Functional Analogy in Human Metabolism: Enzymes with Different Biological Roles or Functional Redundancy?

Decoding functional proteome information in model organisms using protein language models

Protein language models have been tested and proved to be reliable when used on curated datasets but have not yet been applied to full proteomes. Accordingly, we tested how two different machine learning-based methods performed when decoding functional information from the proteomes of selected model organisms. We found that protein language models are more precise and informative than deep learning methods for all the species tested and across the three gene ontologies studied, and that they better recover functional information from transcriptomic experiments. The results obtained indicate that these language models are likely to be suitable for large-scale annotation and downstream analyses, and we recommend a guide for their use.

A novel Tetrahymena thermophila sterol C-22 desaturase belongs to the Fatty Acid Hydroxylase/Desaturase superfamily

Sterols in eukaryotic cells play important roles in modulating membrane fluidity and in cell signaling and trafficking. During evolution, a combination of gene losses and acquisitions gave rise to an extraordinary diversity of sterols in different organisms. The sterol C-22 desaturase identified in plants and fungi as a cytochrome P-450 monooxygenase evolved from the first eukaryotic cytochrome P450 and was lost in many lineages. Although the ciliate Tetrahymena thermophila desaturates sterols at the C-22 position, no cytochrome P-450 orthologs are present in the genome. Here, we aim to identify the genes responsible for the desaturation as well as their probable origin. We used gene knockout and yeast heterologous expression approaches to identify two putative genes, retrieved from a previous transcriptomic analysis, as sterol C-22 desaturases. Furthermore, we demonstrate using bioinformatics and evolutionary analyses that both genes encode a novel type of sterol C-22 desaturase that belongs to the large fatty acid hydroxylase/desaturase superfamily and the genes originated by genetic duplication prior to functional diversification. These results stress the widespread existence of nonhomologous isofunctional enzymes among different lineages of the tree of life as well as the suitability for the use of T. thermophila as a valuable model to investigate the evolutionary process of large enzyme families.

Bioprospecting of microbial enzymes: current trends in industry and healthcare

Microbial enzymes have an indispensable role in producing foods, pharmaceuticals, and other commercial goods. Many novel enzymes have been reported from all domains of life, such as plants, microbes, and animals. Nonetheless, industrially desirable enzymes of microbial origin are limited. This review article discusses the classifications, applications, sources, and challenges of most demanded industrial enzymes such as pectinases, cellulase, lipase, and protease. In addition, the production of novel enzymes through protein engineering technologies such as directed evolution, rational, and de novo design, for the improvement of existing industrial enzymes is also explored. We have also explored the role of metagenomics, nanotechnology, OMICs, and machine learning approaches in the bioprospecting of novel enzymes. Overall, this review covers the basics of biocatalysts in industrial and healthcare applications and provides an overview of existing microbial enzyme optimization tools. KEY POINTS: • Microbial bioactive molecules are vital for therapeutic and industrial applications. • High-throughput OMIC is the most proficient approach for novel enzyme discovery. • Comprehensive databases and efficient machine learning models are the need of the hour to fast forward de novo enzyme design and discovery.

CRISPR/Cas9 knockout of human arylamine N-acetyltransferase 1 in MDA-MB-231 breast cancer cells suggests a role in cellular metabolism

Human arylamine N-acetyltransferase 1 (NAT1), present in all tissues, is classically described as a phase-II xenobiotic metabolizing enzyme but can also catalyze the hydrolysis of acetyl-Coenzyme A (acetyl-CoA) in the absence of an arylamine substrate using folate as a cofactor. NAT1 activity varies inter-individually and has been shown to be overexpressed in estrogen receptor-positive (ER+) breast cancers. NAT1 has also been implicated in breast cancer progression however the exact role of NAT1 remains unknown. The objective of this study was to evaluate the effect of varying levels of NAT1 N-acetylation activity in MDA-MB-231 breast cancer cells on global cellular metabolism and to probe for unknown endogenous NAT1 substrates. Global, untargeted metabolomics was conducted via ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) on MDA-MB-231 breast cancer cell lines constructed with siRNA and CRISPR/Cas9 technologies to vary only in NAT1 N-acetylation activity. Many metabolites were differentially abundant in NAT1-modified cell lines compared to the Scrambled parental cell line. N-acetylasparagine and N-acetylputrescine abundances were strongly positively correlated (r = 0.986 and r = 0.944, respectively) with NAT1 N-acetylation activity whereas saccharopine abundance was strongly inversely correlated (r = −0.876). Two of the most striking observations were a reduction in de novo pyrimidine biosynthesis and defective β-oxidation of fatty acids in the absence of NAT1. We have shown that NAT1 expression differentially affects cellular metabolism dependent on the level of expression. Our results support the hypothesis that NAT1 is not just a xenobiotic metabolizing enzyme and may have a role in endogenous cellular metabolism.

