Indirect Routes to Aminoacyl-tRNA: The Diversity of Prokaryotic Cysteine Encoding Systems

The genetic code is not universal

Recently, a new genetic code with 62 sense codons, coding for 21 amino acids, and only 2 termination codons has been identified in archaea. The authors argue that the appearance of this variant of the genetic code is due to the relatively recent and complete recoding of all UAG stop codons to codons encoding for pyrrolysine. I re-evaluate this discovery by presenting arguments that favour the early, i.e. ancestral, appearance of this variant of the genetic code during the origin of the genetic code itself. These arguments are capable of supporting that during the origin of the organization of the genetic code, at least two versions of the genetic code evolved in the domain of the Archaea. Thus, the genetic code would not be absolutely universal.

The existence of the two domains of life, Bacteria and Archaea, would in itself imply that LUCA and the ancestors of these domains were progenotes

The length of the deepest branches of the tree of life would tend to support the hypothesis that the distance of the branch that separates the sequences of archaea from those of bacteria, i.e. the interdomain one, is longer than the intradomain ones, i.e. those that separate the sequences of archaea and those of bacteria within them. Why should interdomain distance be larger than intradomain distances? The fact that the rate of amino acid substitutions was slowed as the domains of life appeared would seem to imply an evolutionary transition. The slowdown in the speed of evolution that occurred during the formation of the two domains of life would be the consequence of the progenote- > cell evolutionary transition. Indeed, the evolutionary stage of the progenote being characterized by an accelerated tempo and mode of evolution might explain the considerable interdomain distance because the accumulation of many amino acid substitutions on this branch would indicate the progenote stage that is also characterized by a high rate of amino acid substitutions. Furthermore, the fact that intradomain distances are smaller than interdomain distances would corroborate the hypothesis of the achievement of cellularity at the appearance of the main phyletic lineages. Indeed, the cell stage, unlike the progenotic one, definitively establishes the relationship between the genotype and phenotype, lowering the rate of evolution. Therefore, the arguments presented lead to the conclusion that LUCA was a progenote.

Mechanisms and Delivery of tRNA Therapeutics

Transfer ribonucleic acid (tRNA) therapeutics will provide personalized and mutation specific medicines to treat human genetic diseases for which no cures currently exist. The tRNAs are a family of adaptor molecules that interpret the nucleic acid sequences in our genes into the amino acid sequences of proteins that dictate cell function. Humans encode more than 600 tRNA genes. Interestingly, even healthy individuals contain some mutant tRNAs that make mistakes. Missense suppressor tRNAs insert the wrong amino acid in proteins, and nonsense suppressor tRNAs read through premature stop signals to generate full length proteins. Mutations that underlie many human diseases, including neurodegenerative diseases, cancers, and diverse rare genetic disorders, result from missense or nonsense mutations. Thus, specific tRNA variants can be strategically deployed as therapeutic agents to correct genetic defects. We review the mechanisms of tRNA therapeutic activity, the nature of the therapeutic window for nonsense and missense suppression as well as wild-type tRNA supplementation. We discuss the challenges and promises of delivering tRNAs as synthetic RNAs or as gene therapies. Together, tRNA medicines will provide novel treatments for common and rare genetic diseases in humans.

Drying-wetting cycle enhances stress resistance of Escherichia coli O157:H7 in a model soil

Outbreaks of Escherichia coli (E. coli) O157:H7 in farms are often triggered by heavy rains and flooding. Most cells die with the decreasing of soil moisture, while few cells enter a dormant state and then resuscitate after rewetting. The resistance of dormant cells to stress has been extensively studied, whereas the molecular mechanisms of the cross-resistance development of the resuscitated cells are poorly known. We performed a comparative proteomic analysis on O157:H7 before and after undergoing soil dry-wet alternation. A differential expression of 820 proteins was identified in resuscitated cells compared to exponential-phase cells, as determined by proteomics analysis. The GO and KEGG pathway enrichment analyses revealed that up-regulated proteins were associated with oxidative phosphorylation, glycolysis/gluconeogenesis, the citrate cycle (TCA cycle), aminoacyl-tRNA biosynthesis, ribosome activity, and transmembrane transporters, indicating increased energy production and protein synthesis in resuscitated O157:H7. Moreover, proteins related to acid, osmotic, heat, oxidative, antibiotic stress and horizontal gene transfer efficiency were up-regulated, suggesting a potential improvement in stress resistance. Subsequent validation experiments demonstrated that the survival rates of the resuscitated cells were 476.54 and 7786.34 times higher than the exponential-phase cells, with pH levels of 1.5 and 2.5, respectively. Similarly, resuscitated cells showed higher survival rates under osmotic stress, with 7.5%, 15%, and 30% NaCl resulting in survival rates that were 460.58, 1974.55, and 3475.31 times higher. Resuscitated cells also exhibited increased resistance to heat stress, with survival rates 69.64 and 139.72 times higher at 55 °C and 90 °C, respectively. Furthermore, the horizontal gene transfer (HGT) efficiency of resuscitated cells was significantly higher (153.12-fold) compared to exponential phase cells. This study provides new insights into bacteria behavior under changing soil moisture and this may explain O157:H7 outbreaks following rainfall and flooding, as the dry-wet cycle promotes stress cross-resistance development.

