Thermophilic Adaptation in Prokaryotes Is Constrained by Metabolic Costs of Proteostasis

Examining the Effects of Temperature on the Evolution of Bacterial tRNA Pools

The genetic code consists of 61 codons coding for 20 amino acids. These codons are recognized by transfer RNAs (tRNAs) that bind to specific codons during protein synthesis. All organisms utilize less than all 61 possible anticodons due to base pair wobble: the ability to have a mismatch with a codon at its third nucleotide. Previous studies observed a correlation between the tRNA pool of bacteria and the temperature of their respective environments. However, it is unclear if these patterns represent biological adaptations to maintain the efficiency and accuracy of protein synthesis in different environments. A mechanistic mathematical model of mRNA translation is used to quantify the expected elongation rates and error rate for each codon based on an organism's tRNA pool. A comparative analysis across a range of bacteria that accounts for covariance due to shared ancestry is performed to quantify the impact of environmental temperature on the evolution of the tRNA pool. We find that thermophiles generally have more anticodons represented in their tRNA pool than mesophiles or psychrophiles. Based on our model, this increased diversity is expected to lead to increased missense errors. The implications of this for protein evolution in thermophiles are discussed.

Omics data analysis reveals the system-level constraint on cellular amino acid composition

Proteins play a pivotal role in coordinating the functions of organisms, essentially governing their traits, as the dynamic arrangement of diverse amino acids leads to a multitude of folded configurations within peptide chains. Despite dynamic changes in amino acid composition of an individual protein (referred to as AAP) and great variance in protein expression levels under different conditions, our study, utilizing transcriptomics data from four model organisms uncovers surprising stability in the overall amino acid composition of the total cellular proteins (referred to as AACell). Although this value may vary between different species, we observed no significant differences among distinct strains of the same species. This indicates that organisms enforce system-level constraints to maintain a consistent AACell, even amid fluctuations in AAP and protein expression. Further exploration of this phenomenon promises insights into the intricate mechanisms orchestrating cellular protein expression and adaptation to varying environmental challenges.

Identification and evolution of a diterpenoid phytoalexin oryzalactone biosynthetic gene in the genus Oryza

The natural variation of plant-specialized metabolites represents the evolutionary adaptation of plants to their environments. However, the molecular mechanisms that account for the diversification of the metabolic pathways have not been fully clarified. Rice plants resist attacks from pathogens by accumulating diterpenoid phytoalexins. It has been confirmed that the composition of rice phytoalexins exhibits numerous natural variations. Major rice phytoalexins (momilactones and phytocassanes) are accumulated in most cultivars, although oryzalactone is a cultivar-specific compound. Here, we attempted to reveal the evolutionary trajectory of the diversification of phytoalexins by analyzing the oryzalactone biosynthetic gene in Oryza species. The candidate gene, KSLX-OL, which accounts for oryzalactone biosynthesis, was found around the single-nucleotide polymorphisms specific to the oryzalactone-accumulating cultivars in the long arm of chromosome 11. The metabolite analyses in Nicotiana benthamiana and rice plants overexpressing KSLX-OL indicated that KSLX-OL is responsible for the oryzalactone biosynthesis. KSLX-OL is an allele of KSL8 that is involved in the biosynthesis of another diterpenoid phytoalexin, oryzalexin S and is specifically distributed in the AA genome species. KSLX-NOL and KSLX-bar, which encode similar enzymes but are not involved in oryzalactone biosynthesis, were also found in AA genome species. The phylogenetic analyses of KSLXs, KSL8s, and related pseudogenes (KSL9s) indicated that KSLX-OL was generated from a common ancestor with KSL8 and KSL9 via gene duplication, functional differentiation, and gene fusion. The wide distributions of KSLX-OL and KSL8 in AA genome species demonstrate their long-term coexistence beyond species differentiation, suggesting a balancing selection between the genes.

