Genomic and physiological signatures of adaptation in pathogenic fungi

Domain: biology
Published: 2026-01-15
Editorial outcome: minor_revision

Abstract
Emerging fungal pathogens have detrimental impacts on crops, animals, and humans. Despite the mounting threat of these emerging fungal pathogens, little is known about their transition from saprotrophic to pathogenic lifestyles. To gain insights into fungal lifestyle transitions, we study the Trichosporonales order, which includes both saprotrophic species and opportunistic human pathogens, as a system to reveal evolutionary adaptations leading to virulence in fungi. Here we use comparative genome analyses and experimental assays to demonstrate that the transition from saprotroph to opportunistic human pathogen is facilitated by adaptive translation. Codon optimization of metabolic genes grants these fungi the ability to quickly adapt to new environments. In this study, we link genomic data with fungal physiology, highlighting the role of adaptive translation in colonizing different environments and suggesting that gene translation optimization plays a critical role in fungal lifestyle evolution.

Introduction
Fungi are an emerging and fast-growing threat to human and plant health across the globe1,2. Fungi can adapt rapidly in response to new selective pressures in changing environments, a feature enabled by remarkable genomic and phenotypic plasticity. Similarly, fungal pathogens quickly adapt to host physiology and defenses. In clinical and agricultural scenarios, the capacity to rapidly adapt allows fungi to infect new hosts, develop new infection mechanisms and virulence factors, and increase resistance to inhibitors3–5.

While some fungal species are highly adapted to human and animal hosts, such as Candida, Malassezia, or Trichophyton, many species are of environmental origin and are opportunistic pathogens, such as some species of Cryptococcus, Trichosporon, Histoplasma, Blastomyces, or Aspergillus1,6–9. A key feature that defines human pathogens is the ability to survive and disseminate within a given host physiology and body temperature10. Some saprotrophic species (i.e., which acquire nutrients from dead organic matter) can become opportunistic human pathogens through evolutionary adaptations that enable colonisation and survival both in human hosts and in various natural environments11,12. While the opportunistic human pathogens Cryptococcus deneoformans and Cryptococcus gattii (Basidiomycota) are commonly isolated from environmental sources such as trees or soil13, the ability to grow at temperatures up to 40 °C gives them the ability to colonise human and animal hosts, causing systemic infections. In contrast, the exclusively saprotrophic sister species Cryptococcus amylolentus and Cryptococcus floricola can only grow at temperatures up to 30 °C14–16.

Several studies have documented that changes in gene expression patterns are core components of adaptation to new environments17,18. These gene expression changes can be demonstrated via comparative transcriptomic analyses or be reflected in the tRNA pool, which rapidly evolves to meet new translational requirements. This level of adaptation involves changes in the relative numbers of the different tRNA molecules encoded by the genome17. Analyses of tRNA-mediated processes have provided valuable insights into evolutionary mechanisms of fungal adaptation19 and metabolic gene-trait associations20. Proteinsynthesis is regulated in part by the availability of tRNAs that match the codons being translated. This regulation is possible due to codon usage biases, in which synonymous codons are used in unequal frequencies throughout the genome21. This fine-tuning between the available tRNAs and the codon usage bias allows for the regulation of protein expression from individual genes. Highly expressed genes have an optimal codon composition that corresponds to abundant tRNAs, promoting faster and more efficient translation22. Consequently, synonymous mutations in coding regions can be selected to optimise codon usage relative to the available tRNA pool, increasing the translation efficiency of key proteins23,24. Codon frequencies and tRNA copy number may thus provide insight into selection signatures of codon optimisation and evolutionary adaptation to new environments25. Codon optimisation drives genome adaptation in fungal pathogens and is linked to the ability to colonise multiple hosts19. However, little is known about fungal lifestyle dynamics and the evolutionary mechanisms that may allow rapid transitions between lifestyles in some species.

The basidiomycetous order Trichosporonales (Agaricomycotina, Tremellomycetes) comprises many species widely distributed in nature. This order includes several species with clinical, agricultural, and biotechnological importance26. Further, Trichosporonales are emerging pathogens that can cause superficial skin irritations but also invasive, life-threatening, systemic infections. Despite their importance and potential threat, little is known about the evolution, ecology, mechanisms of virulence, and transition to pathogenic lifestyles of fungi belonging to the Trichosporonales. In particular, the genus Trichosporon (Trichosporonales) contains several known human and animal yeast-like pathogens26. Superficial and invasive infections by Trichosporon species are emerging life-threatening conditions6,8,9, with an estimated 77% mortality rate for invasive infections27. Despite the epidemiological significance of Trichosporon infections, few pathogenicity traits are known. While the ability to form biofilm, broad antimycotic resistance, and growth at high temperatures have been reported for these species28–30, adaptive traits related to pathogenicity have not been identified in this genus or related genera. Given that pathogenic species are found across different genera, it can be assumed that lifestyle transitions have occurred independently at multiple times. The underlying mechanisms that allow fast lifestyle transitions are not known, but this knowledge is critical to help predict the emergence of new pathogens.

We investigated the evolutionary events and genomic and physiological features driving the transition from saprotrophic to human pathogenic lifestyles in fungi. The primary aim of our study was to identify genomic signatures and physiological traits associated with lifestyle and host/substrate specialisation that could be used to identify potential human fungal pathogens. To this end, we characterised the genomic composition of a set of closely related Trichosporonales species and performed experimental assays to identify genetic and physiological traits putatively associated with recent lifestyle transitions. Based on their lifestyle, we hypothesised that genomes of saprotrophic species have more genes related to carbohydrate transport and metabolism, while genomes of opportunistic pathogens have higher diversity and copy numbers of genes associated with lipid transport and metabolism. In addition, we hypothesised that lifestyle adaptations are imprinted in the translation machinery, namely in tRNA pools and codon optimisation in pathways relevant to the lifestyle. Due to the ecological versatility of these species, some plasticity is expected in terms of gene regulation and translation, as well as both independent and convergent evolutionary events defining adaptations of species with similar ecological strategies. Furthermore, we hypothesise that opportunistic pathogens have a higher fitness in lipid-rich substrates than saprotrophic fungi.

In this work, we describe evidence that opportunistic fungal pathogens are not strictly defined by gene repertoires, but rather by their ability to readily evolve and adapt to new environments through adaptive translation. In addition, by performing growth assays, we provide experimental evidence that codon optimisation is reflected in fungal fitness.

Results
Opportunistic pathogens emerged independently across genera
To determine the evolutionary relationship among members of the Trichosporonales, we used publicly available genomic data for 50 individuals, representing 37 different species. Based on a high content of duplicated single-copy orthologous genes (duplicated BUSCOs ≥43%), five individuals were predicted to be hybrids or diploids31,32 and therefore removed from further analyses (Supplementary Fig. 1). Notably, among the excluded individuals, we identified a hybrid/diploid genome belonging to the opportunistic pathogen Cutaneotrichosporon dermatis (Supplementary Fig. 1).

We conducted a phylogenomic analysis based on 45 haploid genomes (Supplementary Data 1), including 1438 single-copy orthologous protein sequences present in all genomes. The inferred phylogenomic tree topology indicates that the different genera are monophyletic (bootstrap support ≥99%) with opportunistic pathogenic species mainly found in the Trichosporon and Cutaneotrichosporon genera and 2 additional species found in the Apiotrichum genus (Fig. 1). The distribution of pathogenic species across the phylogeny and the ancestral state reconstruction (Supplementary Fig. 2) suggest that the ability to infect human hosts has emerged independently multiple times during the evolution of the Trichosporonales order. Furthermore, the genera Cryptococcus, Trichosporon, and Cutaneotrichosporon contain species with clearly defined and experimentally supported lifestyles, either as exclusive saprotrophs (e.g., Cryptococcus amylolentus) or as common and well-characterised opportunistic pathogens (e.g., Cryptococcus deneoformans)33. However, the genus Apiotrichum contains mainly saprotrophic species but also includes species which recently were recognised as emerging opportunistic pathogens (i.e., Apiotrichum mycotoxinivorans and Apiotrichum veenhuisii).

The genome composition of saprotrophs and pathogens is indistinguishable
To identify genomic signatures associated with lifestyle and with convergent evolution driven by similar ecological environments, we characterised and compared the genomic structure and composition between opportunistic pathogens and saprotrophic species in the order Trichosporonales.

The haploid genome assemblies among the Trichosporonales species were estimated to range from 17.22 Mb to 33.70 Mb (Figs. 1, 2). We found a significant correlation (Spearman’s Rho = 0.62, P = 0.0067) between genome size and transposable elements (TEs) in the genome, suggesting that TE content is a determinant of genome expansion (Supplementary Fig. 3).

We next investigated whether the predicted fungal lifestyles (saprotrophic or opportunistic pathogen) were reflected in genome structures or genomic feature content among Trichosporonales species. We further annotated the genomes to compare gene composition related to carbohydrate and lipid metabolism. While the genomic features varied considerably between species, we found no significant differences (Wilcoxon test P≥0.05) between saprotrophic and opportunistic pathogens (Fig. 2, Supplementary Fig. 4 and Supplementary Data 2). Intriguingly, the number of genes encoding carbohydrate-active enzymes (CAZymes), genes encoding secreted proteins, and genes involved in carbohydrate and lipid transport and metabolism were similar across saprotrophic and pathogenic species (Supplementary Data 2). We compared the genomic composition of these pathways in saprotrophic and opportunistic pathogenic species of Trichosporonales and Tremellales. While saprotrophic Cryptococcus species encoded more secreted proteins and CAZymes compared to opportunistic pathogens, these differences were not consistent among Apiotrichum and Cutaneotrichosporon species (Supplementary Fig. 4), suggesting different evolutionary histories among genera.

Given the importance of carbohydrate degradation for saprotrophic species and lipid metabolism in pathogenic species, we then assessed the gene composition of these pathways across saprotrophic and opportunistic pathogenic species, focusing on the common pathogenic Cryptococcus, Cutaneotrichosporon, and Trichosporon genera (Supplementary Data 3). However, functional annotation of orthologous genes exclusively detected in genomes of saprotrophic or opportunistic pathogens did not reveal discernible patterns associated with their respective lifestyles (Supplementary Data 4, 5). These findings suggest that other factors beyond gene content play a more critical role in determining fungal lifestyle within the Trichosporonales and Tremellales orders.

tRNA composition and expansion are independent of lifestyle
Due to high tRNA variation among species (Fig. 1 and Supplementary Data 6), we explored the distribution and phylogenetic expansion of these genes (Supplementary Note 1). Our analyses revealed high variation in tRNA gene content (51 to 1455 genes) and an uneven distribution of tRNA genes across species (Supplementary Fig. 5). The anticodon types ranged from 39 to 46 per genome (Supplementary Fig. 6 and Supplementary Data 6), with most genomes containing tRNAs decoding all 20 universal amino acids, although some specific tRNAs were absent for some species, such as tRNAs decoding His or Asp (Supplementary Fig. 7 and Supplementary Data 7). The distribution of tRNA genes may be determined by the underlying phylogenetic relationships of species. In order to account for a putative phylogenetic signal in our analyses of tRNA gene composition, we estimated the phylogenetic signal by using Blomberg’s K and associated significance values (Supplementary Note 1). Despite the high phylogenetic signal of five genes, most of the tRNA gene expansion and composition (38 out of 45 genes) showed a reduced phylogenetic signal, potentially indicating multiple independent evolutionary events (Supplementary Fig. 8). In fact, we observe that the tRNA composition was similar among distantly related species (Supplementary Fig. 9), suggesting convergent evolution with respect to tRNA genes. Interestingly, we observed expansions of certain tRNA gene families across different species, yet the most frequently used synonymous codons did not consistently correspond with the most abundant tRNA gene families in the genomes, suggesting complex relationships between tRNA availability and codon usage patterns (Supplementary Data 8 and Supplementary Fig. 10).

To gain insights into the observed tRNA expansion (Fig. 1, Supplementary Data 6 and Supplementary Fig. 7) we further analysed the intragenomic sequence variation among multiple-copy tRNA genes (Supplementary Note 2, 3). Hereby, we found that the nucleotide variation within each gene family (i.e., genes with the same anticodon sequence) was inversely proportional to gene copy number (Supplementary Fig. 11A). High-copy tRNA genes displayed low sequence variation, while low-copy genes exhibited high divergence, suggesting recent expansion for the most abundant families (Supplementary Fig. 11B). Within each genome, when multiple tRNA genes decoded the same amino acid, at least one showed higher conservation (Supplementary Figs. 12, 13). We speculate that these patterns reflect different selective pressures among tRNA genes, both within and across amino acid groups, which also differed among isolates (Supplementary Fig. 14).

We next addressed whether tRNA composition and signatures of tRNA expansion could reflect functional adaptation to distinct lifestyles. To test this, we investigated if the content of tRNA genes differed among fungal species with different predicted fungal lifestyles. Our results indicated that tRNA content was indistinguishable between species with opportunistic pathogenic and saprotrophic lifestyles (Fig. 2 and Supplementary Data 2). However, tRNA content was strongly correlated with genome size (Supplementary Fig. 15A) among all Trichosporonales (Spearman’s Rho = 0.75, P = 6.5e−06) and among opportunistic human pathogenic (Spearman’s Rho = 0.92, P = 0.001) and saprotrophic (Spearman’s Rho = 0.58, P = 0.01) species, suggesting a strong link between tRNA gene content expansion and genome size. Moreover, we observed a correlation between TE content and tRNA gene content (Spearman’s Rho = 0.64, P = 6.7×10−3), indicating that TEs also may play a role in the diversification of tRNAs (Supplementary Fig. 15B).

Overall, our results highlight a dynamic tRNA repertoire to some extent defined by the phylogenetic relationship of the species, but also by different selective forces, and recent expansion events independent of lifestyle.

Relative codon optimisation of metabolic pathways predicts fungal lifestyle
We subsequently investigated whether tRNA composition influences the translation of genes associated with lifestyles. Based on the tRNA composition of each genome and codon usage in genes, we inferred the translation efficiency for genes associated with carbohydrate and lipid metabolic pathways, which are essential for growth in the environment as saprotrophs and in association with a human host as opportunistic pathogens, respectively. To infer the translational adaptation for these two lifestyles, we predicted the codon optimisation index (S) for lipid and carbohydrate transport and metabolism pathways25. The parameter S and respective statistical significance were estimated based on the tRNA gene pool, codon usage bias, and effective number of coding codons among all genes in the respective metabolic pathway. Therefore, S was used as a proxy for the measure of selection on the overall translational efficiency within these pathways. To account for inter-specific variation in codon usage bias, S values were normalised by comparing the mean S values across genes associated with lipid and carbohydrate metabolic pathways (S ratio) or by normalising pathway-specific S values to the genome-wide mean S (S normalised) (see Materials and Methods). A higher relative codon optimisation was interpreted as a proxy improved translation efficiency.

First, we normalised the codon optimisation (S) values against the genome-wide S for all genes and compared the normalised S across pathways involved in metabolism, cellular processes and signalling, and information storage and processing between saprotrophic and common opportunistic pathogenic species with clearly defined lifestyles (Cryptococcus, Trichosporon, Cutaneotrichosporon). Most pathways showed no significant differences (Wilcoxon test P > 0.05) in the normalised S values, except for the lipid transport and metabolism (Wilcoxon test P = 0.0068), cellular motility (Wilcoxon test P = 0.0039) and post-translational modification, protein turnover, and chaperones (Wilcoxon test P = 0.046) functional categories (Supplementary Fig. 16, Supplementary Data 2 and Supplementary Data 9). These results indicate significantly more optimisation in genes involved in lipid metabolism in opportunistic pathogens compared to saprotrophs and may reflect a key adaptation related to pathogen lifestyle.

In addition, we found that the absolute S and the normalised S values were similar for genes involved in both lipid and carbohydrate metabolic pathways (Wilcoxon test P = 0.77 and P = 0.80, respectively) in opportunistic pathogenic species (Supplementary Fig. 17). However, S was significantly higher for carbohydrate metabolism than for lipid metabolism (Wilcoxon test P = 0.041 and P = 0.002) in saprotrophic species. Moreover, the relative optimisation of the lipid in relation to carbohydrate metabolic pathways was significantly different (Wilcoxon test P = 5.2×10−5) between lifestyles (Figs. 3, 18 and Supplementary Data 2). The direct comparison of single-copy orthologous genes detected in all species further suggested higher normalised S values (Wilcoxon test P = 0.046) in genes related to the lipid metabolism in opportunistic pathogens compared to saprotrophic species (Supplementary Fig. 19 and Supplementary Data 10).

In order to test the predictive value of the S ratio (S lipid:S carbohydrates), we performed a permutation test and trained a decision tree classification model34 using the Cryptococcus, Trichosporon and Cutaneotrichosporon species. Based on the permutation test, the S ratio was able to significantly explain lifestyle (empirical P-value = 0.008). In addition, the decision tree model trained on the full dataset classified the lifestyle accurately for all isolates (100% accuracy). To further test model robustness, we randomly sampled four saprotrophs and ten pathogens to train a model and used the remaining one saprotroph and three pathogens to test the model. In this cross-validation approach, all pathogenic species were correctly predicted (100% accuracy). For the saprotrophic species, the accuracy of the model was of 80%, due to one species (Cryptococcus amylolentus) being consistently misclassified due to the S ratio being near the classification threshold (Supplementary Data 11). The remaining saprotrophic species were correctly classified (100% accuracy). Using this decision tree approach, we conclude that the S ratio is a suitable predictor of fungal lifestyle in the studied species, particularly for distinguishing opportunistic pathogens from saprotrophic species.

