{"paper_id":"152c2da8-91c8-45b3-81d7-b7f273b369d9","body_text":"Codon degeneracy contributes to divergent fitness effects of rare tRNAs with A-1 \nstarting anticodons  2 \n 3 \nParth K Raval1,*, Sungbin Lim2, Jenna Gallie2, and Deepa Agashe1,* 4 \n 5 \n1 National Centre for Biological Sciences (NCBS-TIFR), Bangalore, India 6 \n2 Max Plank Institute for Evolutionary Biology, Plön, Germany 7 \n 8 \n*Correspondence:  9 \nraval@hhu.de 10 \ndagashe@ncbs.res.in 11 \n 12 \n 13 \nABSTRACT  14 \n 15 \nTransfer RNA (tRNA) repertoires vary greatly across genomes, shaped by genetic drift and 16 \nselection. A peculiar pattern across prokaryotes is the near-complete absence of tRNAs with 17 \nunmodified adenine at the 34th (wobble) position (i.e., tRNAANN). Each of these tRNAs are just 18 \na single mutation away from several other tRNAs. Hence, their persistent absence suggests 19 \nfundamental but hitherto unclear constraints. We engineered 36 Escherichia coli strains 20 \nexpressing tRNAs carrying  each theoretically possible ANN anticodon to determine their 21 \nfunctionality and fitness effects. Notably, there was no evidence of broad toxicity due to these 22 \ntRNAs. All five tRNAANN tested underwent post-transcriptional maturation and all seven tested 23 \ncompensated for the deletion of their respective native tRNABNN (carrying G, C or U at the 34th 24 \nposition), demonstrating that tRNA ANN are translationally active.  Furthermore, tRNAANN from 25 \nfour-fold degenerate (4D) codon boxes were unmodified and were generally neutral or 26 \nbeneficial, whereas tRNAANN from two-fold degenerate (2D) boxes underwent A34-to-I34 27 \nmodification and were more likely to impair fitness. We suggest superwobbling by tRNAANN — 28 \ndecoding an entire four-codon set — as one mechanism underlying these differential fitness 29 \neffects. Maximal degeneracy in 4D boxes buffers or exploits tRNAANN superwobbling via 30 \nsynonymous decoding, whereas constrained degeneracy in 2D boxes renders it deleterious, 31 \nlikely through amino acid misincorporation. Thus, these differential fitness effects, sharpens 32 \nthe paradox of neutral or beneficial yet absent 4D tRNAANN, while beginning to empirically 33 \nunravel underlying causes for the absence of 2D tRNAANN. 34 \n 35 \n 36 \nKeywords: tRNA, Mistranslation, Superwobble , tRNA modifying enzymes, Adenine 34 , 37 \nInosine, tadA, ADAT, Codon degeneracy 38 \n39 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 15, 2026. ; https://doi.org/10.64898/2026.05.15.725136doi: bioRxiv preprint \n\n \n \nINTRODUCTION 40 \nTransfer RNA (tRNA) pools, largely determined by tRNA gene copy numbers (GCN), play a 41 \ncrucial role in maintaining translation rate, translation accuracy, growth rate and overall fitness 42 \n(Young et al. 1976; Dong et al. 1996; Scott et al. 2010; Scott and Hwa 2011; Klumpp et al. 43 \n2013; Wilusz 2015; Hu and Lercher 2021; Raval et al. 2023). Within species, tRNA repertoires 44 \nadapt in response to various environmental factors  (Nilsson et al. 2006; Iben et al. 2011; 45 \nBedhomme et al. 2019; Ayan et al. 2020; Khomarbaghi et al. 2024), and across species, they 46 \necho the diversity of ecological niches (Goodenbour and Pan 2006; Vieira -Silva and Rocha 47 \n2010; Fujishima and Kanai 2014; Chan and Lowe 2016; Rak et al. 2018) . For a given amino 48 \nacid, the GCN of tRNAs with distinct anticodons for the same amino acid (‘tRNA isoacceptors’) 49 \nvaries across species. The GCN is often correlated with relative codon usage (Ikemura 1985; 50 \nDong et al. 1996; Berg and Kurland 1997; Elf et al. 2003; Rocha 2004; Novoa and Ribas de 51 \nPouplana 2012; McDonald et al. 2015), contributing to the diversity of tRNA repertoires. The 52 \neffective cytosolic tRNA pools can also vary with expression levels from each tRNA gene copy 53 \n(Kanaya et al. 1999; Dittmar et al. 2004; Dittmar et al. 2006; Bloom -Ackermann et al. 2014; 54 \nSagi et al. 2016; Raval et al. 2023)  and differential charging across  isoacceptors (Elf et al. 55 \n2003; Dittmar et al. 2005). Another layer of variability is generated by tRNA modifying enzymes 56 \n(MEs) that can post-transcriptionally modify tRNAs at about a dozen different sites, particularly 57 \nin the anticodon loop  which extends the initially proposed wobble pairing  (Crick 1966) to an 58 \nexpanding list of modified wobbles (Agris et al. 2007; Grosjean et al. 2010; Iben and Maraia 59 \n2012; Novoa et al. 2012; Agris et al. 2017; Maraia and Arimbasseri 2017; Agris et al. 2018; 60 \nSuzuki 2021) . Despite such numerous mechanisms to specifically generate an immense 61 \ndiversity of functional tRNAs within and across species , tRNAs with specific anticodons are  62 \ncuriously either missing or rare (Maraia and Arimbasseri 2017; Diwan and Agashe 2018; Rak 63 \net al. 2018; Torres 2019; Lei and Burton 2020; Ehrlich et al. 2021; Pernod et al. 2021). 64 \nOf these seemingly ‘prohibited’ tRNAs, perhaps most striking is the lack of those carrying an 65 \nunmodified adenine at the 34th (wobble) position (henceforth, tRNAANN). A total of eight ANN 66 \nanticodons are theoretically possible for the eight family-codon boxes  with four -fold 67 \ndegeneracy (‘4D’; whereby all four codons within a box, differing only at the 3rd base, encode 68 \nthe same amino acid), and another eight for the  split-codon with two-fold degeneracy (‘2D’; 69 \nwhere the four codons within a box encode different amino acids). tRNAANN carrying each of 70 \nthese theoretically possible ANN are a single point mutation away from tRNAUNN, tRNAGNN, 71 \nand tRNACNN (collectively, ‘tRNABNN’) in the same codon box, and should occur frequently in 72 \nbacterial populations. For instance, with a mutation rate of 9.1×10−11 per base per generation, 73 \nan Escherichia coli population that grows from ca. 1,000 cells to 1 billion cells (e.g., a 1 mL 74 \nculture grown in LB medium overnight) will sample at least one mutation leading to a tRNAANN. 75 \nHowever, seven 2D codon tRNAANN and one 4D codon tRNAANN  are universally absent (Fig. 76 \n1A). In addition, seven more 4D tRNAANN are absent in prokaryotes, though Leu-tRNAAAG and 77 \nThr-tRNAAGT do occur in some bacteria (Andachi et al. 1987; Borén et al. 1993; Inagaki et al. 78 \n1995; Phillips and de Crécy -Lagard 2011; Diwan and Agashe 2018; Ehrlich et al. 2021) . 79 \nCuriously, these latter seven 4D codon box tRNA ANN are also absent from eukaryotic 80 \norganelles of bacterial origin and from bacteriophages  (Hatfull 2015; Pope et al. 2015; 81 \nMorgado and Vicente 2019; Ehrlich et al. 2021) . Hence, comparative genomics alone 82 \nunderscore the persistent rarity of tRNAANN, suggesting that their expression may not be well-83 \ntolerated. 84 \nThere are some exceptions  where tRNAANN are genomically encoded; however , their post-85 \ntranscriptional processing again indicates poor tolerance of unmodified tRNAANN. Both bacteria 86 \nand eukaryotes encode Arg-tRNAACG, and it is in fact the preferred Arg-tRNA isoacceptor. But 87 \nin this case, the A34 is post-transcriptionally masked via deamination by the modifying enzyme 88 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 15, 2026. ; https://doi.org/10.64898/2026.05.15.725136doi: bioRxiv preprint \n\n \n \nTadA (Karcher and Bock 2009; Diwan and Agashe 2018; Rafels-Ybern et al. 2019; Ehrlich et 89 \nal. 2021) in prokaryotes or ADAT in eukaryotes, resulting in tRNAs with inosine-34 (tRNAICG) 90 \nin the cytosol  (Gerber and Keller 1999; Wolf et al. 2002; Torres et al. 2014)  and in the 91 \nchloroplast stroma (Delannoy et al. 2009; Karcher and Bock 2009) . Eukaryotes also encode 92 \nand similarly modify six additional 4D codon box tRNAANN (Torres et al. 2014; Rafels-Ybern et 93 \nal. 2018) (Fig. 1A). The only remaining 4D codon box tRNA ANN (Gly-tRNAACC) is universally 94 \nabsent, likely because it is a poor substrate for ADAT  (Saint-Léger et al. 2016)  and TadA 95 \n(Borén et al. 1993). The A-to-I modification appears to be essential for efficient translation in 96 \nbacteria (Wolf et al. 2002), eukaryotes (Torres et al. 2021) and chloroplasts (Delannoy et al. 97 \n2009). Notably, both tRNA ANN and TadA/ADAT appear to be absent in archaea and 98 \nmitochondria (Ehrlich et al. 2021) . Together, the strict co -occurrence of tRNA ANN and 99 \nTadA/ADAT and the universal lack of the poor TadA/ADAT substrates tRNAANN point to ancient 100 \nand strong purifying selection against unmodified tRNAANN, which has likely persisted through 101 \nthe four billion years of life on Earth.  102 \nSingle base mutations should frequently  generate tRNAANN from tRNABNN genes, but are 103 \npresumably removed by selection. A first step towards  understanding the intensity of and 104 \nmechanisms driving such selection is comprehensive in vivo tests of functionality and potential 105 \nfitness impacts of unmodified tRNAANN. Previous studies have largely focused on a few ANN 106 \nanticodons in their native backbones or expressed multiple ANN from  a single backbone, 107 \nlimiting the ability to generalize. For instance, Gly -tRNAACC is unmodified and functional in 108 \nEscherichia coli (Borén et al. 1993)  and humans (Saint-Léger et al. 2016)  and Pro-tRNAAGG 109 \ndoes not affect fitness or translational elongation rates in Salmonella (Chen et al. 2002). Both 110 \nthese tRNAANN were generated from the respective native tRNABNN backbones. In another 111 \nstudy, all sixteen possible ANN anticodons were generated from a single Methanocaldococcus 112 \njannaschii Tyr-tRNA backbone, and these introduced tRNAANN decoded 1-20% of NNU codons 113 \nin E. coli (Biddle et al. 2016; Schmitt et al. 2018; Schmitt et al. 2024) . These observations 114 \nsuggest that unmodified tRNA ANN are likely to be translationally active and non -lethal. 115 \nHowever, the decoding capacities and modifications of tRNAs (including A-to-I) depend on the 116 \nbackbone sequence (Li et al. 1997; Qian et al. 1997; Nakanishi et al. 2005; Schmeing et al. 117 \n2011; Saint-Léger et al. 2016; Roura Frigolé et al. 2019); and fitness impacts of tRNAs depend 118 \non the ecological niche (Bloom-Ackermann et al. 2014; Li et al. 2016; Li and Zhang 2018; 119 \nGabzi et al. 2022; Raval et al. 2023). Therefore, validating function in native tRNA backbones 120 \nand conducting systematic fitness assays across different growth media are both essential to 121 \nquantify and understand the source of the hypothesized selection against tRNAANN.  