Thais G. Rêgo

Origin and evolution of the Peptidyl Transferase Center from proto‐tRNAs

We tested the hypothesis of Tamura (2011) [3] that molecules of tRNA gave origin to ribosomes, particularly to the Peptidyl Transferase Center (PTC) of the 23S ribosomal RNA. We reconstructed the ancestral sequences from all types of tRNA and compared them in their sequences with the current PTC of 23S ribosomal RNA from different organisms. We built an ancestral sequence of proto‐tRNAs that showed a remarkable overall identity of 50.53% with the catalytic site of PTC. We conclude that the Peptidyl Transferase Center was indeed originated by the fusion of ancestral sequences of proto‐tRNA.

Evolution of transfer RNA and the origin of the translation system.

The origin of the translation system is at the center of discussions about the evolution of biological systems. In this context, molecules of transfer RNA (tRNA) are highlighted due to its ability to convey the information contained in nucleic acids with the functional information contained in the proteins. Despite many characteristics shared among tRNAs in various organisms, suggesting a monophyletic origin for this group of molecules, recent discussions have proposed a polyphyletic origin for this group, thus indicating that the shared features are products of evolutionary convergence (Di Giulio, 2013). The main arguments in favor of the model for polyphyletic origin of tRNAs, is based on the theory of exons and suggests that the introns played an important role uniting mini exons or genes, which enabled that minigenes with independent origins were grouped in a single transcription unit at the start of the biological system (Di Giulio, 2012a). Genes for tRNAs have one of the most conserved introns that we know, which would represent a remnant of the process that gave rise to this molecule, being the anticodon loop initially a minigene that was attached to the other loop or hairpin that gave origin to the modern structure of tRNAs (Di Giulio, 2012b). An evidence of this model was found in Nanoarchaeum equitans, where a single tRNA is encoded by two genes that are united after the transcription (Randau et al., 2005). Podar et al. (2013), analyzed the genome of N. equitans and suggested that the organization of genes in this organism is a derived character, being consequence of a process of genomic reduction that is associated with their lifestyle (Podar et al., 2013). Thus, the tRNAs would be monophyletic, having a single ancestor that gave origin to the diversity known today, as suggested by Lacey and Staves (1990).

tRNA Core Hypothesis for the Transition from the RNA World to the Ribonucleoprotein World

Herein we present the tRNA core hypothesis, which emphasizes the central role of tRNAs molecules in the origin and evolution of fundamental biological processes. tRNAs gave origin to the first genes (mRNA) and the peptidyl transferase center (rRNA), proto-tRNAs were at the core of a proto-translation system, and the anticodon and operational codes then arose in tRNAs molecules. Metabolic pathways emerged from evolutionary pressures of the decoding systems. The transitions from the RNA world to the ribonucleoprotein world to modern biological systems were driven by three kinds of tRNAs transitions, to wit, tRNAs leading to both mRNA and rRNA.

A Novel Method to Predict Genomic Islands Based on Mean Shift Clustering Algorithm.

Genomic Islands (GIs) are regions of bacterial genomes that are acquired from other organisms by the phenomenon of horizontal transfer. These regions are often responsible for many important acquired adaptations of the bacteria, with great impact on their evolution and behavior. Nevertheless, these adaptations are usually associated with pathogenicity, antibiotic resistance, degradation and metabolism. Identification of such regions is of medical and industrial interest. For this reason, different approaches for genomic islands prediction have been proposed. However, none of them are capable of predicting precisely the complete repertory of GIs in a genome. The difficulties arise due to the changes in performance of different algorithms in the face of the variety of nucleotide distribution in different species. In this paper, we present a novel method to predict GIs that is built upon mean shift clustering algorithm. It does not require any information regarding the number of clusters, and the bandwidth parameter is automatically calculated based on a heuristic approach. The method was implemented in a new user-friendly tool named MSGIP—Mean Shift Genomic Island Predictor. Genomes of bacteria with GIs discussed in other papers were used to evaluate the proposed method. The application of this tool revealed the same GIs predicted by other methods and also different novel unpredicted islands. A detailed investigation of the different features related to typical GI elements inserted in these new regions confirmed its effectiveness. Stand-alone and user-friendly versions for this new methodology are available at http://msgip.integrativebioinformatics.me.

