 6 flexible loops (red) that directly contact both the peptide and the HLA. The peptide structure is depicted with in green (surface representation). The HLA is depicted in grey (cartoon representation).

Dr. Dinler Amaral Antunes’ research group uses structural bioinformatics methods, such as molecular modeling, molecular docking and molecular dynamics, to investigate protein-ligand interactions with relevant biomedical applications. Since they are constantly pushing the limits of what can be done with available tools, his group is also actively adapting and developing new computational methods to address specific biological problems.

Fig 2. Schematic view of the role of MHCs in T-cell activation. Class I Major Histocompatibility Complexes (MHC-I) are present in almost every cell, being involved in the surface presentation of peptides derived from intracellular proteins. This presentation drives the activation of cytotoxic T-cells, triggering a cellular response. On the other hand, class II MHCs are present only in “professional” antigen-presenting cells (phagocytes), being involved in the surface presentation of peptides derived from extracellular proteins. This presentation drives the activation of helper T-cells, triggering a humoral response. Understanding the structural differences between these two types of MHC receptors and how they bind their ligands is key for the development of better modeling and binding prediction methods. CD, cluster of differentiation. TCR, T-cell receptor. Modified from Antunes et. al, 2019.

In addition to collaborative projects involving broader biomedical applications (e.g., drug discovery), his lab has a particular focus on studying the mechanisms involved in cellular immunity. This type of adaptive immunity is mediated by T-cell lymphocytes and is key for immunological responses targeting both viruses and cancer cells. T-cells can recognize pieces of proteins (i.e., peptides) displayed at the surface of other cells by a family of receptors known as human leukocyte antigens (HLAs). The recognition of peptide-HLA complexes is mediated by the complementarity determining regions (CDRs) of the T-cell receptor (TCR), representing a central step for the activation of T-cells and the development of cellular immunity.

Understanding the molecular features driving the affinity and specificity of the TCR/pHLA interaction can create new opportunities for biomedical applications across different fields, including antiviral vaccine development, cancer immunotherapy, and the treatment of autoimmune diseases. Over the past decade, Dr. Antunes has developed several tools to enable the computational modeling and structural analysis of peptide-HLA complexes. Now, he wants to combine these computational methods with data from high throughput molecular biology and proteomics approaches, to improve the safety and the efficacy of future T-cell-based immunotherapies.


Backbone flexibility of CDR3 and immune recognition of antigens

Conformational entropy is an important component of protein-protein interactions however, there is no reliable method for computing this parameter. We have developed a statistical measure of residual backbone entropy in folded proteins by using the ϕ-ψ distributions of the 20 amino acids in common secondary structures. The backbone entropy patterns of amino acids within helix, sheet or coil form clusters that recapitulate the branching and hydrogen bonding properties of the side chains in the secondary structure type. The same types of residues in coil and sheet have identical backbone entropies, while helix residues have much smaller conformational entropies. We estimated the backbone entropy change for immunoglobulin complementarity-determining regions (CDRs) from the crystal structures of 34 low-affinity T-cell receptors and 40 high-affinity Fabs as a result of the formation of protein complexes. Surprisingly, we discovered that the computed backbone entropy loss of only the CDR3, but not all CDRs, correlated significantly with the kinetic and affinity constants of the 74 selected complexes. Consequently, we propose a simple algorithm to introduce proline mutations that restrict the conformational flexibility of CDRs and enhance the kinetics and affinity of immunoglobulin interactions. Combining the proline mutations with rationally designed mutants from a previous study led to 2400-fold increase in the affinity of the A6 T-cell receptor for Tax-HLAA2. However, this mutational scheme failed to induce significant binding changes in the already-high-affinity C225-Fab/huEGFR interface. Our results will serve as a roadmap to formulate more effective target functions to design immune complexes with improved biological functions.

Keywords: T-cell receptor affinity maturation antibody backbone entropy conformational flexibility.


VIPase

Structural Chemistry

The recombinant light chain is a 27 kDa protein containing three framework regions and three complementarity determining regions . Among the known germline V L genes, the sequence of the anti-VIP light chain is most similar to the germline VL gene 2(70/3) (EMBL/GenBank: Z72384). The anti-VIP light chain contains the J 1 gene. Four sequence substitutions are evident with respect to the germline VL/J genes (germline residues shown in parentheses): His27d(Asp), Thr27e(Ser), Ile34(Asn) and Gln96(His). In addition to the naturally encoded VL domain, the recombinant light chain contains a ten residue c-myc tag and a six residue poly(His) tag at its C-terminus.

