From one amino acid to another: tRNA-dependent amino acid biosynthesis

Aminoacyl-tRNAs (aa-tRNAs) are the essential substrates for translation. Most aa-tRNAs are formed by direct aminoacylation of tRNA catalyzed by aminoacyl-tRNA synthetases. However, a smaller number of aa-tRNAs (Asn-tRNA, Gln-tRNA, Cys-tRNA and Sec-tRNA) are made by synthesizing the amino acid on the tRNA by first attaching a non-cognate amino acid to the tRNA, which is then converted to the cognate one catalyzed by tRNA-dependent modifying enzymes. Asn-tRNA or Gln-tRNA formation in most prokaryotes requires amidation of Asp-tRNA or Glu-tRNA by amidotransferases that couple an amidase or an asparaginase to liberate ammonia with a tRNA-dependent kinase. Both archaeal and eukaryotic Sec-tRNA biosynthesis and Cys-tRNA synthesis in methanogens require O-phosophoseryl-tRNA formation. For tRNA-dependent Cys biosynthesis, O-phosphoseryl-tRNA synthetase directly attaches the amino acid to the tRNA which is then converted to Cys by Sep-tRNA: Cys-tRNA synthase. In Sec-tRNA synthesis, O-phosphoseryl-tRNA kinase phosphorylates Ser-tRNA to form the intermediate which is then modified to Sec-tRNA by Sep-tRNA:Sec-tRNA synthase. Complex formation between enzymes in the same pathway may protect the fidelity of protein synthesis. How these tRNA-dependent amino acid biosynthetic routes are integrated into overall metabolism may explain why they are still retained in so many organisms

Natural reassignment of CUU and CUA sense codons to alanine in Ashbya mitochondria

The discovery of diverse codon reassignment events has demonstrated that the canonical genetic code is not universal. Studying coding reassignment at the molecular level is critical for understanding genetic code evolution, and provides clues to genetic code manipulation in synthetic biology. Here we report a novel reassignment event in the mitochondria of Ashbya (Eremothecium) gossypii, a filamentous-growing plant pathogen related to yeast (Saccharomycetaceae). Bioinformatics studies of conserved positions in mitochondrial DNA-encoded proteins suggest that CUU and CUA codons correspond to alanine in A. gossypii, instead of leucine in the standard code or threonine in yeast mitochondria. Reassignment of CUA to Ala was confirmed at the protein level by mass spectrometry. We further demonstrate that a predicted is transcribed and accurately processed in vivo, and is responsible for Ala reassignment. Enzymatic studies reveal that is efficiently recognized by A. gossypii mitochondrial alanyl-tRNA synthetase (AgAlaRS). AlaRS typically recognizes the G3:U70 base pair of tRNAAla; a G3A change in Ashbya abolishes its recognition by AgAlaRS. Conversely, an A3G mutation in Saccharomyces cerevisiae confers tRNA recognition by AgAlaRS. Our work highlights the dynamic feature of natural genetic codes in mitochondria, and the relative simplicity by which tRNA identity may be switched

Quantitative Analysis of the MGMT Methylation Status of Glioblastomas in Light of the 2021 WHO Classification.

Glioblastomas with methylation of the promoter region of the O(6)-methylguanine-DNA methyltransferase (MGMT) gene exhibit increased sensitivity to alkylating chemotherapy. Quantitative assessment of the MGMT promoter methylation status might provide additional prognostic information. The aim of our study was to determine a quantitative methylation threshold for better survival among patients with glioblastomas. We included consecutive patients ≥18 years treated at our department between 11/2010 and 08/2018 for a glioblastoma, IDH wildtype, undergoing quantitative MGMT promoter methylation analysis. The primary endpoint was overall survival. A total of 321 patients were included. Median overall survival was 12.6 months. Kaplan-Meier and adjusted Cox regression analysis showed better survival for the groups with 16-30%, 31-60%, and 61-100% methylation. In contrast, survival in the group with 1-15% methylation was similar to those with unmethylated promoter regions. A secondary analysis confirmed this threshold. Better survival is observed in patients with glioblastomas with ≥16% methylation of the MGMT promoter region than with <16% methylation. Survival with tumors with 1-15% methylation is similar to with unmethylated tumors. Above 16% methylation, we found no additional benefit with increasing methylation

Agmatidine, a modified cytidine in the anticodon of archaeal tRNA\u3csup\u3eIle\u3c/sup\u3e, base pairs with adenosine but not with guanosine