Altogether, these results indicate that saprotrophic species with a pathogenic lifestyle (i.e., opportunistic pathogens) have similar codon optimisation for both carbohydrates and lipid metabolic pathways, while exclusively saprotrophic species have higher codon optimisation for carbohydrate metabolism (Supplementary Fig. 17). Lifestyles were distinguishable based on the relative codon optimisation (S ratios) of metabolic pathways, which may be relevant to survival in the respective habitats (Fig. 3 and Supplementary Data 2).

These findings indicate selection at the translational level reflected by a significantly higher translational efficiency for carbohydrate-related metabolic genes in saprotrophic species and support the hypothesis that lifestyle adaptations are imprinted in the codon optimisation of lifestyle-relevant pathways.

The emerging opportunistic pathogenic Apiotrichum species (A. mycotoxinovorans and A. veenhuisii) showed a similar codon optimisation between lipid and carbohydrate pathways and were indistinguishable from other well-known opportunistic pathogens (Cryptococcus spp., Cutaneotrichosporon spp., and Trichosporon spp.) (Supplementary Figs. 17, 18, 20). In addition, Apiotrichum species classified as saprotrophic (e.g., A. gracile or A. porosum) showed a similar adaptation for both pathways and were also indistinguishable from other pathogens (Wilcoxon test P = 1, Fig. 3 and Supplementary Fig. 18). This is in contrast to the patterns observed for other genera, where common opportunistic pathogens and their saprotrophic closely related species exhibited clear differences in translational adaptation. The similarity between saprotrophic and pathogenic Apiotrichum species suggests that some species currently considered non-pathogenic may represent potential human pathogens that have remained undetected or misidentified in clinical settings.

Codon optimisation of metabolic pathways predicts fitness
Subsequently, we investigated whether predicted translational adaptation impacted the fitness of the species. We performed growth assays in carbon-rich (potato dextrose) and lipid-rich (oleic acid-based) media. To estimate the fitness effect, we correlated growth parameters with the codon optimisation (S) of the carbohydrate and lipid metabolic pathways, respectively. The maximum growth rate and lag time were used as estimators of fitness for 21 fungal isolates representing 16 species (Supplementary Data 12). The maximum growth rate corresponds to the highest speed at which the cell density increases, while the lag time refers to the time required for the fungal cells to adjust to the surrounding environment, often associated with changes in transcriptome and proteome, before starting exponential growth35.

In carbohydrate-rich media, we observed the highest maximum growth rate for Cutaneotrichosporon dermatis TS-012 (0.99 h−1, standard error 0.05 h−1) and the lowest for Apiotrichum laibachii TS-076 (0.15 h−1, SE 0.006 h−1). In lipid-rich media, Cutaneotrichosporon cyanovorans TS-095 and Trichosporon asahii TS-108 displayed the highest (0.33 h−1, SE 0.006 h−1) and the lowest (0.10 h−1, SE 0.003 h−1) maximum growth rates, respectively. Apiotrichum akiyoshidainum TS-001 had the shortest lag phase (9.96 h, SE 0.50 h) in carbohydrate-rich media, while Cutaneotrichosporon curvatus TS-039 had the longest (31.43 h, SE 0.31 h). In lipid-rich media, Apiotrichum laibachii TS-076 had the shortest lag phase (4.90 h, SE 0.51 h), while Cutaneotrichosporon cyanovorans TS-095 had the longest (19.03 h, SE 0.23 h).

Our analysis revealed no significant correlation (P-value > 0.34) between codon optimisation (S) and maximum growth rate in either substrate (Fig. 4A). However, we observed a significant correlation (R² > 0.6, P-value < 0.013) between lag time and S-values (Fig. 4B), indicating that isolates with higher S of the corresponding metabolic pathway displayed shorter lag phases. These results suggest that a higher codon optimisation of metabolic pathways may provide a fitness advantage by allowing faster adaptation to the surrounding environment and quicker onset of growth.

Saprotrophic species grow at mammal body temperatures
The ability to grow at high temperatures is a critical virulence factor for opportunistic pathogens of humans and other mammal species. Therefore, we further assessed the ability of isolates to grow at 33 °C and 37 °C (Fig. 5). The isolates represented opportunistic pathogenic and saprotrophic species. Most opportunistic pathogenic species (6 isolates) exhibited growth at 33 °C and 37 °C (Fig. 5) except for Cutaneotrichosporon curvatus TS-039, which was only able to grow up to 28 °C (Supplementary Figs. 21, 22). Most saprotrophic species were unable to grow above 28 °C (Supplementary Fig. 21); however, we observed growth of 4 isolates at 33 °C (Vanrija humicola TS-015 and TS-020, Cutaneotrichosporon oleaginosus TS-046, and Cutaneotrichosporon cyanovorans TS-095) and 1 isolate at 37 °C (Cutaneotrichosporon cyanovorans TS-095) (Fig. 5).

These results suggest that growth at high temperatures is not a sole determinant of pathogenicity, since some pathogenic species contain strains not growing at high temperatures, like Cutaneotrichosporon curvatus TS-039 and Cutaneotrichosporon dermatis TS-043 growing at 28 °C and 33 °C, respectively (Supplementary Fig. 22). At the same time, some saprotrophic species were able to grow above 33 °C (e.g., Cutaneotrichosporon oleaginosus TS-046). We speculate that adaptation to higher temperatures may play a role in ecological niche expansion and ability to infect human hosts, however, it is likely one of multiple factors involved in pathogenicity.

Some saprotrophic species (e.g., Vanrija spp.) showed similar relative codon optimisation patterns to those of opportunistic human pathogens (Fig. 3 and Supplementary Fig. 20). The growth temperature experiments (Fig. 5, Supplementary Figs. 21, 22) complement these findings by showing that some saprotrophic species are able to grow at mammal body temperature (e.g., Vanrija spp.), similar to opportunistic human pathogens. Together, these results suggest that some saprotrophic species may have the thermo- and translational adaptation associated with pathogenicity, further suggesting that these may emerge as potential human opportunistic pathogens.

Methods
Genomic data acquisition
Assembled genomic data of 50 fungi were downloaded from the NCBI database, the US DOE Joint Genome Institute’s MycoCosm database (https://mycocosm.jgi.doe.gov), or ATCC Genome Portal (https://genomes.atcc.org/genomes). A total of 5 hybrid or diploid genomes were excluded from further analyses based on literature31 and duplicated BUSCO content (Supplementary Fig. 1). Strains with a predicted proteome completeness >85% and duplicated BUSCOs <5% were selected for further analyses. The final dataset comprised 45 haploid genomes, representing 30 species from the Trichosporonales order and 4 Cryptococcus species from the sister-order Tremellales as outgroups.
Species were considered to be opportunistic human pathogens if 1) they were previously reported in human clinical settings and 2) the identity was confirmed with molecular markers (Supplementary Data 1). Species without clinical reports were considered to be saprotrophic.

Coding gene prediction
To avoid gene prediction biases, all assembled genomes were processed using the funannotate pipeline v1.8.10 with the same settings54. The pipeline included size filtering, cleaning, sorting, and soft masking steps with default parameters, followed by gene prediction using the “cryptococcus” as augustus_species and busco_seed_species and “basidiomycota” as busco_db. Genome completeness was predicted using BUSCO v5.3.2 in comparison to the Basidiomycota lineage database (basidiomycota_odb10)55. For gene comparisons, only coding sequences (CDS) without a predicted stop codon in the inner sequence were considered for further analyses.

Phylogenetic reconstruction
Orthologous protein sequences among the studied Trichosporonales were predicted by using OrthoFinder v2.5.456. A set of 1438 single-copy orthologous genes present in all species were obtained and subsequently individually aligned with MAFFT v7.50557. The individual gene alignments were concatenated and used to calculate a maximum likelihood phylogenetic tree with RAxML v.8.2.12 using 500 bootstrap replicates, the PROTGAMMAWAG model, 123 as seed number for the parsimony inferences, and a random seed of 32158.

tRNA gene prediction and analyses of sequence variation
tRNA genes were predicted with tRNAscan-SE 2.0.9 with default parameters for eukaryotic organisms59. To ensure the high quality of gene predictions, all possible pseudogenes and tRNA genes with a covariance score <30 were excluded from further analyses59. In addition, a subset of tRNA genes were manually curated based on their predicted structure, which validated the quality-filtering criteria, and all curated and quality-filtered genes were subsequently retained. Studies have empirically demonstrated that in fungi and bacteria, the tRNA copy number in the genome correlates with the abundance of each tRNA family in the cell60–63. Therefore, gene copy number in the genome was used as a proxy for the available tRNA pool in the cell.
To explore the overall evolutionary dynamics of tRNA genes, including gene duplications and expansion events, we analysed the full-length tRNA gene sequences, including introns. To estimate sequence variation, the complete tRNA gene sequences were obtained for each genome and aligned for each anticodon type using MAFFT v6.864b57. Only genes with ≥2 copies were considered for intragenomic sequence variation analyses. To estimate intragenomic sequence variation for each tRNA gene family (i.e., anticodon type), the genetic distance was predicted based on sequence alignments using the Kimura-2-parameter distance model64 in the ape R package v5.6-265. Outliers with a distance >10 were excluded, which likely resulted from poor alignments.

Prediction of additional genomic features
Gene functions and pathways were predicted by eggNOG v2.0.866 (Supplementary Data 14). To predict CAZymes, proteomes were scanned against the dbCAN2 v10.0 database67, using HMMER v3.368. Matches with an e-value <1e-15 and a coverage >0.35 were used for further analyses. Secreted proteins were predicted from each proteome with SignalP 6.0g69. Transposable elements were predicted by analysing each genome assembly with REPET3 pipeline70,71, and according to previous studies72. Possibly due to sequencing technologies or assembly methods, the REPET3 approach was unable to predict TEs for some genomes. Therefore, the analyses regarding TEs consider a lower number of species. Repetitive elements were determined by funannotate during soft masking with tantan73.

Codon optimisation and tRNA adaptation index estimation
To assess potential translational selection, the tRNA adaptation index (tAI) and codon optimisation index (S) were calculated with the tAI v0.2.1 R package25. Here, we consider tAI as a proxy for translation efficiency and S as an estimate of the strength of translational selection, respectively. The tAI measures the co-evolution between a given CDS and the respective genomic tRNA pool25,74. The tAI is calculated based on the tRNA gene family count, and the codon count and the distribution per gene in the dataset. The S index estimates the intensity of translational selection based on the correlation between the tAI and the effective number of codons (corrected for GC content at third codon positions)25,74. The statistical significance of the S parameter (the correlation between the tAI and the adjusted codon count) was tested by a Monte Carlo test from the same R package25. The S index, therefore, reflects the intensity of translational selection on gene sets and respective translation efficiency. The synonymous relative codon usage was calculated for the CDS dataset with BioKIT v0.1.175.
The lipid and carbohydrate transport and metabolism-related genes were defined according to the narrowest orthologous groups’ category. The tAI was calculated for each individual gene, while the S was calculated for sets of genes. The normalised translation efficiency related to lipids and carbohydrates was defined as the ratio between the S of the gene sets predicted to be involved in the transport and metabolism in these two metabolic pathways (S lipid transport and metabolism : S carbohydrate transport and metabolism). This ratio was used to address different levels of optimisation between genomes and compare the relative adaptive translation and efficiency of these metabolic pathways within each genome. This ratio indicates a similar level of adaptation to both lipid and carbohydrates (ratio ≈ 1) or a selective translational optimisation towards genes involved in carbohydrate (ratio < 1) or lipid (ratio > 1) transport and metabolism. In addition, the S-value was normalised (Snorm) for each pathway in relation to the genomic S-value, to allow direct comparison between pathways among species. A higher codon optimisation in a given pathway, relative to another pathway or to the genomic background, may reflect functional prioritisation during translation.
To test the predictive value of the S ratio (S lipid:S carbohydrates) for fungal lifestyles, species from the genera Cryptococcus, Trichosporon, and Cutaneotrichosporon were used in a permutation test and for training a decision tree classification model34. These tests were performed with the C5.0 v0.2.076 R package. A permutation test (n=9999) was performed by randomly shuffling lifestyle labels to evaluate the statistical significance of the observed classification accuracy. Furthermore, a decision tree classifier (C5.0) was trained using the S ratio as the only predictor. To assess model robustness, 75% of the data were randomly selected by lifestyle for training (5 saprotrophs and 11 pathogens), and the remaining 25% (1 saprotroph and 3 pathogens) were used for testing. This procedure was repeated over 9999 iterations, and the classification probabilities and accuracies were recorded for each species.

Phylogenetic signal estimation
The strength and direction of trait evolution was estimated based on Blomberg’s K using phylogenetic distance as the only predictor of trait similarity among species77. Blomberg’s K infers stabilising selection (K>1) and neutral (K=1) or convergent (K<1) evolution of a trait relative to the expected variation under the Brownian Motion model of evolution77. This parameter was calculated using the phytools v1.0-3 R package using 100 permutations78. Only K values with P < 0.05 were considered significant. A previously calculated maximum likelihood phylogeny with branch length was used for the calculation (see above).
The influence of both phylogeny and lifestyle on the variance observed across different genomic datasets was tested by decomposing the phylogenetic structure into principal components and analysing them alongside lifestyle using a PERMANOVA approach79. To test for the correlation between tRNA gene composition and phylogenetic distance, the pairwise Bray-Curtis dissimilarity among all genomes was calculated using the vegan v2.6-4 R package, considering the copy number of each tRNA gene family gene80. The pairwise branch length among all genomes was calculated using the phytools v1.0-3 R package and was used as a measure of phylogenetic distance78.

Statistical analyses
Statistical analyses were performed in R v4.2.081 and RStudio 2022.02.282. Data were visualised with ggplot283. For correlative analyses, only 1 strain of each species was used (Supplementary Data 1). Wilcoxon tests were performed with the ggsignif84 and broom85 R packages. Linear regression models, R-values, and P-values were calculated with the ggpmisc R package86. Phylogenetic independent contrasts (PICs) were performed with the ape65 R package.

Yeast strains and media
The yeast cultures used in this study (Supplementary Data 12) were obtained from the Infrastructural Mycosmo Centre and Microbial Culture Collection Ex (EXF), German Collection of Microorganisms and Cell Cultures (DSMZ), Agricultural Research Service Culture Collection, USA (NRRL) and Westerdijk Fungal Biodiversity Institute (CBS). The isolates Apiotrichum porosum TS-027 and Apiotrichum mycotoxinovorans TS-057 were kindly provided by Víctor M. Flores-Núñez (University of Kiel, Germany) and Till Friedrich Schäberle (Fraunhofer IME, Germany), respectively. All isolates were grown on potato dextrose (PD) medium at 23 °C and stored at −80 °C. PD broth medium contained 24 g/L potato dextrose (Sigma-Aldrich). PD agar medium contained 39 g/L potato dextrose agar (Difco) and 10 mL/L 1M Tris HCl (pH 8.0).

