Philippa Kaur

Sea urchins and experimental designSeven hundred small S. intermedius (31.9 ± 0.4 mm of test diameter, mean ± SD) were chosen from an aquaculture farm in Changhai County, Dalian (122° 63′ N, 39° 25′ E) on 23 July 2020. They were subsequently transported to the Key Laboratory of Mariculture and Stock Enhancement in North China’s Sea, Ministry of Agriculture and Rural Affairs at Dalian Ocean University (121° 56′ N, 38° 87′ E) and maintained in a fiberglass tank (a closed culture system, length × width × height: 150 × 100 × 60 cm) with aeration for 7 days to acclimatize to laboratory conditions. The kelp Saccharina japonica, which is the most common food used for S. intermedius culture58, was fed ad libitum under the neutral photoperiod (12 h light:12 h dark). One-half of the seawater was changed daily. Water temperature, pH and salinity were 22.6 ± 0.2 °C, 7.7 ± 0.3 and 30.7 ± 0.1 ‰ (Mean ± SD) according to the daily measurement using a portable water quality monitor (YSI Incorporated, OH, USA), respectively.The rearing space was defined as the ratio of culture volume to the number of sea urchins (cm3 ind−1). Rearing assemblage is the main factor being tested in this study. To simulate the currently used rearing assemblage in longline culture, 24 individuals were placed at plastic devices without layer divisions (length × width × height: 24.5 × 16.8 × 6 cm for culture volume; 25 holes of 0.5 cm diameter/100 cm2) as group A (the control group, 102.9 cm3 ind−1 of initial rearing space, Fig. 7a). To investigate whether multi-layer rearing assemblage improves the survival, food utilization and growth, 24 sea urchins were equally put into the cages where were evenly divided into three layers (8 sea urchins in each layer and length × width × height: 24.5 × 16.8 × 6 cm for each layer, 308.7 cm3 ind−1 of initial rearing space; 25 holes of 0.5 cm diameter/100 cm2; group B; Fig. 7b). Further, to evaluate whether eliminating interaction further contributes to the improvement of these commercially important traits of sea urchins in multi-layer rearing assemblage, 8 sea urchins were divided into eight divisions for each layer in the cages as group C (length × width × height: 8.3 × 5.9 × 6 cm for each division, 297.36 cm3 ind−1 of initial rearing space; 25 holes of 0.5 cm diameter/100 cm2; Fig. 7c). Each treatment had 8 replicates. All devices were placed in a fiberglass tank (length × width × height: 150 × 100 × 60 cm) and immersed in water for ~ 30 cm with aeration. They were easily disassembled for the experimental management.Figure 7Diagrams of the experimental cages used for the groups A (a), B (b) and C (c), the sea urchin with the spotting disease (d) and without the disease (e) and the devices used for measuring the Aristotle’s lantern reflex (f).Full size imageThe experimental period was about ~ 7 weeks (from 31 July 2020 to 20 September 2020) under the neutral photoperiod (12 h light: 12 h dark). The kelp, which was regularly collected in the intertidal waters at Heishijiao, Dalian (121° 58′ E, 38° 87′ N), was daily provided to sea urchins in abundance for all the groups. The remained kelp, feces and dead sea urchins were removed daily. One-half of the seawater was replaced daily by the fresh and filtered seawater which was pumped from the coast of Heishijiao, Dalian. Water temperature was not controlled, ranging from 22.2 to 24.5 °C (the natural seasonal cycle of increasing temperature during summer in the region). Water quality parameters were measured weekly as salinity 29.3 ± 0.6 ‰, pH 7.8 ± 0.2 (mean ± SD) using a portable water quality monitor (YSI Incorporated, OH, USA).To ensure the random sampling, sea urchins were taken out from the experimental device and placed in 24 plastic boxes (labeled from number 1 to number 24, length × width × height: 6 × 6 × 4 cm for each box). Individuals were chosen corresponding to the number (within 24) generated by the “sample” function in R studio (1.1.463). Sampling was re-conducted if the number corresponds to empty, dead or diseased sea urchins.Mortality and morbiditySpotting disease, which appears as spotting lesions with red, purple or blackish color on the test (Fig. 7d), is the most common lethal disease in S. intermedius aquaculture12. Sea urchin without disease is shown in Fig. 7e. Dead sea urchins were removed daily and the number of survivor and diseased sea urchins was recorded weekly for each cage during the experiment (N = 8).Food consumptionThe measurement of food consumption (g dry weight) was conducted once a week (24 h from Tuesday to Wednesday) (N = 8). The total supplied and remained diets were weighted wet by an electric balance (G & G Co., San Diego, USA) after the removal of the surface moisture. The dried weights of feces and samples of supplied and uneaten kelp were determined after 4 days at 80 °C in a convection oven (Yiheng Co., Shanghai, China).Food consumption was calculated as follows (revised from Hu et al.9 for being more concise):$${text{F}} = frac{{{text{A}}_{0} times frac{{{text{A}}_{1} }}{{{text{A}}_{2} }} – {text{B}}_{0} times frac{{{text{B}}_{1} }}{{{text{B}}_{2} }}}}{{text{N}}}$$F = dry food intake per sea urchin (g ind−1 day−1), A0 = wet weight of total supplied diets (g), B0 = wet weight of total uneaten diets (g), A1 = dried weight of sample supplied diets (g), A2 = wet weight of sample supplied diets (g), B1 = dry weight of sample uneaten diets (g), B2 = wet weight of sample uneaten diets (g), N = the number of sea urchins.GrowthTest diameter and lantern length were measured using a digital vernier caliper (Mahr Co., Ruhr, Germany). Body, lantern and gut were weighted wet using an electric balance (G & G Co., San Diego, USA). Test diameter and body weight were evaluated every Wednesday. The average value of the three individuals was considered as the trait value for each replicate (N = 8). Lantern length, wet lantern weight and wet gut weight were recorded in week 4 (29 August 2020) and week 7 (20 September 2020) (N = 8).Aristotle’s lantern reflexAristotle’s lantern reflex, which refers to one cycle from the opening to the closing of the teeth59, was measured using a simple device according to the method of Ding et al.38. There were small compartments (length × width × height: 4.8 × 5.6 × 4.5 cm) with a film (made by 3 g agar and 2 g kelp powder) on the bottom of the device38 (Fig. 7f). The frequency of Aristotle’s lantern reflex was counted within 5 min using a digital camera (Canon Co., Shenzhen, China) under the device in week 4 (29 August 2020) and week 7 (20 September 2020). The average value of all the 5 individuals was considered as Aristotle’s lantern reflex for each replicate (N = 8).5-HT concentrationThe 5-HT is a signaling molecule, playing an important role in regulating feeding behavior52. To evaluate whether 5-HT is involved in Aristotle’s lantern reflex, 5-HT concentration of muscle in lantern was measured for each treatment in week 4 and week 7. 5-HT concentration was considered as the average value of all the 3 healthy individuals for each replicate (N = 8).The concentration of 5-HT was measured using ELISA kits (Nanjing Jiancheng Bio-engineering Institute, Nanjing, China) according to the instructions of the manufacturer. After adding the enzyme-labeled antibody, the substrate became a colored product that was directly related to the amount of the substance tested. The concentrations of 5-HT were calculated by comparing the optical density (O.D.) value of the samples to the standard curve and calculated according to the following formula (according to the kit’s instructions):$${text{Y}} = frac{1}{{({text{a }} + {text{bx}}^{{text{c}}} )}}$$Y = the concentration of 5-HT (ng mL−1), x = the O.D. value of the samples, a = 0.00027, b = 0.12086, c = 1.36806.Pepsin activityPepsin is important for sea urchins to digest protein-rich algae40,60. Pepsin activity was analyzed using the pepsin kits (Nanjing Jiancheng Bio-engineering Institute, Nanjing, China) in week 4 and week 7, following the instructions of the manufacturer. The average value of all the 3 individuals was considered as the pepsin activity for each replicate (N = 8). The procedures include enzyme reaction and color development reaction39. The temperature of reaction was 37 °C and pepsin activities were counted as U mg protein−1. The formula of pepsin activity is shown as follows (according to the kit’s instructions):$${text{P}} = frac{{{text{M}}_{0} – {text{M}}_{1} }}{{{text{M}}_{2} – {text{M}}_{3} }} times frac{{{text{S}}_{0} }}{{{text{S}}_{1} }} times frac{{{text{V}}_{1} times {text{V}}_{2} }}{{{text{V}}_{3} }}$$P = pepsin activity (U/mg prot), M0 = the O.D. value of the sample, M1 = the O.D. value of comparison, M2 = the standard O.D. value, M3 = blank O.D. value, S0 = the standard concentration (50 μg mL−1), S1 = reaction time (10 min), V1 = total volume of reaction solution (0.64 mL), V2 = sample protein concentration (0.04 mL), V3 = sampling volume (mg prot/mL).Gut morphological examinationAfter sea urchins were dissected on week 4 and week 7, all gut tissue samples (~ 1 g for each sample) were fixed in Bouin’s solution (glacial acetic acid: formaldehyde: saturated picric acid solution = 1:5:15) according to the method of Wu et al.61. They were subsequently transferred for gradient dehydration, embedding, cutting, staining and observation62 (N = 24).Statistical analysisKolmogorov–Smirnov test and Levene test were used to analyze the normal distribution and homogeneity of the data, respectively. Rearing assemblage was set as the main factor in the one-way ANOVA with three levels: the control system without layer divisions (group A), a second system with divisions in the cages to simulate the three layers cages (group B) and a third system with individual divisions for each sea urchin (group C). One-way ANOVA was used to analyze the mortality (in weeks 3, 4, 5, 6, 7), morbidity (in weeks 3, 6, 7), food consumption (in weeks 2, 5, 7), test diameter (in weeks 1, 2, 3, 4, 5, 6), body weight (in weeks 1, 4, 5, 7), 5-HT, pepsin activity, lantern length, lantern weight and gut weight. Duncan multiple comparison analysis was performed when significant differences were found in the one-way ANOVA. Kruskal–Wallis test was carried out to compare the differences of mortality (weeks 1, 2), morbidity (weeks 1, 2, 4, 5), food consumption (weeks 1, 3, 4, 6), test diameter (week 7), body weight (weeks 2, 3, 6) and Aristotle’s lantern reflex, because of non-normal distribution and/or heterogeneity of variance. A non-parametric post-hoc test was carried out when significant differences were found in the Kruskal–Wallis test. All data analyses were performed using SPSS 19.0 statistical software. A probability level of P  More

