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L A Nemytikova

Publications and source records attributed to L A Nemytikova.

4 recordsLinked to original sources

Promoter shuffling has occurred during the evolution of the vertebrate growth hormone gene.

Comparative studies of vertebrate gene promoter regions seldom detect gross rearrangements ('promoter shuffling') since such analyses usually employ relatively similar DNA sequences. Conversely, attempts to compare evolutionarily more divergent promoter sequences have been largely unsuccessful owing to the inability of conventional alignment procedures to deal with gross rearrangements. These limitations have been circumvented in the present study by using the novel technique of complexity analysis to identify modular components ('blocks') in the growth hormone (GH) gene promoter sequences of some 22 vertebrate species, from salmon to human. Significant rearrangement of blocks was found to have occurred, indicating that they have evolved as independent units. Some blocks appear to be ubiquitous, whereas others are restricted to a specific taxon. Considerable variation between orthologous GH gene promoters was apparent in terms of block length, copy number and relative location. It may be inferred that a wide variety of different mutational mechanisms have operated upon the GH gene promoter over evolutionary time. These include gross changes such as deletion, duplication, amplification, elongation, contraction, transposition, inversion and fusion, as well as the slow, steady accumulation of single base-pair substitutions. Thus the patchwork structure of the modular GH promoter region, and those of its paralogous GH2 and prolactin (PRL) counterparts, have continually been shuffled into new combinations through the rearrangement of pre-existing blocks. Although some of these changes may have had no influence on promoter function, others could have served to alter either the level of gene expression or the responsiveness of the promoter to external stimuli.

Animals↗

On the complexity measures of genetic sequences.

MOTIVATION: It is well known that the regulatory regions of genomes are highly repetitive. They are rich in direct, symmetric and complemented repeats, and there is no doubt about the functional significance of these repeats. Among known measures of complexity, the Ziv-Lempel complexity measure reflects most adequately repeats occurring in the text. But this measure does not take into account isomorphic repeats. By isomorphic repeats we mean fragments that are identical (or symmetric) modulo some permutation of the alphabet letters. RESULTS: In this paper, two complexity measures of symbolic sequences are proposed that generalize the Ziv-Lempel complexity measure by taking into account any isomorphic repeats in the text (rather than just direct repeats as in Ziv-Lempel). The first of them, the complexity vector, is designed for small alphabets such as the alphabet of nucleotides. The second is based on a search for the longest isomorphic fragment in the history of sequence synthesis and can be used for alphabets of arbitrary cardinality. These measures have been used for recognition of structural regularities in DNA sequences. Some interesting structures related to the regulatory region of the human growth hormone are reported.

Algorithms↗

[A rapid method for detecting interconnections between functionally and/or evolutionary close biological sequences].

It is now common practice to retrieve, by key words, highly specialized selections of sequences from general-purpose databases such as EMBL, GenBank, etc. The sequences included in a selection are often interconnected, which means that there are duplications, embeddings, intersections, homology, common structural elements. Knowledge of these interconnections is necessary for further processing of the sequences. We propose a rapid (single scan) method for identification of such interconnections by means of complexity analysis that generalizes the Lempel-Ziv approach. Analysis of a selection of 5'-flanking regions of vertebrate growth hormone genes from EMBL is presented as an example.

Databases, Nucleic Acid↗