Growth assays
All growth assays were performed on sterile 96-well microplates in a total volume of 200 µL. Growth was measured with an Epoch 2 plate reader using 600 nm absorbance. Measurements were performed every 30 mins until the stationary phase was reached (approximately 138–168 h). All media were inoculated with an initial optical density (OD 600 nm) of 2.5×10−4 (approximately 100 – 1500 CFUs, Supplementary Data 13). Isolates were grown in quadruplicates. To determine maximum growth temperature, each isolate was pre-grown in PD broth medium for 3 days at 23 °C and then incubated at 33 °C and 37 °C in PD broth medium as detailed above. In addition, all isolates were grown at 18 °C, 23 °C, 28 °C, 33 °C and 37 °C in 200 µL as detailed above and in 5 mL in sterile reaction tubes.
To determine the fitness in carbohydrate- or lipid-rich media, isolates were pre-grown in PD broth or lipid-based media and then inoculated in the respective medium at 23 °C as detailed above. The lipid-based medium contained 2 mL/L oleic acid, 20 g/L ox bile, 10 mL/L Tween 40, and 6.7 g/L yeast nitrogen base without amino acids. Maximum growth rate, lag time and respective estimated errors were determined from quadruplicates with omniplate v1.087.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Summary\nIn this study, the Trichosporonales order of fungi is highlighted to investigate the role of adaptive translation in the evolution\nof virulence in fungal pathogens. The Tricosporonales order includes both saprotrophic and opportunistic human pathogens.\nThis species dataset was utilized to test the hypothesis that the transition of saprotrophic to pathogenicity is facilitated by\nadaptive translation of relevant metabolic pathways associated with each lifestyle. Codon optimization of pathways\nassociated with saprotrophic and pathogenic lifestyles was coupled with experimental assays to test\nfungal fitness on substrate specific media and at temperatures relevant to human pathogens. The results are clearly communicated and\nconvincingly indicate that adaptive translation plays a role in fungal lifestyle evolution, as well as raise important questions\nrelating to the convergent evolution of pathogenicity.\nHowever, we have identified a number of major and minor issues that we believe need to be addressed prior to publication.\nMajor Issues\nIssue 1:\nWe have identified issues with the supplementary tRNA analysis results section. These results address interesting\nhypotheses of tRNA gene evolution; however, they distract from the main story. This is further confounded by the concerns\nwe have. Our suggestion would be to exclude these results from the manuscript, or to further discuss our issues with this\nsection (see below).\nThe characterisation results of tRNA gene repertoires for the fungal genomes reports instances of tRNA genes decoding\nhistidine and aspartate being absent from 17 species (page 2). We suggest that the possibility of tRNA-scan isotype and\nanticodon discrepancies should also be discussed as a potential reason for these results. If tRNA-scan did identify such\ngenes for histidine and/or aspartate, this should be shown.\nThe tRNA sequence variation analysis is carried out on whole tRNA gene sequences (including introns). These results are\npresented in the context of selection pressure, however introns are not subject to the same selection pressures as coding\nsequences. Therefore we suggest that justification of including introns is provided, or that only mature tRNA sequences are\nused in this analysis.\nIssue 2:\nMaximum growth rate and lag time were used as estimators for fungal fitness of 16 species. We find no evidence that\nspecies cell size has been accounted for when analyzing and drawing conclusions from the OD measurements. We suggest\nthat if cell size has been accounted for, to state this in methods or to re-analyse this growth data.\nIssue 3:\nThe Trichosporonales genomes used in this study include multiple isolates from the same species. We are concerned that\nthis may have biased the results when comparing pathogenic and non-pathogenic groups. We suggest acknowledgment of\nthe presence of multiple isolates and including a discussion of the potential bias introduced by this. Additionally, we noted\nthat, in Figure 2, the TE content comparison has a smaller number of genomes included. We suggest clarifying across the\nanalyses which datasets (genomic or species level) are used and why certain genomes were omitted from this comparison.\nMinor Issues\nIn Figure 4, the legend states that the fitness of fungal isolates is plotted in relation to translation efficiency and lifestyle. We\ndon’t observe the lifestyle information for the fungal isolates plotted on these graphs. Please define where this information is\nin the legend or amend the figure.\nThe authors propose that the phylogeny suggests human host infection arose independently multiple times (Lines 116-118).\nIt would strengthen this argument to test this hypothesis with an ancestral reconstruction or other evolutionary test.\nThe terms saprotrophic (Line 47) and saprobic (53) should be defined for the broad audience of this journal.\nThe phrase “based on convergent tRNA gene expansion and codon optimization” (Line 265-266) is confusing. It is unclear if\nconvergent describes the tRNA gene expansion (which does not appear to be convergent) or if it is saying that the gene\nexpansion is convergent with the codon optimization.\nThe authors should elaborate on the manual curation of tRNAs (line 369). Are these tRNAs retained in the supplemental\nanalysis?\nIn the statistical analyses methods (line 419), the specific type of linear models calculated are not defined.\n(Remarks on code availability)\nThe code and underlying data appears standard and would allow a researcher to reproduce the figures and analyses\nconducted in the manuscript.", "reviewer_2": "# Short assessment\nIn their study Guerreiro MA and coauthors apply a comparative genomics framework to explore the genetics underlying\npathogenicity in the order the Trichosporonales fungi. Although mainly correlative, this work adds to the growing evidence\nthat codon optimisation likely underpins major traits in fungi.\n# Short summary\nIn the present study, Guerreiro MA and coauthors correlate lifestyle with a set of genomic features in Trichosporonales and\nTremellales fungi. Using a set of 18 opportunistic pathogens and 27 saprotrophs, they conclude that no major genetic\nfeatures, i.e gene-family content, associate with lifestyle. The authors next focus on the predicted translation efficiency of\ndifferent group of protein-coding genes and conclude that saprotrophic lifestyle is characterised by higher codon\noptimisation of genes related to carbohydrate metabolism. Finally, the authors measure the growth-rate and lag-phase of 16\nspecies upon in vitro culture in carbohydrate or lipid-rich media and show that higher codon optimisation correlates with\nshorter lag-phase. In a last experiment, the authors address the link between parhogenicity and the ability to grow at\nelevated temperatures and conclude that growth at 37 degrees celsius is not the sole determinant of pathogenicity in the\nTrichosporonales.\nOverall, the manuscript dives into an interesting and often overlooked aspect of genome evolution, namely codon\noptimisation. However, most of the results presented here are correlative and not properly integrated into their phylogenomic\ncontext.\n# Main comment\n(1) The whole study presented here builds upon the comparative analysis of multiple fungal genomes from the\nTrichosporonales order, without applying the appropriate phylogenetic comparative methods. The authors conclusion that\ntRNA copy number / type or lifestyle has no phylogenetic signal is in contradiction with the results presented (Figures S3, S4\nand S5). In addition, phylogenetic signal for lifestyle as described here was not properly tested. Globally, correlations in a\nphylogenomic framework should account for phylogenetic signal (i.e independent contrasts, phylogenetic generalised least\nsquares methods).\nFor reference, here is a very similar study on fungi that recently investigated lifestyle transitions under the scope of codon\noptimisation - https://academic.oup.com/mbe/article/39/4/msac085/6575681\n# Minor comments\n(1) The whole study builds on the comparison of lipid and carbohydrate-related gene pools, but no mention of how these\nsets were built or which genes they include. My understanding is that codon optimisation was calculated across lipid and\ncarbohydrate-related gene pools independently of orthology assignment? Directly comparing single-copy orthogroups\nassigned to either functional group would help strengthen the claim that these underly adaptation.\n(2) The use of different S ratios (genome or metabolism ratios) along the study is confusing and can be misleading. The S\nvalue already accounts for the genomic context so that they are directly comparable between genomes. Given the small\ndifferences highlighted in the study, it would be interesting to discuss what does a ratio of 1.12 corresponds to in terms of\ntranslation efficiency (difference of 0.09 in S values).\n(3) The main result presented Figure 4 is very interesting - linking higher codon optimisation to shorter lag phase in vitro -\nlooking at the source data (Source_growth_parameters.txt) do I understand it well that all fungi were not tested on both\nmedia (lipid and carbohydrate-rich)? Or how do lipid-optimised species perform on carbohydrate-rich media and vice-versa?\nNote that lifestyle does not appear in the figure 4.\n(4) The last results paragraph on the growth at multiple temperatures for a subset of fungal species reads disconnected from\nthe above. Reorganising the results section could help clarifying the rationale?\n# Side notes\n(1) I find it hard to navigate the different species/lifestyle/S ratio datasets presented along the manuscript (i.e common\npathogen, emerging pathogen, saprotroph, growth-rate, growth temperature .. ), perhaps a global table summarising these\ndifferent sets and what they were used for could help clarify.\n(2) Regarding tRNA variation, could the authors trace back recent tRNA duplications linked to anticodon change?\n(3) Many supplementary figures are not mentioned in the main results. Could grouping the supplementary results along the\nmain results help clarify some aspects? Similarly, having all supplementary tables in one file could help navigating the data.\n(4) the title and the claim in the summary that this study \"demonstrate that the transition from saprotroph to opportunistic\nhuman pathogen is facilitated by adaptive translation\" is overstated as most results presented are correlative and linked to a\nspecific gene category and fungal order.\n(5) Although mentioned along main results, convergence / independence / selection / adaptation is not formally addressed in\nthe frame of this study.\n# Main text\n(1) line 121. \"convergent lifestyle-induced evolution\" unclear.\n(2) line 124. mentioning that some genomes were excluded here when the final set of 45 genomes was already introduced\njust before got me confused.\n(3) line 157. why not adding supplementary results to main results?\n(4) line 161. not properly tested\n(5) line 180/181. “regulation” is not properly tested here\n(6) line 188. selection not properly tested (note that the probability of selection acting on codon optimization can be\ndetermined using the ts.test function from the same dos Reis et al, 2004 study)\n(7) line 240. higher genome / lipid / carbohydrate S?\n(8) line 257. \"respectively\"\n(9) line 292-294. \"evidence that adaptive evolution shapes the composition of tRNA genes and codons to tune the\nexpression of genes associated with distinct lifestyles\" reads slightly overstated given the presented results.\n(10) line 296. what is considered a codon optimised gene here?\n(11) line 389. not directly inferring translational selection\n(12) line 421. what does it mean? A summary table with the list of species/isolates present in each dataset / experiment\ncould help clarify.\n(Remarks on code availability)\n# Code\nAll the code and source data linked to the present work appears to be present on the provided github page. Note that I dind't\ntry to reproduce the analysis with the code provided.", "reviewer_3": "The study by Guerreiro et al. investigates codon usage in fungi of the Trichosporonales order in search for genomic\nsignatures associated with a switch from a saprotrophic to a pathogenic lifestyles. Undestanding how genomes adapt to a\npathogenic lifestyle is of prime conceptual and practical relevance. Regulation of virulence at the epitranscriptomic and\nprotein translation levels is often overlooked in comparative and evolutionary genomics, so that this manuscript offers a\ntruely original contribution to the field. The most striking finding is the discovery of a bias towards better codon optimisation\nin genes associated with lipid transport and metabolism relative to genes associated with carbohydrate transport and\nmetabolism in pathogenic species. This signature associates with a faster onset of growth in vitro in lipid-rich media for\npathogenic species compared to saprotrophs. The rationale for the study is straighforward and clearly explained. I found this\na very interesting lead but would need additional information to fully appreciate the biological relevance of the findings.\nMajor comments:\n1. I found that an important limitation of this report is the lack of experimental evidence that the genes and pathways\nidentified based on codon usage are actually important for pathogenicity. If genetic manipulation is not feasible in the\nspecies under consideration, a survey of global gene expression could provide a proxy to support the involvement of lipid-\nrelated genes in the infection process.\n2. Estimates for tRNA gene copy number and codon usage can be very dependent on the quality of genome assemblies and\ngene annotation. Some controls should be provided to ensure that genome data quality did not affect the codon usage\nanalysis. The addition of RNAseq data might be useful to improve gene annotation if needed.\n3. Another test for the biological significance of the major finding from this work would be to demonstrate that it can be used\nto predict yet unknown opportunistic pathogens (or strains able to initiate growth on lipids faster) in other fungal lineages.\nHas this approach been tested?\n4. Are the observed codon usage patterns the result of drift or selection? Is the reduced codon adaptation in carbohydrate\ntransport and metabolism genes of pathogens due to drift, following relaxed selection on codon usage (as step towards\npseudogenization)? or is this rather due to an increase in translationnal selection on lipid transport and metabolism genes?\n5. The observation that Trichonosporales species have a similar repertoire of CAZYmes, secreted proteins, lipid transport\nand metabolism does not necessarily means that they have the sale metabolic capacity (differences in enzymes promiscuity,\nregulation and pathway topology may cause differences). A more systematic metabolic phenotyping of the strains or\nmetabolomic analyses would clarify this point.\n6. The strategy used in this study relies on the hypothesis that \"the tRNA copy number in the genome correlates with the\nabundance of each tRNA family in the cell\" (L370). This generally sounds like a reasonnable assumption, however the\ncontext of the present study might require additional validation. Indeed, differential regulation of tRNA genes might explain\nwhy \"the most frequently used synonymous codons did not consistently correspond with the most abundant tRNA gene\nfamilies in the genomes\" (L163). Furthermore, the relative abundance of individual tRNAs varies in response to stresses and\nchanges in growth conditions (see for instance 10.1126/scisignal.aat6409 and 10.1093/nar/gku945). Taking into accound\ntRNA genes regulation might reveal other pathways harboring translational selection signatures in pathogens.\nMinor comments:\n. L212 \"Apiotrichum species classified as saprotrophic (e.g. A. gracile or A. porosum) showed a similar adaptation (Wilcoxon\ntest P = 1) in both pathways and were indistinguishable from other pathogens\" What seems to distinguish them is that they\ndo not have a higher Slipid/Scarbohydrate ratio than their saprotrophic relatives. This does not seem consistent with a\n\"significantly higher translational efficiency for carbohydrate-related metabolic genes in saprotrophic species\". It is not very\nclear to me what is special about these species and how they contribute to the hypothesis tested here.\n. The experiments presented in this work indicate that there is no strict segregation between saprotrophic and opportunistic\npathogen strains according to high temperature growth, this seems in contradiction with the hypothesis that \"adaptation to\nhigher temperatures may play a role in ecological niche expansion and in pathogenicity\" (L259), could you please clarify?\n. Is growth on lipid-rich medium affecting the expression of small secreted proteins (effectors)?\n. L226 instead of \"adapt\", prefer \"accomodate\" to avoid confusion with the evolutionary term\n. Title suggest the topic is about all kind of genomic signatures of adaptation across all lineages of fungi, whereas this is\nabout one particular signature in one lineage of fungi. This does not diminish the relevance and interest of the manuscript\nbut should be made more explicit from the title.\n. Fig 2: the search for genomic signatures distinguishing saprotrophic and opportunistic pathogens might be better adressed\nby a PCA?\n. Fig 3: Is Wilcoxon test the most adapted for comparing ratios (which might not follow a Normal distribution)?\n. A N-terminal translational ramp is typical of codon-optimized genes (10.1016/j.cell.2010.03.031). Is this ramp detected in\nlipid and carbohydrate-related genes? more or less so between saprotrophic and pathogenic species?\n. signal peptides are biased towards translationally inefficient codons (10.1016/j.bbamem.2018.09.010). Was this taken into\naccount when comparins lipid and carbohydrate related genes?\n. Fig 4: The slope of the correlation between S and lag time is steeper for growth on carbohydrates-rich medium than on\nlipid-rich medium, how can this be interpreted?\n(Remarks on code availability)", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.\n(Remarks on code availability)"}, "author_response": "Point-to-point response to reviewers’ comments\nReviewer #1 (Remarks to the Author):\nSummary\nIn this study, the Trichosporonales order of fungi is highlighted to investigate the role of adaptive\ntranslation in the evolution of virulence in fungal pathogens. The Tricosporonales order includes\nboth saprotrophic and opportunistic human pathogens. This species dataset was utilized to test the\nhypothesis that the transition of saprotrophic to pathogenicity is facilitated by adaptive translation\nof relevant metabolic pathways associated with each lifestyle. Codon optimization of pathways\nassociated with saprotrophic and pathogenic lifestyles was coupled with experimental assays to test\nfungal fitness on substrate specific media and at temperatures relevant to human pathogens. The\nresults are clearly communicated and convincingly indicate that adaptive translation plays a role in\nfungal lifestyle evolution, as well as raise important questions relating to the convergent evolution\nof pathogenicity.\nHowever, we have identified a number of major and minor issues that we believe need to be\naddressed prior to publication.\nMajor Issues\nIssue 1:\nWe have identified issues with the supplementary tRNA analysis results section. These results\naddress interesting hypotheses of tRNA gene evolution; however, they distract from the main story.\nThis is further confounded by the concerns we have. Our suggestion would be to exclude these\nresults from the manuscript, or to further discuss our issues with this section (see below).\nResponse: We appreciate the remarks on the supplementary results. Although we recognize that\nthese results might be distracting, we believe that the tRNA evolution is an important side aspect to\nsupport our main conclusions. Therefore, we would like to keep them as supplementary information.