The light chain exists predominantly in monomer form in neutral aqueous solution. Small amounts of dimers of the light chain can be detected at concentrations greater than 1 μM.

The catalytic triad identified of the VIPase light chain has provided a structural template for engineering tailor-made proteolytic antibodies. Incorporation of the Ser27a-His93-Asp1 triad into a noncatalytic light chain by site-directed mutations imparted proteolytic activity to the light chain [26] . Consistent with the expected nucleophilic mechanism, the peptidase activity of the mutant light chain was inhibited by the serine protease inhibitor diisopropylfluorophosphate.

The VH domain of antibodies is often a major contributor towards noncovalent antigen binding. Pairing of the VIPase VL domain with its natural VH domain partner imparted to the resultant single chain Fv fragment an improved binding affinity for VIP (decreased Km) and kinetic efficiency (kcat/Km) [27] . By molecular modeling, considerable spatial separation of the VH domain residues from the Ser 27a nucleophile was evident (>10 Å), suggesting that the VH domain effects are attributable to remote recognition of the extended transition state of VIP hydrolysis. Consistent with the nucleophilic mechanism of catalysis, the light chain displayed irreversible binding of biotinylated phosphonate diesters detectable by denaturing electrophoresis [28] . VIP inhibited the labeling of the light chain by phosphonate diesters, confirming that the binding is active site directed.

The domain pairing approach can be extended to engineer novel catalytic specificities. Mutations were introduced into the third complementarity determining region of the constituent VH domain in the VIPase single chain Fv fragment cited in the preceding paragraph to direct the catalytic specificity to amyloid β-peptide [29] . Pairing of a VIP-hydrolyzing light chain with the heavy chain subunit of an antibody specific to a hepatitis C virus antigen was described to afford a catalytic antibody capable of hydrolyzing the viral antigen [30] .


Introduction

Antibodies are tetrameric proteins consisting of two heavy and two light chains that are held together by inter-chain disulfide bonds. The light chain comprises a variable (VL) and a constant domain (CL), while the heavy chain of IgG1 antibody subtype consists of one variable domain (VH) and three constant domains (CH1, CH2, CH3). Each domain is stabilized by one disulfide bond. The variable domains each have three hypervariable loops, known as the complementarity determining regions (CDRs), which are the main regions engaged in antigen binding. The CDRs are supported by the framework region which determines the structure of the variable domain.

Variable fragments (Fv) are the smallest fragments of an antibody that can bind to the antigen with similar affinity and specificity of full length antibody. Non-covalently associated functional Fv fragments were produced in the periplasm of E. coli already in 1988 [1]. However, the non-covalent re-association of Fv fragments proved unstable, therefore Bird et al circumvented this obstacle by linking the VH domain to the VL domain through a short flexible peptide, generating a single chain Fv or scFv, so that both domains could be expressed from one gene and provide equimolar expression of both Fv [2]. Each Fv contains an intra-domain disulfide bond, therefore scFv expression usually requires an oxidizing environment such as found in the eukaryotic endoplasmic reticulum or bacterial periplasm. The disulfide bond of each Fv domain is highly conserved and critical for domain stability and solubility [3,4]. The overall stability of an antibody or an antibody fragment depends not only on intrinsic stability of each domain, but also on the stability of domain interfaces [5,6]. Only intrinsically very stable scFv can fold in the absence of both disulfide bonds [7] and in reducing environments, such as found in the cytoplasm of a cell, most scFv will form non-functional insoluble aggregates.

Although antibodies are secreted proteins there is increasing interest in intrabodies i.e. antibodies or antibody fragments that can be expressed and retained intracellularly. Targeting an intra-body to the cell enables the study of protein function in vivo or the modulation of molecular events inside the cell, e.g. stabilization of protein–protein interactions, neutralization of intracellular antigens or even catalyzing reactions [8–10]. The potential intrabody needs to be hyperstable to fold in the reducing environment of the cytoplasm.