1.Lesk, C., Rowhani, P. & Ramankutty, N. Influence of extreme weather disasters on global crop production. Nature 529, 84–87 (2016).CAS 

Google Scholar 
2.Zhang, J. et al. Effect of drought on agronomic traits of rice and wheat: a meta-analysis. Int. J. Environ. Res. Public Health 15, 839 (2018).3.Hirasawa, T., in Genetic Improvement of Rice for Water-Limited Environments (eds Ito, O, O’Toole, J. C. & Hardy, B.) 89–98 (International Rice Research Institute, 1999).4.Pandey, V. & Shukla, A. Acclimation and tolerance strategies of rice under drought stress. Rice Sci. 22, 147–161 (2015).
Google Scholar 
5.Compant, S., van der Heijden, M. G. A. & Sessitsch, A. Climate change effects on beneficial plant-microorganism interactions. FEMS Microbiol. Ecol. 73, 197–214 (2010).CAS 

Google Scholar 
6.de Vries, F. T., Griffiths, R. I., Knight, C. G., Nicolitch, O. & Williams, A. Harnessing rhizosphere microbiomes for drought-resilient crop production. Science 368, 270–274 (2020).
Google Scholar 
7.Busby, P. E. et al. Research priorities for harnessing plant microbiomes in sustainable agriculture. PLoS Biol. 15, e2001793 (2017).PubMed 
PubMed Central 

Google Scholar 
8.Santos-Medellín, C., Edwards, J., Liechty, Z., Nguyen, B. & Sundaresan, V. Drought stress results in a compartment-specific restructuring of the rice root-associated microbiomes. mBio 8, e00764-17 (2017).PubMed 
PubMed Central 

Google Scholar 
9.Naylor, D., DeGraaf, S., Purdom, E. & Coleman-Derr, D. Drought and host selection influence bacterial community dynamics in the grass root microbiome. ISME J. https://doi.org/10.1038/ismej.2017.118 (2017).10.Fitzpatrick, C. R. et al. Assembly and ecological function of the root microbiome across angiosperm plant species. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.1717617115 (2018).11.Edwards, J. A. et al. Compositional shifts in root-associated bacterial and archaeal microbiota track the plant life cycle in field-grown rice. PLoS Biol. 16, e2003862 (2018).PubMed 
PubMed Central 

Google Scholar 
12.Zhang, J. et al. Root microbiota shift in rice correlates with resident time in the field and developmental stage. Sci. China Life Sci. 61, 613–621 (2018).
Google Scholar 
13.Xu, L. et al. Drought delays development of the sorghum root microbiome and enriches for monoderm bacteria. Proc. Natl Acad. Sci. USA 115, E4284–E4293 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
14.Liechty, Z. et al. Comparative analysis of root microbiomes of rice cultivars with high and low methane emissions reveals differences in abundance of methanogenic archaea and putative upstream fermenters. mSystems 5, e00897-19 (2020).PubMed 
PubMed Central 

Google Scholar 
15.Rong, X. & Huang, Y. Taxonomic evaluation of the Streptomyces griseus clade using multilocus sequence analysis and DNA–DNA hybridization, with proposal to combine 29 species and three subspecies as 11 genomic species. Int. J. Syst. Evol. Microbiol. 60, 696–703 (2010).CAS 

Google Scholar 
16.Lin, L. & Xu, X. Indole-3-acetic acid production by endophytic Streptomyces sp. En-1 isolated from medicinal plants. Curr. Microbiol. 67, 209–217 (2013).CAS 

Google Scholar 
17.Legault, G. S., Lerat, S., Nicolas, P. & Beaulieu, C. Tryptophan regulates thaxtomin A and indole-3-acetic acid production in Streptomyces scabiei and modifies its interactions with radish seedlings. Phytopathology 101, 1045–1051 (2011).CAS 

Google Scholar 
18.Guo, J. et al. Seed-borne, endospheric and rhizospheric core microbiota as predictor for plant functional traits across rice cultivars are dominated by deterministic processes. New Phytol. https://doi.org/10.1111/nph.17297 (2021).19.de Vries, F. T. et al. Soil bacterial networks are less stable under drought than fungal networks. Nat. Commun. 9, 3033 (2018).PubMed 
PubMed Central 

Google Scholar 
20.de Vries, F. T. & Shade, A. Controls on soil microbial community stability under climate change. Front. Microbiol. 4, 265 (2013).PubMed 
PubMed Central 

Google Scholar 
21.Borken, W. & Matzner, E. Reappraisal of drying and wetting effects on C and N mineralization and fluxes in soils. Glob. Change Biol. 15, 808–824 (2009).
Google Scholar 
22.Lueders, T. & Friedrich, M. W. Effects of amendment with ferrihydrite and gypsum on the structure and activity of methanogenic populations in rice field soil. Appl. Environ. Microbiol. 68, 2484–2494 (2002).CAS 
PubMed 
PubMed Central 

Google Scholar 
23.Linquist, B. A. et al. Reducing greenhouse gas emissions, water use, and grain arsenic levels in rice systems. Glob. Change Biol. 21, 407–417 (2015).
Google Scholar 
24.Speirs, L. B. M., Rice, D. T. F., Petrovski, S. & Seviour, R. J. The phylogeny, biodiversity, and ecology of the chloroflexi in activated sludge. Front. Microbiol. 10, 2015 (2019).PubMed 
PubMed Central 

Google Scholar 
25.Thomas, S. H. et al. The mosaic genome of Anaeromyxobacter dehalogenans strain 2CP-C suggests an aerobic common ancestor to the delta-proteobacteria. PLoS ONE 3, e2103 (2008).PubMed 
PubMed Central 

Google Scholar 
26.Yang, T. H., Coppi, M. V., Lovley, D. R. & Sun, J. Metabolic response of Geobacter sulfurreducens towards electron donor/acceptor variation. Microb. Cell Fact. 9, 90 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
27.Keller, K. L. & Wall, J. D. Genetics and molecular biology of the electron flow for sulfate respiration in desulfovibrio. Front. Microbiol. 2, 135 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
28.Zhalnina, K. et al. Dynamic root exudate chemistry and microbial substrate preferences drive patterns in rhizosphere microbial community assembly. Nat. Microbiol. https://doi.org/10.1038/s41564-018-0129-3 (2018).29.Williams, A. & de Vries, F. T. Plant root exudation under drought: implications for ecosystem functioning. New Phytol. 225, 1899–1905 (2020).
Google Scholar 
30.Vries, F. T. et al. Changes in root-exudate-induced respiration reveal a novel mechanism through which drought affects ecosystem carbon cycling. New Phytol. 224, 132–145 (2019).PubMed 
PubMed Central 