122 \nTo this end, we introduced 20 tRNAANN via point mutation in the native tRNA genes of E. coli, 123 \nand tested their expression, maturation and functionality. Our results provide some of the first 124 \nempirical evidence for folding, maturation and translational activity of a broad set of tRNAANN 125 \nin an endogenous context. Most of the tRNAANN were accommodated in the cytosol 126 \nunmodified, did not alter  fitness under tested conditions , and a subset of tested tRNA ANN 127 \nevidently effectively replaced native tRNABNN. However, of tRNAANN that affected fitness, 2D 128 \nand 4D tRNAANN differed from each other in that 2D tRNAANN appeared prone to modification 129 \nand reduced fitness when overexpressed , whereas 4D tRNA ANN appeared unmodified and 130 \nimproved fitness, especially in nutrient poor media . We propose that within-codon box 131 \nsuperwobbling (decoding of all four codons) may underlie the observed fitness differences 132 \nwherein the degeneracy of the code will buffer or profit from superwobbling of 4D tRNAANN but 133 \nnot from 2D tRNAANN due to the limited degeneracy of 2D codon boxes. Overall, our results 134 \nhighlight the lack of any clear overarching source of purifying selection on most tRNAANN and 135 \nunderscore benefits of a majority of 4D tRNAANN, deepening the mystery of their near-universal 136 \nabsence. However, our work also points at mistranslation as a potential source of counter -137 \nselection for 2D codon amino acids, which merits further research. 138 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 15, 2026. ; https://doi.org/10.64898/2026.05.15.725136doi: bioRxiv preprint \n\n \n \nMETHODS 139 \nGenerating strains 140 \nWe conducted all tRNA gene manipulations in E. coli MG1655 (the wild type, WT). The native 141 \ntRNA genes were amplified from the genome and cloned into  an IPTG-inducible low copy 142 \nnumber plasmid (pACDH, a derivative of pACD with an ACYC origin of replication  (Mangroo 143 \nand RajBhandary 1995; Rao and Varchney 2001) ) or a high copy number plasmid  (pUC19 144 \n(Norrander et al. 1983)). We introduced an B34-to-A34 point mutation (‘B34A’)  in the native 145 \ntRNABNN genes via PCR mutagenesis (Hoa et al. 1989) , followed by Sanger sequencing to 146 \nconfirm the mutation. To introduce tRNAANN on the genome, we utilized a scar-free, two-step 147 \nallelic exchange method that utilizes  the pKOV vector that carries a chloramphenicol 148 \nresistance marker (Link et al. 1997). Briefly, we amplified the locus with the native tRNA gene 149 \nof interest (along with ca. 200 -300bp upstream and downstream regions) from the genome 150 \nwhile also introducing a B34A substitution in the amplicon  through PCR mutagenesis . We 151 \ncloned the amplicons with mutated alleles into pKOV and transformed the WT with pKOV -152 \ntRNAANN. We selected colonies with  genomic integration of pKOV-tRNAANN on 153 \nchloramphenicol (20 µg/ml) at 43°C (where the pKOV plasmid replicates very inefficiently) and 154 \nconfirmed the chromosomal insertion via PCR. We selected cells that had removed pKOV, by 155 \ngrowing the population first overnight in LB (Lysogeny Broth, Difco) with 5% sucrose at 37°C 156 \nand then on LB agar with 5% sucrose  to isolate single colonies  at 37°C. The presence of 157 \nsucrose in both these steps selects against cells with pKOV since the expression of sacB from 158 \npKOV is lethal. proL B34A was introduced on the genome via the Datsenko-Wanner method 159 \n(Datsenko and Wanner 2000) . Briefly, kanamycin cassette was  first inserted 60  bp 160 \ndownstream of proL. The proL::Kan region was amplified through a mutagenesis PCR that 161 \nintroduced B34A via the forward primer. The proL B34A::Kan amplicon was electroporated 162 \ninto the WT carrying plasmid pKD46 where the red recombinase switched the WT proL with 163 \nproL B34A::Kan. The Kan cassette was cured using PCP20 resulting in the proL B34A strain. 164 \nWe screened the mutant strains for the intended mutation, and to rule out any other mutations, 165 \nvia PCR followed by Sanger sequencing and further confirmed the m via Next Generation 166 \nSequencing (Illumina HiSeq PE150, 4-5 million reads per strain, >100x depth). We stored all 167 \nthe strains thus generated as glycerol cryostocks at –80°C. 168 \n 169 \nMeasuring growth parameters 170 \nWe revived strains from cryostocks by streaking onto LB (Lysogeny Broth, Difco) agar plates, 171 \nwith appropriate antibiotics  where applicable. We inoculated individual colonies in LB and 172 \nincubated at 37°C (180 rpm shaking) for 14-16 hours to grow the preculture. We set up growth 173 \nmeasurement experiments by sub -culturing 1% v/v  of precultures  in 48 well microplates 174 \n(Corning) in 500 µL growth medium: LB or M9 minimal medium (M9 salts, 1mM CaCl2, 2.5 175 \nmM MgSO4) supplemented with specific carbon and nitrogen sources (“GA”: 0.4% w/v 176 \nglucose and cas amino acids; or carbon sources alone: glucose  (Glu) 0.2% w/v, galactose  177 \n(Gal) 0.2%, pyruvate (Pyr) 50 mM, or glycerol (Gly) 0.6% w/v) (Raval et al. 2023)  and with 178 \nappropriate antibiotics where applicable. To test for fitness costs that may accumulate over 179 \nlonger periods of time, we passaged the strains  with a B34A substitution (six independent 180 \nlines per strain) for eight days (about 80 generations) by diluting 1% v/v every 24h in  500 µL 181 \nLB in 48 well microplates incubated at 37°C (180 rpm shaking). We stored cryostocks at fourth 182 \nand eighth day, revived and measured their fitness as per above. 183 \nWe estimated various growth proxies by measuring optical density at 600 nm  (OD600) in a 184 \nTecan microplate reader, an automated system (LiconiX incubator, robotic arm and Tecan 185 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 15, 2026. ; https://doi.org/10.64898/2026.05.15.725136doi: bioRxiv preprint \n\n \n \nmicroplate reader)  for strains where tRNAANN were introduced from the plasmid , or a 186 \nmicroplate reader from Agilent Biotek (Epoch2) for strains with a B34A substitution. We 187 \nincluded the WT in every microplate as a reference, to account for differences across plate 188 \nreaders and over time. We inferred growth rate by fitting an exponential equation to 189 \napproximately the first one third of each growth curve (region where growth was exponential) 190 \nusing Curvefitter software (Delaney et al. 2013). We inferred the lag phase length as the time 191 \ntaken to reach early log phase , defined as approximately 1/3rd of the final OD600 in a given 192 \ngrowth medium (OD600 0.2 for LB and M9 GA; 0.15 for M9 Glu, Gal and Gly; and 0.12 for M9 193 \nPyr). We normalized fitness of WT carrying tRNAANN, with that of WT carrying the respective 194 \ntRNABNN control for each case of tRNAANN expression from a plasmid. 195 \nFinally, we approximated the overall fitness effect of each 2D codon tRNAANN expressed from 196 \nhigh copy plasmid by qualitatively scoring its impact on the three growth parameters across 197 \nsix media, assigning +1, 0, and –1 to positive, neutral, and negative effects, respectively. We 198 \ndefined the sum of these scores, across media and across parameters, as the ‘fitness effect 199 \nindex’. We estimated the mistranslation likelihood for each tRNAANN as the ratio of non-cognate 200 \nto cognate codon usage across the E. coli genome. 201 \n 202 \nEstimating adenine to inosine modification and mature tRNA levels  203 \nWe measured the relative abundance of each tRNA isotype , for strains with B34A 204 \nsubstitutions. Prior work suggests that adenine to inosine modification  can be reliably 205 \nestimated using the frequency of cytosine in the complementary strand, i.e., unmodified A34 206 \nappears as T34 in reverse-complemented cDNA, whereas I34 appears as C34 (Schmitt et al. 207 \n2024). We therefore  utilized YAMAT -Seq (Shigematsu et al. 2017; Ayan et al. 2020)  as 208 \noptimized previously for E. coli (Raval et al. 2023). Briefly, we grew three biological replicates 209 \nof each strain in LB till mid-log phase (ca. OD 0.3) and isolated total RNA. After deacylation, 210 \nwe ligated Y-shaped, DNA/RNA hybrid adapters (Shigematsu et al. 2017) (Eurofins) to the 5'-211 \nNCCA-3' and 3'-inorganic phosphate-5' ends of uncharged tRNAs, reverse transcribed , and 212 \nfurther amplified the cDNA products by PCR with sample -specific indices (Illumina). After a 213 \nquality and quantity check through bioanalyzer (Agilent DNA7500 Series II kit and Agilent 2100 214 \nBioanalyzer Systems), we combined equimolar amounts of each sample and further purified 215 \nthe fully assembled cDNA (with adapters and indices ligated at both ends  of the tRNA 216 \nsequence) by separating them from unligated adapters based on size on a 5% poly-acrylamide 217 \ngel. The final products were sequenced using an Illumina NextSeq 550 High-Output 2x75 bp 218 \nkit (Single-end, 150 bp reads) at the Max Planck Institute for Evolutionary Biology. YAMAT-219 \nseq for proL-ANN (with WT control) was performed with size-based separation by magnetic 220 \nbeads (Ampure XP beads) followed by verification of the size of purified products (200 -223 221 \nbp) using TapeStation DNA D1000 HS Screen Tape and sequencing on the Novaseq platform 222 \n(paired end, 150 bp) at the National Centre for Biological Sciences.  223 \nWe sorted the combined raw reads into reads derived from individual strains (available from 224 \nNCBI GEO; accession number GSE328815) based on the unique Illumina barcodes. We 225 \ndownloaded all native tRNA sequences predicted by GtRNAdb 2.0 (Chan and Lowe 2009) for 226 \nWT and manually added additional tRNAANN sequences by introducing B34A substitution in 227 \nthe sequence of tRNABNN isoacceptors mutated in our study (e.g. to get the gly -tRNAACC 228 \nsequence, we replaced the C34 in gly-tRNAGCC sequence with A34). We mapped bases 80 -229 \n151 (expected to tRNA sequences) of the raw reads from each strain against the collection of 230 \nnative and ANN tRNA sequences using Geneious Prime (version 11.1.4). We used the 231 \nfollowing previously mapping criteria : 10 % mismatch/gap rate, max 2 -bp gap size, max. 5 232 \nambiguities; no iterations, discard reads that align equally well to multiple reference sequences 233 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 15, 2026. ; https://doi.org/10.64898/2026.05.15.725136doi: bioRxiv preprint \n\n \n \n(Khomarbaghi et al. 2024). On average, at least around 95% of the reads aligned with tRNAs, 234 \nthe number of reads aligned to each tRNA isoacceptor were exported by Geneious and used 235 \nfor calculating within-sample proportions of each tRNA isoacceptor.  