DeepPPThermo: A Deep Learning Framework for Predicting Protein Thermostability Combining Protein-Level and Amino Acid-Level Features

Using wet experimental methods to discover new thermophilic proteins or improve protein thermostability is time-consuming and expensive. Machine learning methods have shown powerful performance in the study of protein thermostability in recent years. However, how to make full use of multiview sequence information to predict thermostability effectively is still a challenge. In this study, we proposed a deep learning-based classifier named DeepPPThermo that fuses features of classical sequence features and deep learning representation features for classifying thermophilic and mesophilic proteins. In this model, deep neural network (DNN) and bi-long short-term memory (Bi-LSTM) are used to mine hidden features. Furthermore, local attention and global attention mechanisms give different importance to multiview features. The fused features are fed to a fully connected network classifier to distinguish thermophilic and mesophilic proteins. Our model is comprehensively compared with advanced machine learning algorithms and deep learning algorithms, proving that our model performs better. We further compare the effects of removing different features on the classification results, demonstrating the importance of each feature and the robustness of the model. Our DeepPPThermo model can be further used to explore protein diversity, identify new thermophilic proteins, and guide directed mutations of mesophilic proteins.

Determining the effects of temperature on the evolution of bacterial tRNA pools

The genetic code consists of 61 codon coding for 20 amino acids. These codons are recognized by transfer RNAs (tRNA) that bind to specific codons during protein synthesis. Most organisms utilize less than all 61 possible anticodons due to base pair wobble: the ability to have a mismatch with a codon at its third nucleotide. Previous studies observed a correlation between the tRNA pool of bacteria and the temperature of their respective environments. However, it is unclear if these patterns represent biological adaptations to maintain the efficiency and accuracy of protein synthesis in different environments. A mechanistic mathematical model of mRNA translation is used to quantify the expected elongation rates and error rate for each codon based on an organism's tRNA pool. A comparative analysis across a range of bacteria that accounts for covariance due to shared ancestry is performed to quantify the impact of environmental temperature on the evolution of the tRNA pool. We find that thermophiles generally have more anticodons represented in their tRNA pool than mesophiles or psychrophiles. Based on our model, this increased diversity is expected to lead to increased missense errors. The implications of this for protein evolution in thermophiles are discussed.

Main Factors Shaping Amino Acid Usage Across Evolution

The standard genetic code determines that in most species, including viruses, there are 20 amino acids that are coded by 61 codons, while the other three codons are stop triplets. Considering the whole proteome each species features its own amino acid frequencies, given the slow rate of change, closely related species display similar GC content and amino acids usage. In contrast, distantly related species display different amino acid frequencies. Furthermore, within certain multicellular species, as mammals, intragenomic differences in the usage of amino acids are evident. In this communication, we shall summarize some of the most prominent and well-established factors that determine the differences found in the amino acid usage, both across evolution and intragenomically.

Learning deep representations of enzyme thermal adaptation

Temperature is a fundamental environmental factor that shapes the evolution of organisms. Learning thermal determinants of protein sequences in evolution thus has profound significance for basic biology, drug discovery, and protein engineering. Here, we use a data set of over 3 million BRENDA enzymes labeled with optimal growth temperatures (OGTs) of their source organisms to train a deep neural network model (DeepET). The protein-temperature representations learned by DeepET provide a temperature-related statistical summary of protein sequences and capture structural properties that affect thermal stability. For prediction of enzyme optimal catalytic temperatures and protein melting temperatures via a transfer learning approach, our DeepET model outperforms classical regression models trained on rationally designed features and other deep-learning-based representations. DeepET thus holds promise for understanding enzyme thermal adaptation and guiding the engineering of thermostable enzymes.

Survival of the cheapest: how proteome cost minimization drives evolution

Darwin's theory of evolution emphasized that positive selection of functional proficiency provides the fitness that ultimately determines the structure of life, a view that has dominated biochemical thinking of enzymes as perfectly optimized for their specific functions. The 20th-century modern synthesis, structural biology, and the central dogma explained the machinery of evolution, and nearly neutral theory explained how selection competes with random fixation dynamics that produce molecular clocks essential e.g. for dating evolutionary histories. However, quantitative proteomics revealed that selection pressures not relating to optimal function play much larger roles than previously thought, acting perhaps most importantly via protein expression levels. This paper first summarizes recent progress in the 21st century toward recovering this universal selection pressure. Then, the paper argues that proteome cost minimization is the dominant, underlying 'non-function' selection pressure controlling most of the evolution of already functionally adapted living systems. A theory of proteome cost minimization is described and argued to have consequences for understanding evolutionary trade-offs, aging, cancer, and neurodegenerative protein-misfolding diseases.