Google Scholar 
31.Casartelli, A. et al. Exploring traditional aus-type rice for metabolites conferring drought tolerance. Rice 11, 9 (2018).PubMed 
PubMed Central 

Google Scholar 
32.Pérez-Jaramillo, J. E. et al. Linking rhizosphere microbiome composition of wild and domesticated Phaseolus vulgaris to genotypic and root phenotypic traits. ISME J. https://doi.org/10.1038/ismej.2017.85 (2017).33.Kang, D.-J. & Futakuchi, K. Effect of moderate drought-stress on flowering time of interspecific hybrid progenies (Oryza sativa L. × Oryza glaberrima Steud.). J. Crop Sci. Biotechnol. 22, 75–81 (2019).
Google Scholar 
34.Guo, X. et al. Host-associated quantitative abundance profiling reveals the microbial load variation of root microbiome. Plant Commun. 1, 100003 (2020).PubMed 
PubMed Central 

Google Scholar 
35.Varoquaux, N. et al. Transcriptomic analysis of field-droughted sorghum from seedling to maturity reveals biotic and metabolic responses. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.1907500116 (2019).36.Li, P. et al. Physiological and transcriptome analyses reveal short-term responses and formation of memory under drought stress in rice. Front. Genet. 10, 55 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
37.Vandenkoornhuyse, P., Quaiser, A., Duhamel, M., Le Van, A. & Dufresne, A. The importance of the microbiome of the plant holobiont. New Phytol. 206, 1196–1206 (2015).PubMed 
PubMed Central 

Google Scholar 
38.Toju, H. et al. Core microbiomes for sustainable agroecosystems. Nat. Plants 4, 247–257 (2018).
Google Scholar 
39.Shade, A. & Stopnisek, N. Abundance-occupancy distributions to prioritize plant core microbiome membership. Curr. Opin. Microbiol. 49, 50–58 (2019).
Google Scholar 
40.Suralta, R. R. et al. Plasticity in nodal root elongation through the hardpan triggered by rewatering during soil moisture fluctuation stress in rice. Sci. Rep. 8, 4341 (2018).PubMed 
PubMed Central 

Google Scholar 
41.Hamedi, J. & Mohammadipanah, F. Biotechnological application and taxonomical distribution of plant growth promoting actinobacteria. J. Ind. Microbiol. Biotechnol. 42, 157–171 (2015).CAS 

Google Scholar 
42.Vurukonda, S. S. K. P., Vardharajula, S., Shrivastava, M. & SkZ, A. Enhancement of drought stress tolerance in crops by plant growth promoting rhizobacteria. Microbiol. Res. 184, 13–24 (2016).
Google Scholar 
43.Aznar, A. & Dellagi, A. New insights into the role of siderophores as triggers of plant immunity: what can we learn from animals? J. Exp. Bot. 66, 3001–3010 (2015).CAS 

Google Scholar 
44.Viaene, T., Langendries, S., Beirinckx, S., Maes, M. & Goormachtig, S. Streptomyces as a plant’s best friend? FEMS Microbiol. Ecol. https://doi.org/10.1093/femsec/fiw119 (2016).45.Meena, K. K. et al. Abiotic stress responses and microbe-mediated mitigation in plants: the omics strategies. Front. Plant Sci. 8, 172 (2017).PubMed 
PubMed Central 

Google Scholar 
46.Mukamuhirwa, A. et al. Effect of intermittent drought on grain yield and quality of rice (Oryza sativa L.) grown in Rwanda. J. Agro Crop Sci. 206, 252–262 (2020).CAS 

Google Scholar 
47.Fleta-Soriano, E. & Munné-Bosch, S. Stress memory and the inevitable effects of drought: a physiological perspective. Front. Plant Sci. 7, 143 (2016).PubMed 
PubMed Central 

Google Scholar 
48.Ding, Y., Fromm, M. & Avramova, Z. Multiple exposures to drought ‘train’ transcriptional responses in Arabidopsis. Nat. Commun. 3, 740 (2012).
Google Scholar 
49.de la Fuente Cantó, C. et al. An extended root phenotype: the rhizosphere, its formation and impacts on plant fitness. Plant J. 103, 951–964 (2020).
Google Scholar 
50.Kittas, C., Bartzanas, T. & Jaffrin, A. Temperature gradients in a partially shaded large greenhouse equipped with evaporative cooling pads. Biosyst. Eng. 85, 87–94 (2003).
Google Scholar 
51.Edwards, J. et al. Soil domestication by rice cultivation results in plant–soil feedback through shifts in soil microbiota. Genome Biol. 20, 221 (2019).PubMed 
PubMed Central 

Google Scholar 
52.Edwards, J., Santos-Medellín, C. & Sundaresan, V. Extraction and 16S rRNA sequence analysis of microbiomes associated with rice roots. Bio. Protoc. 8, e2884 (2018).53.Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl Acad. Sci. USA 108, 4516–4522 (2011).CAS 

Google Scholar 
54.Masella, A. P., Bartram, A. K., Truszkowski, J. M., Brown, D. G. & Neufeld, J. D. PANDAseq: paired-end assembler for illumina sequences. BMC Bioinform. 13, 31 (2012).CAS 

Google Scholar 
55.Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
56.Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
57.DeSantis, T. Z. et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl. Environ. Microbiol. 72, 5069–5072 (2006).CAS 
PubMed 
PubMed Central 

Google Scholar 
58.Weimer, B. C. 100K Pathogen Genome Project. Genome Announc. 5, e00594-17 (2017).59.Kong, N. et al. Draft genome sequences of 1,183 Salmonella strains from the 100K Pathogen Genome Project. Genome Announc. 5, e00518–17 (2017).PubMed 
PubMed Central 

Google Scholar 
60.Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
61.Bankevich, A. et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. 19, 455–477 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
62.Seemann, T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30, 2068–2069 (2014).CAS 

Google Scholar 
63.Medema, M. H. et al. antiSMASH: rapid identification, annotation and analysis of secondary metabolite biosynthesis gene clusters in bacterial and fungal genome sequences. Nucleic Acids Res. 39, W339–W346 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
64.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2018); https://www.R-project.org/65.McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
66.Lozupone, C. & Knight, R. UniFrac: a new phylogenetic method for comparing microbial communities. Appl. Environ. Microbiol. 71, 8228–8235 (2005).CAS 
PubMed 
PubMed Central 

Google Scholar 
67.Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).PubMed 
PubMed Central 

Google Scholar 
68.McMurdie, P. J. & Holmes, S. Waste not, want not: why rarefying microbiome data is inadmissible. PLoS Comput. Biol. 10, e1003531 (2014).PubMed 
PubMed Central 

Google Scholar 
69.Paradis, E., Claude, J. & Strimmer, K. APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289–290 (2004).CAS 
PubMed 
PubMed Central 

Google Scholar 
70.Oksanen, J. et al. vegan: Community Ecology Package (2018).71.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).72.Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest package: tests in linear mixed effects models. J. Stat. Softw. 82, 13 (2017).
Google Scholar 
73.Lenth, R., Singmann, H., Love, J., Buerkner, P. & Herve, M. Emmeans: estimated marginal means, aka least-squares means. R package v.1, 3 (R Foundation for Statistical Computing, 2018).74.Kassambara, A. Rstatix: pipe-friendly framework for basic statistical tests. R package v.0.6.0 (R Foundation for Statistical Computing, 2020).75.Graves, S., Piepho, H.-P., Selzer, L. & Dorai-Raj, S. multcompView: visualizations of paired comparisons. R package v.0.1-7 (R Foundation for Statistical Computing, 2015).76.Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
77.Liaw, A. & Wiener, M. Classification and regression by randomForest. R. News 2, 18–22 (2002).
Google Scholar 
78.Subramanian, S. et al. Persistent gut microbiota immaturity in malnourished Bangladeshi children. Nature 510, 417–421 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar  More

1.Ceballos, G., Ehrlich, P. R. & Dirzo, R. Biological annihilation via the ongoing sixth mass extinction signaled by vertebrate population losses and declines. Proc. Natl. Acad. Sci. U. S. A. 114, E6089–E6096 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
2.Briggs, J. C. Marine extinctions and conservation. Mar. Biol. 158, 485–488 (2011).Article 