236 \n 237 \nAnalyses of tRNA sequences from GtRNA DB and TadA homologues 238 \nWe downloaded all predicted tRNA sequences from all bacteria and archaea available on the 239 \nGenomic tRNA database, GtRNAdb (Chan and Lowe 2016; Thornlow et al. 2020)  including 240 \nthe taxonomy of each species . For 14 genera where at least one tRNA ANN (other than Arg-241 \ntRNAACG) was present in all of its species (a total of 273 species across genera) we attempted 242 \nto retrieve protein coding sequences (CDS) as nucleotide sequences  from NCBI Genome 243 \nDatasets (v18.24.0) , using the taxonomic IDs provided on GtRNAdb . We could retrieve 244 \ngenomes of 263 species using this approach for which we retrieved genome AT content from 245 \nthe metadata of the genome assemblies, and calculated codon usage from nucleotide CDS 246 \n(eight Lactobacilli and two Streptococci species, the genomes could not be fetched readily 247 \nusing the taxonomy IDs).  Lastly, out of 555 bacterial genomes from GtRNAdb with at least 248 \none tRNAANN (other than Arg-tRNAACG), we could retrieve amino acid CDS for 539 genomes 249 \nwhich were used to searched for TadA homologues using the E. coli tadA  amino acid 250 \nsequence as a query (diamond BLAST v2.1.24.178 (Buchfink et al. 2014) ; e-value<10e-6, 251 \npercentage identity >30%, query coverage >50%). 252 \n 253 \nData analysis 254 \nWe used in -house python scripts (except for YAMAT -Seq) for all statistical analysis and 255 \ngenerating plots. We used Mann–Whitney U tests to compare the fitness of strains expressing 256 \ntRNAANN with relevant control strains (WT or strain expressing tRNABNN). To quantify skew in 257 \nfitness effects relative to zero  (neutral), we computed a zero -anchored quantile asymmetry 258 \nmetric (Q-asym0) defined as Q(+) 0.9 -  |Q(-)0.1| / Q(+) 0.9 +  |Q(-)0.1|, where Q(+) 0.9 is the 90th 259 \npercentile of positive values  of log2(magnitude of fitness)  and Q(-)0.1 the 10th percentile of 260 \nnegative values. This metric compares the extent of the positive and negative tails relative to 261 \nzero, yielding a normalized measure of tail imbalance scaled between −1 ( deleterious skew) 262 \nand +1 (beneficial skew), with 0 indicating symmetry. While similar in essence to Bowley’s 263 \ncoefficient (a measure based on quantiles and median ), Q-asym0 is zero -anchored. 264 \nSignificance was assessed by bootstrap (10,000 resamples), with percentile -based 95% 265 \nconfidence intervals; skew was considered significant when the interval excluded zero. All raw 266 \ndata used for analysis are provided in the source data files for each figure, along with the 267 \nstatistics; in -house scripts to conduct the analyses and plots are available on Zenodo 268 \n(https://zenodo.org/records/20180916).  269 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 15, 2026. ; https://doi.org/10.64898/2026.05.15.725136doi: bioRxiv preprint \n\n \n \nRESULTS 270 \nGeneration of an E. coli strain library encompassing all theoretically possible A-271 \nstarting anticodons  272 \nIn theory, sixteen anticodons can start with an adenosine, but fifteen of these are absent from 273 \nbacterial tRNA repertoires, including that of E. coli MG1655 (the wild type, WT). The WT tRNA 274 \nrepertoire consists of 37 genes for tRNAs with G-, U- or C-starting anticodons (‘tRNABNN’) that 275 \ndecode family or four-fold degenerate codon boxes (‘4D’ codon boxes, where all four codons 276 \nwithin a codon box are assigned to the same amino acid) and 45 genes for tRNAs that decode 277 \nsplit or two-fold degenerate codon boxes (‘2D’ codon boxes, where the four codons within a 278 \ncodon box are split across two different amino acids). Thus, a total of 82 native tRNABNN genes 279 \n(Fig. S1) could generate a tRNA with an A-starting anticodon (‘tRNA ANN’) through a single 280 \nsubstitution leading to adenosine at the 34th base (‘B34A substitution’) (Fig. 1A, Fig. S1).  281 \nThe functional and fitness effects of these tRNAANN should be determined by a combination of 282 \ntRNA backbone and the extent of degeneracy  of the codons decoded by tRNA ANN through 283 \ncanonical, wobble and superwobble base -paring. Under canonical or wobble pairing, the 284 \nbackbone alone determines amino acid charging, such that a G34 to  (‘G34A’) will always 285 \nretain the original amino acid identity and should hence be tolerated due to degeneracy in 286 \nboth 4D codon and 2D codon boxes. On the other hand, a C34A or U34A substitution will lead 287 \nto mistranslation in a 2D codon box due to a lack of four-fold degeneracy (Fig. 1A). Hence, 288 \nthe probability of mistranslation is higher in tRNA ANN generated from a tRNA CNN or tRNAUNN 289 \nbackbone of the adjacent 2D codon box, and could potentially explain selection against C34A 290 \nor U34A substitution s. However, an additional complication is revealed  by prior work 291 \nsuggesting that tRNA ANN can superwobble, i.e., read all four codons in a box , in bacteria, 292 \nmitochondria and eukaryotic cytosol  (Sibler et al. 1986; Andachi et al. 1987; Borén et al. 1993; 293 \nInagaki et al. 1995; Watanabe et al. 1997; Von Nickisch -Rosenegk et al. 2001; Chen et al. 294 \n2002; Aldinger et al. 2012; Yokobori et al. 2013; Soma et al. 2023; Kompatscher et al. 2024; 295 \nSchmitt et al. 2024) . Under this scenario, even within a 2D codon box, a G34 A substitution 296 \ncould result in mistranslation because the tRNA will still be charged with the cognate amino 297 \nacid but it can decode the non-cognate codons via superwobble (Fig. 1B). We hypothesized 298 \nthat such mistranslation through superwobbling may impair fitness, generating selection 299 \nagainst 2D codon tRNAANN derived from a tRNAGNN isoacceptor. In contrast, since a 4D codon 300 \ntRNAANN remains an isoacceptor regardless of which isoacceptor tRNABNN backbone it arose 301 \nfrom (Fig. 1C), 4D tRNAANN should have weaker fitness effects. 302 \n 303 \nFigure 1: Expected range of decoding by potential tRNAANN that could arise via single 304 \nmutations in native tRNABNN. (A) All theoretically possible anticodons (written from 5’-to-3’) 305 \norganized across 4D and 2D codon boxes in the codon table. ANNs that are universally rare 306 \nand modified in different domains (summarized from a previous study (Ehrlich et al. 2021)) are 307 \ncolor coded as per the key on the bottom. The native BNN tRNAs mutated to ANN in this study 308 \n(within their respective codon boxes) are indicated in bold; ANNs expressed from a plasmid 309 \nare underlined, and those expressed from  the genome indicated by asterisk s. (B–C) 310 \nSchematics illustrate decoding in examples of 2D and 4D codon boxes under Watson-Crick 311 \n(WC; indicated by black lines ), wobble  (indicated by dotted black lines)  and superwobble 312 \n(indicated by dotted orange lines)  in the wild type (WT) and after B34A substitutions in distinct 313 \ntRNABNN. In each case, mRNA codons are written  from 5’ -to-3’ at the bottom , and 314 \ncomplementary anticodons written from 3’-to-5’ on top, with flanking lines and attached single 315 \nletter codes indicating the tRNA backbone and amino acid after charging, respectively. The 316 \n34th base is shown in bold  and anticodon mutations leading to  A34 are marked in red . 317 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 15, 2026. ; https://doi.org/10.64898/2026.05.15.725136doi: bioRxiv preprint \n\n \n \nPredicted mistranslation is indicated by red arrows . (B) A codon box split between 318 \nphenylalanine and leucine, with two codons each decoded by Phe-tRNA and Leu-tRNA in the 319 \nWT. A G34A substitution would generate Phe-tRNAAAG charged with phenylalanine , which 320 \nunder WC pairing reads the phenylalanine codon UUU but can decode the leucine codons 321 \nUUA and UUG  through superwobbling, resulting in mistranslation.  Similarly, U34A or C34A  322 \nsubstitutions would result in tRNAAAG charged with leucine, resulting in leucine-to-323 \nphenylalanine mistranslation. Superwobbling resulting in mistranslation is indicated by red 324 \narrows and is expected to be harmful.  (C) The leucine 4D codon box, where all four codons 325 \nare decoded by tRNAGAG, tRNAUAG and tRNACAG isoacceptors. An B34A substitution in any of 326 \nthese isoacceptors should generate Leu-tRNAAAG charged with leucine. Under canonical base 327 \npairing, Leu-tRNAAAG decodes the CUU anticodon and under superwobbling it decodes CUC, 328 \nCUA and CUG. In either case, the lysine charged Leu -tRNAAAG decodes lysine codons, and 329 \ndoes not result in mistranslation. Hence, in  tRNABNN from 4D codon boxes, B34A mutations 330 \nare unlikely to  mistranslate. Superwobbling by tRNAANN within 4D codon boxes is indicated 331 \nby green arrows and is expected to be tolerated or beneficial. Such superwobbling is indicated 332 \nonly for a tRNAANN resulting from G34A substitution, but it is equally applicable for any B34A 333 \nsubstitution within a 4D codon box. 334 \n  335 \n 336 \n 337 \nTo test these hypotheses, we constructed tRNAANN genes by introducing B34A substitutions 338 \nin 20 diverse WT tRNABNN genes (Fig. 1A, Fig. S1). From each of the eight 2D codon boxes, 339 \nwe derived one tRNAANN from the native tRNAGNN (mimicking possible transition mutations) to 340 \naddress fitness effects potentially stemming from mistranslation due to superwobbling. Of the 341 \neight 4D codon boxes, one is decoded by tRNAACG whereas the remaining seven are decoded 342 \nby different combinations of tRNABNN isoacceptors with different backbones (Fig. S2). We 343 \nintroduced B34A substitutions in a subset of these tRNABNN isoacceptor genes. We generated 344 \none tRNA ANN each from proline, valine, alanine and glycine 4D codon boxes. For serine, 345 \nleucine and threonine 4D codon boxes, we generated tRNAANN from a subset of isoacceptors 346 \ndiffering from each other in their backbone sequences by more than 30%, resulting in two 347 \ntRNAANN from leucine codon boxes, and three from the threonine and serine codon box.  348 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 15, 2026. ; https://doi.org/10.64898/2026.05.15.725136doi: bioRxiv preprint \n\n \n \nThis set of 20 tRNAANN genes thus covered all fifteen ANN anticodons absent across bacteria, 349 \nwith serine, leucine and threonine ANN  generated from more than one backbone. We 350 \nintegrated five of these tRNA ANN into the WT genome by replacing the corresponding native 351 \ntRNABNN with its tRNA ANN variant (Fig. 1A), generating strains with tRNA ANN expressed from 352 \nthe genome (individually referred with the gene name followed by ‘ANN’, e.g. pheV-ANN, and 353 \ncollectively referred to as ‘ANN strains’). We also expressed seven tRNAANN through a high-354 \ncopy plasmid in E. coli strains lacking their respective native tRNA BNN, generated previously 355 \n(Raval et al. 2023)  (individually referred with Δ followed by gene name , e.g. ΔpheV, and 356 \ncollectively referred as knockouts, ‘KOs’). In the WT, we additionally expressed five of these 357 \ntRNAANN from a low-copy plasmid and finally, 19 of the tRNAANN from a high-copy plasmid. The 358 \nstrains thus generated (Table S1) provided a comprehensive system to probe specific aspects 359 \nof tRNAANN functionality and fitness effects. 