RESUMO
The analysis of transcriptional temporal noise could be an interesting means to study gene expression dynamics and stochasticity in eukaryotes. To study the statistical distributions of temporal noise in the eukaryotic model system Saccharomyces cerevisiae, we analyzed microarray data corresponding to one cell cycle for 6200 genes. We found that the temporal noise follows a lognormal distribution with scale invariance at the genome, chromosomal and sub-chromosomal levels. Correlation of temporal noise with the codon adaptation index suggests that at least 70% of all protein-coding genes are a noise minimization core of the genome. Accordingly, a mathematical model of individual gene expression dynamics was proposed, using an operator theoretical approach, which reveals strict conditions for noise variability and a possible global noise minimization/optimization strategy at the genome level. Our model and data show that minimal noise does not correspond to genes obeying a strictly deterministic dynamics. The natural strategy of minimization consists in equating the mean of the absolute value of the relative variation of the expression level (alpha) with noise (eta). We hypothesize that the temporal noise pattern is an emergent property of the genome and shows how the dynamics of gene expression could be related to chromosomal organization.
Assuntos
Genoma Fúngico , Saccharomyces cerevisiae/genética , Transcrição Gênica , Regulação Fúngica da Expressão Gênica , Redes Reguladoras de Genes , Modelos Genéticos , Modelos Estatísticos , Modelos Teóricos , Análise de Sequência com Séries de Oligonucleotídeos , Saccharomyces cerevisiae/metabolismo , Fatores de TempoRESUMO
The objective of this study was to identify midgut microvillar proteins in insects appearing earlier (Coleoptera) and later (Lepidoptera) in evolution. For this, cytoskeleton-free midgut microvillar membrane from Spodoptera frugiperda (Lepidoptera) and Tenebrio molitor (Coleoptera) were used to raise antibodies. These were used for screening midgut cDNA expression libraries. Positive clones were sequenced, assembled and searched for similarities with gene/protein databases. The predicted midgut microvillar proteins from T. molitor were: cockroach allergens (unknown function), peritrophins (peritrophic membrane proteins), digestive enzymes (aminopeptidase, alpha-mannosidase) and unknown proteins. Predicted S. frugiperda midgut proteins may be grouped into six classes: (a) proteins involved in protection of midgut (thioredoxin peroxidase, aldehyde dehydrogenase, serpin and juvenile hormone epoxide hydrolase); (b) digestive enzymes (astacin, transporter-like amylase, aminopeptidase, and carboxypeptidase); (c) peritrophins; (d) proteins associated with microapocrine secretion (gelsolin, annexin); (e) membrane-tightly bound-cytoskeleton proteins (fimbrin, calmodulin) and (f) unidentified proteins. The novel approach is compared with others and microvillar function is discussed in the light of the predicted proteins.
Assuntos
Trato Gastrointestinal/metabolismo , Biblioteca Gênica , Proteínas de Insetos/metabolismo , Microvilosidades/metabolismo , Spodoptera/metabolismo , Tenebrio/metabolismo , Animais , Anticorpos/imunologia , Evolução Biológica , Trato Gastrointestinal/imunologia , Perfilação da Expressão Gênica , Proteínas de Insetos/genética , Proteínas de Insetos/imunologia , Microvilosidades/imunologia , Spodoptera/genética , Spodoptera/imunologia , Tenebrio/genética , Tenebrio/imunologiaRESUMO
The analysis of transcriptional temporal noise could be an interesting means to study gene expression dynamics and stochasticity in eukaryotes. To study the statistical distributions of temporal noise in the eukaryotic model system Saccharomyces cerevisiae, we analyzed microarray data corresponding to one cell cycle for 6200 genes. We found that the temporal noise follows a lognormal distribution with scale invariance at the genome, chromosomal and sub-chromosomal levels. Correlation of temporal noise with the codon adaptation index suggests that at least 70% of all protein-coding genes are a noise minimization core of the genome. Accordingly, a mathematical model of individual gene expression dynamics was proposed, using an operator theoretical approach, which reveals strict conditions for noise variability and a possible global noise minimization/optimization strategy at the genome level. Our model and data show that minimal noise does not correspond to genes obeying a strictly deterministic dynamics. The natural strategy of minimization consists in equating the mean of the absolute value of the relative variation of the expression level (alpha) with noise (eta). We hypothesize that the temporal noise pattern is an emergent property of the genome and shows how the dynamics of gene expression could be related to chromosomal organization.