\nThe evolution of tRNA gene composition, expansion and sequence is a fundamental component of\nadaptive translation, and these results offer additional support for the observed patterns related to\nlifestyles. To ensure that the manuscript maintains its focus and is within space limitations, we decided\nto keep these results as supplementary. However, we are open to include some of them in the main\ntext, if considered essential.\nThe characterisation results of tRNA gene repertoires for the fungal genomes reports instances of\ntRNA genes decoding histidine and aspartate being absent from 17 species (page 2). We suggest\nthat the possibility of tRNA-scan isotype and anticodon discrepancies should also be discussed as a\npotential reason for these results. If tRNA-scan did identify such genes for histidine and/or\naspartate, this should be shown.\nResponse: During the tRNA annotation with tRNAscan, some tRNA genes were indeed classified as\npotential pseudogenes and we excluded these from further analyses. However, some of these\npseudogenes were potentially decoding histidine and aspartate. It is important to note that there was\nno bias towards these tRNA gene families decoding histidine and aspartate. Additionally, we excluded\ntRNA genes with a covariance score <30, as recommended by literature. While this approach increases\nthe confidence of the gene prediction and minimizes false positives, it may also contribute to the\nobserved absence of certain tRNA genes. We now include a supplementary table (Supplementary\nTable 7) reporting the number of pseudogenes that were excluded, per genome and address this issue\nin the Supplementary Results and Supplementary Discussion.\nIn the respective section of the Supplementary Results, we now include the following underlined\nsentence:\n“However, tRNAs genes decoding histidine (tRNAHis) were in low abundance overall and even absent\nin 15 genomes (Ap. domesticum, Ap. montevideense, Ap. porosum, Cutaneotrichosporon\narboriformis, Cu. curvatus, Cu. cyanovorans, Cu. dermatis, P. fragicola, Trichosporon asahii, and Tr.\ninkin), while the aspartate-encoding tRNA (tRNAAsp) was absent in Pascua and Takashimella spp.\nSome of these genes were, however, detected as pseudogenes during tRNA prediction\n(Supplementary Table 7).”\nIn the Supplementary Discussion we added the following underlined sentence:\n“Previous studies have reported that codon usage and codon abundance in protein-coding genes do\nnot always correspond to the most abundant associated tRNA gene, and some tRNAs may even remain\nundetected in the genome 2,4. It has been suggested that in fungi, tRNA wobbling of the third base\nposition might enable a tRNA to recognize several unspecific synonymous codons and thereby allow\nthe decoding of these amino acids 2,4,5. The absence of some tRNA could also be due to technical\nlimitations of predictions tools, such as classification of tRNAs as pseudogenised genes\n(Supplementary Table 7) or incorrect assignment of the isotype.”\nThe tRNA sequence variation analysis is carried out on whole tRNA gene sequences (including\nintrons). These results are presented in the context of selection pressure, however introns are not\nsubject to the same selection pressures as coding sequences. Therefore we suggest that justification\nof including introns is provided, or that only mature tRNA sequences are used in this analysis.\nResponse: Thank you for pointing out this very important point. We completely acknowledge that the\nselection pressure is different between introns and exons of tRNA genes. In this section, our aim was\nto analyse the overall evolutionary dynamics of the tRNA genes, including introns. This approach\nallowed us to address gene duplication and expansion events (e.g. Supplementary Figure 6), for which\ndiversification of the gene sequence might be more pronounced within the intronic regions. Since our\nfocus was not limited to the functional regions, we consider that including introns provide valuable\ninformation in an evolutionary context. We added an explanation to the text.\nWe now include in the Materials and Methods the following underlined changes for clarity:\n“To explore the overall evolutionary dynamics of tRNA genes, including gene duplications and\nexpansion events, we analyzed the full-length tRNA gene sequences, including introns. To estimate\nsequence variation, the complete tRNA gene sequences were obtained for each genome and aligned\nfor each anticodon type using MAFFT v6.864b 46”\nIssue 2:\nMaximum growth rate and lag time were used as estimators for fungal fitness of 16 species. We find\nno evidence that species cell size has been accounted for when analyzing and drawing conclusions\nfrom the OD measurements. We suggest that if cell size has been accounted for, to state this in\nmethods or to re-analyse this growth data.\nResponse: To account for potential biases introduced by cell size, we counted CFUs among isolates for\nthe same initial absorbance that was used in these assays (OD = 2.5 × 10-4). We observed that the\n600nm\ninitial number of CFUs (approx. 100 – 1500 CFUs) across isolates did not correlate with any growth\nparameter and could not explain the growth rate or the lag time. Given this, we consider that the cell\nsize does not play a role in these particular experiments. We added additional information in the\nmaterials and methods section to address this issue. Additionally, we now include a supplementary\ntable (Supplementary Table 12) with the CFU counts per isolate at the initial OD of the growth\nexperiments (OD = 2.5 × 10-4).\n600nm\nIssue 3:\nThe Trichosporonales genomes used in this study include multiple isolates from the same species.\nWe are concerned that this may have biased the results when comparing pathogenic and non-\npathogenic groups. We suggest acknowledgment of the presence of multiple isolates and including\na discussion of the potential bias introduced by this.\nResponse: Thank you for bringing up this important observation. We indeed performed all analyses\nconsidering only one isolate per species. The results for single isolates were congruent with the ones\nobtained with multiple isolates per species (e.g. Trichosporon asahii or Cutaneotrichosporon\noleaginosus). For some of the analyses (e.g. Fig. 2) only one isolate per species was considered in the\nanalyses. We now provide an additional supplementary figure for Fig. 3 (Supplementary Figure 18),\nwhere we include only one isolate per species. We find relevant to show on Fig. 3 that, while even\nconsidering interspecies variation, our results are consistent among individuals of the same species\nand not specific to one genome. In the Supplementary Table 1 we indicate which genomes were\nconsidered as representatives for the species. Additionally, we now clarify on the legend of Fig. 3 that\nthe analysis considers multiple isolates per species by adding the following information:\n“In order to consider interspecific variation, the analysis considers several genomes for some species.\nThe results considering 1 genome per species are consistent with the ones presented and are shown\nin Supplementary Figure 18.”\nAdditionally, we noted that, in Figure 2, the TE content comparison has a smaller number of\ngenomes included. We suggest clarifying across the analyses which datasets (genomic or species\nlevel) are used and why certain genomes were omitted from this comparison.\nResponse: Thank you for noticing that we missed to clarify this detail in the text. During TE\nidentification, the pipeline we used (REPET3) was unable to identify any TEs in some genomes. We\nbelieve this might be due to the sequencing technologies used or assembly methods in the respective\noriginal studies. Therefore, these genomes are omitted in this particular panel. We now added, in the\nmethods section, the following underlined sentences for clarity:\n“Transposable elements were predicted by analyzing each genome assembly with REPET3 pipeline\n59,60, and according to previous studies 61. Possibly due to sequencing technologies or assembly\nmethods, the REPET3 approach was unable to predict TEs for some genomes. Therefore, the analyses\nregarding TEs consider a lower number of species.”\nMinor Issues\nIn Figure 4, the legend states that the fitness of fungal isolates is plotted in relation to translation\nefficiency and lifestyle. We don’t observe the lifestyle information for the fungal isolates plotted on\nthese graphs. Please define where this information is in the legend or amend the figure.\nResponse: Indeed, there is no lifestyle depicted in the figure. We corrected the legend.\nThe authors propose that the phylogeny suggests human host infection arose independently\nmultiple times (Lines 116-118). It would strengthen this argument to test this hypothesis with an\nancestral reconstruction or other evolutionary test.\nResponse: We appreciate the suggestion. We now provide an ancestral reconstruction and additional\nphylogenetic tests that account for the influence of phylogenetic structure and lifestyle on our\nanalyses. The ancestral reconstruction indicates that the pathogenic lifestyle emerged in the\nTrichosporonales independently in the Apiotrichum genus and in the clade containing the\nTrichosporon and Cutaneotrichosporon genera. We also noted that the ancestral reconstruction\nindicates a second lifestyle transition event in the pathogenic Cutaneotrichosporon genus to an\nexclusively saprotrophic lifestyle.\nThis reconstruction supports our main conclusion that lifestyle transitions among this group of fungi\nare dynamic.\nWe now provide this reconstruction in the supplementary figures.\nThe terms saprotrophic (Line 47) and saprobic (53) should be defined for the broad audience of this\njournal.\nResponse: We use these two terms as synonyms. For consistency and clarity, we replaced the saprobic\nterm with saprotrophic throughout the text and added a definition in the introduction.\n“Some saprotrophic species (i.e. which acquire nutrients from dead organic matter) can become\nopportunistic human pathogens…”\nThe phrase “based on convergent tRNA gene expansion and codon optimization” (Line 265-266) is\nconfusing. It is unclear if convergent describes the tRNA gene expansion (which does not appear to\nbe convergent) or if it is saying that the gene expansion is convergent with the codon optimization.\nResponse: We recognize that this the sentence could be confusing and corrected it.\nThe authors should elaborate on the manual curation of tRNAs (line 369). Are these tRNAs retained\nin the supplemental analysis?\nResponse: During manual curation, we manually verified a subset of tRNA genes secondary structures.\nAll of the gene structures were as expected. We performed this curation step in order to verify that\nexcessive copy numbers were not due to automatic gene annotation errors. This was performed based\non the quality-filtered genes, filtered based on covariance score and pseudogene classification.\nTherefore, all the curated sequences and all the quality-filtered genes were retained.\nIn the statistical analyses methods (line 419), the specific type of linear models calculated are not\ndefined.\nResponse: We now clarify this in the methods.\n“Linear regression models, R values, and P values were calculated with the ggpmisc R package 73”\nReviewer #1 (Remarks on code availability):\nThe code and underlying data appears standard and would allow a researcher to reproduce the\nfigures and analyses conducted in the manuscript.\nResponse: We appreciate the time and effort in reviewing our codes and data. Our code and data in\nthe GitHub repository were updated to reflect the modifications on our analyses, including the new\nanalyses.\nReviewer #2 (Remarks to the Author):\n# Short assessment\nIn their study Guerreiro MA and coauthors apply a comparative genomics framework to explore the\ngenetics underlying pathogenicity in the order the Trichosporonales fungi. Although mainly\ncorrelative, this work adds to the growing evidence that codon optimisation likely underpins major\ntraits in fungi.\n# Short summary\nIn the present study, Guerreiro MA and coauthors correlate lifestyle with a set of genomic features\nin Trichosporonales and Tremellales fungi. Using a set of 18 opportunistic pathogens and 27\nsaprotrophs, they conclude that no major genetic features, i.e gene-family content, associate with\nlifestyle. The authors next focus on the predicted translation efficiency of different group of protein-\ncoding genes and conclude that saprotrophic lifestyle is characterised by higher codon optimisation\nof genes related to carbohydrate metabolism. Finally, the authors measure the growth-rate and lag-\nphase of 16 species upon in vitro culture in carbohydrate or lipid-rich media and show that higher\ncodon optimisation correlates with shorter lag-phase. In a last experiment, the authors address the\nlink between parhogenicity and the ability to grow at elevated temperatures and conclude that\ngrowth at 37 degrees celsius is not the sole determinant of pathogenicity in the Trichosporonales.\nOverall, the manuscript dives into an interesting and often overlooked aspect of genome evolution,\nnamely codon optimisation. However, most of the results presented here are correlative and not\nproperly integrated into their phylogenomic context.\nResponse: We appreciate the recognition of our study and all the comments provided. We now\nprovide more clear evidence regarding the phylogenomic signal in several of our analyses.\n# Main comment\n(1) The whole study presented here builds upon the comparative analysis of multiple fungal\ngenomes from the Trichosporonales order, without applying the appropriate phylogenetic\ncomparative methods.\nResponse: We appreciate the reviewer’s criticism regarding the phylogenetic signal on our\ncomparative analyses. We now address the question by testing for the phylogenetic signal in our main\nanalyses, following the approach outlined by Mesny and Vannier (2020), which was previously applied\nin the study by Hill et al. 2022, to test for the influence of both phylogeny and lifestyle on our results.\nFor this, the phylogenetic structure was decomposed into principal components and tested together\nwith lifestyle to assess their effects on different genomic datasets.\nIn these results, we observe that the variance in genome composition and structure is explained\nmainly by phylogeny and not by lifestyle. In addition, the variance in the parameter “S” of genes\nrelated to carbohydrate transport and metabolism is also only explained by the phylogeny. However,\nthe parameter “lifestyle” explains more than the phylogeny regarding the variance in the S of genes\nrelated to lipid transport and metabolism. Additionally, the most striking observation is on the ratio\nof the S of these two pathways, where the phylogeny does not explain the variance of S, but rather\nthe lifestyle explains the variance in S with a coefficient of determination (R²) of 0.69.\nIn summary, we consider that lifestyle itself is not a phylogenetically inherited trait in our dataset,\nsince both pathogenic and saprotrophic species are present in multiple clades, further indicating that\nlifestyle is not due to phylogenetic relatedness. We appreciate the recommendation by the reviewer\nand we now include this table in the supplements to support our claims.\nDataset Formula Effect Df R2 F p\nAll orthologous genes count All OGs ~ PC1 + PC2 + lifestyle PC1 1 0.16 9.70 1e-04\nPC2 1 0.10 6.20 1e-04\nlifestyle 1 0.05 3.10 5e-04\nAll genome metrics Stats ~ PC1 + PC2 + lifestyle PC1 1 0.21 13.33 0.0001\nPC2 1 0.12 7.80 0.0031\nlifestyle 1 0.02 0.97 0.3456\nMetrics – Genome size size ~ PC1 + PC2 + lifestyle PC1 1 0.21 14.14 0.0004\nPC2 1 0.16 10.96 0.0006\nlifestyle 1 0.02 1.38 0.2422\nMetrics – GC content GC ~ PC1 + PC2 + lifestyle PC1 1 0.52 60.21 0.0001\nPC2 1 0.13 15.06 0.0002\nlifestyle 1 0.02 2.85 0.0894\nMetrics – tRNA genes count tRNAs ~ PC1 + PC2 + lifestyle PC1 1 0.26 17.56 0.0001\nPC2 1 0.13 8.89 0.0003\nlifestyle 1 0.04 2.44 0.0712\nMetrics – Repeat content repeats ~ PC1 + PC2 + lifestyle PC1 1 0.10 4.48 0.0240\nPC2 1 0.30 1.42 0.2434\nlifestyle 1 0.00 0.13 0.9034\nMetrics – Proteins count proteins ~ PC1 + PC2 + lifestyle PC1 1 0.18 9.67 0.0024\nPC2 1 0.00 0.16 0.7944\nlifestyle 1 0.04 2.08 0.1447\nMetrics – Secreted proteins Sec-proteins ~ PC1 + PC2 + PC1 1 0.30 17.76 0.0001\ncount lifestyle PC2 1 0.01 0.79 0.4272\nlifestyle 1 0.01 0.76 0.4293\nMetrics – CAZymes count CAZymes ~ PC1 + PC2 + lifestyle PC1 1 0.16 9.97 0.0014\nPC2 1 0.11 6.88 0.0080\nlifestyle 1 0.02 1.07 0.3037\nMetrics – Secreted CAZymes Sec-CAZymes ~ PC1 + PC2 + PC1 1 0.20 11.69 0.0006\ncount lifestyle PC2 1 0.12 7.05 0.0070\nlifestyle 1 0.01 0.70 0.4547\nMetrics – Carbohydrate genes carbos ~ PC1 + PC2 + lifestyle PC1 1 0.12 6.65 0.0101\ncount PC2 1 0.03 1.43 0.2307\nlifestyle 1 0.05 2.65 0.1005\nMetrics – Lipid genes count lipids ~ PC1 + PC2 + lifestyle PC1 1 0.74 144.72 0.0001\nPC2 1 0.04 7.47 0.0052\nlifestyle 1 0.01 2.83 0.0864\ntRNA gene counts by anticodon tRNA anticodon ~ PC1 + PC2 + PC1 1 0.24 15.94 0.0001\nlifestyle PC2 1 0.12 7.75 0.0001\nlifestyle 1 0.03 2.25 0.0674\nS carbohydrate transport and S carbos ~ PC1 + PC2 + lifestyle PC1 1 0.59 31.16 0.0001\nmetabolism for PC2 1 0.40 20.98 0.0001\nCutaneotrichosporon, lifestyle 1 0.06 3.18 0.0809\nTrichosporon, and Cryptococcus\nS lipid transport and S lipids ~ PC1 + PC2 + lifestyle PC1 1 0.24 17.22 0.0006\nmetabolism for PC2 1 0.14 9.92 0.0049\nCutaneotrichosporon, lifestyle 1 0.59 41.64 0.0001\nTrichosporon, and Cryptococcus\nS carbohydrate:lipid ratio for S ratio ~ PC1 + PC2 + lifestyle PC1 1 0.01 0.43 0.5485\nCutaneotrichosporon, PC2 1 0.01 0.52 0.5069\nTrichosporon, and Cryptococcus lifestyle 1 0.69 40.96 0.0001\nThe authors conclusion that tRNA copy number / type or lifestyle has no phylogenetic signal is in\ncontradiction with the results presented (Figures S3, S4 and S5).\nIn addition, phylogenetic signal for lifestyle as described here was not properly tested.\nResponse: We recognize the issues with our conclusions based on these analyses. We corrected our\nstatements related to the tRNA evolution in relation to lifestyle and phylogeny, accordingly.\nAdditionally, we now test for phylogenetic signal in our analyses. These indeed show that the tRNA\ncopy number and type are shaped only by phylogenetic structure. After including new analyses where\nwe correct for the phylogenetic signal, the main message of our study remains the same, that codon\noptimization and translation efficiency are linked to lifestyle in this group of fungi.\nGlobally, correlations in a phylogenomic framework should account for phylogenetic signal (i.e\nindependent contrasts, phylogenetic generalised least squares methods).\nFor reference, here is a very similar study on fungi that recently investigated lifestyle transitions\nunder the scope of codon optimisation - https://academic.oup.com/mbe/\narticle/39/4/msac085/6575681\nResponse: We appreciate the recommendation and we followed the approaches used in the study to\naccount for phylogenetic signal in our analyses (please see above).\n# Minor comments\n(1) The whole study builds on the comparison of lipid and carbohydrate-related gene pools, but no\nmention of how these sets were built or which genes they include. My understanding is that codon\noptimisation was calculated across lipid and carbohydrate-related gene pools independently of\northology assignment? Directly comparing single-copy orthogroups assigned to either functional\ngroup would help strengthen the claim that these underly adaptation.\nResponse: Thank you for pointing this out. The analyses related to the pathways were based on the\nfunctional annotations of each predicted gene by eggNOG, namely the narrowest orthologous groups.\nIndeed, the codon optimisation was calculated independently of orthology. We now clarify this in the\nmaterials and methods section. We also include an additional table with the eggNOG annotation for\neach gene considered in these datasets.\nThe direct comparison of single-copy orthogroups present in all tested species with well-defined\nlifestyles (Cryptococcus, Cutaneotrichosporon and Trichosporon) shows the trend that opportunistic\npathogens have a higher codon optimization in genes related to lipid metabolism than the\nsaprotrophic species (P = 0.046). Due to the reduced dataset, the P-value remains significant but close\nto 0.05, however the patterns are still observable.