360 \n 361 \ntRNAANN are translationally active 362 \nA parsimonious mechanism by which tRNAANN can affect cellular fitness is through translation 363 \nrate and/or accuracy , although evidence for their translational activity remains indirect and 364 \nlimited (Borén et al. 1993; Biddle et al. 2016; Schmitt et al. 2018; Schmitt et al. 2024) . A key 365 \nprerequisite for translational activity is accurate post-transcriptional folding and maturation of 366 \nthe tRNA transcripts. To quantify the relative abundance of mature tRNAANN within the total 367 \ntRNA pool, we utilized YAMAT -seq ( Y-shaped Adapter-ligated MAture TRNA sequencing)  368 \n(Shigematsu et al. 2017; Ayan et al. 2020; Raval et al. 2023) . In this method, two ends of Y-369 \nshaped adapters are ligated to the  conserved 3′-CCA and to 5’ -OH, respectively,  in the  370 \nacceptor arms of fully folded and matured tRNAs . Adapter-ligated tRNAs are then reverse -371 \ntranscribed, PCR -amplified using adapter -specific primers, and the resulting libraries are 372 \nsequenced and analyzed using a standard small-RNA sequencing work-flow. We quantified 373 \nthe relative abundance  of each tRNA species  in all five strains where we replaced native 374 \ntRNABNN genes by tRNAANN variants. For four of these ANN strains, w e also included strains 375 \nlacking the corresponding native tRNABNN (KOs) (Raval et al. 2023), as a control for potential 376 \ncompensatory upregulation from the remaining gene copies. 377 \nThe 2D codon Phe-tRNAGAA is encoded by two gene copies in E. coli: pheV and pheU. We 378 \nnote that quantitative inferences about Phe-tRNAGAA remain challenging since this is amongst 379 \nthe most difficult tRNA species to detect by YAMAT (Ayan et al. 2020; Khomarbaghi et al. 380 \n2024). Nonetheless, the ΔpheV strain had decreased Phe-tRNAGAA proportion relative to WT 381 \nwhereas pheV-ANN had a higher Phe-tRNAGAA proportion than ΔpheV (Fig. 2A). This indicated 382 \nthat upregulation from pheU may not be sufficient to restore Phe-tRNAGAA levels in ΔpheV, as 383 \nalso suggested by the fitness defect observed in ΔpheV previously (Raval et al. 2023) . We 384 \nthus inferred that at least a fraction of  Phe-tRNAGAA in pheV-ANN is due to  Phe-tRNAAAA 385 \nundergoing A34-to-inosine 34 modification, which appears in reverse complemented cDNA as 386 \nC34 and therefore reads as G34 in the gene sequence  (Delannoy et al. 2009; Torres et al. 387 \n2015; Saint-Léger et al. 2016) . In contrast, all four 4D codon tRNAANN that we tested were 388 \nexpressed and detected unmodified among mature tRNAs (Fig. 2A). For instance, the ΔserX 389 \nstrain showed Ser-tRNAGGA levels (encoded by serX and serW) comparable to WT, suggesting 390 \npotential upregulation of serW. However, serX -ANN showed a significant reduction in Ser -391 \ntRNAGGA (from the unaltered serW) and significant proportion of Ser-tRNAAGA (from serX-392 \nAGA). Proportions of Ser-tRNAGGA and Ser-tRNAAGA, were equal, and together matched Ser-393 \ntRNAGGA levels in the WT  (Fig. 2A).  Similarly, the Pro -tRNAAGG expressed on the genome 394 \n(carrying a G34A mutation in the single-copy tRNA gene proL) was also detected unmodified 395 \nin the cytosolic pool at levels comparable to the native Pro-tRNAGGG in the WT. Likewise, Thr-396 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 15, 2026. ; https://doi.org/10.64898/2026.05.15.725136doi: bioRxiv preprint \n\n \n \ntRNAGGU (encoded by thrT and thrV) was lower than WT in both ΔthrT and thrT-ANN strains. 397 \nHowever, thrT-ANN expressed both Thr-tRNAGGU and Thr-tRNAAGU, whose combined 398 \nproportions matched those of Thr-tRNAGGU in the WT. The Gly-tRNACCC (single copy, encoded 399 \nby glyU) was absent in the tRNA pool of the ΔglyU, whereas glyU-ANN showed Gly-tRNAACC 400 \nproportions similar to Gly-tRNACCC in the WT. The observation that 4D tRNAANN proportions 401 \nwere similar to WT (for Pro -tRNAAGG and Gly -tRNAACC) or added up to WT  together with  402 \ntRNABNN (for Ser -tRNAAGA and Thr -tRNAAGU) suggested that tRNAANN were tolerated 403 \nunmodified in cells, without any inhibition of expression  from G34A alleles or post-404 \ntranscriptional degradation. Furthermore, in strains carrying tRNAANN, relative levels of 405 \nisoacceptors or other tRNA species were unaltered (Fig. S 3), suggesting that tRNAANN 406 \nexpression is tolerated without major changes in the overall cytosolic tRNA pools.  Taken 407 \ntogether, YAMAT-seq confirmed that both 4D and 2D codon tRNAANN are expressed, correctly 408 \nfolded, and fully mature akin to native tRNABNN. The 4D codon tRNAANN remained unmodified, 409 \nwhereas the 2D codon tRNAANN was modified to tRNAINN. 410 \nWe reasoned that if mature tRNAANN are also translationally active,  the fitness of strains 411 \ncarrying the G34A substitution should  be similar to WT , and tRNA ANN should rescue any 412 \ndeleterious effect of losing the respective tRNABNN. Indeed, whereas ΔpheV and ΔthrT showed 413 \nlower growth rate than WT, the growth rates of pheV-ANN and thrT-ANN were comparable to 414 \nWT (Fig. 2B). Strains ΔpheV and ΔthrT also showed a longer lag phase in LB, which was 415 \nshortened in strains carrying the respective ANNs (Fig. S 4A). The f inal OD was largely 416 \nindistinguishable (± 5% changes) from WT for most KO and ANN strains, and was rescued in 417 \nthrT-ANN (Fig. S4B). Better growth parameters of ANN strains as compared to the respective 418 \nKO strains suggested that tRNAANN functionally replaced the native tRNABNN. Growth rates of 419 \nserX-ANN, pro-ANN, and glyU -ANN were indistinguishable from WT, suggesting that these 420 \ntRNAANN were also well tolerated. tRNAANN strains passaged in LB for eight days (about 80 421 \ngenerations) also did not show any fitness costs, suggesting a lack of long-term fitness effects 422 \nof B34A substitutions on the genome (Fig. 2C). These observations further suggested that 423 \ntRNAANN can successfully replace tRNABNN on the genome without significant fitness costs.  424 \nTo further validate that tRNAANN can compensate for the loss of the respective tRNABNN, we 425 \nintroduced plasmid-borne tRNAANN and tRNABNN into four of the 2D codon and three of the 4D 426 \ncodon tRNA KOs. Across all five growth media and seven tRNAANN tested (35 combinations), 427 \ntRNAANN improved overall growth of the respective KOs (Fig. S5A). Across all 35 combinations 428 \ntested, they shortened the lag phase  of the respective KOs (statistically significant for 25 429 \ncombinations, Fig. S5B). All strains showed exponential growth in two nutrient-rich media (LB 430 \nand M9GA0.4) , where complementation by tRNAANN increased growth rate for all 14 431 \ncombinations tested (Fig. 2D) and  increased final OD for 10 combinations (Fig. S 5C).  432 \nMoreover, complementation by tRNAANN and respective tRNABNN increased the fitness of KOs 433 \nto a similar extent, further corroborating the functionality of tRNAANN.  434 \nThus, all five tRNAANN introduced on the genome were folded, matured, and could functionally 435 \nreplace their respective tRNABNN; and seven tRNAANN expressed from a plasmid could also 436 \nfunctionally replace their native tRNABNN genes. A priori, a G34A substitution is unlikely to 437 \nrender a tRNA translationally inactive as described earlier, and prior studies also suggest that 438 \nsuch tRNAANN have translational activity (Borén et al. 1993; Biddle et al. 2016; Schmitt et al. 439 \n2018; Schmitt et al. 2024) . Taken together, we concluded that tRNAANN are generally 440 \ntranslationally active.  441 \n 442 \nFigure 2: tRNAANN undergo normal maturation and can compensate for the loss of native 443 \ntRNAs. (A) For a subset of tRNA genes (pheV, serX, thrT, glyU), we quantified the relative 444 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 15, 2026. ; https://doi.org/10.64898/2026.05.15.725136doi: bioRxiv preprint \n\n \n \nproportion of each tRNA species within the mature tRNA pool, for strains lacking the tRNABNN 445 \ngene (KO, in orange), strains carrying the G34A substitution in the same tRNABNN gene (tRNA-446 \nANN, in green), and the wild type (WT, in grey). For proL, we quantified the relative proportion 447 \nof each tRNA species for proL-ANN and WT. Bar graphs show the relative abundance of 448 \ntRNABNN and tRNA ANN of interest (n=3, mean  ± standard deviation ). ‘Total’ indicates the 449 \ncombined abundance of tRNA BNN and tRNA ANN in ANN strains . Statistical significance is 450 \nindicated only for relevant comparisons: blue brackets indicate p <0.05  in pairwise t -tests, 451 \nblack indicate p>0.05. See the source data file for Fig. 2A for all statistical comparisons and  452 \nFig. S3 for proportions of all tRNA species. (B) Growth rates relative to WT, for tRNA KO and 453 \nstrains with tRNAANN expression from the genome (as measured in Fig. 2A ). KO strains that 454 \nare significantly different from WT and ANN strains significantly different from KO are indicated 455 \nby thick borders. (C) Growth rates of the five strains with tRNAANN expression from the genome 456 \nin LB after transferring 1% v/v every 24 hours for 8 days. Each cell shows the mean growth 457 \nrate of six such populations (relative to WT ancestor) for each strain at the fourth and eighth 458 \nday. None of these growth rates were significantly different from WT  (paired t test s). (D) 459 \nGrowth rates relative to WT+pACDH  (empty vector control) , for KO+pACDH, KO+pACDH -460 \ntRNABNN and KO+pACDH-tRNAANN for a subset of genes (asnU, asnV, aspV, tyrV, proL, thrW, 461 \nglyU). The KO+pACDH strains that were significantly different from WT+pACDH control, and 462 \nthe complementation strains (KO+pACDH -tRNABNN and KO+pACDH-tRNAANN) significantly 463 \ndifferent from the KO+pACDH , are indicated by thick borders. Asterisks for KO+pACDH-464 \ntRNAANN cells indicate a significant differen ce from KO+pACDH -tRNABNN (p<0.05, Mann-465 \nWhitney U test). 