Google Scholar 
3.Heupel, M. R., Knip, D. M., Simpfendorfer, C. A. & Dulvy, N. K. Sizing up the ecological role of sharks as predators. Mar. Ecol. Prog. Ser. 495, 291–298 (2014).ADS 
Article 

Google Scholar 
4.TRAFFIC East Asia. Shark product trade in Hong Kong and mainland China and implementation of the CITES shark listings. TRAFFIC East Asia (2004).5.Pacoureau, N. et al. Half a century of global decline in oceanic sharks and rays. Nature 589, 567–571 (2021).ADS 
CAS 
PubMed 
Article 

Google Scholar 
6.Dulvy, N. K. et al. Extinction risk and conservation of the world’s sharks and rays. Elife 3, 1–34 (2014).Article 

Google Scholar 
7.Dwyer, R. G. et al. Individual and population benefits of marine reserves for reef sharks. Curr. Biol. 30, 480-489.e5 (2020).CAS 
PubMed 
Article 

Google Scholar 
8.Kerwath, S. E., Winker, H., Götz, A. & Attwood, C. G. Marine protected area improves yield without disadvantaging fishers. Nat. Commun. 4, 1–6 (2013).Article 

Google Scholar 
9.Cabral, R. B. et al. A global network of marine protected areas for food. Proc. Natl. Acad. Sci. U. S. A. 117, 28134–28139 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
10.Camhi, M. D., Fordham, S. V. & Fowler, S. L. Domestic and International Management for Pelagic Sharks. in Sharks of the Open Ocean: Biology, Fisheries and Conservation (eds. M. D. Camhi, E. K. Pikitch & E. A. Babcock) 418–444 (Blackwell, 2009). https://doi.org/10.1002/9781444302516.ch34.11.Schiller, L., Alava, J. J., Grove, J., Reck, G. & Pauly, D. The demise of Darwin’s fishes: evidence of fishing down and illegal shark finning in the Galápagos Islands. Aquat. Conserv. Mar. Freshw. Ecosyst. 25, 431–446 (2015).Article 

Google Scholar 
12.Feitosa, L. M. et al. DNA-based identification reveals illegal trade of threatened shark species in a global elasmobranch conservation hotspot. Sci. Rep. 8, 1–11 (2018).CAS 
Article 

Google Scholar 
13.Reck, G. Development of the Galápagos Marine Reserve. in The Galapagos Marine Reserve. Social and Ecological Interactions in the Galapagos Islands (ed. Denkinger J, V. L.) 139‒158 (Springer, 2014).14.PNG (Parque Nacional Galápagos). Barco chino deberá pagar 6 millones por daño ambiental dispone Sala de lo Penal. https://www.galapagos.gob.ec/barco-chino-debera-pagar-6-millones-por-dano-ambiental-dispone-sala-de-lo-penal/ (2017).15.Fiscalía General del Estado Ecuatoriano. Boletín de Prensa FGE N. 096-DC-2019: Corte Nacional aceptó recurso de casación por delito contra la flora y fauna silvestres en Galápagos. https://www.fiscalia.gob.ec/corte-nacional-acepto-recurso-de-casacion-por-delito-contra-la-flora-y-fauna-silvestres-en-galapagos/ (2019).16.D’Afflisio, E., Braca, P., Millefiori, L. M. & Willett, P. Maritime Anomaly Detection Based on Mean-Reverting Stochastic Processes Applied to a Real-World Scenario. in 2018 21st International Conference on Information Fusion, FUSION 2018 1171–1177 (Institute of Electrical and Electronics Engineers Inc., 2018). https://doi.org/10.23919/ICIF.2018.8455854.17.Cutlip, K. Our Data Suggests Transhippment Involved in Refrigerated Cargo Vessel Just Sentenced to $5.9 Million and Jail Time for Carrying Illegal Sharks. https://globalfishingwatch.org/impacts/policy-compliance/transhippment-involved-in-reefer-sentenced-for-carrying-illegal-sharks/ (2017).18.Compagno, L., Dando, M. & Fowler, S. Sharks of the World (Princeton University Press, 2005).
Google Scholar 
19.Bradley, D. et al. Leveraging satellite technology to create true shark sanctuaries. Conserv. Lett. 12, 1–8 (2019).Article 

Google Scholar 
20.Cardeñosa, D. et al. Species composition of the largest shark fin retail-market in mainland China. Sci. Rep. 10, 1–10 (2020).Article 
CAS 

Google Scholar 
21.IATTC. Resolution C-11-10. Resolution on the conservation of oceanic whitetip sharks caught in association with fisheries in the Antigua convention area. (IATCC, 2011).22.Gonzalez-Pestana, A., Kouri J., C. & Velez-Zuazo, X. Shark fisheries in the Southeast Pacific: A 61-year analysis from Peru. F1000Research 3, 164 (2014).23.Martínez-Ortiz, J., Aires-Da-silva, A. M., Lennert-Cody, C. E. & Maunderxs, M. N. The ecuadorian artisanal fishery for large pelagics: Species composition and spatio-temporal dynamics. PLoS ONE 10, 1–29 (2015).Article 
CAS 

Google Scholar 
24.Bustamante, C. & Bennett, M. B. Insights into the reproductive biology and fisheries of two commercially exploited species, shortfin mako (Isurus oxyrinchus) and blue shark (Prionace glauca), in the south-east Pacific Ocean. Fish. Res. 143, 174–183 (2013).Article 

Google Scholar 
25.Hinton, M. G. et al. Stock Status Indicators for Fisheries of the Eastern Pacific Ocean. INTER-AMERICAN TROPICAL TUNA COMISSION, 19, 142–182 (2011).26.Duffy, L. M., Lennert-Cody, C. E., Olson, R. J., Minte-Vera, C. V. & Griffiths, S. P. Assessing vulnerability of bycatch species in the tuna purse-seine fisheries of the eastern Pacific Ocean. Fish. Res. 219, 105316 (2019).Article 

Google Scholar 
27.Clarke, S. C., Harley, S. J., Hoyle, S. D. & Rice, J. S. Population trends in Pacific Oceanic sharks and the utility of regulations on shark finning. Conserv. Biol. 27, 197–209 (2013).PubMed 
Article 

Google Scholar 
28.Martinez Ortiz, J. et al. Abundancia estacional de Tiburones desembarcados en Manta-Ecuador. EPESPO-PMRC, 9–27 (2007).29.Román-Verdesoto, M. Updated summary regarding hammerhead sharks caught in the tuna fisheries in the Eastern Pacific Ocean 6th Meeting of the Scientific Advisory Committee IATTC. (2015).30.IATTC. Resolution C-16-06: Conservation Measures for Shark Species, with Special Emphasis on the Silky Shark (Carcharhinus falciformis), for the years 2017, 2018, and 2019. (IATTC, 2016).31.Alava, J. J. Massive Chinese Fleet Jeopardizes Threatened Shark Species around the Galápagos Marine Reserve and Waters off Ecuador. Int. J. Fish. Sci. Res. 1, 8–10 (2017).
Google Scholar 
32.El Universo. Se detectan tres flotas pesqueras chinas cerca de Galápagos . https://Www.Eluniverso.Com/Noticias/2019/03/21/Nota/7244318/Se-Detectan-Tres-Flotas-Pesqueras-Chinas-Cerca-Galapagos (2019).33.El Universo. Armada del Ecuador detecta flota pesquera con 260 barcos en las cercanías de Galápagos. https://www.eluniverso.com/noticias/2020/07/16/nota/7908768/armada-ecuador-detecta-flota-pesquera-260-barcos-cercanias. (2020).34.El Universo. Varios barcos chinos, que integran la flota extranjera que pesca cerca de Ecuador, estarían emitiendo ‘falsas coordenadas’; aparecen en Nueva Zelanda. https://www.eluniverso.com/noticias/2020/08/06/nota/7932429/flota-china-pesquera-galapagos-ecuador-nueva-zelanda-ecuador#cxrecs_s. (2020)35.Stuff. Chinese vessels off Galapagos ‘cloaking’ in New Zealand. https://www.stuff.co.nz/environment/122339295/chinese-vessels-off-galapagos-cloaking-in-new-zealand. (2020).36.Mas, F., Forselledo, R. & Domingo, A. Length-length relationships for six pelagic shark species. Collect. Vol. Sci. Pap. ICCAT 70, 2441–2450 (2014).
Google Scholar 
37.D’Alberto, B. M. et al. Age, growth and maturity of oceanic whitetip shark (Carcharhinus longimanus) from Papua New Guinea. Mar. Freshw. Res. 68, 1118–1129 (2017).Article 