So far only a few scFv have been reported to be soluble and functional in the absence of both disulfide bonds. The seminal works in the field towards intrabody production were circa 20 years ago. i) Ohage and Steipe showed that by rational engineering it was possible to construct hyperstable VL domains which were able to fold in the cytoplasm [11] ii) Proba et al by means of molecular evolution (DNA shuffling and phage display) generated stable and functional scFv lacking disulfide bonds in both VH and VL [12] based on the scFv fragment of the levan binding antibody ABPC48, which naturally misses one of the conserved cysteine residues in VH [13] iii) Martineau et al through random genetic mutation, screening and selection generated high yields of disulfide free scFv against β-galactosidase in the cytoplasm of E. coli [14] iv) Tavladoraki et al showed that scFv derived from the anti-viral antibody F8 was functionally expressed in the cytoplasm of a transgenic plant and E. coli, and had free sulfhydryl groups [8].

The cysteines involved in disulfide bonds are located in the scFv framework which could imply that these regions are responsible for disulfide-dependency of scFv folding and stability. It has been reported that intrinsically stable scFv could be used as a scaffold for grafting antigen binding loops from other antibodies to generate more soluble and stable scFv [15,16]. Grafting of antigen binding regions was first applied for antibody humanization, in order to decrease the risk of immune response triggered by a rodent antibody [17]. Carter et al humanized anti-HER2 murine antibody (mumAb4D5) and generated an antibody with a very favourable folding properties [18]: Jung and Plückthun used the framework of anti-HER2 scFv (under the name “4D5”) for grafting CDRs of aggregation prone fluorescein binding antibody 4-4-20 and the resulting scFv showed improved solubility and thermal stability [15]. Anti-HER2 scFv framework also served as scaffold for transplantation of CDRs from EGP-2-binding MOC31 scFv and the generated scFv showed increased stability and better expression [16]. Moreover, Wörn and Plückthun generated a biologically functional cysteine-free derivative of anti-HER2 scFv by mutating cysteine residues to valine or alanine however this scFv variant formed inclusion bodies in the periplasm of E. coli and in vitro refolding was necessary to obtain active protein (7).

While transplanting hypervariable loops has proved to be sufficient to preserve the antigen binding affinity [19,20], in many cases it has been necessary to transfer also several framework residues from the paternal scFv to ensure retention of the binding properties: these framework residues were shown have crucial influence on the conformation of particular CDR or even to be directly involved in antigen binding [21–24].

It has been known for nearly 20 years that some scFv antibody fragments are able to fold in the absence of disulfide bonds [8,12,13]. The disulfide-dependence/independence of folding must be linked to the stability of the native-like fold with no disulfide bond [e.g. 11] but it is unclear the degree to which this is dependent on the framework region of the scFv or the CDRs. Since the CDRs are in the core of the framework and are not in contact with the disulfide bond in either the VL or VH domain [25], the simplest hypothesis is that the framework is more essential than the CDRs for disulfide-dependence/independence of folding and if this is true then it should be possible to swap CDRs from disulfide-dependent scFv into disulfide-independent frameworks and obtain folded protein in the absence of disulfide formation, but not vice versa. To test this hypothesis a rapid screen is required and one which does not introduce a bias due to different compartments being used or due to the sole use of cysteine mutants in the scFv. It has been reported that folding for disulfide-independent scFv is limited by prolyl isomerization [4,26] and so the presence of sufficient levels of catalysts for this slow folding step is essential. Previously [27] we reported soluble expression of variety of scFv and Fabs in the cytoplasm of E. coli, in the presence of folding catalysts, a sulphydryl oxidase Erv1p and disulfide bond isomerase, PDI, that form a system called CyDisCo (cytoplasmic formation of disulfide bonds in E. coli). We tested expression of fragments of different antibody isotypes (IgG, IgM, IgA, IgE), subclasses (IgG1, IgG2, IgG4, IgA1) and derived from different species (human, murine, humanized). Out of eleven tested scFv soluble expression was observed for ten. Yields varied from 4 mg/L up to 271 mg/L with no correlation between the expression level and any specific antibody type, with the most abundant and the most poorly expressed being IgG1. Each scFv expression was tested in parallel in the absence of folding helpers and only two scFv, natalizumab and trastuzumab (in [27] designated as “Tysabri” and “Herceptin”, respectively), were efficiently expressed solubly in the absence of catalysts of disulfide bond formation. The trastuzumab derived scFv has identical VH and VL domains as the anti-HER2 scFv used by Jung and Plückthun [7]. When using CyDisCo the six cytoplasmic E. coli peptidyl-prolyl cis-trans isomerases are present and hence prolyl isomerization should be efficiently catalyzed. Furthermore parallel +/- CyDisCo screening for production is facile in any E. coli strain and no physiological changes in response to CyDisCo have been reported. Hence CyDisCo could be used to screen for disulfide-dependency of folding of scFv.

