Synonymous mutations and neutral evolution in bacteria
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Analysis of Transcriptional Data of Human Neural Progenitor Cells Exposed to Lead
Molecular Evolution. Codon Bias. Long term evolution experiment. Adaptiveness index. Positive selection. Synonymous mutation.
Lead (Pb) exposure. Transcriptogramer. RNA-Seq. Transcriptome analysis. Time series. Network inference. Data integration. Network visualization.
Part I
The study of molecular evolution allows inferring how genetic mutations work over time. Long Term Evolution Experiments (LTEE) brings to us new way to corroborate theories wich was based only limited and restricted experiments. This work aims to verify the influence of synonymous mutations (Syn) over prokaryotes fitness, its correlation with genome codon bias and develop a method to measure this correlation. Later we try to correlate observed mutation under a systemic approach where it was compared to the genome plasticity index and expression data. Therefore we use data of an LTEE involving 50,000 generations of E. coli REL606 where 12 bacteria populations had two clones sequenced over 11 distinct generations. For each group population/age, all observed Single Nucleotide Variants (SNV) was classified as Syn or Nonsynonymous (Non) and recorded. Was create one genome per group with all observed mutations to act as a reference for next generation and the codon adaptation index (w) was calculated. We propose a w variation index (∆w), based on the ratio of mutation's w over reference's w, to quantify the influence of codon bias over Syn preservation. The analysis of ∆w values could allow inferring if a hypothetical slowing down on the translation ratio of mutated mRNA have any impact on selection, creating a selective pressure over Syn. We created a random and selection free comparison base using a 20,000 rounds Monte Carlo simulation (MC) where aleatory mutations are inserted into reference genome, classified and it ∆w was calculated. In the end, we couldn't confirm the initial hypothesis, and an unequivocal relation between mutations and neutral selection couldn't be established.
Part II
Implications of lead poisoning are important for human health. Lead virtually affects all human systems, especially nervous system. Health concern about lead poisoning relies in its irreversible detriment to neurodevelopment, affecting normal memory consolidation and learning processes in children. Lead has been reported to interact with many cellular components, such as ion binding proteins, transduction signaling proteins, membrane ion channels, and transcription factors. Here, we applied the transcriptogramer R/Bioconductor package pipeline to evaluate the transcriptional profile of human neural progenitor cells (NPCs) treated with lead acetate 30μM in a time-dependent manner. Transcriptogramer is a non-supervised system biology-based method designed to identify functionally associated gene groups differentially expressed in case-control designed experiments. The pipeline was able to identify differentially expressed clusters in early time of lead treatment. Those clusters increased in number of genes as times goes on. Enrichment analysis of differentially expressed clusters revealed the modification in glycosylation pathways, lipid biosynthesis, G-protein pathways, cytoskeleton organization, ion channels metabolism, and cellular division process in early time of treatment. In late time of treatment, nearby genes were incorporated on each cluster and new GO terms have been identified, such as zinc and calcium related terms. The transcriptogramer pipeline was able to identify several genetic systems transcriptionally altered in lead-treated NPCs, including pathways welldescribed as affected by lead. In conclusion, lead induces huge transcriptional modifications in NPCs which can be related to system damage and/or system adaptation in response to lead.