Google Scholar 
38.Oshitani, S., Nakano, H. & Tanaka, S. Age and growth of the silky shark Carcharhinus falciformis from the Pacific Ocean. Fish. Sci. 69, 456–464 (2003).CAS 
Article 

Google Scholar 
39.Joung, S. J., Chen, N. F., Hsu, H. H. & Liu, K. M. Estimates of life history parameters of the oceanic whitetip shark, Carcharhinus longimanus, in the Western North Pacific Ocean. Mar. Biol. Res. 12, 758–768 (2016).Article 

Google Scholar 
40.Naylor, G. J. P. et al. A DNA sequencebased approach to the identification of shark and ray species and its implications for global elasmobranch diversity and parasitology. Bull. Am. Museum Nat. Hist. 21, 1–262 (2012).
Google Scholar 
41.Peñafiel, N., Flores, D. M., Rivero De Aguilar, J., Guayasamin, J. M. & Bonaccorso, E. A cost-effective protocol for total DNA isolation from animal tissue. Neotrop. Biodivers. 5, 69–74 (2019).42.Kearse, M. et al. Geneious Basic: An integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28, 1647–1649 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
43.Katoh, K., Rozewicki, J. & Yamada, K. D. MAFFT online service: Multiple sequence alignment, interactive sequence choice and visualization. Brief. Bioinform. 20, 1160–1166 (2018).Article 
CAS 

Google Scholar 
44.Maddison, W. P. & Maddison, D. R. Mesquite: A modular system for evolutionary Mesquite installation for evolutionary analysis. (2003).45.Aparicio-Puerta, E. et al. SRNAbench and sRNAtoolbox 2019: intuitive fast small RNA profiling and differential expression. Nucleic Acids Res. 47, W530–W535 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
46.Nguyen, L. T., Schmidt, H. A., Von Haeseler, A. & Minh, B. Q. IQ-TREE: A fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).CAS 
PubMed 
Article 

Google Scholar 
47.Trifinopoulos, J., Nguyen, L. T., von Haeseler, A. & Minh, B. Q. W-IQ-TREE: A fast online phylogenetic tool for maximum likelihood analysis. Nucleic Acids Res. 44, W232–W235 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
48.Kalyaanamoorthy, S., Minh, B. Q., Wong, T. K. F., Von Haeseler, A. & Jermiin, L. S. ModelFinder: Fast model selection for accurate phylogenetic estimates. Nat. Methods 14, 587–589 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
49.Seki, T., Taniuchi, T., Nakano, H. & Shimizu, M. Age, growth and reproduction of the oceanic Whitetip shark from the Pacific Ocean. Fish. Sci. 64, 14–20 (1998).CAS 
Article 

Google Scholar 
50.Bergman, B. Reefer Fined $5.9 Million for Endangered Catch in Galapagos Recently Rendezvoused with Chinese Longliners. https://skytruth.org/2017/08/galapagos-reefer-fined-5-9-million/ (2017).51.Romero-Caicedo, A. F., Galván-Magaña, F. & Martínez-Ortiz, J. Reproduction of the pelagic thresher shark Alopias pelagicus in the equatorial Pacific. J. Mar. Biol. Assoc. U. K. 94, 1501–1507 (2014).Article 

Google Scholar 
52.Chen, C., Liu, K. & Chang, Y. Reproductive biology of the bigeye thresher shark, Alopias superciliosus (Lowe, 1839) (Chondrichthyes: Alopiidae), in the northwestern Pacific. Ichthyol. Res. 44, 227–236 (1997).Article 

Google Scholar 
53.Bradley, D. et al. Growth and life history variability of the grey reef shark (Carcharhinus amblyrhynchos) across its range. PLoS ONE 12, 1–20 (2017).
Google Scholar 
54.Holmes, B. J. et al. Age and growth of the tiger shark Galeocerdo cuvier off the east coast of Australia. J. Fish Biol. 87, 422–448 (2015).CAS 
PubMed 
Article 

Google Scholar 
55.Nakano, H Stevens, J. The biology and ecology of the blue shark, Prionace glauca. in Sharks of the open ocean: Biology, fisheries and conservation (Vol. 1) (ed. Camhi, Merry D Pikitch, E K Babcock, E. A.) 140‒151 (Blackwell Scientific Publications, 2008).56.Gubanov, Y. E. The reproduction of some species of pelagic sharks from the equatorial zone of the Indian Ocean. J. Ichthyol. 18, 781–792 (1978).
Google Scholar 
57.Fahmi & Sumadhiharga, K. Size, sex and length at maturity of four common sharks caught from Western Indonesia. Mar. Res. Indones. 32, 7–19 (2007).58.Nava, P. N. & Márquez-Farías, J. F. Talla de madurez del tiburón martillo, Sphyrna zygaena, capturado en el Golfo de California. Hidrobiologica 24, 129–135 (2014).
Google Scholar 
59.Saïdi, B., Bradaï, M. N. & Bouaïn, A. Reproductive biology of the smooth-hound shark Mustelus mustelus (L.) in the Gulf of Gabès (south-central Mediterranean Sea). J. Fish Biol. 72, 1343–1354 (2008). More

Numerical sex ratioIn each population, the total number of male individuals was higher than that of female individuals, regardless of caste (binomial tests for Okinawa alates, total female:male individuals = 2,427:3,468, p  More

1.Bershtein, S., Serohijos, A. W. & Shakhnovich, E. I. Curr. Opin. Struct. Biol. 42, 31–40 (2017).CAS 
Article 

Google Scholar 
2.Elena, S. F. & Lenski, R. E. Nat. Rev. Genet. 4, 457–469 (2003).CAS 
Article 

Google Scholar 
3.Fowler, D. M. & Fields, S. Nat. Methods 11, 801–807 (2014).CAS 
Article 

Google Scholar 
4.Jasinska, W. et al. Nat. Ecol. Evol. 4, 437–452 (2020).Article 

Google Scholar 
5.Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature https://doi.org/10.1038/s41586-021-03819-2 (2021).6.Diss, G. & Lehner, B. eLife 7, e32472 (2018).Article 

Google Scholar 
7.Schmiedel, J. M. & Lehner, B. Nat. Genet. 51, 1177–1186 (2019).CAS 
Article 

Google Scholar 
8.Guan, Y., Dunham, M. J. & Troyanskaya, O. G. Genetics 175, 933–943 (2007).CAS 
Article 

Google Scholar 
9.Soria, P. S., McGary, K. L. & Rokas, A. Mol. Biol. Evol. 31, 984–992 (2014).CAS 
Article 

Google Scholar 
10.Gabaldon, T. & Koonin, E. V. Nat. Rev. Genet. 14, 360–366 (2013).CAS 
Article 

Google Scholar 
11.Montelione, G. T. F1000 Biol. Rep. 4, 7 (2012).Article 

Google Scholar 
12.Laskowski, R. A., Watson, J. D. & Thornton, J. M. J. Mol. Biol. 351, 614–626 (2005).CAS 
Article 

Google Scholar 
13.Lee, D., Redfern, O. & Orengo, C. Nat. Rev. Mol. Cell Biol. 8, 995–1005 (2007).CAS 
Article 

Google Scholar 
14.Redfern, O. C., Dessailly, B. H., Dallman, T. J., Sillitoe, I. & Orengo, C. A. PLoS Comput. Biol. 5, e1000485 (2009).Article 

Google Scholar 
15.Harms, M. J. & Thornton, J. W. Curr. Opin. Struct. Biol. 20, 360–366 (2010).CAS 
Article 

Google Scholar 
16.Levin, L. & Mishmar, D. Nat. Ecol. Evol. 1, 41 (2017).Article 

Google Scholar 
17.Aadland, K. & Kolaczkowski, B. Biol. Evol. 12, 1549–1565 (2020).CAS 

Google Scholar 
18.Kleiner, D. et al. J. Mol. Biol. 431, 4796–4816 (2019).CAS 
Article 

Google Scholar 
19.Boehr, D. D., Nussinov, R. & Wright, P. E. Nat. Chem. Biol. 5, 789–796 (2009).CAS 
Article 

Google Scholar 
20.Bershtein, S. et al. PLoS Genet. 11, e1005612 (2015).Article 

Google Scholar 
21.Senior, A. W. et al. Nature 577, 706–710 (2020).CAS 
Article 

Google Scholar 
22.Gershoni, M. et al. J. Mol. Biol. 404, 158–171 (2010).CAS 
Article 

