Abstract
We consider the problem of biological complexity via a projection of protein-coding genes of complex organisms onto the functional space of the proteome. The latter can be defined as a set of all functions committed by proteins of an organism. Alternative splicing (AS) allows an organism to generate diverse mature RNA transcripts from a single mRNA strand and thus it could be one of the key mechanisms of increasing of functional complexity of the organism's proteome and a driving force of biological evolution. Thus, the projection of transcription units (TU) and alternative splice-variant (SV) forms onto proteome functional space could generate new types of relational networks (e.g. SV-protein function networks, SFN) and lead to discoveries of novel evolutionarily conservative functional modules. Such types of networks might provide new reliable characteristics of organism complexity and a better understanding of the evolutionary integration and plasticity of interconnection of genome-transcriptome-proteome functions.ResultsWe use the InterPro and UniProt databases to attribute descriptive features (keywords) to protein sequences. UniProt database includes a controlled and curated vocabulary of specific descriptors or keywords. The keywords have been assigned to a protein sequence via conserved domains or via similarity with annotated sequences. Then we consider the unique combinations of keywords as the protein functional labels (FL), which characterize the biological functions of the given protein and construct the contingency tables and graphs providing the projections of transcription units (TU) and alternative splice-variants (SV) onto all FL of the proteome of a given organism. We constructed SFNs for organisms with different evolutionary history and levels of complexity, and performed detailed statistical parameterization of the networks.ConclusionsThe application of the algorithm to organisms with different evolutionary history and level of biological complexity (nematode, fruit fly, vertebrata) reveals that the parameters describing SFN correlate with the complexity of a given organism. Using statistical analysis of the links of the functional networks, we propose new features of evolution of protein function acquisition. We reveal a group of genes and corresponding functions, which could be attributed to an early conservative part of the cellular machinery essential for cell viability and survival. We identify and provide characteristics of functional switches in the polyform group of TUs in different organisms. Based on comparison of mouse and human SFNs, a role of alternative splicing as a necessary source of evolution towards more complex organisms is demonstrated.The entire set of FL across many organisms could be used as a draft of the catalogue of the functional space of the proteome world.
Highlights
Information content of genome coding sequences unfolds via functions of proteins
We consider the unique combinations of keywords as the protein functional labels (FL), which characterize the biological functions of the given protein and construct the contingency tables and graphs providing the projections of transcription units (TU) and alternative splice-variants (SV) onto all FL of the proteome of a given organism
The application of the algorithm to organisms with different evolutionary history and level of biological complexity reveals that the parameters describing Splice-Function Networks (SFN) correlate with the complexity of a given organism
Summary
Information content of genome coding sequences unfolds via functions of proteins. Alternative splicing is one of the ways an organism uses for genome manifestation into its proteome. We consider the problem of projection of genetic information into the functional space of the proteome, where the latter is defined as a set of molecular functions performed by proteins. Not all of the functions of proteins manifest themselves at a level of macroscopic phenotype and the notion of redundancy of proteins could arise. An inventory of biological functions of protein is documented in resources such as the FunCat [2] and partly in the Gene Ontology [3] and these use biological knowledge. They include a hierarchical list of all known functions performed by biomolecules in a cell. The protein modules detected can be attributed to basic metabolic pathways and well-characterized cellular systems on a global scale
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