Modification of the cytidine in the first anticodon position of the AUA decoding tRNAIle (tRNAIle 2 ) of bacteria and archaea is essential for this tRNA to read the isoleucine codon AUA and to differentiate between AUA and the methionine codon AUG. To identify the modified cytidine in archaea, we have purified this tRNA species from Haloarcula marismortui, established its codon reading properties, used liquid chromatography–mass spectrometry (LC-MS) to map RNase A and T1 digestion products onto the tRNA, and used LC-MS/MS to sequence the oligonucleotides in RNase A digests. These analyses revealed that the modification of cytidine in the anticodon of tRNAIle 2 adds 112 mass units to its molecular mass and makes the glycosidic bond unusually labile during mass spectral analyses. Accurate mass LC-MS and LC-MS/MS analysis of total nucleoside digests of the tRNAIle 2 demonstrated the absence in the modified cytidine of the C2-oxo group and its replacement by agmatine (decarboxy-arginine) through a secondary amine linkage. We propose the name agmatidine, abbreviation C+, for this modified cytidine. Agmatidine is also present in Methanococcus maripaludis tRNAIle 2 and in Sulfolobus solfataricus total tRNA, indicating its probable occurrence in the AUA decoding tRNAIle of euryarchaea and crenarchaea. The identification of agmatidine shows that bacteria and archaea have developed very similar strategies for reading the isoleucine codon AUA while discriminating against the methionine codon AUG

Emergent rules for codon choice elucidated by editing rare arginine codons in Escherichia coli

The degeneracy of the genetic code allows nucleic acids to encode amino acid identity as well as noncoding information for gene regulation and genome maintenance. The rare arginine codons AGA and AGG (AGR) present a case study in codon choice, with AGRs encoding important transcriptional and translational properties distinct from the other synonymous alternatives (CGN). We created a strain of Escherichia coli with all 123 instances of AGR codons removed from all essential genes. We readily replaced 110 AGR codons with the synonymous CGU codons, but the remaining 13 “recalcitrant” AGRs required diversification to identify viable alternatives. Successful replacement codons tended to conserve local ribosomal binding site-like motifs and local mRNA secondary structure, sometimes at the expense of amino acid identity. Based on these observations, we empirically defined metrics for a multidimensional “safe replacement zone” (SRZ) within which alternative codons are more likely to be viable. To evaluate synonymous and nonsynonymous alternatives to essential AGRs further, we implemented a CRISPR/Cas9-based method to deplete a diversified population of a wild-type allele, allowing us to evaluate exhaustively the fitness impact of all 64 codon alternatives. Using this method, we confirmed the relevance of the SRZ by tracking codon fitness over time in 14 different genes, finding that codons that fall outside the SRZ are rapidly depleted from a growing population. Our unbiased and systematic strategy for identifying unpredicted design flaws in synthetic genomes and for elucidating rules governing codon choice will be crucial for designing genomes exhibiting radically altered genetic codes.United States. Department of Energy (DE-FG02-02ER63445

Development and early diagnosis of critical illness myopathy in COVID-19 associated acute respiratory distress syndrome.

BACKGROUND The COVID-19 pandemic has greatly increased the incidence and clinical importance of critical illness myopathy (CIM), because it is one of the most common complications of modern intensive care medicine. Current diagnostic criteria only allow diagnosis of CIM at an advanced stage, so that patients are at risk of being overlooked, especially in early stages. To determine the frequency of CIM and to assess a recently proposed tool for early diagnosis, we have followed a cohort of COVID-19 patients with acute respiratory distress syndrome and compared the time course of muscle excitability measurements with the definite diagnosis of CIM. METHODS Adult COVID-19 patients admitted to the Intensive Care Unit of the University Hospital Bern, Switzerland requiring mechanical ventilation were recruited and examined on Days 1, 2, 5, and 10 post-intubation. Clinical examination, muscle excitability measurements, medication record, and laboratory analyses were performed on all study visits, and additionally nerve conduction studies, electromyography and muscle biopsy on Day 10. Muscle excitability data were compared with a cohort of 31 age-matched healthy subjects. Diagnosis of definite CIM was made according to the current guidelines and was based on patient history, results of clinical and electrophysiological examinations as well as muscle biopsy. RESULTS Complete data were available in 31 out of 44 recruited patients (mean [SD] age, 62.4 [9.8] years). Of these, 17 (55%) developed CIM. Muscle excitability measurements on Day 10 discriminated between patients who developed CIM and those who did not, with a diagnostic precision of 90% (AUC 0.908; 95% CI 0.799-1.000; sensitivity 1.000; specificity 0.714). On Days 1 and 2, muscle excitability parameters also discriminated between the two groups with 73% (AUC 0.734; 95% CI 0.550-0.919; sensitivity 0.562; specificity 0.857) and 82% (AUC 0.820; CI 0.652-0.903; sensitivity 0.750; specificity 0.923) diagnostic precision, respectively. All critically ill COVID-19 patients showed signs of muscle membrane depolarization compared with healthy subjects, but in patients who developed CIM muscle membrane depolarization on Days 1, 2 and 10 was more pronounced than in patients who did not develop CIM. CONCLUSIONS This study reports a 55% prevalence of definite CIM in critically ill COVID-19 patients. Furthermore, the results confirm that muscle excitability measurements may serve as an alternative method for CIM diagnosis and support its use as a tool for early diagnosis and monitoring the development of CIM