Google Scholar 
23.Sutto, L., Marsili, S., Valencia, A. & Gervasio, F. L. Proc. Natl Acad. Sci. USA 112, 13567–13572 (2015).CAS 
Article 

Google Scholar  More

1.Goordial J, Davila A, Lacelle D, Pollard W, Marinova MM, Greer CW, et al. Nearing the cold-arid limits of microbial life in permafrost of an upper dry valley, Antarctica. ISME J. 2016;10:1613.PubMed 
PubMed Central 
Article 

Google Scholar 
2.Mykytczuk NC, Foote SJ, Omelon CR, Southam G, Greer CW, Whyte LG. Bacterial growth at −15 C; molecular insights from the permafrost bacterium Planococcus halocryophilus Or1. ISME J. 2013;7:1211.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
3.Margesin R, Miteva V. Diversity and ecology of psychrophilic microorganisms. Res Microbiol. 2011;162:346–61.PubMed 
Article 

Google Scholar 
4.De Maayer P, Anderson D, Cary C, Cowan DA. Some like it cold: understanding the survival strategies of psychrophiles. EMBO Rep. 2014;15:508–17.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
5.Hassan N, Rafiq M, Hayat M, Shah AA, Hasan F. Psychrophilic and psychrotrophic fungi: a comprehensive review. Rev Environ Sci Bio. 2016;15:147–72.Article 

Google Scholar 
6.Christner BC, Mosley‐Thompson E, Thompson LG, Reeve JN. Bacterial recovery from ancient glacial ice. Environ Microbiol. 2003;5:433–6.CAS 
PubMed 
Article 

Google Scholar 
7.Raymond-Bouchard I, Goordial J, Zolotarov Y, Ronholm J, Stromvik M, Bakermans C, et al. Conserved genomic and amino acid traits of cold adaptation in subzero-growing Arctic permafrost bacteria. FEMS Microbiol Ecol. 2018;94:fiy023.Article 
CAS 

Google Scholar 
8.Raymond-Bouchard I, Tremblay J, Altshuler I, Greer CW, Whyte LG. Comparative transcriptomics of cold growth and adaptive features of a eury-and steno-psychrophile. Front Microbiol. 2018;9:1565.PubMed 
PubMed Central 
Article 

Google Scholar 
9.Buzzini P, Margesin R. Cold-adapted yeasts: a lesson from the cold and a challenge for the XXI century. In: Buzzini P, Margesin R, editors. Cold-adapted yeasts. Heidelberg: Springer; 2014. p. 3–22.Chapter 

Google Scholar 
10.Altshuler I, Goordial J, Whyte LG. Microbial life in permafrost. In: Margesin R, editor. Psychrophiles: from biodiversity to biotechnology. 2nd edn. Cham: Springer; 2017. p. 153–79.Chapter 

Google Scholar 
11.Gilichinsky D, Wilson G, Friedmann E, McKay C, Sletten R, Rivkina E, et al. Microbial populations in Antarctic permafrost: biodiversity, state, age, and implication for astrobiology. Astrobiology. 2007;7:275–311.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
12.de Menezes GCA, Porto BA, Amorim SS, Zani CL, de Almeida Alves TM, Junior PAS, et al. Fungi in glacial ice of Antarctica: diversity, distribution and bioprospecting of bioactive compounds. Extremophiles. 2020;24:367–76.PubMed 
Article 
PubMed Central 

Google Scholar 
13.Zhang T, Wang N, Yu L. Soil fungal community composition differs significantly among the Antarctic, Arctic, and Tibetan Plateau. Extremophiles. 2020;24:821–9.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
14.Coleine C, Zucconi L, Onofri S, Pombubpa N, Stajich JE, Selbmann L. Sun exposure shapes functional grouping of fungi in cryptoendolithic Antarctic communities. Life. 2018;8:19.PubMed Central 
Article 
CAS 

Google Scholar 
15.Gunde-Cimerman N, Zalar P, de Hoog S, Plemenitaš A. Hypersaline waters in salterns–natural ecological niches for halophilic black yeasts. FEMS Microbiol Ecol. 2000;32:235–40.CAS 

Google Scholar 
16.Perini L, Gostinčar C, Anesio AM, Williamson C, Tranter M, Gunde-Cimerman N. Darkening of the Greenland Ice Sheet: fungal abundance and diversity are associated with algal bloom. Front Microbiol. 2019;10:557.PubMed 
PubMed Central 
Article 

Google Scholar 
17.Tojo M, Newsham KK. Snow moulds in polar environments. Fungal Ecol. 2012;5:395–402.Article 

Google Scholar 
18.Rosa LH, Vaz AB, Caligiorne RB, Campolina S, Rosa CA. Endophytic fungi associated with the Antarctic grass Deschampsia antarctica Desv.(Poaceae). Polar Biol. 2009;32:161–7.Article 

Google Scholar 
19.Gianoli E, Inostroza P, Zúñiga-Feest A, Reyes-Díaz M, Cavieres LA, Bravo LA, et al. Ecotypic differentiation in morphology and cold resistance in populations of Colobanthus quitensis (Caryophyllaceae) from the Andes of central Chile and the maritime Antarctic. Arct Antarct Alp Res. 2004;36:484–9.Article 

Google Scholar 
20.Duncan SM, Farrell RL, Thwaites JM, Held BW, Arenz BE, Jurgens JA, et al. Endoglucanase‐producing fungi isolated from Cape Evans historic expedition hut on Ross Island, Antarctica. Environ Microbiol. 2006;8:1212–9.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
21.Starmer WT, Lachance M-A. Yeast ecology. In: Kurtzman CP, Fell JW, Boekhout T, eds. The yeasts. 5ft ed. London: Elsevier; 2011. p. 65–83.Chapter 

Google Scholar 
22.Shivaji S, Prasad G. Antarctic yeasts: biodiversity and potential applications. In: Satyanarayana T, Kunze G, editors. Yeast biotechnology: diversity and applications. New Delhi: Springer; 2009. p. 3–18.Chapter 

Google Scholar 
23.Gunde-Cimerman N, Plemenitaš A, Buzzini P. Changes in lipids composition and fluidity of yeast plasma membrane as response to cold. In: Buzzini P, Margesin R, editors. Cold-adapted yeasts. Heidelberg: Springer; 2014. p. 225–42.Chapter 

Google Scholar 
24.Goordial J, Raymond-Bouchard I, Riley R, Ronholm J, Shapiro N, Woyke T, et al. Improved high-quality draft genome sequence of the eurypsychrophile Rhodotorula sp. JG1b, isolated from permafrost in the hyperarid upper-elevation mcmurdo dry valleys, Antarctica. Genome Announc. 2016;4:e00069–16.PubMed 
PubMed Central 
Article 

Google Scholar 
25.Yen H-W, Liao Y-T, Liu YX. Cultivation of oleaginous Rhodotorula mucilaginosa in airlift bioreactor by using seawater. J Biosci Bioeng. 2016;121:209–12.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
26.Buzzini P, Turk M, Perini L, Turchetti B, Gunde-Cimerman N. Yeasts in polar and subpolar habitats. In: Buzzini P, Lachance M-A, Yurkov A, editors. Yeasts in natural ecosystems: diversity. Cham: Springer; 2017. p. 331–65.Chapter 

Google Scholar 
27.Margesin R, Fonteyne P-A, Schinner F, Sampaio JP. Rhodotorula psychrophila sp. nov., Rhodotorula psychrophenolica sp. nov. and Rhodotorula glacialis sp. nov., novel psychrophilic basidiomycetous yeast species isolated from alpine environments. Int J Syst Evol Micr. 2007;57:2179–84.CAS 
Article 

Google Scholar 
28.Sabri A, Jacques P, Weekers F, Bare G, Hiligsmann S, Moussaif M, et al. Effect of temperature on growth of psychrophilic and psychrotrophic members of Rhodotorula aurantiaca. In: Walt DR, editor. Applied biochemistry and biotechnology. New York: Springer Science+Business Media; 2000. p. 391–9.
Google Scholar 
29.Marchant DR, Head JW III. Antarctic dry valleys: microclimate zonation, variable geomorphic processes, and implications for assessing climate change on Mars. Icarus 2007;192:187–222.Article 