466 \n 467 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 15, 2026. ; https://doi.org/10.64898/2026.05.15.725136doi: bioRxiv preprint \n\n \n \n  468 \n 469 \nOverexpression of 2D tRNAANN is often neutral or deleterious 470 \nThe observation that expression of translationally active tRNAANN did not impair fitness of WT 471 \n(Fig. 2 B-C) was puzzling since their persistent rarity suggest ed otherwise, at least for 2D 472 \ncodon tRNAANN that should be prone to mistranslation (Fig. 1). However, we noticed that all 473 \n4D codon tRNAANN tested were tolerated unmodified whereas the 2D codon tRNAANN was 474 \nmasked by A34 -to-I34 modification which can potentially restrict supperwobbling (Curran 475 \n1995; Gerber and Keller 1999; Wolf et al. 2002; Yokobori et al. 2013; Schmitt et al. 2024) and 476 \nmitigate potential toxicity. Furthermore, while tRNAANN expression from a single copy on the 477 \ngenome closely mimics  potential B34A substitutions in natural bacterial populations, the 478 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 15, 2026. ; https://doi.org/10.64898/2026.05.15.725136doi: bioRxiv preprint \n\n \n \ntRNAANN thus expressed may also get outcompeted by native tRNABNN and may not contribute 479 \nsubstantially to translation . To reconcile a lack of clear fitness effect s (even after ca. 80 480 \ngenerations) and the rarity of tRNAANN in nature, we reasoned that  the fitness effects of 481 \ntRNAANN may be attenuated when they are expressed from a single copy but compounded 482 \nover evolutionary time scale s. If so, tRNAANN overexpressed from a multi-copy plasmid may 483 \naccentuate fitness effects and allow them to be  detected within the timeframe of our 484 \nexperiments. Similarly, temperature stress may also exaggerate weak mistranslation-induced 485 \nfitness consequences (Rydbn and Isaksson 1984; Thorbjarnardottir et al. 1991; Dahlgren and 486 \nRydén-Aulin 2000; Lyu et al. 2023; Romero Romero et al. 2024).  487 \nTo test these hypotheses, we first overexpressed a subset of tRNAANN from a low-copy-number 488 \nIPTG-inducible plasmid under permissive growth conditions (37°C, LB medium). We observed 489 \nthat both the 2D codon Asn-tRNAAUU and Asp-tRNAAUC reduced relative growth rate (R rel) 490 \nacross all four IPTG concentrations, and Asn-tRNAAUU also lowered the final OD600 (Fig. 3A, 491 \nFig. S6A-C). However, the three 4D codon tRNAANN were largely neutral both with respect to 492 \ngrowth rate (Fig. 3A, Fig. S6B) and final OD600 (Fig. 3B, Fig. S6C). We next assessed fitness 493 \nunder temperature stress (42° C and 30°C), where we also included Asn-tRNAAUU under its 494 \nnative promoter to allow for native gene regulation. At 42°C Asp-tRNAATC reduced growth rate 495 \n(Fig. 3C, Fig. S6D), whereas Leu-tRNAAAG lowered the final OD600 (Fig. 3D, Fig. S6E). At 30°C, 496 \nhowever, none of the tRNAANN showed any fitness impacts. The 2D codons Asp-tRNAAUC and 497 \nAsn-tRNAAUU (expressed from their native promoter) lowered the final OD 600 (Fig. 3C-D, Fig. 498 \nS6D-E). Thus, temperature stress and overexpression indeed revealed some negative fitness 499 \nimpacts, and further suggested that 2D codon box tRNAANN may be more likely to impair fitness 500 \nthan 4D codon box tRNAANN.  501 \n 502 \nFigure: 3 Overexpression of tRNAANN from a low-copy number plasmid. Heatmaps show 503 \ngrowth rates (A) and final OD (B) of strains carrying tRNAANN relative to that of their respective 504 \ntRNABNN. Throughout the study, we compared fitness effects of overexpression of tRNA ANN 505 \nwith that of respective tRNA BNN to account for fitness effect stemming purely from 506 \noverexpression. To calculate relative fitness parameters, we divided growth rate or OD of the 507 \nstrain expressing tRNAANN by that of the strain expressing tRNA BNN. Hence values of these 508 \nparameters above 1 indicate a benefit (shown in green) and below 1 indicate a cost (shown in 509 \nred) of tRNAANN expression. tRNA genes were expressed on the plasmid pACDH and strains 510 \nwere grown in LB medium at 37°C across a gradient of inducer concentration (IPTG). Cases 511 \nwhere tRNAANN were significantly different from tRNA BNN (Mann Whitney test, p<0.05; N=4) 512 \nwith an  effect size higher than 5% are further indicated by red or green thick borders. 513 \nStatistically significant differences of effect sizes smaller than 5% (potentially within the range 514 \nof noise resulting from detection limits and fitting of the growth equation) are indicated by thick 515 \ngrey borders. Relative growth rates (C) and relative final OD (D) of the same strains under 516 \n30°C and 42°C, in LB with 0.5 mM IPTG. 517 \n 518 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 15, 2026. ; https://doi.org/10.64898/2026.05.15.725136doi: bioRxiv preprint \n\n \n \n 519 \n 520 \nTo test for potential differences between the fitness effects of 2D and 4D codon tRNAANN more 521 \nsystematically, w e next investigated the fitness effects of tRNA carrying all theoretically 522 \npossible ANN anticodons  (Fig. S1) introduced into the WT background from a high copy 523 \nplasmid, which we expected to further accentuate fitness consequences. In half the tRNAANN-524 \nmedia combinations tested, there was no significant fitness effect (Fig. 4A -C, Fig. S7A, Fig. 525 \nS8). Three out of the eight tested 2D codon tRNAANN were deleterious: P he-tRNAAAA, Cys-526 \ntRNAACA and His-tRNAAUG significantly prolonged the lag phase and reduced growth rate (Fig. 527 \n4A-B, Fig. S8A-B) in at least two of six media. Phe -tRNAAAA and Cys-tRNAACA also reduced 528 \nfinal OD in four of the six media  (Fig. 4C, Fig. S8C). Tyr-tRNAAUA, Asn-tRNAAUU and Asp-529 \ntRNAAUC prolonged the lag phase and reduced growth rate in at least one medium (Fig. 4A-B, 530 \nFig. S8 A). Note tha t overexpression of Phe -tRNAAAA from a high -copy plasmid impaired 531 \nfitness, in contrast to expression from a single genome -encoded copy (Fig. 2B-C). This is 532 \nexpected because expression from a single copy should result in tRNA levels low enough for 533 \nthe modification system (Fig. 2A) to mitigate fitness effects, whereas a substantial fraction of 534 \noverexpressed Phe-tRNAAAA is likely to have remained unmodified , reducing fitness. Finally, 535 \nSer-tRNAACU improved all three parameters across at least two media (Fig. 4A-C, Fig. S8) and 536 \nIle-tRNAAAU increased growth rate (Fig. 4A , Fig. S8B) and OD (Fig. 4C , Fig. S8C) in at least 537 \ntwo media. The magnitude of fitness effects (Fig. 4D -F) was skewed, significantly increasing 538 \nthe lag phase length and also lowering final OD; whereas effects on growth rate were less 539 \nasymmetric and small (i.e., equally likely to be beneficial or deleterious). Overall, these results 540 \nsuggested that the deleterious effects of 2D  tRNAANN were larger and more consistent than 541 \nbeneficial effects, and on average, 2D tRNAANN are likely to be neutral or deleterious.  542 \n 543 \nFigure 4: Overexpression of 2D codon box tRNA ANN from a high-copy plasmid. (A–C) 544 \nHeat maps show growth parameters of WT strains carrying tRNAANN, relative to those carrying 545 \nthe corresponding native tRNABNN gene on the high copy plasmid pUC19 induced with 0.5mM 546 \nIPTG at 37°C . Positive effects on growth (tRNA ANN/ tRNABNN > 1) are shown in green and 547 \nnegative impacts (tRNAANN/ tRNABNN < 1) in red.  Significant differences between tRNAANN and 548 \nthe corresponding tRNA BNN (Mann-Whitney test, p < 0.05; N = 4 ) with effect size >5% are 549 \nhighlighted by a thick border. Statistically significant differences  with effect size < 5% 550 \n(potentially reflecting noise from detection limits and fitting of the growth equation) are 551 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 15, 2026. ; https://doi.org/10.64898/2026.05.15.725136doi: bioRxiv preprint \n\n \n \nindicated by thick grey borders. (A) Relative length of lag phase, (B) relative growth rate and 552 \n(C) relative final OD are shown across different media, with values of each parameter given 553 \nin each cell . The first two rows include nutrient -rich media and the next four rows include 554 \nnutrient-poor media. In some cases, data for one or more replicates from either the tRNA ANN 555 \nstrain or the respective tRNA BNN variant did not fit the exponential growth equation whereas 556 \nthe other variant grew; e.g., Cys-tRNAANN in Gly 0.6 medium did not grow, unlike Cys-tRNABNN 557 \n(raw OD600 vs. time curves are shown in Fig. S7). Such cases yielded infinite or infinitesimal 558 \nvalues for relative growth rate, so the corresponding cells in the heatmaps are empty; but the 559 \nfill colours and outlines indicate the qualitative direction of relative growth (green if only 560 \ntRNAANN grew and red if only tRNABNN grew). Cases where neither tRNAANN nor tRNABNN grew 561 \nsufficiently well to fit an exponential growth equation or to calculate lag phase (e.g. , AsnT-562 \ntRNAs in Gly 0.6) are indicated by cells with a cross. The absolute values of growth parameters 563 \nare shown in Fig. S8 and summarised in the source data file for Fig. S8. (D-F) Distribution of 564 \nthe mean relative fitness effect shown in the heatmaps in Fig. 4A-C, after log2 transformation  565 \n(bin width= 0.1). Smoothed lines (using bin width 0.4)  indicate the underlying probability 566 \ndistribution estimated using the kernel density. Q-asym0 estimates magnitude of fitness effects 567 \nskew towards beneficial (+1) to harmful ( -1), values with asterisks indicate statistically 568 \nsignificant skew; red/green denote harmful/beneficial skew , grey denote no significant skew  569 \n(see methods for calculation of Q-asym0 and statistical analysis). 