DNA sequence of the constant gene region of the mouse immunoglobulin kappa chain

135

Section: E S G V P D R F T 0 S C S 0 T D F T L T I S S V Q a E D Lmentioning

confidence: 55%

Two murine natural polyreactive autoantibodies are encoded by nonmutated germ-line genes.

Baccalà

Quang

Gilbert

et al. 1989

135

“…To evaluate possible mechanisms, information on the distribution of somatic mutations is undoubtedly required (5,(16)(17)(18)(19). Both murine and human immunoglobulin gene sequences show a lack of somatic mutation in the constant (C) region exons of H and K chains (5,17,20,21). However, Cleary et al (22) reported the occurrence of somatic mutation in the human CA exon.…”

mentioning

confidence: 99%

Somatic mutation in constant regions of mouse lambda 1 light chains.

Motoyama

Okada

Azuma

1991

To study the distribution of somatic mutation, we determined nucleotide sequences of rearranged Alchain genomic DNA from four hybridomas obtained from C57BL/6 mice that had been immunized with (4-hydroxy-3-nitrophenyl)acetyl-conjugated chicken gamma globulin. In total, 114 nucleotide substitutions were observed, with neither insertion nor deletion. Sixty-one mutations occurred in the variable-joining region genes (VAI-JA1) and 49 in joiningconstant (JAI-CAl) introns. Although frequency decreased with distance from the VAI-JAl coding region, somatic mutations occurred in the entire JAI-CAI intron and even in the CAI region. We found four nucleotide substitutions in CA, genes, all of which were replacement mutations. Therefore, the mechanism responsible for somatic mutation is operative into the CA, exons. Nucleotide sequences of rearranged but inactive A2-chain genes from two hybridomas were also examined and compared with those of Al-chain genes. The clustering of replacement mutations in complementarity-determining regions in the inactive A2-chain genes similar to the active Al-chain genes suggested a mechanism that induces somatic mutation preferentially in this region even in the absence of antigenic selection.Sequence diversity in the variable (V) domains of immunoglobulins is generated by random recombination of V, diversity (D), and joining (J) gene segments of heavy (H) chains or V and J segments of light (L) chains (1). Insertion of the N region and flexibility in the recombination sites also result in the variation in the V domain sequences (2). Somatic mutation introduces additional variation to VH and VL domains (1, 3-6). It has been shown that somatic mutation is triggered by stimulation with thymus-dependent antigens (3, 4, 6, 7) and is found in rearranged immunoglobulin genes both active and inactive (8)(9)(10)(11)(12). The somatic mutation mechanism is as yet unclear, although several models have been proposed (13-15). To evaluate possible mechanisms, information on the distribution of somatic mutations is undoubtedly required (5,(16)(17)(18)(19). Both murine and human immunoglobulin gene sequences show a lack of somatic mutation in the constant (C) region exons of H and K chains (5,17,20,21). However, Cleary et al. (22) reported the occurrence of somatic mutation in the human CA exon. Because of the complex genetics and gene structure of human immunoglobulins, it is rather difficult to distinguish between nucleotide variations arising from somatic mutation and those from other mechanisms.Murine A chains are particularly useful for examining the distribution of somatic mutations because of their simple gene structures (23-25). The A-chain genes possess only two VL genes (VA1 and VA2), which recombine with JA1, JA2, orJA3.Selective recombination of VA, to JA1CA1 or JA3-CA3 has been commonly observed (26,27). Therefore, a variation in sequence as a result of somatic mutation could be easily distinguished from that generated by other mechanisms.Furthermore, the J-C introns of A-chain genes di...

“…Chicken 3-globin 15 11 Human IgG K chain constant 16 12 Mouse IgG K chain constant 17 13 Mouse IgG1 constant 18 14 Mouse IgG K chain variable 19 15 Mouse IgG2 variable 20 16 Human preproinsulin 21, 22 17 Rat preproinsulin 23 18 Chicken preproinsulin 24 …”

mentioning

confidence: 99%

“…The other 9 globins are well-represented by a single Markov chain of order 1 ( Table 3 (rows [14][15][16][17]. In most cases, they are less similar than pairs of noncoding sequences in Table 4.…”

mentioning

confidence: 99%

“…This fact may indicate the presence of very different structures in the noncoding sequences for controlling the distinct expression of these genes of disparate function. The two rat noncoding sequences (17,20) (prolactin, proinsulin) and the two chicken noncoding sequences (18,29) (ovalbumin, proinsulin) are also intrapair distant. Similarly, while the seven immunoglobulins form a well-distinguished cluster in the coding tree, they are distributed all over the noncoding tree (11,12,13,14,15,35,36), perhaps also indicating distinct controls of expression, as seems reasonable.…”

mentioning

confidence: 99%

See 1 more Smart Citation

A measure of the similarity of sets of sequences not requiring sequence alignment.

Blaisdell¹

1986

311

192

Determination of first-and second-order Markov chain homogeneity of sets of nuclear eukaryotic DNA sequences, both coding and noncoding, finds similarities imperceptible to the standard Needleman-Wunsch base matching or dot-matrix algorithms. These measures of the similarities of the distributions of adjacent pairs or triplets are in agreement with accepted evolutionary-tree topologies. Hierarchical clustering of the distributions of doublets of 30 miscellaneous coding sequences gives clusters in reasonable agreement with accepted biological classifications. In addition to similarity by homology, there is also observed similarity of disparate genes in the same organism-for example, all three disparate yeast genes (two enzymes and actin) form a well-distinguished cluster.Sixty-four miscellaneous eukaryotic DNA sequences, half coding and half noncoding, have recently been examined as expressions of first-, second-, or third-order Markov chains (1). Standard statistical tests (2, 3) found that 61/64 required at least a first-order Markov chain (that is, not zeroth order) for their expression, 37/64 required at least second order, and a few required at least third order. For 64/64 sequences, the one-step second-order transition count matrix (counts of consecutive triples of bases in sites 1, 2, and 3) made a better prediction from the occupants of sites 1, 2, and 3 of the occupant at site 4 than did the assumption of random occupancy at site 4, and similarly for 56/64 sequences at site 5. The statistical papers (2, 3) also provide tests of the homogeneity of several realizations of Markov chains of a given order-that is, tests of the hypothesis that the several observed sequences are samples generated by the same stochastic Markov chain. This paper presents the results of such tests ofhomogeneity ofMarkov chain representations of orders 1 and 2 for selected sets of sequences and compares the results with the Needleman-Wunsch base-matching (4) and dot-matrix (5) assessments of sequence similarity. METHODThe information theoretic test (3) of the homogeneity of n samples of transition matrices of order 1-that is, of distributions of pairs of contiguous bases, is as follows: n 4 4