Google Scholar 
30.Kurtzman C, Fell JW, Boekhout T, editors. The yeasts: a taxonomic study. 5ft ed. London: Elsevier; 2011.
Google Scholar 
31.Kornerup A, Wanscher JH, editors. Methuen handbook of colour. 2nd ed. London: Methuen and Co.; 1967.
Google Scholar 
32.Xing W, Yin M, Lv Q, Hu Y, Liu C, Zhang J. Oxygen solubility, diffusion coefficient, and solution viscosity. In: Xing W, Yin G, Zhang J, editors. Rotating electrode methods and oxygen reduction electrocatalysts. London: Elsevier; 2014. p. 1–31.
Google Scholar 
33.Viti C, Decorosi F, Marchi E, Galardini M, Giovannetti L. High-throughput phenomics. In: Mengoni A, Galardini M, Fondi M, editors. Bacterial pangenomics. Methods and protocols. New York: Springer; 2015. p. 99–123.Chapter 

Google Scholar 
34.Rico A, Preston GM. Pseudomonas syringae pv. tomato DC3000 uses constitutive and apoplast-induced nutrient assimilation pathways to catabolize nutrients that are abundant in the tomato apoplast. Mol Plant Microbe. 2008;21:269–82.CAS 
Article 

Google Scholar 
35.Patro R, Duggal G, Love MI, Irizarry RA, Kingsford C. Salmon provides fast and bias-aware quantification of transcript expression. Nat Methods. 2017;14:417.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
36.Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
37.Kanehisa M, Sato Y, Morishima K. BlastKOALA and GhostKOALA: KEGG tools for functional characterization of genome and metagenome sequences. J Mol Biol. 2016;428:726–31.CAS 
PubMed 
Article 

Google Scholar 
38.Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215:403–10.CAS 
Article 

Google Scholar 
39.Krüger J, Rehmsmeier M. RNAhybrid: microRNA target prediction easy, fast and flexible. Nucleic Acids Res. 2006;34:W451–54.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
40.Rehmsmeier M, Steffen P, Höchsmann M, Giegerich R. Fast and effective prediction of microRNA/target duplexes. RNA. 2004;10:1507–17.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
41.Greetham D. Phenotype microarray technology and its application in industrial biotechnology. Biotechnol Lett. 2014;36:1153–60.CAS 
PubMed 
Article 

Google Scholar 
42.Bochner BR. Global phenotypic characterization of bacteria. FEMS Microbiol Rev. 2008;33:191–205.PubMed 
Article 
CAS 

Google Scholar 
43.Maldonado F, Packard T, Gómez M. Understanding tetrazolium reduction and the importance of substrates in measuring respiratory electron transport activity. J Exp Mar Biol Ecol. 2012;434:110–8.Article 
CAS 

Google Scholar 
44.Barclay BJ, DeHaan CL, Hennig UG, Iavorovska O, von Borstel RW, Von, et al. A rapid assay for mitochondrial DNA damage and respiratory chain inhibition in the yeast Saccharomyces cerevisiae. Environ Mol Mutagen. 2001;38:153–8.CAS 
PubMed 
Article 

Google Scholar 
45.Jenkins CL, Lawrence SJ, Kennedy AI, Thurston P, Hodgson JA, Smart KA. Incidence and formation of petite mutants in lager brewing yeast Saccharomyces cerevisiae (syn. S. pastorianus) populations. J Am Soc Brew Chem. 2009;67:72–80.CAS 

Google Scholar 
46.Glab N, Wise R, Pring D, Jacq C, Slonimski P. Expression in Saccharomyces cerevisiae of a gene associated with cytoplasmic male sterility from maize: respiratory dysfunction and uncoupling of yeast mitochondria. Mol Gen Genet. 1990;223:24–32.CAS 
PubMed 
Article 

Google Scholar 
47.Goldring ES, Grossman LI, Krupnick D, Cryer DR, Marmur J. The petite mutation in yeast: loss of mitochondrial deoxyribonucleic acid during induction of petites with ethidium bromide. J Mol Biol. 1970;52:323–35.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
48.Vogel C, Marcotte EM. Insights into the regulation of protein abundance from proteomic and transcriptomic analyses. Nat Rev Genet. 2012;13:227–32.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
49.Pinatel E, Peano C. RNA sequencing and analysis in microorganisms for metabolic network reconstruction. In: Fondi M, editor. Metabolic network reconstruction and modeling. Methods and protocols. New York: Springer; 2018. p. 239–65.Chapter 

Google Scholar 
50.Raymond‐Bouchard I, Chourey K, Altshuler I, Iyer R, Hettich RL, Whyte LG. Mechanisms of subzero growth in the cryophile Planococcus halocryophilus determined through proteomic analysis. Environ Microbiol. 2017;19:4460–79.PubMed 
Article 
CAS 

Google Scholar 
51.Bhuiyan M, Tucker D, Watson K. Gas chromatography–mass spectrometry analysis of fatty acid profiles of Antarctic and non-Antarctic yeasts. Anton Leeuw. 2014;106:381–9.CAS 
Article 

Google Scholar 
52.López-Malo M, Chiva R, Rozes N, Guillamon JM. Phenotypic analysis of mutant and overexpressing strains of lipid metabolism genes in Saccharomyces cerevisiae: implication in growth at low temperatures. Int J Food Microbiol. 2013;162:26–36.PubMed 
Article 
CAS 

Google Scholar 
53.Rossi M, Buzzini P, Cordisco L, Amaretti A, Sala M, Raimondi S, et al. Growth, lipid accumulation, and fatty acid composition in obligate psychrophilic, facultative psychrophilic, and mesophilic yeasts. FEMS Microbiol Ecol. 2009;69:363–72.CAS 
PubMed 
Article 

Google Scholar 
54.Contreras G, Barahona S, Sepúlveda D, Baeza M, Cifuentes V, Alcaíno J. Identification and analysis of metabolite production with biotechnological potential in Xanthophyllomyces dendrorhous isolates. World J Micro Biot. 2015;31:517–26.CAS 
Article 

Google Scholar 
55.Libkind D, Arts M, Van Broock M. Fatty acid composition of cold-adapted carotenogenic basidiomycetous yeasts. Rev Argent Microbiol. 2008;40:193–7.CAS 
PubMed 

Google Scholar 
56.Thomas-Hall S, Watson K. Cryptococcus nyarrowii sp. nov., a basidiomycetous yeast from Antarctica. Int J Syst Evol Micr. 2002;52:1033–8.CAS 

Google Scholar 
57.López-Malo M, García-Ríos E, Chiva R, Guillamon JM. Functional analysis of lipid metabolism genes in wine yeasts during alcoholic fermentation at low temperature. Micro Cell. 2014;1:365.Article 
CAS 

Google Scholar 
58.Tai SL, Daran-Lapujade P, Walsh MC, Pronk JT, Daran J-M. Acclimation of Saccharomyces cerevisiae to low temperature: a chemostat-based transcriptome analysis. Mol Biol Cell. 2007;18:5100–12.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
59.Mao C, Wadleigh M, Jenkins GM, Hannun YA, Obeid LM. Identification and characterization of Saccharomyces cerevisiae dihydrosphingosine-1-phosphate phosphatase. J Biol Chem. 1997;272:28690–4.CAS 
PubMed 
Article 

Google Scholar 
60.Mata-Gómez LC, Montañez JC, Méndez-Zavala A, Aguilar CN. Biotechnological production of carotenoids by yeasts: an overview. Micro Cell Fact. 2014;13:12.Article 
CAS 

Google Scholar 
61.Moliné M, Flores MR, Libkind D. del Carmen Diéguez M, Farías ME, van Broock M. Photoprotection by carotenoid pigments in the yeast Rhodotorula mucilaginosa: the role of torularhodin. Photoch Photobio Sci. 2010;9:1145–51.Article 
CAS 

Google Scholar 
62.Liu GY, Nizet V. Color me bad: microbial pigments as virulence factors. Trends Microbiol. 2009;17:406–13.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
63.Rodrigues DF, Tiedje JM. Coping with our cold planet. Appl Environ Micro. 2008;74:1677–86.CAS 
Article 

Google Scholar 
64.Villarreal P, Carrasco M, Barahona S, Alcaíno J, Cifuentes V, Baeza M. Tolerance to ultraviolet radiation of psychrotolerant yeasts and analysis of their carotenoid, mycosporine, and ergosterol content. Curr Microbiol. 2016;72:94–101.CAS 
PubMed 
Article 

Google Scholar 
65.Moliné M, Libkind D, del Carmen DiéguezM, van Broock M. Photoprotective role of carotenoids in yeasts: response to UV-B of pigmented and naturally-occurring albino strains. J Photoch Photobio B 2009;95:156–61.Article 
CAS 