570 \n 571 \n 572 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 15, 2026. ; https://doi.org/10.64898/2026.05.15.725136doi: bioRxiv preprint \n\n \n \nOverexpression of 4D tRNAANN is often neutral or beneficial   573 \nWe next investigated fitness effects of overexpressing 4D codon tRNAANN, which should be 574 \nbetter tolerated due to complete degeneracy in their target codon  boxes. Indeed, six of the 575 \neleven 4D codon tRNAANN significantly improved lag phase length in at least two media, and 576 \nfour were neutral (Fig. 5A, Fig. S7B, Fig. S8A), in contrast to the results for 2D tRNAANN (Fig. 577 \n4A, Fig. S7A, Fig. S8A). Exponential growth rate was also improved by five 4D tRNAANN and 578 \nremained unaffected by overexpression of four other 4D codon tRNAANN (Fig. 5B, Fig. S8B). 579 \nThree of the 4D codon tRNAANN improved final OD and the remaining eight appeared to be 580 \nneutral (Fig. 5C, Fig. S8C). Val-tRNAAAC reduced growth rate in three media (Fig. 5B, Fig. S8B) 581 \nand Gly-tRNAACC (the only universally absent 4D codon tRNAANN) impaired lag phase as well 582 \nas growth rate (Fig. 5A-B, Fig. S8A-B). Overall, apart from the universally absent Gly-tRNAACC, 583 \n4D codon tRNAANN either did not affect growth or improved it. Similar to 2D tRNAANN, in half of 584 \nthe media -tRNAANN combinations tested, there were no significant fitness effects.  In the 585 \nremaining half  of the cases tested , 4D codon tRNA ANN also showed skewed fitness 586 \nmagnitudes, albeit opposite to that of 2D tRNAANN. Overexpression of 4D tRNAANN skewed 587 \neach of the three growth parameters significantly towards more beneficial effects (Fig. 5D-F). 588 \nThus, we concluded that on average, 4D tRNA ANN are likely to be  neutral or substantially 589 \nbeneficial. 590 \n 591 \nFigure 5: Overexpression of 4D codon box tRNAANN from a high copy plasmid . (A–C) 592 \nHeat maps show growth parameters of WT strains carrying tRNAANN, relative to those carrying 593 \nthe corresponding native tRNABNN gene on the high copy plasmid pUC19 induced with 0.5mM 594 \nIPTG at 37°C. Positive effects on growth (tRNA ANN/ tRNABNN > 1) are shown in green and 595 \nnegative impacts (tRNAANN/ tRNABNN < 1) in red.  Significant differences between tRNAANN and 596 \nthe corresponding tRNA BNN (Mann-Whitney test, p < 0.05; N = 4 ) with effect size >5% are 597 \nhighlighted by a thick border. Statistically significant differences with effect size < 5% 598 \n(potentially reflecting noise from detection limits and fitting of the growth equation) are 599 \nindicated by thick grey borders. (A) Relative length of lag phase, (B) relative growth rate and 600 \n(C) relative final OD are shown across different media, with values of each parameter given 601 \nin each cell. The first two rows include nutrient -rich media and the next four rows include 602 \nnutrient-poor media. In some cases, data for one or more replicates from either the tRNA ANN 603 \nstrain or the respective tRNA BNN variant did not fit the exponential growth equation whereas 604 \nthe other variant grew; e.g., Ser-tRNAGGA in Gal 0.2 medium did not grow unlike Ser-tRNAAGA 605 \n(raw OD600 vs. time curves are shown in Fig. S7). Such cases yielded infinite or infinitesimal 606 \nvalues for relative growth rate, so the corresponding cells in the heatmaps are empty; but the 607 \nfill colours and outlines indicate the qualitative direction of relative growth (green if only 608 \ntRNAANN grew and red if only tRNA BNN grew). The absolute values of growth parameters are 609 \nshown in Fig. S8 and summarised in the source data file for Fig. S8. (D-F) Distribution of the 610 \nmean relative fitness effect shown in the heatmaps in Fig. 5A-C, after log2 transformation  (bin 611 \nwidth= 0.1). Smoothed lines (using bin width 0.4) indicate the underlying probability distribution 612 \nestimated using the kernel density. Q-asym0 estimates magnitude of fitness effects skew 613 \ntowards beneficial (+1) to harmful ( -1), values with asterisks indicate statistically significant 614 \nskew; red/green denote deleterious/beneficial skew, grey denote s no significant skew (see 615 \nmethods for calculation of Q-asym0 and statistical analysis). 616 \n 617 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 15, 2026. ; https://doi.org/10.64898/2026.05.15.725136doi: bioRxiv preprint \n\n \n \n 618 \n 619 \nOn the whole,  a majority of  both 2D and 4D  tRNAANN appeared to be neutral  (Fig. 4-5, Fig. 620 \nS9A-B). However, when tRNAANN did affect fitness, 4D codon tRNAANN were consistently more 621 \nlikely to improve it than to impair it  (Fig. 5D-F, Fig. S9 A-B). 2D codon tRNAANN, in contrast, 622 \nappeared more likely to impair early growth (lag phase) and less likely to improve later stages 623 \n(exponential growth and final OD) , compared to 4D codon tRNAANN (Fig. 4D-F, Fig. S9A-B). 624 \nRecall that  the tendency of 2D codon tRNAANN to impair fitness is expected  from 625 \nsuperwobbling, which can increase mistranslation in 2D codon boxes (Fig. 1). Although direct 626 \nmeasurement of mistranslation by 2D codon tRNAANN will require more detailed studies, we 627 \nindirectly tested for this effect by estimating the genome-wide mistranslation likelihood for each 628 \n2D codon tRNA ANN (Fig. S9 D), expecting that higher mistranslation likelihood should 629 \ncorrespond to lower fitness.  2D codon Phe-tRNAAAA, Cys-tRNAACA, Asn-tRNAATT with high 630 \nmistranslation likelihood impaired fitness whereas S er-tRNAACU and Ile-tRNAAAU with low 631 \nmistranslation likelihood improved it. This yielded a weak negative correlation between the 632 \noverall fitness effect of each tRNAANN and its mistranslation likelihood (Fig. S9E), though we 633 \nacknowledge that this analysis is constrained because only eight tRNAANN carrying 2D 634 \nanticodon are theoretically possible. This is also further confounded by potential outliers such 635 \nas His-tRNAAUG which did not impair fitness despite a high mistranslation likelihood . One 636 \nexplanation could be A34-to-I34 modification, which restricts superwobbling. While prior work 637 \nshows modification of His-tRNAAUG in an orthogonal tRNA backbone (Biddle et al. 2016; 638 \nSchmitt et al. 2024),  it remains to be directly investigated what fraction of His-tRNAAUG or any 639 \nother tRNAANN introduced here from a plasmid is modified. Nevertheless, this analysis at least 640 \nqualitatively suggested that mistranslation resulting from superwobbling may contribute to the 641 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 15, 2026. ; https://doi.org/10.64898/2026.05.15.725136doi: bioRxiv preprint \n\n \n \nnegative fitness effects of 2D codon tRNAANN. Superwobbling by 4D tRNAANN on the other 642 \nhand, may be tolerated or even exploited due to the degeneracy of the genetic code. 643 \n 644 \nB34A substitutions are generally rare, but better tolerated in 4D codon box tRNAs 645 \nThe beneficial and neutral fitness effects of 4D codon tRNAANN were unexpected due to their 646 \nnear-universal absence, with the exception of two 4D codon tRNAANN in Leuconostocaceae 647 \n(Leuconostoc and Enococcus sp.) reported in previous analyses of a relatively small number 648 \nof bacterial genomes(Diwan and Agashe 2018; Ehrlich et al. 2021). We re-evaluated the rarity 649 \nof tRNAANN using the entire prokaryotic tRNA repertoire known to date, which includes 246,393 650 \npredicted tRNA sequences across 4047 bacteria and 10,517 tRNAs from 220 archaea in the 651 \nGtRNA database (Chan and Lowe 2016; Thornlow et al. 2020) . Across 10,517 predicted 652 \narchaeal tRNAs, only three cases of tRNAANN were found, all from 4D codon boxes (Fig. 6A). 653 \nOf 8383 bacterial tRNAANN genes, 7,688 encoded Arg-tRNAACG, supporting the rarity of other 654 \nANN anticodons across bacteria (Fig. 6A). However, the 695 non-ACG tRNAANN sequences 655 \ndid include all other theoretically possible ANN. 81% of these were 4D codon tRNAANN, with 656 \nLeu-tRNAAAG and Thr -tRNAAGT being most frequent (Fig. 6B). Of the remaining  (19%) 2D 657 \ntRNAANN sequences, half were Phe -tRNAAAA and His -tRNAAUG, which are  potentially 658 \ncompatible with the A-to-I modification system.  659 \nFirmicutes encoded the highest number of tRNAANN (Fig. 6B, Fig. S10), and within this phylum, 660 \nall Lactobacillus, Lactococcus and Streptococcus species encoded Leu-tRNAAAG (Fig. 6C). 661 \nThese three genera lacked Leu-tRNAGAG, suggesting a G34A substitution dating back at least 662 \nuntil their common ancestor (Fig. 6C). Thr-tRNAAGT also showed a similar pattern, albeit for 663 \nfewer genera. The presence of an ANN anticodon in lieu of a GNN is similar to Arg -tRNAs 664 \nwhere ACG is preferred and GCG is absent, and suggests that these genera not only encode, 665 \nbut potentially prefer these specific 4D tRNAANN. Interestingly, these two ANNs only replaced 666 \nGNNs, but not other BNN isoacceptors, suggesting that potential superwobbling by 667 \nunmodified ANNs may be suboptimal even in 4D codon boxes (e.g., due to slower translation 668 \nrate). In contrast, 2D-codon tRNAANN were present sporadically across species from different 669 \ngenera (Fig. S10) and always in the presence of tRNAGNN (Fig. S11). We also found some 670 \ntRNAANN genes in Proteobacteria (genera Salmonella, Escherichia) and Tenericutes 671 \n(subphylum Mollicutes), although their occurrence was scattered within the subphyla or 672 \ngenera, suggesting lineage specific acquisition or loss (Fig. 6B, S10). 673 \n 674 \nFigure 6: Occurrence of tRNA ANN across prokaryotes. (A) Fractions of tRNA ANN and 675 \ntRNABNN within all bacterial and archaeal tRNAs  reported in the GtRNA DB . The number of 676 \ntRNA genes i s indicated in parentheses. (B) Number of  bacterial genomes carrying each 677 \ntRNAANN gene, grouped by  phylum. 4D and 2D tRNA ANN are labelled in green and red, 678 \nrespectively. For tRNAANN present in fewer than 50 species, refer to the right hand Y axis. (C) 679 \nBacterial genera (colored by phylum, see panel B) where at least one tRNAANN is found in all 680 \nspecies (number of species is indicated in parentheses). The coloured portion of each cell 681 \nrepresents the fraction of species where the tRNAANN is present. 4D and 2D tRNA ANN are 682 \nlabelled in green and red, respectively. For comparison, presence of the respective tRNAGNN 683 \nis also shown (labelled in black).  Instances where a tRNANN potentially replaced a tRNAGNN 684 \n(indicated by a concomitant lack of the respective tRNAGNN) are highlighted by black squares. 685 \n(D) For the genera shown in panel C, average genome AT content (1 indicating 100% AT) , 686 \nfraction of U-ending codons out of all codons  used in the genome , and fraction of U -ending 687 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 15, 2026. ; https://doi.org/10.64898/2026.05.15.725136doi: bioRxiv preprint \n\n \n \ncodons within the codon box where the tRNAANN is present (averaged across all species within 688 \na genus). 