Mutation severity spectrum of rare alleles in the human genome is predictive of disease type

The human genome harbors a variety of genetic variations. Single-nucleotide changes that alter amino acids in protein-coding regions are one of the major causes of human phenotypic variation and diseases. These single-amino acid variations (SAVs) are routinely found in whole genome and exome sequencing. Evaluating the functional impact of such genomic alterations is crucial for diagnosis of genetic disorders. We developed DeepSAV, a deep-learning convolutional neural network to differentiate disease-causing and benign SAVs based on a variety of protein sequence, structural and functional properties. Our method outperforms most stand-alone programs, and the version incorporating population and gene-level information (DeepSAV+PG) has similar predictive power as some of the best available. We transformed DeepSAV scores of rare SAVs in the human population into a quantity termed "mutation severity measure" for each human protein-coding gene. It reflects a gene's tolerance to deleterious missense mutations and serves as a useful tool to study gene-disease associations. Genes implicated in cancer, autism, and viral interaction are found by this measure as intolerant to mutations, while genes associated with a number of other diseases are scored as tolerant. Among known disease-associated genes, those that are mutation-intolerant are likely to function in development and signal transduction pathways, while those that are mutation-tolerant tend to encode metabolic and mitochondrial proteins.

Evidence for novel epigenetic marks within plants

Variation in patterns of gene expression can result from modifications in the genome that occur without a change in the sequence of the DNA; such modifications include methylation of cytosine to generate 5-methylcytosine (5mC) resulting in the generation of heritable epimutation and novel epialleles. This type of non-sequence variation is called epigenetics. The enzymes responsible for generation of such DNA modifications in mammals are named DNA methyltransferases (DNMT) including DNMT1, DNMT2 and DNMT3. The later stages of oxidations to these modifications are catalyzed by Ten Eleven Translocation (TET) proteins, which contain catalytic domains belonging to the 2-oxoglutarate dependent dioxygenase family. In various mammalian cells/tissues including embryonic stem cells, cancer cells and brain tissues, it has been confirmed that these proteins are able to induce the stepwise oxidization of 5-methyl cytosine to 5-hydroxymethylcytosine (5hmC), 5-formylcytosine (5fC), and finally 5-carboxylcytosine (5caC). Each stage from initial methylation until the end of the DNA demethylation process is considered as a specific epigenetic mark that may regulate gene expression. This review discusses controversial evidence for the presence of such oxidative products, particularly 5hmC, in various plant species. Whereas some reports suggest no evidence for enzymatic DNA demethylation, other reports suggest that the presence of oxidative products is followed by the active demethylation and indicate the contribution of possible TET-like proteins in the regulation of gene expression in plants. The review also summarizes the results obtained by expressing the human TET conserved catalytic domain in transgenic plants.

Mining of potential drug targets through the identification of essential and analogous enzymes in the genomes of pathogens of Glycine max, Zea mays and Solanum lycopersicum

Pesticides are one of the most widely used pest and disease control measures in plant crops and their indiscriminate use poses a direct risk to the health of populations and environment around the world. As a result, there is a great need for the development of new, less toxic molecules to be employed against plant pathogens. In this work, we employed an in silico approach to study the genes coding for enzymes of the genomes of three commercially important plants, soybean (Glycine max), tomato (Solanum lycopersicum) and corn (Zea mays), as well as 15 plant pathogens (4 bacteria and 11 fungi), focusing on revealing a set of essential and non-homologous isofunctional enzymes (NISEs) that could be prioritized as drug targets. By combining sequence and structural data, we obtained an initial set of 568 cases of analogy, of which 97 were validated and further refined, revealing a subset of 29 essential enzymatic activities with a total of 119 different structural forms, most belonging to central metabolic routes, including the carbohydrate metabolism, the metabolism of amino acids, among others. Further, another subset of 26 enzymatic activities possess a tertiary structure specific for the pathogen, not present in plants, men and Apis mellifera, which may be of importance for the development of specific enzymatic inhibitors against plant diseases that are less harmful to humans and the environment.