Here we examine the contribution of CDRs and framework regions to the disulfide-dependency of folding of scFv. Four scFv were selected for the study, two that showed a high degree of disulfide-independence (trastuzumab and natalizumab) and two more disulfide-dependent (IgA1 and Maa48). We swapped CDRs and tested for disulfide-dependence of folding in the cytoplasm of E. coli using the CyDisCo system. To verify disulfide-independent folding we mutated all four cysteines to alanine in the trastuzumab and natalizumab scFv. We also studied the thermal stability and secondary structure of solubly expressed scFv to examine for correlations with disulfide-dependency. While not directly relevant to the question of disulfide-dependence of folding we also undertook binding studies by western blotting or dot blotting or surface plasmon resonance of most of the scFv produced to aid other researchers in the field of hybrid scFv production.


Immunoprofiling: How it works

As previously discussed, immunoprofiling is the quantitative measurement of antigen receptors (ARs antibodies or T-cell receptors) in a sample. Massively parallel (NextGen) DNA sequencing is a commonly used method for this purpose because receptor diversity can be quickly and cost-effectively measured with the added benefit that individual receptors can be quantified.

AR diversity is a result of random recombination plus DNA base insertions

To understand the basis and power of DNA sequencing assays, we need to understand the basics of AR development and maturation. The adaptive immune systems of all jawed vertebrates are similar in that an immense AR diversity is created through a DNA rearrangement process [1] that recombines genes within different groups together. Each receptor locus has discrete groups of genes that are called Variable (V), Diversity (D), and Joining (J). There are also Constant (C) genes, but for the purpose of antigen recognition the V(D)J genes form the "business" end of the AR. Hence this post focuses on V(D)J recombination and does to discuss C genes and class switching.

Human AR loci. The genes for each AR chain exist in separate locations (loci) in the genome. Color shades are used to indicate the different gene groups (V: red, D: green, J: yellow, and C: blue). General gene lengths (in nucleotides [nt, bases] are given below the gene group names. The number of genes for each group is indicated below each locus diagram. The chromosome ( chr ) where the loci are located is indicated at the end of each diagram. Data for the figure were obtained from the IMGT database: http://www.imgt.org/IMGTrepertoire/LocusGenes/genetable/human/geneNumber. Nov 14, 2018

In the AR recombination process a gene in each gene group (V, D, of J) is combined with a gene in another group. As an example, an antibody (BCR) is created from one heavy chain molecule (IGH) and one light chain molecule (kappa [IGK] or lambda [IGL]). The heavy chain has V, D, and J genes, whereas the light chain loci only has V and J genes. In the IGH recombination process, a D gene is combined with a J gene and the resulting DJ gene is combined with a V gene. Light chain recombination simply combines a V gene with a J gene. Because the process is random, the number of possible VDJ or VJ genes is the product of the number of V, D and J genes, or V and J genes, respectively. The total diversity is the product of the VDJ and IGL VJ combinations plus the product of the VDJ and IGK VJ combinations (BCRs are dimers of one IGH and either an IGL, or an IGK chain).

When the above math is done the number of possible receptors is in the millions. While millions seems big, it is actually small when one considers that number of antigens that can be recognized is limitless. How is this possible? The simple answer is that the recombination process is “sloppy.” During recombination the gene segments are brought together via a protein complex that places the genes together and loops out the intervening DNA [2]. The loop is cleaved to create blunt terminal ends of DNA that are then joined together with enzymes that can add a variable number of random DNA bases at each V, D, or J junction to create a nearly limitless number of receptor sequences.