\nWe now include these results in the supplementary figures.\n(2) The use of different S ratios (genome or metabolism ratios) along the study is confusing and can\nbe misleading. The S value already accounts for the genomic context so that they are directly\ncomparable between genomes. Given the small differences highlighted in the study, it would be\ninteresting to discuss what does a ratio of 1.12 corresponds to in terms of translation efficiency\n(difference of 0.09 in S values).\nResponse: We understand that the usage of different ratios might be confusing, but by normalizing\nthese values, we are able to compare the relative codon optimization across species. We agree that S\naccounts for the genomic context, however, we also consider that accounting for species-level\ndifferences in codon usage is necessary when comparing different species. Therefore, we considered\nratios as a normalization step to remove species-specific biases in order to compare whether a\nparticular subset of genes is more or less optimized relatively to the rest of the genome, rather than\nfocusing exclusively on absolute S values. However, we also considered the absolute S values, and we\nprovide these analyses on Supplementary Figure 17, where both absolute and normalized S values are\ncompared for the different pathways.\nWe are happy to provide additional discussion; however, we were not able to find the context of the\n1.12 ratio regarding the translation efficiency or the difference of 0.09 in the S values mentioned by\nthe reviewer.\n(3) The main result presented Figure 4 is very interesting - linking higher codon optimisation to\nshorter lag phase in vitro - looking at the source data (Source_growth_parameters.txt) do I\nunderstand it well that all fungi were not tested on both media (lipid and carbohydrate-rich)? Or\nhow do lipid-optimised species perform on carbohydrate-rich media and vice-versa? Note that\nlifestyle does not appear in the figure 4.\nResponse: For these analyses, we performed the experiments with 4 replicates per isolate. When\nanalysing the growth curves for each isolate, we excluded the data from the isolate from further\nanalyses when replicates were not consistent or when the isolate showed an abnormal growth. Due\nto some degree of dimorphism of some isolates under certain conditions, we were unable to collect\nconsistent data after several experiments for these isolates. Therefore, the dataset is incomplete. We\nnote the challenge of standardizing experimental procedures across a large diversity of fungal species.\nThe isolates for which the data is presented showed a yeast-like growth and the growth among\nreplicates were comparable.\nWe corrected the legend of figure 4 to make our procedures and handling of replicates clearer.\n(4) The last results paragraph on the growth at multiple temperatures for a subset of fungal species\nreads disconnected from the above. Reorganising the results section could help clarifying the\nrationale?\nResponse: We acknowledge that this paragraph might seem disconnected from the above, however,\nwe believe these last 2 sections follow a logical progression of our results.\nWe suggest in the last sentence of the translation efficiency section that the translation efficiency\ncontributes to fitness advantage (adaptation speed). The temperature growth section follows\nnaturally by addressing the growth temperature, which is especially relevant for transitioning from\nsaprotrophic to pathogenic lifestyles.\nWe acknowledge that some rephrasing and sentence restructuring could improve the clarity of the\nresults, however we believe the current paragraph structure provides coherence. However, we are\nopen for suggestions to rephrase or restructure segments of the text which might be unclear or\nmissing context to the reviewer.\n# Side notes\n(1) I find it hard to navigate the different species/lifestyle/S ratio datasets presented along the\nmanuscript (i.e common pathogen, emerging pathogen, saprotroph, growth-rate, growth\ntemperature .. ), perhaps a global table summarising these different sets and what they were used\nfor could help clarify.\nResponse: We appreciate the feedback of the reviewer and we will take this into account to refine the\npresentation of our results. We understand that different concepts throughout the study might be\nconfusing, however, we believe that the current organization of the manuscript provides sufficient\ncontext for each dataset and metric. Each dataset (e.g., common pathogen, emerging pathogen,\nsaprotroph, growth rate, growth temperature) is introduced in the respective section, ensuring that\ntheir purpose to the analysis and meaning are clear. Given this structure, we do not find it necessary\nto include a global summary table. However, we will ensure that these concepts and differences are\nclear within each section and we are adding brief clarifications where needed to improve clarity.\n(2) Regarding tRNA variation, could the authors trace back recent tRNA duplications linked to\nanticodon change?\nResponse: This is indeed an interesting question. While in this study we did not specifically investigate\nanticodon changes, we were able to identify tRNA gene sequences differing only by 1 bp in the\nanticodon, however these were encoding for the same amino acid (e.g. tRNA-AGC and tRNA-CGC\nencoding Ala). In these cases, the intronic regions were very different, implying that this might not be\na recent event. We additionally identified tRNAs encoding different amino acids that were rather\nsimilar in sequence, but already differed in some positions in the tRNA body sequence. We are not\nable to say if this in indeed a duplication followed by a few mutations leading to a codon change. Due\nto the observed sequence diversity, a fast mutation rate is a possible scenario, which is also observed\nin other publications (e.g. Yona et al. 2013). To properly address this question, we are currently\nplanning a future study on anticodon shifts in tRNA genes, where we could address the evolution of\ncodon recognition and translational adaptation.\n(3) Many supplementary figures are not mentioned in the main results. Could grouping the\nsupplementary results along the main results help clarify some aspects? Similarly, having all\nsupplementary tables in one file could help navigating the data.\nResponse: We have merged all the supplementary tables into a single Excel file. Additionally, we\nrevised the text and we now mention all the supplementary information in the main text.\n(4) the title and the claim in the summary that this study \"demonstrate that the transition from\nsaprotroph to opportunistic human pathogen is facilitated by adaptive translation\" is overstated as\nmost results presented are correlative and linked to a specific gene category and fungal order\nResponse: We think our experimental and computational analyses provide evidence for a hitherto\npoorly considered mechanism of fungal niche adaptation, namely by modification of the translation\nefficiency. In the revised version of the manuscript, we include new analyses. Hereby we revisit several\nof our analyses to account for a putative phylogenetic signal in the results. These results clearly\ndemonstrate that phylogenetic relatedness does not explain the observed distribution of fungal\nlifestyles. Rather our analyses support dynamic transitions of lifestyles among fungal species, which\nto some extent can be explained by translation efficiency of key pathways.\nWe are aware that future functional studies focusing on specific genes or certain species, would be of\ngreat interest to further understanding niche adaptation. However, the scope of this study was the\ncomparative analyses of a large number of species.\n(5) Although mentioned along main results, convergence / independence / selection / adaptation is\nnot formally addressed in the frame of this study.\nResponse: We believe that our study reveals adaptive evolution occurring at the level of translation\nefficiency. We have discussed the role of natural selection in shaping translation efficiency of different\ngroups of genes (adaptation to different substrates), and how to see this in the light of convergence.\nIf the reviewer feels that these terms should be further emphasized and explained, we will be happy\nto follow further advice to specific elements of the manuscript.\n# Main text\n(1) line 121. \"convergent lifestyle-induced evolution\" unclear.\nResponse: We rephrased this sentence. It now reads:\n“To identify genomic signatures associated with lifestyle and with convergent evolution driven by\nsimilar ecological environments, we characterized and compared the genomic structure and\ncomposition between opportunistic pathogens and saprotrophic species in the order\nTrichosporonales.”\n(2) line 124. mentioning that some genomes were excluded here when the final set of 45 genomes\nwas already introduced just before got me confused.\nResponse: We restructured this part of the results for clarity. We now present the total number of\ngenomes and the criteria to exclude some of these before mentioning the final set of 45 genomes.\n(3) line 157. why not adding supplementary results to main results?\nResponse: We now add additional supplementary results to the main text. We acknowledge that these\nresults provide additional relevant details, however, to maintain clarity and focus on the main findings,\nas suggested by Reviewer 1 (see above), we have decided to keep most of these results in the\nsupplementary materials.\n(4) line 161. not properly tested\nResponse: This sentence has been edited. We now include additional tests addressing the\nphylogenetic context and corrected some of our interpretations throughout the text.\n(5) line 180/181. “regulation” is not properly tested here\nResponse: We replaced “regulation” with “influence”.\n(6) line 188. selection not properly tested (note that the probability of selection acting on codon\noptimization can be determined using the ts.test function from the same dos Reis et al, 2004 study)\nResponse: We appreciate mentioning this important point. We now provide the results from the\nstatistical test for the determination of S. Instead of using a Monte Carlo test of permutation, we\nperformed Pearson and Spearman correlation tests, following the same principles as the ts.test\nfunction. We observed that all the S values used in our study were significant (P-value < 0.05) by both\ncorrelation tests. For consistency, since the S values are calculated based on Pearson correlations, we\nnow provide the respective Pearson P-values in the Supplementary Tables 9 and 10.\n(7) line 240. higher genome / lipid / carbohydrate S?\nResponse: We rephrased this sentence. It now reads:\n“However, we observed a significant correlation (R² > 0.6, P-value <0.013) between lag time and S\nvalues (Fig. 4B), indicating that isolates with higher S of the corresponding metabolic pathway\ndisplayed shorter lag phases.”\n(8) line 257. \"respectively\"\nResponse: We corrected this.\n(9) line 292-294. \"evidence that adaptive evolution shapes the composition of tRNA genes and\ncodons to tune the expression of genes associated with distinct lifestyles\" reads slightly overstated\ngiven the presented results.\nResponse: We rephrased this sentence. It now reads:\n“Based on our comparative genome analysis and our in-vitro fitness assays, our results indicate that\nadaptive evolution may shape the composition of tRNA genes and codons to tune the expression of\ngenes associated with distinct lifestyles 24.”\n(10) line 296. what is considered a codon optimised gene here?\nResponse: We edited the sentence and now clarify that this is “optimal codon usage”.\n(11) line 389. not directly inferring translational selection\nResponse: We rephrased this sentence. It now reads:\n“To assess for potential translational selection, the tRNA adaptation index (tAI) and codon\noptimization index (S) were calculated with the tAI v0.2.1 R package 25. Here, we consider tAI as a\nproxy for translation efficiency and S as an estimate of the strength of translational selection,\nrespectively.”\n(12) line 421. what does it mean? A summary table with the list of species/isolates present in each\ndataset / experiment could help clarify.\nResponse: We now clarify in the figure legends which isolates are being considered. Regarding the\ngenomic data, we indicate in Supplementary Table 1 which individuals were considered as\nrepresentatives of each species.\nReviewer #2 (Remarks on code availability):\n# Code\nAll the code and source data linked to the present work appears to be present on the provided\ngithub page. Note that I dind't try to reproduce the analysis with the code provided.\nResponse: We appreciate the time and effort in reviewing our codes and data. Our code and data in\nthe GitHub repository were updated to reflect the modifications on our analyses, including the new\nanalyses.\nReviewer #3 (Remarks to the Author):\nThe study by Guerreiro et al. investigates codon usage in fungi of the Trichosporonales order in\nsearch for genomic signatures associated with a switch from a saprotrophic to a pathogenic\nlifestyles. Undestanding how genomes adapt to a pathogenic lifestyle is of prime conceptual and\npractical relevance. Regulation of virulence at the epitranscriptomic and protein translation levels\nis often overlooked in comparative and evolutionary genomics, so that this manuscript offers a\ntruely original contribution to the field. The most striking finding is the discovery of a bias towards\nbetter codon optimisation in genes associated with lipid transport and metabolism relative to genes\nassociated with carbohydrate transport and metabolism in pathogenic species. This signature\nassociates with a faster onset of growth in vitro in lipid-rich media for pathogenic species compared\nto saprotrophs. The rationale for the study is straighforward and clearly explained. I found this a\nvery interesting lead but would need additional information to fully appreciate the biological\nrelevance of the findings.\nMajor comments:\n1. I found that an important limitation of this report is the lack of experimental evidence that the\ngenes and pathways identified based on codon usage are actually important for pathogenicity. If\ngenetic manipulation is not feasible in the species under consideration, a survey of global gene\nexpression could provide a proxy to support the involvement of lipid-related genes in the infection\nprocess.\nResponse: The experimental validation of gene function is currently out of the scope of our study.\nHowever, gene expression analyses of Cryptococcus neoformans (also analysed in our study) during\ninfection show that genes involved in the metabolism of carbohydrates and lipids are reported to be\nhighly expressed and also enriched in human cerebrospinal fluid\n(https://doi.org/10.1128/mBio.02313-21). Although we did not analyse the gene expression in our\ndatasets, the above-mentioned study supports our hypotheses that these gene sets are involved in\npathogenesis and that codon optimization in these pathways are linked to the pathogenic lifestyle.\nWe added the following sentence to the discussion to reflect this limitation on our study:\n“Although we did not analyze gene expression in our study, previous studies on Cryptococcus\ndeneoformans show that genes involved in carbohydrate and lipid metabolism are highly expressed\nduring infection and enriched in human cerebrospinal fluids 37, supporting our hypothesis that these\ngene sets are important for pathogenesis.”\n2. Estimates for tRNA gene copy number and codon usage can be very dependent on the quality of\ngenome assemblies and gene annotation. Some controls should be provided to ensure that genome\ndata quality did not affect the codon usage analysis. The addition of RNAseq data might be useful\nto improve gene annotation if needed.\nResponse: We recognize the concern regarding the impact of the genome quality. To prevent and\nmitigate any biases, we processed all genomes with the same pipelines and settings and used only\ngenomes with at least 85% completeness on their predicted proteome. Additionally, we have\nperformed manual curation of some of the predicted tRNA genes to validate the predictions. We also\nobserve that the completeness of a genome does not correlate with the corresponding tRNA gene\ncopy number, by applying a linear regression model (R² = 0.08, P >0.05) and correlation tests with\ncorrection (adjusted P) for multiple correlations (Pearson P.adj >0.05; Spearman P.adj >0.05; Kendall\nP.adj >0.05). We also verified that our results (131 tRNA genes, 8 tRNA pseudogenes, Supplementary\nTables 6 and 7) are in accordance with what is known for some of the species, namely Cryptococcus\ndeneoformans (131 tRNA genes, 8 tRNA pseudogenes according to\nhttps://gtrnadb.ucsc.edu/GtRNAdb2/genomes/eukaryota/Cryp_neof_var_neoformans_JEC21/). We\nare confident on our tRNA gene predictions and we believe that adding RNAseq data for improved\ngene annotation is out of the scope of the current study.\n3. Another test for the biological significance of the major finding from this work would be to\ndemonstrate that it can be used to predict yet unknown opportunistic pathogens (or strains able to\ninitiate growth on lipids faster) in other fungal lineages. Has this approach been tested?\nResponse: That is a great question and we are currently planning to explore it in detail in a follow-up\nstudy on this topic.\n4. Are the observed codon usage patterns the result of drift or selection? Is the reduced codon\nadaptation in carbohydrate transport and metabolism genes of pathogens due to drift, following\nrelaxed selection on codon usage (as step towards pseudogenization)? or is this rather due to an\nincrease in translationnal selection on lipid transport and metabolism genes?\nResponse: This is an extremely important and an intriguing question, however it is challenging to\ndistinguish between genetic drift and relaxed selection acting on codon usage with the current\ndatasets available. What we can currently say, is that our results indicate that the codon optimization\non the lipid pathway is highly influenced by lifestyle. It is possible that the increased codon\noptimization in the lipid pathway in pathogenic species might reflect translational selection favouring\ntranslation of these proteins rather than relaxed selection, since both pathways are crucial for survival.\n5. The observation that Trichonosporales species have a similar repertoire of CAZYmes, secreted\nproteins, lipid transport and metabolism does not necessarily means that they have the sale\nmetabolic capacity (differences in enzymes promiscuity, regulation and pathway topology may\ncause differences). A more systematic metabolic phenotyping of the strains or metabolomic\nanalyses would clarify this point.\nResponse: We agree that having a similar gene repertoire does not necessarily imply identical\nmetabolic capacity. Although our study focuses on genome evolution, a comprehensive metabolic\nphenotyping combined with genomic data could help clarify their metabolic capacity. This is an\nimportant direction for future work that we are currently also considering to better understand the\nmetabolic diversity and pathogenesis within the Trichosporonales order.\n6. The strategy used in this study relies on the hypothesis that \"the tRNA copy number in the\ngenome correlates with the abundance of each tRNA family in the cell\" (L370). This generally sounds\nlike a reasonnable assumption, however the context of the present study might require additional\nvalidation. Indeed, differential regulation of tRNA genes might explain why \"the most frequently\nused synonymous codons did not consistently correspond with the most abundant tRNA gene\nfamilies in the genomes\" (L163). Furthermore, the relative abundance of individual tRNAs varies in\nresponse to stresses and changes in growth conditions (see for instance 10.1126/scisignal.aat6409\nand 10.1093/nar/gku945). Taking into accound tRNA genes regulation might reveal other pathways\nharboring translational selection signatures in pathogens.