The Selenoproteome as a Dynamic Response Mechanism to Oxidative Stress in Hydrogenotrophic Methanogenic Communities

Methanogenesis is a critical process in the carbon cycle that is applied industrially in anaerobic digestion and biogas production. While naturally occurring in diverse environments, methanogenesis requires anaerobic and reduced conditions, although varying degrees of oxygen tolerance have been described. Microaeration is suggested as the next step to increase methane production and improve hydrolysis in digestion processes; therefore, a deeper understanding of the methanogenic response to oxygen stress is needed. To explore the drivers of oxygen tolerance in methanogenesis, two parallel enrichments were performed under the addition of H2/CO2 in an environment without reducing agents and in a redox-buffered environment by adding redox mediator 9,10-anthraquinone-2,7-disulfonate disodium. The cellular response to oxidative conditions is mapped using proteomic analysis. The resulting community showed remarkable tolerance to high-redox environments and was unperturbed in its methane production. Next to the expression of pathways to mitigate reactive oxygen species, the higher redox potential environment showed an increased presence of selenocysteine and selenium-associated pathways. By including sulfur-to-selenium mass shifts in a proteomic database search, we provide the first evidence of the dynamic and large-scale incorporation of selenocysteine as a response to oxidative stress in hydrogenotrophic methanogenesis and the presence of a dynamic selenoproteome.

Unconventional genetic code systems in archaea

Archaea constitute the third domain of life, distinct from bacteria and eukaryotes given their ability to tolerate extreme environments. To survive these harsh conditions, certain archaeal lineages possess unique genetic code systems to encode either selenocysteine or pyrrolysine, rare amino acids not found in all organisms. Furthermore, archaea utilize alternate tRNA-dependent pathways to biosynthesize and incorporate members of the 20 canonical amino acids. Recent discoveries of new archaeal species have revealed the co-occurrence of these genetic code systems within a single lineage. This review discusses the diverse genetic code systems of archaea, while detailing the associated biochemical elements and molecular mechanisms.

Machine Learning Algorithms Highlight tRNA Information Content and Chargaff’s Second Parity Rule Score as Important Features in Discriminating Probiotics from Non-Probiotics

Simple Summary

Probiotics are a group of beneficial microorganisms that are symbionts of the human gut microbiome. The identification of new probiotics is therefore of paramount importance from both public health and commercial perspectives. In this study, we show for the first time that Artificial Intelligence algorithms can identify novel probiotics and also discriminate them from pathogenic organisms in the human gut. We were also able to determine the information content within tRNA sequences as the key genomic features capable of characterizing probiotics.

Abstract

Probiotic bacteria are microorganisms with beneficial effects on human health and are currently used in numerous food supplements. However, no selection process is able to effectively distinguish probiotics from non-probiotic organisms on the basis of their genomic characteristics. In the current study, four Machine Learning algorithms were employed to accurately identify probiotic bacteria based on their DNA characteristics. Although the prediction accuracies of all algorithms were excellent, the Neural Network returned the highest scores in all the evaluation metrics, managing to discriminate probiotics from non-probiotics with an accuracy greater than 90%. Interestingly, our analysis also highlighted the information content of the tRNA sequences as the most important feature in distinguishing the two groups of organisms probably because tRNAs have regulatory functions and might have allowed probiotics to evolve faster in the human gut environment. Through the methodology presented here, it was also possible to identify seven promising new probiotics that have a higher information content in their tRNA sequences compared to non-probiotics. In conclusion, we prove for the first time that Machine Learning methods can discriminate human probiotic from non-probiotic organisms underlining information within tRNA sequences as the most important genomic feature in distinguishing them.

The tRNA discriminator base defines the mutual orthogonality of two distinct pyrrolysyl-tRNA synthetase/tRNAPyl pairs in the same organism

Site-specific incorporation of distinct non-canonical amino acids into proteins via genetic code expansion requires mutually orthogonal aminoacyl-tRNA synthetase/tRNA pairs. Pyrrolysyl-tRNA synthetase (PylRS)/tRNAPyl pairs are ideal for genetic code expansion and have been extensively engineered for developing mutually orthogonal pairs. Here, we identify two novel wild-type PylRS/tRNAPyl pairs simultaneously present in the deep-rooted extremely halophilic euryarchaeal methanogen Candidatus Methanohalarchaeum thermophilum HMET1, and show that both pairs are functional in the model halophilic archaeon Haloferax volcanii. These pairs consist of two different PylRS enzymes and two distinct tRNAs with dissimilar discriminator bases. Surprisingly, these two PylRS/tRNAPyl pairs display mutual orthogonality enabled by two unique features, the A73 discriminator base of tRNAPyl2 and a shorter motif 2 loop in PylRS2. In vivo translation experiments show that tRNAPyl2 charging by PylRS2 is defined by the enzyme's shortened motif 2 loop. Finally, we demonstrate that the two HMET1 PylRS/tRNAPyl pairs can simultaneously decode UAG and UAA codons for incorporation of two distinct noncanonical amino acids into protein. This example of a single base change in a tRNA leading to additional coding capacity suggests that the growth of the genetic code is not yet limited by the number of identity elements fitting into the tRNA structure.