The recombination process. For loci with D genes the first step (1) is to combine one D gene with one J gene. Next (2), a V gene is combined with the DJ gene to create a VDJ unit (3). The additional bases are indicated by pink bars between V-D-J junctions. As V genes also contain promoters for transcription, a pre-mRNA is made that has the VDJ unit, any extra J genes, an intron, and the adjacent C gene (4). The last step is to splice out the intron, and any "extra" J genes (5) to create the mature AR mRNA.

Immunoprofiling samples the sequences of V(D)J regions

As one can expect, the V(D)J* junctions are the areas of highest diversity in ARs. The antigen recognition domains of the AR protein has three regions that interact with antigens. Also known as complementarity determining regions (CDRs), the first two, CDR1 and CDR2, are encoded by the V gene. CDR3 is encoded by the V(D)J junction region, and from an immunoprofiling perspective this is the most important region. As CDR3 segments are between 60 and 100 bases in length they are ideal candidates for massively parallel high-throughput short-read sequencing on the Illumina platform. Hence, immunoprofiling is a growth area in biotechnology.

In immunoprofiling assays, the CDR3 segment is sequenced from DNA or RNA (converted to cDNA). In either case PCR is used to amplify DNA containing CDR3 using V gene and J gene primers**. Even though the combinations of V(D)J that are possible in a sample is large, the number of primers required is simply the total number of V and J genes for a given AR locus. For example, profiling the TCR beta receptors (above figure) requires 61 primers (48 V gene and 13 J gene).

Despite the modest number of primers needed in an immunoprofiling assay the sequences that the primers bind to will result in significant differences in PCR amplification frequencies between individual receptor molecules. This is due to hybridization efficiency which is affect by the local DNA sequence. Primers that bind more efficiently result in greater amounts of amplified DNA. Thus to make immunoprofiling a quantitative assay, amplification differences need to be accounted for.

To account for amplification differences, Adaptive Biotechnologies (Seattle WA) developed synthetic DNA molecules that contain the same V and J gene primer binding sequences as the receptors that are being sequenced [3]. In our TCR beta example, up to 624 synthetic DNAs are needed. The synthetic DNA molecules are added (spiked in) to assays at a defined concentration. Once sequencing is complete the final dataset will have some number of sequences, called reads, that match each synthetic DNA. The ratio of the number of reads to their corresponding number of spiked in molecules corresponds to the PCR amplification frequency for that primer sequence combination. As the reads derived from sample material will also have many, if not all, ot the same primer combinations - with very different “middle” sequences - we can apply the ratios determined from the synthetic DNA reads to normalize the data.

Structure of V(D)J region and immunoprofiling . Either DNA or RNA (cDNA) can be sequenced with the same V and J gene primers. The bottom diagram shows the AR binding region. CDRs are flanked by Framework regions (FWR). V gene and J gene primers bind to the FWR3 (V) and FWR4 (J) regions, respectively. The pink regions indicate the additional bases that are added during recombination.

DNA sequencing-based immunoprofiling quantitatively measures AR diversity in samples by determining the sequences of V(D)J junctions. AR receptor diversity is vast due to a combinatorial rearrangement process that inserts a variable number of random DNA bases at each junction. In the sequencing process V(D)J junctions are amplified with V and J gene specific primers and, to be quantitative, differences in amplification rates that are due to primer sequences must be factored into each assay.

References / notes:

* The (D) in V(D)J is to note that a D gene is it present in some chains (IGH, TCRB, TCRD) but not others (IGL, IGK, TCRA, TCRG).

1. Litman GW, Rast JP, Fugmann SD. The origins of vertebrate adaptive immunity. Nat Rev Immunol. 201010(8):543-53.

3. Carlson CS, Emerson RO, Sherwood AM, Desmarais C, Chung MW, Parsons JM, Steen MS, LaMadrid-Herrmannsfeldt MA, Williamson DW, Livingston RJ, Wu D, Wood BL, Rieder MJ, Robins H. Using synthetic templates to design an unbiased multiplex PCR assay. Nat Commun. 20134:2680.

** There are other ways to prepare DNA for immunoprofiling that do not require individual primers. Such assays will have more steps that can introduce other kinds of artifacts, and (or) be limited to RNA sequencing.


Watch the video: Antigens u0026 Antibodies (June 2022).