A proposal of the proteome before the last universal common ancestor (LUCA)

The search for understanding the biological nature of the last universal common ancestor (LUCA) has been a theoretical challenge and has sparked intense debate in the scientific community. We reconstructed the ancestral sequences of tRNAs in order to test the hypothesis that these molecules originated the first genes. The results showed that the proteome before LUCA may have been composed of basal energy metabolism, namely, compounds with three carbons in the glycolytic pathway, which operated as a distribution centre of substrates for the development of metabolic pathways of nucleotides, lipids and amino acids. Thus, we present a proposal for metabolism in organisms before LUCA that was the initial core for the assembly of further metabolic pathways. Received 6 August 2015, accepted 20 October 2015

Peptidyl Transferase Center and the Emergence of the Translation System

In this work, the three-dimensional (3D) structure of the ancestral Peptidyl Transferase Center (PTC) built by concatamers of ancestral sequences of tRNAs was reconstructed, and its possible interactions with tRNAs molecules were analyzed. The 3D structure of the ancestral PTC was also compared with the current PTC of T. thermophilus. Docking experiments between the ancestral PTC and tRNAs suggest that in the origin of the translation system, the PTC functioned as an adhesion center for tRNA molecules. The approximation of tRNAs charged with amino acids to the PTC permitted peptide synthesis without the need of a genetic code.

Origin and Evolution of RNA-Dependent RNA Polymerase

RNA-dependent RNA polymerases (RdRp) are very ancient enzymes and are essential for all viruses with RNA genomes. We reconstruct the origin and evolution of this polymerase since the initial stages of the origin of life. The origin of the RdRp was traced back from tRNA ancestors. At the origin of the RdRp the most ancient part of the protein is the cofactor-binding site that had the capacity of binding to simple molecules as magnesium, calcium, and ribonucleotides. Our results suggest that RdRp originated from junctions of proto-tRNAs that worked as the first genes at the emergence of the primitive translation system, where the RNA was the informational molecule. The initial domain, worked as a building block for the emergence of the fingers and thumb domains. From the ancestral RdRp, we could establish the evolutionary stages of viral evolution from a rooted ancestor to modern viruses. It was observed that the selective pressure under the RdRp was the organization and functioning of the genome, where RNA double-stranded and RNA single-stranded virus formed a separate group. We propose an evolutionary route to the polymerases and the results suggest an ancient scenario for the origin of RNA viruses.

Structural evolution of Glycyl-tRNA synthetases alpha subunit and its implication in the initial organization of the decoding system

The origin and evolution of the genetic code is a fundamental challenge in modern biology. At the center of this problem is the correct interaction between amino acids and tRNAs. Aminoacyl-tRNA synthetase is the enzyme responsible for the correct binding between amino acids and tRNAs. Among the 20 canonical amino acid, glycine was the most abundant in prebiotic condition and it must have been one of the first to be incorporated into the genetic code. In this work, we derive the ancestral sequence of Glycyl-tRNA synthetase (GlyRS) and predict its 3D-structure. We show, via molecular docking experiments, the capacity of ancestral GlyRS to bind the tRNA anticodon stem loop, cofactors and substrates. These bindings exhibit high affinity and specificity. We propose that the primordial function of these interactions was to stabilize both compounds to make possible the catalysis. In this context, the anticodon stem loop did contribute to the encoding system and just with the emergence of the mRNA it was co-opted for codification. Thus, we present a model for the origin of the genetic code in which the operational and the anticodon codes did not evolve independently.

Information Theory-Based Detection of Noisy Bit Planes in Medical Images

Mammographic Computer-Aided Diagnosis systems are applications designed to assist radiologists in diagnosis of malignancy in mammographic findings. Most methods described in the literature do not perform a proper preprocessing step in mammographic images prior to classification, which can generate inconsistent results due to the potentially large amount of noise in medical images. This paper proposes a new method based on Information Theory and Data Compression for detection of random noise in image bit planes. In order to validate the efficiency of the proposed noise removal method, we used Machine Learning algorithms to classify mammographic findings from the Digital Database for Screening Mammography. Results using texture features indicate that a reduction in the radiometric resolution of 4 or 5 bit planes in digitized screen film mammographic images result in a better classification performance.