\nResponse: We appreciate the insightful comment. The idea of considering tRNA gene regulation\nduring infection is extremely intriguing and might indeed help us find additional translational selection\nsignatures. The relative abundance of individual tRNA genes varies in responses to stress and this is\noften overlooked in fungal biology. Our study considers the genomic tRNA copy number as a proxy for\nthe tRNA pool in the cell, however, we acknowledge that the optimum conditions might differ for each\nisolate, and due to different levels of stress, the tRNA abundance might be different from the\ntheoretical. It will be important to determine how stress conditions affect the tRNA pool in the cell to\nfurther understand the role of translational selection in different lifestyles and during challenging\nconditions, namely during infection.\nMinor comments:\n. L212 \"Apiotrichum species classified as saprotrophic (e.g. A. gracile or A. porosum) showed a\nsimilar adaptation (Wilcoxon test P = 1) in both pathways and were indistinguishable from other\npathogens\" What seems to distinguish them is that they do not have a higher Slipid/Scarbohydrate\nratio than their saprotrophic relatives. This does not seem consistent with a \"significantly higher\ntranslational efficiency for carbohydrate-related metabolic genes in saprotrophic species\". It is not\nvery clear to me what is special about these species and how they contribute to the hypothesis\ntested here.\nResponse: The Apiotrichum genus represent a very intriguing case where a few pathogenic species\nseem to have emerged among exclusively saprotrophic species. Our hypothesis is that the Apiotrichum\nspecies are emerging human pathogens and all the species analysed in this study (with genomic data)\nare potential human pathogens, undetected or misidentified so far in clinical settings. To our\nknowledge, the pathogenic species in this genus were detected for the first time in clinical settings in\n2009 (10.1128/JCM.00460-09) and 2016 (10.1080/00275514.2019.1637645), respectively. This is in\ncontrast with other pathogens like Trichosporon, Cutaneotrichosporon or Cryptococcus which are well\nknown human pathogens for many decades. The genomic signatures are similar among these\npathogens and distinct from the saprotrophic counterparts, which are also similar among them.\nTherefore, we found it very peculiar that a group of saprotrophic species showed no differences from\nother pathogens. By testing our approach in other fungal lineages in a follow-up study, we aim to\npredict potential human pathogens based on genomic data.\nWe modified the text in order to bring forward some of this information and clarify our choice to\ninclude these species and the respective hypotheses.\n. The experiments presented in this work indicate that there is no strict segregation between\nsaprotrophic and opportunistic pathogen strains according to high temperature growth, this seems\nin contradiction with the hypothesis that \"adaptation to higher temperatures may play a role in\necological niche expansion and in pathogenicity\" (L259), could you please clarify?\nResponse: We recognize that this might be confusing and we edited the text to clarify this point. In\nour experiments, we observed that some isolates of known human pathogen species are unable to\ngrow above 33 °C. While high-temperature adaptation is important for human pathogens, we\nhypothesise that it is not the only determinant of pathogenicity but rather it may contribute to\necological niche expansion and the ability of some species to infect human hosts. Likewise, the ability\nof certain saprotrophic species to tolerate high temperatures may indicate independent adaptations\nthat are not necessarily linked to pathogenicity.\nAs examples, we see that both the pathogenic Cutaneotrichosporon dermatis and the saprotrophic\nCutaneotrichosporon oleaginosus grow only up to 33 °C; however, they show distinct codon\noptimization for the metabolic pathways. On another hand, Cutaneotrichosporon dermatis and\nTrichosporon asahii are both pathogenic fungi with similar codon optimization; however, the latter is\nable to grow at 37 °C.\nWe edited the text for clarity and the sentence now reads:\n“We speculate that adaptation to higher temperatures may play a role in ecological niche expansion\nand ability to infect human hosts, however, it is likely one of multiple factors involved in\npathogenicity.”\n. Is growth on lipid-rich medium affecting the expression of small secreted proteins (effectors)?\nResponse: We did not specifically analyse the expression of proteins or effectors, as it was out of the\nscope of our study, so we cannot directly address this point based on our current data. However, given\nthat effectors are often linked with host interactions and environmental adaptation, it would be\ninteresting to investigate whether lipid availability influences their expression.\n. L226 instead of \"adapt\", prefer \"accomodate\" to avoid confusion with the evolutionary term\nResponse: We replaced the word “adapt” with “adjust”.\n. Title suggest the topic is about all kind of genomic signatures of adaptation across all lineages of\nfungi, whereas this is about one particular signature in one lineage of fungi. This does not diminish\nthe relevance and interest of the manuscript but should be made more explicit from the title.\nResponse: We acknowledge the concern and clarify that our study investigates multiple aspects of\ngenomic adaptation in two phylogenetically different fungal orders (Trichosporonales and\nTremellales). Specifically, we study signatures of adaptation on genome structure (e.g. GC content,\ngenome size), functional subsets of gene content and composition (e.g. tRNAs, CAZymes, genes in\nmetabolic pathways) and also codon usage bias and translation efficiency across major functional\ngenomic pathways (e.g. energy production, cytoskeleton or chromatin structure pathways).\nWe now include additional analyses where we demonstrate that the observed signatures are driven\nby lifestyle rather than by phylogenetic relatedness. We also note that signatures of pathogenicity can\nvary across fungal groups. For example, in some fungal lineages, pathogenicity is linked with genome\ncontraction (e.g. Malassezia), while in others with expansion of genes associated with secondary\nmetabolite production (e.g. Aspergillus) in comparison to non-pathogenic closely related species.\nWhile a comprehensive analysis of all fungal lineages is beyond the scope of our study, we focus on\nwell-studied groups to establish a foundation for broader investigations. Given the species diversity\nand the multiple genomic signatures analysed in our study, we believe that the current title accurately\nreflects our study. Furthermore, we consider that readers of Nature Communication are from diverse\nfields of research and most may not be familiar with the specific taxa studied. Therefore, we prefer to\nkeep the title as it is and hope the reviewer can follow our arguments.\n. Fig 2: the search for genomic signatures distinguishing saprotrophic and opportunistic pathogens\nmight be better adressed by a PCA?\nResponse: We now provide PERMANOVA analyses, where we decompose the phylogenetic\nrelationships into principal components and test the effects of the phylogenetic structure and lifestyle\non the variation on different datasets. These are now available in the Supplementary Table 2.\n. Fig 3: Is Wilcoxon test the most adapted for comparing ratios (which might not follow a Normal\ndistribution)?\nResponse: The Wilcoxon test is a non-parametric test that is suitable for distributions which are\nNormal or not. One advantage is that it is also less sensitive to outliers than other statistical tests (e.g.\nt-test). Additionally, the results from Wilcoxon test are congruent with PERMANOVA tests, which does\nnot make assumptions on distribution or sample homogeneity. Therefore, we believe that these\nresults are reliable from the statistical point of view.\n. A N-terminal translational ramp is typical of codon-optimized genes (10.1016/j.cell.2010.03.031).\nIs this ramp detected in lipid and carbohydrate-related genes? more or less so between saprotrophic\nand pathogenic species?\nResponse: This is indeed an interesting aspect of codon optimization and might be an interesting\nquestion for future analyses. However, in our study we focus on the role of the overall codon usage\nbias on pathway level. We believe that this provides a comprehensive view on the translational\nadaptation given how complex these pathways are.\n. signal peptides are biased towards translationally inefficient codons\n(10.1016/j.bbamem.2018.09.010) . Was this taken into account when comparins lipid and\ncarbohydrate related genes?\nResponse: We acknowledge that signal peptides can influence codon bias, however since our analyses\nconsider the entire coding sequence, we believe that it captures the overall trends in codon usage,\nand any bias is present in every genome, since all genomes were processed and analysed in the same\nway. Additionally, we analysed genes involved in transport and metabolism, which also include\nintracellular proteins. Therefore, we did not consider signal peptides in our analyses, besides when\naddressing secreted proteins.\n. Fig 4: The slope of the correlation between S and lag time is steeper for growth on carbohydrates-\nrich medium than on lipid-rich medium, how can this be interpreted?\nResponse: We interpret the slopes based on the protein diversity in each pathway. In fungi, the\ncarbohydrate metabolic pathway contains functionally redundant proteins and multifunctional\nproteins, while in the lipid pathway, the proteins involved are rather specific for their function. The\ncodon optimization of genes involved in carbohydrate metabolism might influence several\nsubprocesses in the pathway. In addition to this, the metabolism of carbohydrates releases quicker\nand directly available energy. In contrast, the metabolism of lipids involves additional steps, such as\nthe breakdown of lipids into fatty acids and subsequent β-oxidation, which delays energy availability\nand could be reflected on the slope of these correlations.\nWe added the following sentences to the Discussion:\n“Our experimental data link codon optimization in genes related to certain metabolic pathways to\nincreased fitness in the respective substrates. We propose that codon optimization and improved\ntranslation efficiency represent a mechanism of faster adaptation (Fig. 4). Physiological adjustment\ntime (here determined as the lag-time), was more strongly reduced in carbohydrate-rich conditions\nthan in lipid-rich conditions, which may be influenced by the codon optimization in the corresponding\nmetabolic genes. In fungi, the carbohydrate metabolism involves redundant and multifunctional\nproteins, while lipid metabolism relies mainly on functionally specific proteins. We speculate that the\ncodon optimization in genes involved in carbohydrate metabolism may involve a multitude of\nsubprocesses. In addition, the energy released during carbohydrate metabolism is more immediately\navailable than for lipid metabolism, which requires additional processing. Hence, we propose that the\ndifference in translation efficiency for carbohydrate and lipid metabolism may reflect a difference in\ncomplexity of the underlying cellular processes.”\nReviewer #4 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part\nof the Nature Communications initiative to facilitate training in peer review and to provide\nappropriate recognition for Early Career Researchers who co-review manuscripts.\nResponse: We appreciate the time and effort in reviewing our codes and data. Our code and data in\nthe GitHub repository were updated to reflect the modifications on our analyses, including the new\nanalyses."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have addressed all the points raised in the original review with text edits or supplemental analysis.\nThe manuscript explores the hypothesis that the transition of saprotrophic to pathogenicity is facilitated by adaptive\ntranslation of relevant metabolic pathways associated with each lifestyle. This work builds on a growing field that recognizes\nadaptive evolution on translational efficiency as important for trait evolution. It faces several of the same challenges as other\nworks in the field including limitations in genomic tRNA annotation. Nevertheless it contributes to our understanding of the\nevolution of pathogenicity.\n(Remarks on code availability)\nCode appears sufficient for re-analysis.", "reviewer_2": "## Short summary\nIn their revised manuscript, Guerreiro et al. provide a more detailed analysis of phylogenetic signal across genome-wide\nmetrics. They conclude that while phylogenetic relatedness explains variation in most metrics, lifestyle (pathogen vs.\nsaprotroph) partly explains variation in codon optimisation of lipid metabolism-related genes (Table S2). They also analyse\ncodon optimisation (S values) in single-copy orthogroups and find no overall difference between pathogens and\nsaprotrophs. However, they report higher relative codon optimisation of metabolism-related genes in pathogens (Figure\nS19).\n## Short assessment\nThe additional analyses improved the manuscript. However, as presented, the results on codon optimisation are misleading\nand main conclusions remain speculative.\n## Main points\n1. The codon optimisation analysis is misleading. The text suggests direct comparisons of S values (which are largely\nsimilar between lifestyles), but the main results (and conclusions) rely on S ratios, whose biological interpretation regarding\ntranslational efficiency is unclear.\n2. The reported R² values (Pearson? Spearman?) do not account for phylogenetic non-independence (e.g. via phylogenetic\nindependent contrasts).\n3. While S is an established proxy for translational selection, the use of \"translation\" throughout is confusing,\nparticularly without experimental validation. \"Codon optimisation\" is more precise and appropriate for the audience.\n## In text\nline 199-209 : this part reads more related to the previous paragraph titled \"tRNA composition and expansion are\nindependent of lifestyle\"\nline 219 : typo \"within in these pathways\"\nline 221-222 : \"common\nopportunistic pathogenic species well-defined lifestyles\" reads confusing.\nline 221-222 : misleading, looking at figure S16 I see that ratios are being compared, not S values. It should be clear in the\ntext. In addition the list of genes assigned to each functional category as presented in the Table S9 is missing.\nline 230-233 : misleading as here again ratios are being compared, not S values (although they are presented in Figure\nS17).\nline 236 : presented statistics are for ratios and not S values.\nline 238 : not clear in Figure S19 what the ratio is (S lipid + S carbohydrate / S genome ?)\nline 242-244 : reads vague, plus S are not predictive of lifestyle but S ratios.\nline 252-254 : confusing, it reads like P relates to the comparison of both pathways.\nline 281-286 : clarify here that correlations are for metabolism-specific S values (and not genome-wide) in link with the\ngrowth media. Therefore \"results suggest that a higher codon optimisation of metabolic pathways ..\"\nline 309 : \"is linked to a higher relative codon optimisation of the lipid metabolic pathway\". tRNA gene pool not linked to\nlifestyle according to paragraph \"tRNA composition and expansion are independent of lifestyle\" and translation not\nexperimentally tested.\nline 356-357 : \"the role of codon optimisation in translational speed\"?\nline 371 and abstract : \"ecology\" does not appear in results.\nline 372-373 : the sentence reads redundant and confusing\n(Remarks on code availability)", "reviewer_3": "As previously mentioned, this manuscript puts forward very interesting hypotheses on fungal pathogen evolution taking an\noriginal angle. The authors have made a commendable effort at clarifying the text and refining some analyses. I appreciated\nthat the authors considered the use of one or multiple isolate per species (comment from reviewer 1) and tested for\nphylogenetic signal in several analyses (comments from reviewer 2). However, efforts at providing further experimental\nsupport has been limited. I found that the included revisions are not yet completely satisfactory and would need to be\nstrengthened by additional information.\nImportant points:\n1. According to the introduction, “the primary aim of [this] study was to identify genomic signatures and physiological traits\nassociated with lifestyle and host/substrate specialization that could be used to identify potential human fungal pathogens”.\nA key finding here is that lag phase on carbohydrates and lipids is anticorrelated with translation efficiency of the\ncorresponding genes (Fig. 4). The extent to which this finding enables the identification of potential human pathogens\nremains unclear. Is there evidence for substrate specialization in the selected species and how it relates to lifestyle needs to\nbe clarified. More thorough comparative analysis of lipid and carbohydrate metabolic pathways would be useful to determine\nwhether orthogroups showing dramatic translation efficiency variation across species are ecologically relevant.\n2. section \"Translation efficiency of metabolic pathways predicts fungal lifestyle\". what is the predictive value of the S ratio?\nIn other words, what is the likelihood of correctly predicting lifestyle when shuffling S values for key metabolic pathways\nbetween species (null model)?\n3. In spite of their arguments, I think the authors need to come up with a more specific and accurate title for their study (see\nalso Reviewer 1 comment 4). First, the number of genes encoding CAZYmes and other secreted is sometimes used as a\nsignature for fungal lifestyles, whereas this parameter does not appear discriminating in the present study. Second, the lack\nof specificity suggests that genomic signatures have been assessed systematically, including for instance any type of gene\nfamily, although this study is focused on testing specific hypotheses. Third, the authors agree in their rebuttal letter that\nfurther functional studies will be required to establish a causal link between codon usage and fitness, but this causal\nrelationship is implicit in the current title.\nSuggestion: the authors compared codon optimization (S) across several pathways. A more systematic approach would be\nto search for pathways enriched among genes with the highest codon optimization. Has this approach been tested?\nMinor comments and suggestions:\n. L250 the S ratio used in figure 3 should be introduced in the main text\n. Fig4 \"Data are presented for isolates with consistent growth patterns across four independent replicates\" > need to\nrepresent variability of the data along the Y axis\n. The section \"Saprotrophic species grow at mammal body temperatures\" is not related to codon usage analysis and does\nadd predictive power to the approach. how many times was the experiment repeated (this is not mentionned in sup fig. 21).\nstill appears disconnected to the rest of the manuscript (reviewer 2 minor comment 4)\n. \"host infection arose independently multiple times\": based on the ancestral state reconstruction provided, that opportunistic\npathogenicity arose 3 times or that it was lost 3 times in the lineages analyses seems equaly likely to me\n. L186: \"we observe that the tRNA composition was similar among distantly related species (Supplementary Fig. 9),\nsuggesting convergent evolution with respect to tRNA genes.\" In this figure, the similarity in tRNA composition decreases\nwith phylogenetic distance at the genus level but not beyond – this does not seem sufficient to infer some form of convergent\nevolution\n. L188: “the most frequently used synonymous codons did not consistently correspond with the most abundant tRNA gene\nfamilies in the genomes” – this seems to argue against adaptive translation at whole genome level, is there a better match\nbetween codon usage and tRNA repertoire when considering specific gene subsets?\n(Remarks on code availability)\nI have not tested the code but appreciate that the authors make it available. The readme file might be expanded to help\nreaders navigate among the different files provided.