689 \n 690 \n 691 \n 692 \nNext, we analysed genomic features of species that do have tRNAANN. The 15 genera with at 693 \nleast one tRNAANN found in all species ( i.e., candidates for acquisition by their respective 694 \nancestors) all had high genomic AT content ranging from 60-70% (Fig. 6D) and 30-40% of all 695 \ncodons in these genomes were U-ending. In genera such as Streptococcus and Lactococcus, 696 \nU-ending codons were also preferred over their synonyms in the same codon  box where 697 \ntRNAANN was pres ent. However, in other genera, this preference was only moderate , 698 \nsuggesting that factors other than codon usage contribute to the retention of tRNAANN in some 699 \nlineages. Lastly, 88% of the species with at least one non -ACG tRNAANN encode a TadA 700 \nhomologue (Fig. S10) which may modify these tRNAANN (e.g., Leu-tRNAGAG is modified in 701 \nOenococcus (Rafels-Ybern et al. 2019)  and Streptococcus (Wulff et al. 2024) ). Such large-702 \nscale genome-based predictions are somewhat limited due to prediction errors and a lack of 703 \ndirect evidence for gene expression and functionality. Nonetheless, this analysis of 704 \nexceptional non -ACG tRNAANN suggests that 4D codon tRNAANN (Leu-tRNAGAG and Thr-705 \ntRNAAGT) are retained across evolutionary timescales and potentially even preferred in some 706 \ngenera; and that overall, other 4D codon tRNAANN are generally better tolerated than 707 \nunmodified 2D codon tRNAANN, as shown by our experimental results.  708 \n  709 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 15, 2026. ; https://doi.org/10.64898/2026.05.15.725136doi: bioRxiv preprint \n\n \n \nDISCUSSION 710 \n“It seems likely that inosine will be formed enzymically from an adenine in the nascent sRNA. 711 \nThis may mean that A in this position will be rare or absent, depending upon the exact 712 \nspecificity of the enzyme(s) involved.”  (Crick 1966) 713 \nAnticipation of constraints on anticodon space dates back to the 1960s when only a handful 714 \nof tRNAs were sequenced (Ingram 1963; Holley R.W. 1965; Crick 1966) . These sequences 715 \nalready showed adenosine 34 to inosine 34 (A-to-I) modification and Crick argued that I34 at 716 \nthe first or wobble base of the anticodon can allow pairing with codons ending in U, C or A and 717 \ndrive amino acid misincorporation (mistranslation) during decoding of two -fold degenerate 718 \n(2D) codon boxes. He speculated in a footnote (see above) that A34 may therefore be rare  719 \namong tRNAs decoding 2D codon boxes (Crick 1966). In essence, selection for translational 720 \nfidelity can constrain the anticodon space.  While his was an argument against wobbling by 721 \nI34, subsequent research showed that unmodified A34 itself can decode all four anticodons 722 \nin a codon box (supperwobble) in bacteria, mitochondria and eukaryotic cytosol (Sibler et al. 723 \n1986; Andachi et al. 1987; Borén et al. 1993; Inagaki et al. 1995; Watanabe et al. 1997; Von 724 \nNickisch-Rosenegk et al. 2001; Chen et al. 2002; Aldinger et al. 2012; Yokobori  et al. 2013; 725 \nSoma et al. 2023; Kompatscher et al. 2024; Schmitt et al. 2024) .Therefore, unmodified A34 726 \nmay be more harmful than I34 in 2D codon boxes, though both are expected to be suboptimal 727 \nand rare in 2D codon boxes.  728 \nGenome sequencing over the last three decades revealed that A34 in tRNA genes decoding 729 \ntwo-fold degenerate codon boxes (‘2D codon tRNAANN’) are indeed extremely rare, and that 730 \nwhen A34 is observed in  tRNAs decoding four -fold degenerate codon boxes (‘4D codon 731 \ntRNAANN’) it typically co-occurs with A-to-I modifying enzymes (MEs) (Chan and Lowe 2016; 732 \nDiwan and Agashe 2018; Ehrlich et al. 2021) . The near-universal absence of unmodified 2D 733 \nand 4D codon tRNAANN implies strong purifying selection, arguably acting since early cellular 734 \nevolution (Fig. 7A). Although numerous sources of purifying selection can be speculated (Fig. 735 \n7B), a key first step towards explaining the absence of tRNA ANN is to directly determine the 736 \nfunctionality and fitness effects of expressing such tRNAs. Our results show that tRNAANN in 737 \ntheir native backbones are tolerated, folded, matured and translationally active  in E. coli. A 738 \nmajority of the 4D codon tRNAANN were also tolerated in an unmodified state and showed 739 \nneutral or positive fitness effects when overexpressed. However, 2D codon box tRNAANN were 740 \nmore likely to be deleterious  when overexpressed  and their magnitude of fitness effect 741 \nappeared to scale with a coarsely estimated likelihood of mistranslation. 742 \nOrthogonal tRNAANN, introduced from a medium -copy number plasmid and designed to 743 \nincorporate tyrosine against each sense codon , reduced growth rate by 10-50% (Schmitt et 744 \nal. 2018). Similar global mistranslation, albeit less severe in magnitude,  likely contributed to 745 \nthe fitness cost s of 2D codon tRNA ANN. Furthermore, all  tested 4D codon tRNAANN were 746 \ntolerated unmodified, whereas the only tested 2D codon Phe -tRNAAAA was modified to Phe -747 \ntRNAIAA. The INN anticodon can decode only three codons and thus it is likely to mis -748 \nincorporate amino acids at fewer codons than unmodified ANN. A prior study tested all 749 \ntRNAANN from a non-native backbone and found that only a 2D codon His-tRNAAUG underwent 750 \nsuch modification  (Biddle et al. 2016; Schmitt et al. 2024) . The enzyme responsible for 751 \nmodification of these “novel”  bacterial tRNAANN substrates for I34 modification remains 752 \nunknown, though a parsimonious explanation is TadA which canonically modifies Arg -753 \ntRNAACG and was recently shown to modify numerous mRNAs  in E. coli (Arad et al. 2026) .  754 \nOur study thus suggests potential substrate flexibility of this ancient and essential enzyme  755 \n(Wolf et al. 2002; Delannoy et al. 2009; Diwan and Agashe 2018; Torres et al. 2021) that may 756 \nmitigate the deleterious effects of other tRNA ANN and confer additional advantages, though 757 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 15, 2026. ; https://doi.org/10.64898/2026.05.15.725136doi: bioRxiv preprint \n\n \n \nthis remains to be confirmed. Intriguingly, the two tRNAANN that are compatible with 758 \nmodification also constituted 50% of the rarely found 2D codon tRNAANN across bacteria (Fig 759 \n6B), suggesting that A-to-I modification might mitigate mistranslation and fitness impairment 760 \nby such tRNAs. Taken together, selection for translational fidelity hence emerges as one 761 \nplausible explanation for the absence of 2D codon tRNAANN. 762 \n 763 \nFigure 7: A deep mystery of scarce tRNAANN: (A) The near-universal absence of unmodified 764 \ntRNAANN suggests purifying selection on tRNAANN genes and the evolution of mechanisms to 765 \nmitigate deleterious effects of tRNAANN that are retained and expressed. (B) Potential sources 766 \nof negative selection (in the red box) could involve translation and include  error-prone or 767 \ninefficient decoding by unmodified tRNAs (A-U pairing is also the least efficient (Pernod et al. 768 \n2021)) ribosomal stalling or disassembly. However, they may also include non-translational 769 \nsources of selection such as  toxic effects of fragments resulting from tRNA degradation 770 \n(suggested for some tRNABNN in eukaryotes (Magee and Rigoutsos 2020; Polacek and Ivanov 771 \n2020)), interreference with non -translational processes (suggested for some tRNA BNN in 772 \neukaryotes (Seligmann 2010; Katz et al. 2016; Balasubramaniam et al. 2017; Hamdani et al. 773 \n2019; Su et al. 2020; Ehrlich et al. 2021)), or more speculatively, DNA structure-level effects. 774 \nPotential mechanisms for removing or managing tRNAANN genes (in the green box) include 775 \nsubstitutions in the anticodon loop (e.g., A34B) or elsewhere in the gene , causing 776 \npseudogenization, loss or silencing of the gene. Known mitigation mechanisms (in the yellow 777 \nbox) are likely ancient and include changes in the tRNA backbone (e.g., pairing between bases 778 \n32 and 38, which reduces miscoding) and post-transcriptional modifications. Substrate tRNAs 779 \nand respective modifying enzymes (MEs) always co -occur, and beyond mitigating the 780 \ntranslational effects of unmodified anticodons (e.g. , of tRNAANN and tRNAGNN), modification 781 \nmay also confer additional advantages that drive selection favouring substrate tRNAs and 782 \ncounter-selection against poor substrates. MEs (e.g., TadA) introduced into the archaeal host 783 \nby endosymbiosis likely allowed eukaryotic tRNA repertoires to expand (e.g., tRNAANN). 784 \nOverall, while translational fidelity appears to be a key factor  explaining why tRNAANN are 785 \nabsent, alternative sources of negative selection remains to be investigated.  786 \n 787 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 15, 2026. ; https://doi.org/10.64898/2026.05.15.725136doi: bioRxiv preprint \n\n \n \n 788 \n 789 \nIn contrast to 2D codon tRNAANN, superwobbling by a tRNA ANN within a 4D codon box is not 790 \nexpected to cause mistranslation. Indeed, due to four-fold degeneracy, all 4D tRNAANN (except 791 \nGly- tRNAACC) were neutral or substantially beneficial. 4D tRNAANN were also more likely to be 792 \nretained or even preferred as compared to 2D codon tRNAANN among the exceptional cases 793 \nof A34 occurrences across bacteria. Nevertheless,  seven of eight 4D  tRNAANN are missing 794 \nfrom 99.99% of predicted bacterial tRNA repertoires. Both bacteria and eukaryotes decode 795 \nthe arginine 4D codon box with Arg -tRNAACG; whereas for the other seven 4D codon boxes, 796 \nbacteria prefer tRNAUNN and eukaryotes use tRNAANN (Novoa et al. 2012). Each of these 4D 797 \ncodon tRNAs is modified via  A34-to-I34 or U34-to-cmo5U34 (Novoa et al. 2012; Diwan and 798 \nAgashe 2018) and tRNAGNN alternatives are absent. Codons recognized by these modified 799 \ntRNAs are enriched in highly expressed genes, and these tRNAs (Novoa et al. 2012) and A-800 \nto-I modification appear to be essential (Wolf et al. 2002; Delannoy et al. 2009; Torres et al. 801 \n2021), further underscoring a crucial role of tRNA modification in decoding 4D codon boxes. 