The molecular chaperone Hsp70 from the thermotolerant Diptera species differs from the Drosophila paralog in its thermostability and higher refolding capacity at extreme temperatures

Previously, we demonstrated that species of the Stratiomyidae family exhibit higher tolerance to thermal stress in comparison with that of many representatives of Diptera, including Drosophila species. We hypothesized that species of this group inherited the specific structures of their chaperones from an ancestor of the Stratiomyidae family, and this enabled the descendants to colonize various extreme habitats. To explore this possibility, we cloned and expressed in Escherichia coli copies of the Hsp70 genes from Stratiomys singularior, a typical eurythermal species, and Drosophila melanogaster, for comparison. To investigate the thermal sensitivity of the chaperone function of the inducible 70-kDa heat shock proteins from these species, we used an in vitro refolding luciferase assay. We demonstrated that under conditions of elevated temperature, S. singularior Hsp70 exhibited higher reactivation activity in comparison with D. melanogaster Hsp70 and even human Hsp70. Similarly, S. singularior Hsp70 was significantly more thermostable and showed in vitro refolding activity after preheatment at higher temperatures than D. melanogaster paralog. Thermally induced unfolding experiments using differential scanning calorimetry indicated that Hsp70 from both Diptera species is formed by two domains with different thermal stabilities and that the ATP-binding domain of S. singularior is stable at temperatures 4 degrees higher than that of the D. melanogaster paralog. To the best of our knowledge, this study represents the first report that provides direct experimental data indicating that the evolutionary history of a species may result in adaptive changes in the structures of chaperones to enable them to elicit protective functions at extreme environments.

Machine Learning Applied to Predicting Microorganism Growth Temperatures and Enzyme Catalytic Optima

Enzymes that catalyze chemical reactions at high temperatures are used for industrial biocatalysis, applications in molecular biology, and as highly evolvable starting points for protein engineering. The optimal growth temperature (OGT) of organisms is commonly used to estimate the stability of enzymes encoded in their genomes, but the number of experimentally determined OGT values are limited, particularly for thermophilic organisms. Here, we report on the development of a machine learning model that can accurately predict OGT for bacteria, archaea, and microbial eukaryotes directly from their proteome-wide 2-mer amino acid composition. The trained model is made freely available for reuse. In a subsequent step we use OGT data in combination with amino acid composition of individual enzymes to develop a second machine learning model-for prediction of enzyme catalytic temperature optima ( T_opt). The resulting model generates enzyme T_opt estimates that are far superior to using OGT alone. Finally, we predict T_opt for 6.5 million enzymes, covering 4447 enzyme classes, and make the resulting data set available to researchers. This work enables simple and rapid identification of enzymes that are potentially functional at extreme temperatures.

Multidisciplinary involvement and potential of thermophiles

The full biotechnological exploitation of thermostable enzymes in industrial processes is necessary for their commercial interest and industrious value. The heat-tolerant and heat-resistant enzymes are a key for efficient and cost-effective translation of substrates into useful products for commercial applications. The thermophilic, hyperthermophilic, and microorganisms adapted to extreme temperatures (i.e., low-temperature lovers or psychrophiles) are a rich source of thermostable enzymes with broad-ranging thermal properties, which have structural and functional stability to underpin a variety of technologies. These enzymes are under scrutiny for their great biotechnological potential. Temperature is one of the most critical parameters that shape microorganisms and their biomolecules for stability under harsh environmental conditions. This review describes in detail the sources of thermophiles and thermostable enzymes from prokaryotes and eukaryotes (microbial cell factories). Furthermore, the review critically examines perspectives to improve modern biocatalysts, its production and performance aiming to increase their value for biotechnology through higher standards, specificity, resistance, lowing costs, etc. These thermostable and thermally adapted extremophilic enzymes have been used in a wide range of industries that span all six enzyme classes. Thus, in particular, target of this review paper is to show the possibility of both high-value-low-volume (e.g., fine-chemical synthesis) and low-value-high-volume by-products (e.g., fuels) by minimizing changes to current industrial processes.