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.\n(Remarks on code availability)"}, "author_response": "Point-to-point response to reviewers’ comments\nReviewer #1 (Remarks to the Author):\nThe authors have addressed all the points raised in the original review with text edits or\nsupplemental analysis.\nThe manuscript explores the hypothesis that the transition of saprotrophic to pathogenicity is\nfacilitated by adaptive translation of relevant metabolic pathways associated with each lifestyle.\nThis work builds on a growing field that recognizes adaptive evolution on translational efficiency as\nimportant for trait evolution. It faces several of the same challenges as other works in the field\nincluding limitations in genomic tRNA annotation. Nevertheless it contributes to our understanding\nof the evolution of pathogenicity.\nReviewer #1 (Remarks on code availability):\nCode appears sufficient for re-analysis.\nResponse: We appreciate the very constructive comments, as well as the recognition of our efforts\nand the value of our study.\nReviewer #2 (Remarks to the Author):\n## Short summary\nIn their revised manuscript, Guerreiro et al. provide a more detailed analysis of phylogenetic signal\nacross genome-wide metrics. They conclude that while phylogenetic relatedness explains variation\nin most metrics, lifestyle (pathogen vs. saprotroph) partly explains variation in codon optimisation\nof lipid metabolism-related genes (Table S2). They also analyse codon optimisation (S values) in\nsingle-copy orthogroups and find no overall difference between pathogens and saprotrophs.\nHowever, they report higher relative codon optimisation of metabolism-related genes in pathogens\n(Figure S19).\n## Short assessment\nThe additional analyses improved the manuscript. However, as presented, the results on codon\noptimisation are misleading and main conclusions remain speculative.\nResponse: We appreciate all the constructive comments and all the suggestions to improve the clarity\nand accuracy of the text.\n## Main points\n1. The codon optimisation analysis is misleading. The text suggests direct comparisons of S values\n(which are largely similar between lifestyles), but the main results (and conclusions) rely on S ratios,\nwhose biological interpretation regarding translational efficiency is unclear.\nResponse: We agree that our conclusions rely on comparisons of S ratios rather than absolute S values,\nand we acknowledge the importance of clearly distinguishing between the two. We revised the text\nin the Results and Discussion sections to refer to S ratios when appropriate and clarified that these\nrepresent relative codon optimization between specific metabolic pathways or comparisons of S for\nspecific metabolic pathways and the genome-wide average.\nWhile the biological interpretation of S ratios may be less direct than the interpretation of absolute S\nvalues, we use these ratios to compare codon usage among pathways. A higher codon optimization in\na given pathway, relative to another pathway or to the genomic background, may reflect an\noptimization of translation and thereby a footprint of natural selection. We revised the text to make\nthis interpretation clearer.\nWe added the following sentences to the “Translation efficiency of metabolic pathways predicts fungal\nlifestyle” subsection of the Results:\n“To account for interspecific variation in codon usage bias, S values were normalized by comparing\nthe mean S values across genes associated with lipid and carbohydrate metabolic pathways (S ratio)\nor by normalizing pathway-specific S values to the genome-wide mean S (S normalized) (see Materials\nand Methods). A higher relative codon optimization was interpreted as a proxy improved translation\nefficiency.”\nWe added the following sentences to the “Codon optimization and tRNA adaptation index estimation”\nsubsection of the Materials and Methods:\n“In addition, the S value was normalized (S norm) for each pathway in relation to the genomic S value,\nto allow direct comparison between pathways among species. A higher codon optimization in a given\npathway, relative to another pathway or to the genomic background, may reflect functional\nprioritization during translation.”\n2. The reported R² values (Pearson? Spearman?) do not account for phylogenetic non-independence\n(e.g. via phylogenetic independent contrasts).\nResponse: We now provide the correlation values calculated using phylogenetically independent\ncontrasts (PICs). We also clarify which model (Spearman) was used to test for correlations. The\nSupplementary Figures 3 and 15 were updated to consider phylogenetic independent contrasts. These\nnew results are consistent with our previous analyses and add further support to our final conclusion.\nThe values mentioned in the text were updated.\n3. While S is an established proxy for translational selection, the use of \"translation\" throughout is\nconfusing, particularly without experimental validation. \"Codon optimisation\" is more precise and\nappropriate for the audience.\nResponse: We revised the text and replaced “translation” with “codon optimization” where\nappropriate.\n## In text\nline 199-209 : this part reads more related to the previous paragraph titled \"tRNA composition and\nexpansion are independent of lifestyle\"\nResponse: This paragraph was moved to the previous section.\nline 219 : typo \"within in these pathways\"\nResponse: We corrected this.\nline 221-222 : \"common opportunistic pathogenic species well-defined lifestyles\" reads confusing.\nResponse: We rephrased the sentence. It now reads:\n“Furthermore, the genera Cryptococcus, Trichosporon, and Cutaneotrichosporon contain species\nwith clearly defined and experimentally supported lifestyles, either as exclusive saprotrophs (e.g.\nCryptococcus amylolentus) or as common and well-characterized opportunistic pathogens (e.g.\nCryptococcus deneoformans) 33.“\nline 221-222 : misleading, looking at figure S16 I see that ratios are being compared, not S values. It\nshould be clear in the text. In addition the list of genes assigned to each functional category as\npresented in the Table S9 is missing.\nResponse: We rephrased the sentence to clarify the use of ratios. It now reads:\n“First, we normalized the codon optimization (S) values against the genome-wide S for all genes and\ncompared the normalized S across pathways involved in metabolism (…)”\nAdditionally, we now provide a table (Supplementary Table 14) with the list of all genes predicted in\nthis study, functional assignments and the respective predicted tAI values.\nline 230-233 : misleading as here again ratios are being compared, not S values (although they are\npresented in Figure S17).\nResponse: We clarified this and now provide the P values for both S and normalized S (ratio against\nthe genome-wide S). It now reads:\n“Additionally, we found that the absolute S and the normalized S values were similar for genes\ninvolved in both lipid and carbohydrate metabolic pathways (Wilcoxon test P = 0.77 and P = 0.80,\nrespectively) in opportunistic pathogenic species (Supplementary Fig. 17).”\nline 236 : presented statistics are for ratios and not S values.\nResponse: We clarified this. It now reads:\n“The direct comparison of single-copy orthologous genes detected in all species further suggested\nhigher normalized S values (Wilcoxon test P = 0.046) in genes (…)”\nThe concept of normalized S values is introduced in the previous paragraph.\nline 238 : not clear in Figure S19 what the ratio is (S lipid + S carbohydrate / S genome ?)\nResponse: We clarified this. The figure legend now reads:\n“Direct comparison of the codon optimization (S values) among single-copy orthogroups. Protein\nsequences of genes involved in carbohydrate and lipid transport and metabolism were clustered into\northogroups. The S value was determined based on single-copy orthogroups present in all tested\nspecies with well-defined lifestyles (Cryptococcus, Cutaneotrichosporon, and Trichosporon). The S\nratio represents the relative codon optimization between both pathways (S lipid : S carbohydrate) for\neach species.”\nline 242-244 : reads vague, plus S are not predictive of lifestyle but S ratios.\nResponse: We rephrased this. It now reads:\n“Lifestyles were distinguishable based on the relative codon optimization (S ratios) of metabolic\npathways, which may be relevant to survival in the respective habitats (Fig. 3 and Supplementary Table\n2)”.\nline 252-254 : confusing, it reads like P relates to the comparison of both pathways.\nResponse: We moved the P value for clarity. It now reads:\n“Additionally, Apiotrichum species classified as saprotrophic (e.g. A. gracile or A. porosum) showed a\nsimilar adaptation for both pathways and were also indistinguishable from other pathogens (Wilcoxon\ntest P = 1, Fig. 3 and Supplementary Fig. 18).”\nline 281-286 : clarify here that correlations are for metabolism-specific S values (and not genome-\nwide) in link with the growth media. Therefore \"results suggest that a higher codon optimisation of\nmetabolic pathways ..\"\nResponse: We clarified this. It now reads:\n“These results suggest that a higher codon optimization of metabolic pathways may provide a fitness\nadvantage by allowing faster adaptation to the surrounding environment and quicker onset of\ngrowth.”\nline 309 : \"is linked to a higher relative codon optimisation of the lipid metabolic pathway\". tRNA\ngene pool not linked to lifestyle according to paragraph \"tRNA composition and expansion are\nindependent of lifestyle\" and translation not experimentally tested.\nResponse: We rephrased this. It now reads:\n“Our results suggest that the emergence of pathogenicity may be associated with codon optimization\nfor the lipid metabolism. We further show that some species with an optimized codon composition\nfor lipid metabolism, can grow at higher temperatures. Taken together, our findings reflect\nadaptations relevant for infection of mammal hosts.”\nline 356-357 : \"the role of codon optimisation in translational speed\"?\nResponse: We rephrased this. It now reads:\n“For example, in human cells, the translation rate of genes with optimal codon usage is 58% faster\n(4.9 codons/s) than non-optimized genes (3.1 codons/s) 44, emphasizing the role of codon optimization\nin translational speed.”\nline 371 and abstract : \"ecology\" does not appear in results.\nResponse: We removed the mention of ecology in the Abstract and Discussion.\nline 372-373 : the sentence reads redundant and confusing\nResponse: We rephrased this. It now reads:\n“We detected expansion of the translation machinery (i.e. tRNAs) independently of lifestyle\n(Supplementary Results), while the translation efficiency of metabolic genes showed signatures of\nadaptation (i.e. codon optimization) linked to lifestyle.”\nReviewer #3 (Remarks to the Author):\nAs previously mentioned, this manuscript puts forward very interesting hypotheses on fungal\npathogen evolution taking an original angle. The authors have made a commendable effort at\nclarifying the text and refining some analyses. I appreciated that the authors considered the use of\none or multiple isolate per species (comment from reviewer 1) and tested for phylogenetic signal in\nseveral analyses (comments from reviewer 2). However, efforts at providing further experimental\nsupport has been limited. I found that the included revisions are not yet completely satisfactory and\nwould need to be strengthened by additional information.\nResponse: We appreciate the feedback and the recognition of our revisions. We have further revised\nthe text to clarify the mentioned issues.\nImportant points:\n1. According to the introduction, “the primary aim of [this] study was to identify genomic signatures\nand physiological traits associated with lifestyle and host/substrate specialization that could be\nused to identify potential human fungal pathogens”. A key finding here is that lag phase on\ncarbohydrates and lipids is anticorrelated with translation efficiency of the corresponding genes\n(Fig. 4). The extent to which this finding enables the identification of potential human pathogens\nremains unclear. Is there evidence for substrate specialization in the selected species and how it\nrelates to lifestyle needs to be clarified. More thorough comparative analysis of lipid and\ncarbohydrate metabolic pathways would be useful to determine whether orthogroups showing\ndramatic translation efficiency variation across species are ecologically relevant.\nResponse: We agree that the relevance of the anticorrelation between phenotype and codon\noptimization was not clear in the text. We now clarify this point in the Discussion.\nWe added the following sentences to the Discussion:\n“Substrate specialization is ecologically relevant in the context of the transition from saprotrophic to\npathogenic lifestyles. The opportunistic pathogen Cryptococcus deneoformans shows an increased\nexpression of lipid-related genes during infection 39,40. This further suggests that in mammal hosts,\nlipid-rich environments may select for opportunistic pathogens with optimized lipid metabolism.”\n“These results suggest that codon optimization patterns could serve as potential predictive genomic\nmarkers for fungal lifestyles.”\nRegarding the orthogroups, we previously analysed single-copy orthogroups involved in lipid and\ncarbohydrate metabolism (Supplementary Figure 19). This analysis revealed that the codon\noptimization (S values) for the two pathways was similar in opportunistic pathogens, whereas\nsaprotrophic species displayed a significantly higher codon optimization for genes involved in\ncarbohydrate metabolism. This supports support the idea that lipid metabolism is selectively\noptimized in pathogens, reflecting an adaptation to lipid-rich environments, such as mammal hosts.\nThis also suggests that the ability to degrade carbohydrates and lipids is equally important for\npathogenic species, which colonize both natural environments and mammal hosts.\nFor species associated with mammals, lipid metabolism is a vital trait. For example, Cryptococcus\nneoformans is a saprotrophic yeast but also an opportunistic human pathogen. This opportunistic\npathogen shows increased expression of lipid-related gene during murine infection (Fan et al. 2005,\nHu et al. 2008).\nIn our study, we also show that pathogenic Cryptococcus and other pathogenic species exhibit a higher\ncodon optimization for genes involved in lipid metabolism compared to saprotrophic species,\nincluding saprotrophic Cryptococcus (Supplementary Figure 16, Supplementary Figure 17,\nSupplementary Figure 19). This supports the hypothesis of an evolutionary adaptation to mammals by\nopportunistic pathogens. Furthermore, our results also indicate that opportunistic pathogens show\nsimilar levels of codon optimization in both lipid and carbohydrate metabolic genes, while\nsaprotrophic species show significantly higher codon optimization for carbohydrate-related genes\n(Supplementary Figure 17). This suggests an adaptation of opportunistic pathogens to both mammal\nand natural environments, whereas saprotrophic species are better adapted to natural environments.\nOur results emphasize the importance of the lipid metabolism in opportunistic pathogens and\ncarbohydrate metabolism for both saprotrophs and opportunistic pathogens. The patterns observed\nin the codon optimization analyses are further supported by our experimental data, which, as pointed\nout by the reviewer, statistically support a link between the codon optimization and the phenotype\non lipid or carbohydrate substrates.\nOverall, our study links codon optimization with the respective ecological context. While further\nexperimental validation is needed, our results suggest that relative codon optimization of lipid\nmetabolic genes may serve as a genomic signature for pathogenicity.\nFan, Weihua; Kraus, Peter R.; Boily, Marie-Josee; Heitman, Joseph (2005): Cryptococcus neoformans\ngene expression during murine macrophage infection. In Eukaryotic cell 4 (8), pp. 1420–1433. DOI:\n10.1128/EC.4.8.1420-1433.2005.\nHu, Guanggan; Cheng, Po-Yan; Sham, Anita; Perfect, John R.; Kronstad, James W. (2008): Metabolic\nadaptation in Cryptococcus neoformans during early murine pulmonary infection. In Molecular\nmicrobiology 69 (6), pp. 1456–1475. DOI: 10.1111/j.1365-2958.2008.06374.x.\n2. section \"Translation efficiency of metabolic pathways predicts fungal lifestyle\". what is the\npredictive value of the S ratio? In other words, what is the likelihood of correctly predicting lifestyle\nwhen shuffling S values for key metabolic pathways between species (null model)?\nResponse: To test for the predictive value of the S ratio (S lipids : S carbohydrates), we performed a\npermutation test and a decision tree model test. For this, we used subset of species with well\ncharacterized lifestyles (i.e. Cryptococcus, Trichosporon and Cutaneotrichosporon).\nFor the permutation test, we performed 9999 iterations which resulted on a significant empirical p-\nvalue (P = 0.008), indicating that the S ratio is able to significantly explain lifestyle.\nAdditionally, we used a C5.0 boosted decision tree model with rule-based output to test the predictive\naccuracy of the S ratio (S lipids : S carbohydrates). First, we built a model based on all 18 isolates,\nwhich was able to classify the isolates correctly without errors (100% accuracy). In the next approach,\nfrom 18 genomes, we randomly sampled 14 genomes (4 saprotrophs and 10 pathogens) to train a\nmodel and used the remaining 4 genomes (1 saprotroph and 3 pathogens) to test the model. We\nperformed 9999 iterations as well. In this approach, the model was able to predict all pathogenic\nspecies (100% accuracy). For the saprotrophic species, the accuracy was of 80%, due to one isolate\n(Cryptococcus amylolentus) being consistently misclassified (100% inaccuracy). The remaining\nsaprotrophic species were correctly classified (100% accuracy).\nThe inaccuracy of predicting C. amylolentus lifestyle is due to the low sample size in the training\ndataset and to the S ratio in this species being on the upper limit of the thresholds characterizing a\nsaprotrophic lifestyle, defined by the algorithm during training.\nWe are confident of the predicting accuracy of the S ratio, for the studied species. We are currently\nworking on testing this model on a wider range of species for a follow-up publication.\nWe added this paragraph to the Results section:\n“In order to test the predictive value of the S ratio (S lipid : S carbohydrates), we performed a\npermutation test and trained a decision tree classification model 34 using the Cryptococcus,\nTrichosporon and Cutaneotrichosporon species. Based on the permutation test, the S ratio was able\nto significantly explain lifestyle (empirical P value = 0.016). Additionally, the decision tree model\ntrained on the full dataset classified the lifestyle accurately for all isolates (100% accuracy). To further\ntest model robustness, we randomly sampled four saprotrophs and ten pathogens to train a model\nand used the remaining one saprotroph and three pathogens to test the model. In this cross-validation\napproach, all pathogenic species were correctly predicted (100% accuracy). For the saprotrophic\nspecies, the accuracy of the model was of 80%, due to one species (Cryptococcus amylolentus) being\nconsistently misclassified due to the S ratio being near the classification threshold (Supplementary\nTable 11). The remaining saprotrophic species were correctly classified (100% accuracy). Using this\ndecision tree approach, we conclude that the S ratio is a suitable predictor of fungal lifestyle in the\nstudied species, particularly for distinguishing opportunistic pathogens from saprotrophic species.”\nWe added the respective information to the Materials and Methods.\n3. In spite of their arguments, I think the authors need to come up with a more specific and accurate\ntitle for their study (see also Reviewer 1 comment 4). First, the number of genes encoding CAZYmes\nand other secreted is sometimes used as a signature for fungal lifestyles, whereas this parameter\ndoes not appear discriminating in the present study. Second, the lack of specificity suggests that\ngenomic signatures have been assessed systematically, including for instance any type of gene\nfamily, although this study is focused on testing specific hypotheses. Third, the authors agree in\ntheir rebuttal letter that further functional studies will be required to establish a causal link between\ncodon usage and fitness, but this causal relationship is implicit in the current title.