802 \nIndeed, it has been proposed that coevolution between MEs and tRNA repertoires shaped the 803 \nancient preference for modifiable 4D codon tRNAUNN in bacteria and tRNAANN in eukaryotes 804 \n(Novoa et al. 2012) . The sources of selection against unmodified 4D codon tRNA ANN and 805 \ntRNAGNN remain incompletely understood, although unmodified 4D codon tRNAGNN (absent 806 \nacross eukaryotes and present with G34 -to-queuosine modification in most bacteria (Diwan 807 \nand Agashe 2018) ) makes the second and third bases error -prone, causing mistranslation 808 \noutside the cognate codon boxes , and driving cellular toxicity  (Pernod et al. 2021) . This 809 \ninherent propensity to error and toxicity is mitigated by a canonical base pairing between the 810 \n32nd and 38 th bases of the tRNA , which respectively mark the beginning and end of the 811 \nanticodon loop (Pernod et al. 2021). This additional base pairing changes the conformation of 812 \nthe anticodon loop and lowers miscoding by tRNAGNN. This underscores the idea that specific 813 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 15, 2026. ; https://doi.org/10.64898/2026.05.15.725136doi: bioRxiv preprint \n\n \n \nanticodons — potentially also ANN — in specific backbones are inherently error-prone, unless 814 \nmitigated by additional features of the backbone or anticodon modifications . Thus, selection 815 \nfor translational fidelity may also contribute to the absence of 4D tRNAANN (Fig. 7B).  816 \nOur observation that both 2D and 4D tRNAANN showed lower growth rates at 42°C than at 817 \n30°C hints at such mistranslation, exacerbated by high temperature.  While mistranslation is 818 \ngenerally thought to be deleterious  (O’connor et al. 1992; Beebe et al. 2003; Bacher et al. 819 \n2004; Kohanski et al. 2008; Berg et al. 2019; Kelly et al. 2019), it can also be beneficial under 820 \nspecific conditions, and especially under stress (Fan et al. 2015; Samhita et al. 2020; Samhita 821 \net al. 2021; Samhita 2022) . The extent of mistranslation also varies across tRNAANN due to 822 \ndifferences in stability across superwobble base -pairs (e.g., A-A is least stable). 823 \nConsequences of amino acid misincorporation on protein structure and fitness also depends 824 \non the physico-chemical differences between the exchanged amino acids ; thus,  mis-825 \nincorporation by some tRNAANN (e.g., replacing Lys with Asn) may be less detrimental than 826 \nothers (e.g., Arg to Ser) (Sengupta et al. 2007). Fitness effects of mistranslation are therefore 827 \nlikely to vary across specific  tRNAANN and environment s, and our suggestion that 828 \nmistranslation contributes to the absence of tRNAANN remains to be directly tested. A first step 829 \nwould be to measure the extent of mistranslation in cells expressing tRNAANN. Strains with 830 \nhigh-fidelity ribosomes (Ruusala et al. 1984; Chumpolkulwong et al. 2004; Agarwal et al. 2015) 831 \nand downregulation of tadA could also be used to test mistranslation -driven fitness defects, 832 \nwhich should be alleviated and exacerbated in the respective strain backgrounds. Likewise, 833 \nantibiotics reducing ribosomal fidelity should also amplify such fitness defects. Lastly, we note 834 \nthat despite our normalization of fitness effects of overexpressed tRNA ANN with that of 835 \noverexpressed tRNABNN, we cannot rule out that overexpression of tRNAANN caused extremely 836 \nhigh mistranslation or resulted in other effects  qualitatively different from those observed in 837 \nnature. Thus, such overexpression may not have amplified natural fitness effects as we had 838 \nintended. We hope that future studies using more sensitive fitness assays and  long term 839 \nevolution may provide further insights.  840 \nOverall, while mistranslation is an attractive hypothesis, given these causes of substantial 841 \nvariation in its effects across specific tRNAANN and environments, it is unlikely to be the sole 842 \nexplanation for the rarity of all tRNAANN across prokaryotes. Therefore, we speculate important 843 \ncontributions from other mechanisms, likely as universal as core features of the translation 844 \nmachinery. For instance, correct pairing at the A site  in bacterial ribosomes  causes 845 \nA1492/A1493 to flip out from 16S rRNA into the minor groove of the first two codon-anticodon 846 \nbase pair and promotes EF-Tu GTP hydrolysis and amino acylated tRNA accommodation 847 \n(Schmeing and Ramakrishnan 2009) . It is possible that an adenosine at the first anticodon 848 \nposition causes steric hindrance (due to the presence of two additional As in the vicinity) and 849 \nimpairs this essential step inside the decoding center, which is conserved across the tree of 850 \nlife (Rodnina and Wintermeyer 2009; Schmeing and Ramakrishnan 2009; Dever et al. 2018; 851 \nTirumalai et al. 2021) . However, an adenosine is tolerated on the mRNA (e.g. , in A-ending 852 \ncodons), suggesting that any steric hindrance is likely asymmetric and specifically unable to 853 \naccommodate tRNA with A34. While this argument remains to  be tested via structural or 854 \nmolecular dynamics  analyses, it serves as a useful example for the nature of potential 855 \nfundamental explanations for the ancient and persistent rarity of tRNAANN.  856 \nA34 may also be rare because tRNA genes at large are highly conserved despite several 857 \ntimes higher mutations rates than the rest of the genome (Thornlow et al. 2018). For instance, 858 \nout of 62,941 mutations observed across 50,000 generations of adaptive evolution in twelve 859 \nE. coli evolution populations, only 50 were in tRNA genes and none in the anticodons (Table 860 \nS2). Analysis of ca. 700 mutations from E. coli mutation accumulation lines (Sane et al. 2025) 861 \nalso captured only one insertion at the first base of the leuW tRNA gene, suggesting that even 862 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 15, 2026. ; https://doi.org/10.64898/2026.05.15.725136doi: bioRxiv preprint \n\n \n \nunder a regime largely dominated by drift, mutations inside tRNA genes are rare. A saturation 863 \nmutagenesis study of the yeast Arg-tRNACCU also found most substitutions to be detrimental, 864 \nwith those in the anticodon loop reducing fitness by more than 25%  (Li et al. 2016) . Hence, 865 \noverall strong purifying selection suggests that any disadvantage of B34A substitutions (e.g., 866 \nlow levels of  mistranslation) may be penalized especially severely and contribute to  the 867 \nscarcity of A34 over evolutionary timescales.  Apart from substitutions, the presence or 868 \nabsence of entire tRNA genes can also be under selection, e.g., due to ecological factors such 869 \nas nutrient availability (Raval et al. 2023). Even in the current study, tRNAANN were more likely 870 \nto affect fitness in nutrient poor media (Fig. 4,5, Fig. S9A-C); whereas in nutrient-rich media, 871 \nmost tRNAANN, particularly 4D codon tRNA ANN, were neutral. It is therefore expected that if 872 \nsome tRNAANN genes enhance translation capacity , they  may be retained due to  drift or 873 \npositive selection  following nutrient upshifts . Fitness assays during nutrient fluctuations, 874 \nincluding competition with WT, might reveal further fitness effects of unmodified tRNAANN. 875 \nIn closing, t he near-complete absence of  unmodified tRNAANN remains intriguing, more so 876 \ngiven the largely neutral and sometimes beneficial effects that we observed. At a population 877 \nsize of a billion and assuming a 10% fitness advantage (i.e., selection coefficient s=0.1), a 878 \nmutation creating a tRNAANN from a tRNABNN has about 17% chance of reaching fixation in an 879 \naverage of 410 generations, as inferred from 1000 discrete -time Wright–Fisher transitions 880 \n(Fig. S12). Even a tRNAANN with a 10% disadvantage can persist for tens of generations, and 881 \na tRNAANN with a disadvantage smaller than 1% can persist for over a thousand generations. 882 \nNonetheless, tRNAANN are remarkably rare. Although cases with negative fitness effects of 2D 883 \ntRNAANN suggest that selection for translational fidelity likely contributes to their absence, the 884 \nneutral and beneficial effects observed for 4D tRNAANN suggests that other fundamental 885 \nconstraints on these molecules, operating in nature, remain hidden. 886 \n 887 \nDATA AVAILABILITY  888 \nSupplementary figures are available with this submission. Raw reads for YAMAT -seq are 889 \navailable on from NCBI GEO; accession number GSE328815 . Additional data are available 890 \non Zenodo (10.5281/zenodo.20180916) which includes, but is not limited to, the following 891 \nsupplementary data and in-house scripts:  892 \nSource data for the main and supplementary figures 893 \nTable S1 (Primers and strains from this study) 894 \nTable S2 (tRNA gene mutations in long term evolution experiment) 895 \nScripts, Source files for the scripts, Output files and plots. 896 \n 897 \nACKNOWLEDGEMENTS 898 \nWe thank  Saurabh Mahajan,  Laasya Samhita, S upratim Sengupta and members of the 899 \nAgashe lab for discussion and critical comments on the manuscript; the NCBS NGS facility for 900 \nhelp with genome and YAMAT sequencing; Gaurav Diwan and Joshua Miranda for setting up 901 \nand maintaining our automated growth measurement system; Gunda Dechow-Seligmann and 902 \nSven Künzel for helping with data collection for YAMAT-seq; George Stoletov, Raagini Biswas, 903 \nAdrita Chakraborty and Pratibha Sanjenbam for helping with the experiments; and the NCBS 904 \nlaboratory kitchen staff for their crucial support. PKR acknowledges the use of ChatGPT 905 \n(OpenAI) for assistance with writing Python scripts. We acknowledge funding and support 906 \nfrom the National Centre for Biological Sciences (NCBS-TIFR) and the Department of Atomic 907 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 15, 2026. ; https://doi.org/10.64898/2026.05.15.725136doi: bioRxiv preprint \n\n \n \nEnergy, Government of India (Project Identification No. RTI 4006) to DA, a CSIR-UGC-NET 908 \nJune/2018/430 fellowship to PKR, the Max Planck Society (JG and SL), and the International 909 \nMax Planck Research School for Evolutionary Biology (SL). 910 \n 911 \nAUTHOR CONTRIBUTIONS 912 \nPKR: Conceptualization, Experimental design, Methodology, Investigation, Data curation, 913 \nValidation, Formal analysis, Visualization, Writing - original draft, review and editing. SL: 914 \nInvestigation, Data curation, Formal analysis  JG: Experimental design, Funding acquisition, 915 \nResources, Methodology, Writing - review and editing.  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