\nResponse: In our study, we compare several genomic traits (e.g., CAZyme content, GC content) that\nhave previously reported to be associated with lifestyle in other fungal groups. While CAZymes and\nmost other traits did not distinguish lifestyles, we identified codon optimization as a genomic feature\nstrongly associated with lifestyle. We applied robust comparative and statistical methods to identify\nsuch associations. We consider the expression “genomic signatures” appropriate, as it reflects the\nidentification of genomic features that correlate with lifestyle. Importantly, we interpret “signatures”\nas consistent genomic patterns associated with a lifestyle, rather than causality. We also believe that\nthe current title is accessible and informative for the broad readership of Nature Communications. If\nthe editor believes that the title must be change, we are happy to do so.\nSuggestion: the authors compared codon optimization (S) across several pathways. A more\nsystematic approach would be to search for pathways enriched among genes with the highest codon\noptimization. Has this approach been tested?\nResponse: Instead of analysing pathway enrichment among genes with the highest codon\noptimization values, we compared S values between lifestyles across all annotated COG functional\ncategories (Supplementary Figure 16). This allowed us to compare full pathways and identify\nconsistent differences in codon optimization between lifestyles. While enrichment analyses can be\nuseful, they rely on thresholds (e.g. genes with the 5% highest S values) and may miss broader trends\nacross entire functional groups. We believe that our approach is suitable to detect genomic signatures\nassociated with lifestyle across the genome, without introducing biases.\nMinor comments and suggestions:\n. L250 the S ratio used in figure 3 should be introduced in the main text\nResponse: We now introduce the S ratio in the main text. We added the following sentences:\n“To account for interspecific variation in codon usage bias, S values were normalized by comparing\nthe mean S values across genes associated with lipid and carbohydrate metabolic pathways (S ratio)\nor by normalizing pathway-specific S values to the genome-wide mean S (S normalized) (see Materials\nand Methods). A higher relative codon optimization was interpreted as a proxy improved translation\nefficiency.”\n. Fig4 \"Data are presented for isolates with consistent growth patterns across four independent\nreplicates\" > need to represent variability of the data along the Y axis\nResponse: We updated the figure to depict the variability.\n. The section \"Saprotrophic species grow at mammal body temperatures\" is not related to codon\nusage analysis and does add predictive power to the approach. how many times was the experiment\nrepeated (this is not mentionned in sup fig. 21).\nstill appears disconnected to the rest of the manuscript (reviewer 2 minor comment 4)\nResponse: This experiment was performed for 4 replicates per isolate. We have now added this\ninformation to the figure legend as well the variation among replicates. To provide further support to\nthis figure, we now include qualitative growth data (Supplementary Figure 22). For this figure, we\nrepeated the same growth experiment at different temperatures, but in 5 mL of medium in reaction\ntubes. We provide the photos of each tube at each condition. The results are similar between the\ndifferent approaches.\nWe agree that this section is not directly related to codon optimization. However, it complements our\ngenomic analyses by assessing a critical virulence factor, such as the ability to grow at mammal body\ntemperatures. We show that some saprotrophic species (e.g., Vanrija spp.) exhibit codon optimization\npatterns similar to those of opportunistic pathogens. Additionally, we also show that these species\ncan also grow at mammal body temperatures. These results support the hypothesis that they may\npossess thermotolerance and translational adaptations relevant for pathogenicity in a mammal host.\nWe added the following paragraph to this section of the Results to clarify the meaning of these results:\n“Some saprotrophic species (e.g. Vanrija spp.) showed similar codon optimization patterns to those\nof opportunistic human pathogens (Figure 3 and Supplementary Fig. 20). The growth temperature\nexperiments (Figure 5, Supplementary Fig. 21 and Supplementary Fig. 22) complement these findings\nby showing that some saprotrophic species are able to grow at mammal body temperature (e.g.\nVanrija spp.), similar to opportunistic human pathogens. Together, these results suggest that some\nsaprotrophic species may have the thermo- and translational adaptation associated with\npathogenicity, further suggesting that these may emerge as potential human opportunistic\npathogens.”\n. \"host infection arose independently multiple times\": based on the ancestral state reconstruction\nprovided, that opportunistic pathogenicity arose 3 times or that it was lost 3 times in the lineages\nanalyses seems equaly likely to me\nResponse: We agree that both gain and loss of pathogenicity are possible scenarios. However, we\nconsider that the gain of pathogenicity is a more likely scenario. This is suggested by the distribution\nof pathogenic species across at least three phylogenetically distinct clades, that are separated by\nsaprotrophic lineages and by that most fungal species in the Tremellales are not mammal pathogens.\nThese patterns suggest independent adaptations to mammal hosts rather than a single ancestral\npathogenic state followed by multiple losses.\n. L186: \"we observe that the tRNA composition was similar among distantly related species\n(Supplementary Fig. 9), suggesting convergent evolution with respect to tRNA genes.\" In this figure,\nthe similarity in tRNA composition decreases with phylogenetic distance at the genus level but not\nbeyond – this does not seem sufficient to infer some form of convergent evolution\nResponse: We agree that the similarity in tRNA composition between genera alone is insufficient to\ninfer convergent evolution. However, we also detect a low phylogenetic signal (Blomberg’s K < 1) for\nmost tRNA genes (38 out of 45 genes), indicating that closely related species are less similar in tRNA\ncomposition than expected under a Brownian motion model of evolution. These results suggest that\ntRNA gene composition may be under selective pressure independent of phylogeny, which is\nconsistent with convergent evolution. However, in the current study, we are not able to identify the\nspecific drivers involved in this.\n. L188: “the most frequently used synonymous codons did not consistently correspond with the\nmost abundant tRNA gene families in the genomes” – this seems to argue against adaptive\ntranslation at whole genome level, is there a better match between codon usage and tRNA\nrepertoire when considering specific gene subsets?\nResponse: In our analyses, we see evidence for adaptive translation on pathways relevant for the\nlifestyle (Supplementary Figure 16). The codon optimization value (S) integrates codon usage, effective\nnumber of codons, tRNA genes copy number and GC content (on the 3rd codon position). Therefore,\nthe codon usage and tRNA repertoire is integrated in the S value. By directly comparing the relative\nsynonymous codon usage (RSCU) and the tRNA repertoire (Supplementary Table 8), we are able to\nidentify differences between the RSCU of the whole genome and the subset of genes involved in lipid\nor carbohydrate metabolism. However, given the complexity of the codon usage and tRNA pool data,\nwe consider that the comparison of S values is more informative and reliable than the direct\ncomparison between only RSCU and tRNA repertoires.\nReviewer #3 (Remarks on code availability):\nI have not tested the code but appreciate that the authors make it available. The readme file might\nbe expanded to help readers navigate among the different files provided.\nResponse: We appreciate the suggestion and we updated the readme with additional details.\nReviewer #4 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part\nof the Nature Communications initiative to facilitate training in peer review and to provide\nappropriate recognition for Early Career Researchers who co-review manuscripts.\nResponse: We appreciate all the constructive comments and we are glad that our manuscript could\ncontribute to the training of a Researcher.\nPoint-to-point response to reviewers’ comments\nReviewer #2 (Remarks to the Author):\nIn this revised version, the authors have incorporated a phylogenetic perspective into their\ncorrelation analyses, although the methods used for this part are not described.\nResponse: We now provide the respective details in the Materials and Methods.\nThey have also clarified the results concerning codon optimization. However, the main conclusions\nstill rely on comparing ratios of codon optimization values across different functional categories,\nand for which the biological meaning remains unclear. Since S values are directly comparable across\nspecies, there is no need to \"account for interspecific variation\" as stated in the text and methods.\nIn their analysis, S values do not differ between pathogens and saprotrophs, contradicting the\nstatement that codon optimization predicts lifestyle.\nResponse: We have now added a paragraph in the Discussion (please see below) to address the\nbiological meaning of the different S values (absolute, normalized and ratio).\nWe are happy to provide additional clarifications; however, it is unclear which results are being\nreferred to by the reviewer. All of our analyses consistently show that the S values are able to\ndistinguish between pathogens and saprotrophs. The only exceptions are within the 1) Apiotrichum\ngenus, which we discuss that the species classified as saprotrophic might be emerging pathogens (e.g.\nFigure 3); and 2) in the single-copy orthogroups comparison (Supplementary Figure 19), where the\npatterns are still observable although the p-values are significant but close to 0.05 due to the reduced\nsize and power of the single-gene dataset.\nReviewer #3 (Remarks to the Author):\nThe authors have adressed previous comments adequately.\nI still think the title of the manuscript lacks specificity and clarity but I will let the Journal Editor\ndecide on that matter.\nOne point that may deserve further discussion: the authors argue that gene content is not a\ndeterminant of lifestyle in these fungal orders (L162) and later quickly elude the role of transcription\n(L412) with no justification. A short discussion on how the regulation of gene expression at the\ntranscription level (in addition to protein translation) may determine lifestyle transitions, and the\npossible molecular mechanisms involved, seems like an important element of context to provide.\nResponse: We added the following paragraph to the Discussion to address these concerns:\n“Gene expression and regulation are directly affected by several molecular mechanisms, including\nchromatin remodelling, epigenetic modifications, transcription factor binding, and the use of\nalternative promoters 45,46. Transcriptional reprogramming can be linked to shifts between\nsaprotrophic and host-associated lifestyles, for example via the upregulation of stress-response\npathways, secreted enzymes, or virulence factors 47,48. Hence, transcription regulation defines the\nrepertoire of expressed genes, while translational adaptation regulates the protein synthesis\nefficiency to meet specific requirements. These mechanisms suggest that ecological transitions are\nenabled by both transcriptional regulation and translational adaptation. While we show that\ntranslational adaptation provides an advantage in niche adaptation (Figure 4), future studies\naddressing the relevance of transcriptional regulation and how this interacts with translation\nefficiency will be crucial to fully understand the molecular mechanisms involved in lifestyle\ntransitions.”\nOverall, the revised manuscript provides convincing evidence for a role of adaptive translation in\ntransition to pathogenic lifestyle in a fungal lineage, and clearly discusses the impact and limitations\nof the study. This is an insightful and original contribution to our understanding of fungal evolution.\nResponse: We appreciate the recognition of our study and all the constructive feedback throughout\nthe reviewing process.\n***\nPlease see below my opinion on concerns on NCOMMS-24-59407B regarding the use of S ratio:\nS values measure the degree of co-adaptation between the tRNA pool of a cell and codon usage in\ngenes. They take into account both individual gene sequences (codon usage) and the cellular\nbackground (tRNA pool, generally inferred from number of copies in the genome).\nS values are directly comparable across species (as said by reviewer 2) in the sense that if S is higher\nfor gene1 in species X than in Y, it means the co-adaptation between tRNAs and codon usage for\ngene1 is better in X than in Y.\nBut a strictly identical gene sequence (same codon usage) will have different S values across species\ndepending on the cellular background (tRNA pool). This means that the distribution range of S\nvalues in a genome varies from species to species. In other words, with a same S value, gene1 can\nbe the best optimized gene in species X and just average relative to other genes in species Y. Its\ndegree of co-adaptation with tRNAs will be identical strictly speaking, but the meaning for the cell\nfunctionning will be different if many other genes outcompete it for tRNAs recruitment (species Y)\nor not (species X). This is probably what the authors call “interspecific variation”.\nResponse: This is a correct interpretation of the S values between different species.\nFor this reason, I think the ratio approach of the authors has merits and offers biological insights by\nshowing with a single metric which of two pathways has better co-evolved with the tRNA pool\nacross species. But I agree with reviewer 2 that they need to be more specific so that their\nconclusions are not confusing. Typically, if conclusions are based on S1/S2 ratio comparisons, then\nthe \"relative codon optimization of pathway 1 and 2 predicts lifestyle\" and not simply \"codon\noptimization\".\nResponse: We agree with the suggestion and we revised the text to further emphasize that our results\nand conclusions are based on the relative codon optimization instead of codon optimization.\nIn short, my recommendations would be as follows:\n1) - More clearly present and comment S values for each pathway in each species, as well as the\ndistribution of S values in all the genomes analyzed (as supplementary material) to document the\neffect of codon usage and tRNA pool variation on the S metrics, so that the choice of ratios is clearly\njustified\nResponse: We provide now an additional panel to Supplementary Figure 17 (Supplementary Figure\n17B), where we show the distribution of the genome-wide, absolute and normalized S values. In this\nanalysis, the saprotrophic species clearly show a higher codon optimization (S) for the genes involved\nin carbohydrate metabolism in comparison to lipids. The S value in opportunistic pathogens for both\npathways is much closer to each other, suggesting co-optimization of the codon usage. We also\nobserve variability in the genome-wide S value among the species.\nThe ratio between both pathways and the normalization of each pathways against the genome-wide\nvalue, allow for comparison across species, considering the genomic background of each individual.\n2) - Another approach to compare individual genes across species would be to quantile normalize S\nvalues across genomes. Individual normalized S values would then be directly comparable across\nspecies (top S values will be equal in all species). Personally, I had favored normalization as it makes\nthe whole S value distributions comparable and does not rely on one reference gene/pathway as\nthe ratio does. I would suggest the authors to try this approach and verify that they reach the same\nconclusions as with ratios.\nResponse: We appreciate the suggestion of quantile normalization.\nWe would like to emphasize that the S value is a single value that represents the co-adaptation\nbetween the tRNA gene pool and the codon usage bias of a given dataset, and further accounts for\nthe effective number of codons and GC content at the 3rd position.\nIn our analyses, we normalized the pathway S values against the genome-wide S (Supplementary\nFigures 16 and 17). In these analyses, we also compared these normalized values with the absolute\nvalues. Both metrics (Supplementary Figures 16 and 17), as well as the S ratios (Figure 3 and\nSupplementary Figure 18), provided consistent results. In addition, we also compared the S value of\nsingle copy orthologous genes present in all genomes analysed by this approach (Supplementary\nFigure 19). This approach provided further support to the previous findings based on the ratio,\nalthough the statistical signal was weaker (P = 0.046) due to low sample size.\nOur current normalization approach accounts for the genomic background of codon usage and tRNA\npool composition, allowing direct comparisons across species without relying on S ratios. Quantile\nnormalization would equalize the distributions by rank and thereby force all genomes to have identical\nS distributions, removing the biological information regarding codon optimization intensity. By\nconsidering the genome-wide S as a reference, we can infer whether a metabolic pathway is more\nstrongly optimized than expected based on the genomic background. We therefore consider the\ngenome-wide normalization to be more appropriate to address our research questions, as it considers\nthe genomic context, while allowing robust comparisons across species.\n3) - Invite authors to carefully revise their manuscript to make sure interpretations and conclusions\nare strictly accurate, that the analysis strategy is properly and fully justified. A short paragraph\ndedicated to the interpretation of the S ratio/normalized S/raw S values and their meaning in the\ncontext of this study might be useful to add to the discussion section.\nResponse: We added the following paragraph to the Discussion to clarify the interpretation of the\ndifferent values:\n“In this study, we used different representations of codon optimization (S) to capture complementary\naspects of translational adaptation. Absolute S values directly reflect the co-adaptation between\ncodon usage bias and the genomic tRNA pool within each genome, while also integrating the effective\nnumber of codons and the GC content at the third position. Since individuals and species often show\nvariations in the tRNA pool and codon usage biases, absolute S values may not be directly comparable\nacross genomes. To account for different genomic backgrounds, we calculated normalized S values,\nwhich express the codon optimization of a given pathway relative to the genome-wide S value (S\nabsolute : S genome). This metric indicates whether a pathway is more or less optimized than expected\nwithin the same genome. Finally, S ratios provide a direct comparison between two functional\ncategories within a genome (S pathway 1 : S pathway 2), which reflects whether a pathway is more\nstrongly optimized relative to another. Altogether, we consider the three metrics to provide\ncomplementary insights: absolute S values assess the global level of translational adaptation in a\ngenome, normalized S values emphasize deviations from the genomic baseline, and S ratios determine\nrelative optimization between pathways. By combining these metrics, we assess the contribution of\ncodon optimization to fungal lifestyle adaptation.”\nReviewer #3 (Remarks on code availability):\nI have not tested the code but appreciate that the authors make it available\nResponse: We have updated the code to reflect the revisions."}]

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