Google Scholar 
66.Huang G-T, Ma S-L, Bai L-P, Zhang L, Ma H, Jia P, et al. Signal transduction during cold, salt, and drought stresses in plants. Mol Biol Rep. 2012;39:969–87.PubMed 
Article 
CAS 

Google Scholar 
67.Heino P, Palva ET. Signal transduction in plant cold acclimation. In: Hirt H, Shinozaki K, editors. Plant responses to abiotic stress. Berlin: Springer; 2003. p. 151–86.Chapter 

Google Scholar 
68.Storey KB, Storey JM. Signal transduction and gene expression in the regulation of natural freezing survival. In: Storey KB, Storey JM, editors. Protein adaptations and signal transduction. London: Elsevier; 2001. p. 1–19.
Google Scholar 
69.Li W-H, Yang J, Gu X. Expression divergence between duplicate genes. Trends Genet. 2005;21:602–7.PubMed 
Article 
CAS 

Google Scholar 
70.Vollmers J, Voget S, Dietrich S, Gollnow K, Smits M, Meyer K, et al. Poles apart: arctic and Antarctic Octadecabacter strains share high genome plasticity and a new type of xanthorhodopsin. Plos One. 2013;8:e63422.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
71.Wagner A. Asymmetric functional divergence of duplicate genes in yeast. Mol Biol Evol. 2002;19:1760–8.CAS 
PubMed 
Article 

Google Scholar 
72.Varki A, Gagneux P. Biological functions of glycans. In: Varki A, Cummings RD, Esko JD, Stanley P, Hart GW, Aebi M, et al. editors. Essentials of glycobiology. 3rd ed. Cold Spring Harbor: Cold Spring Harbor Laboratory Press; 2017.
Google Scholar 
73.Colley K, Varki A, Kinoshita T. Cellular organization of glycosylation. In: Varki A, Cummings RD, Esko JD, Stanley P, Hart GW, Aebi M, et al. editors. Essentials of glycobiology. 3rd ed. Cold Spring Harbor: Cold Spring Harbor Laboratory Press; 2017.
Google Scholar 
74.Pavlova K, Panchev I, Hristozova T. Physico-chemical characterization of exomannan from Rhodotorula acheniorum MC. World J Micro Biot. 2005;21:279–83.CAS 
Article 

Google Scholar 
75.Cho DH, Chae HJ, Kim EY. Synthesis and characterization of a novel extracellular polysaccharide by Rhodotorula glutinis. Appl Biochem Biotech. 2001;95:183–93.CAS 
Article 

Google Scholar 
76.Flemming HC, Neu TR, Wingender J. The perfect slime. Microbial extracellular polymeric substances (EPS). London: IWA Publishing; 2016.Book 

Google Scholar 
77.Nichols WW, Evans MJ, Slack MP, Walmsley HL. The penetration of antibiotics into aggregates of mucoid and non-mucoid Pseudomonas aeruginosa. Microbiology. 1989;135:1291–303.CAS 
Article 

Google Scholar 
78.Selbmann L, Onofri S, Fenice M, Federici F, Petruccioli M. Production and structural characterization of the exopolysaccharide of the Antarctic fungus Phoma herbarum CCFEE 5080. Res Microbiol. 2002;153:585–92.CAS 
PubMed 
Article 

Google Scholar 
79.Rini JM, Esko JD. Glycosyltransferases and glycan-processing enzymes. In: Varki A, Cummings RD, Esko JD, Stanley P, Hart GW, Aebi M, et al. editors. Essentials of glycobiology. 3rd ed. Cold Spring Harbor: Cold Spring Harbor Laboratory Press; 2017.
Google Scholar 
80.Strassburg K, Walther D, Takahashi H, Kanaya S, Kopka J. Dynamic transcriptional and metabolic responses in yeast adapting to temperature stress. Omics. 2010;14:249–59.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
81.Becerra M, Lombardia L, Gonzalez-Siso M, Rodriguez-Belmonte E, Hauser N, Cerdán M. Genome-wide analysis of the yeast transcriptome upon heat and cold shock. Int J Genomics. 2003;4:366–75.CAS 

Google Scholar 
82.Homma T, Iwahashi H, Komatsu Y. Yeast gene expression during growth at low temperature. Cryobiology. 2003;46:230–7.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
83.Sahara T, Goda T, Ohgiya S. Comprehensive expression analysis of time-dependent genetic responses in yeast cells to low temperature. J Biol Chem. 2002;277:50015–21.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
84.Schade B, Jansen G, Whiteway M, Entian KD, Thomas DY. Cold adaptation in budding yeast. Mol Biol Cell. 2004;15:5492–502.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
85.Mikami K, Kanesaki Y, Suzuki I, Murata N. The histidine kinase Hik33 perceives osmotic stress and cold stress in Synechocystis sp. PCC 6803. Mol Microbiol. 2002;46:905–15.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
86.Tsuji M. Cold-stress responses in the Antarctic basidiomycetous yeast Mrakia blollopis. R Soc Open Sci. 2016;3:160106.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
87.Sarkar D, Bhowmik PC, Kwon Y-I, Shetty K. Clonal response to cold tolerance in creeping bentgrass and role of proline-associated pentose phosphate pathway. Bioresour Technol. 2009;100:5332–9.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
88.Bura R, Vajzovic A, Doty SL. Novel endophytic yeast Rhodotorula mucilaginosa strain PTD3 I: production of xylitol and ethanol. J Ind Microbiol Biot. 2012;39:1003–11.CAS 
Article 

Google Scholar 
89.da Silva TL, Feijão D, Roseiro JC, Reis A. Monitoring Rhodotorula glutinis CCMI 145 physiological response and oil production growing on xylose and glucose using multi-parameter flow cytometry. Bioresour Technol. 2011;102:2998–3006.PubMed 
Article 
CAS 

Google Scholar 
90.Johansson B, Hahn-Hägerdal B. The non-oxidative pentose phosphate pathway controls the fermentation rate of xylulose but not of xylose in Saccharomyces cerevisiae TMB3001. FEMS Yeast Res. 2002;2:277–82.CAS 
PubMed 
PubMed Central 

Google Scholar 
91.Eliasson A, Boles E, Johansson B, Österberg M, Thevelein J, Spencer-Martins I, et al. Xylulose fermentation by mutant and wild-type strains of Zygosaccharomyces and Saccharomyces cerevisiae. Appl Microbiol Biot. 2000;53:376–82.CAS 
Article 

Google Scholar 
92.Mohamad N, Mustapa Kamal S, Mokhtar M. Xylitol biological production: a review of recent studies. Food Rev Int. 2015;31:74–89.CAS 
Article 

Google Scholar 
93.Shetty K, Wahlqvist M. A model for the role of the proline-linked pentose-phosphate pathway in phenolic phytochemical bio-synthesis and mechanism of action for human health and environmental applications. Asia Pac J Clin Nutr. 2004;13:1–24.CAS 
PubMed 
PubMed Central 

Google Scholar 
94.Fonseca P, Moreno R, Rojo F. Growth of Pseudomonas putida at low temperature: global transcriptomic and proteomic analyses. Environ Microbiol Rep. 2011;3:329–39.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
95.Rao R, Bhadra B, Shivaji S. Isolation and characterization of ethanol‐producing yeasts from fruits and tree barks. Lett Appl Microbiol. 2008;47:19–24.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
96.Kourkoutas Y, Komaitis M, Koutinas A, Kaliafas A, Kanellaki M, Marchant R, et al. Wine production using yeast immobilized on quince biocatalyst at temperatures between 30 and 0 C. Food Chem. 2003;82:353–60.CAS 
Article 

Google Scholar 
97.Kanellaki M, Koutinas AA. Low temperature fermentation of wine and beer by cold-adapted and immobilized yeast cells. In: Margesin R, Schinner F, editors. Biotechnological applications of cold-adapted organisms. Berlin: Springer; 1999. p. 117–45.Chapter 

Google Scholar 
98.Bakoyianis V, Kanellaki M, Kaliafas A, Koutinas A. Low-temperature wine making by immobilized cells on mineral kissiris. J Agr Food Chem. 1992;40:1293–6.CAS 
Article 

Google Scholar 
99.Tiwari R, Singh S, Shukla P, Nain L. Novel cold temperature active β-glucosidase from Pseudomonas lutea BG8 suitable for simultaneous saccharification and fermentation. RSC Adv. 2014;4:58108–15.CAS 
Article 

Google Scholar 
100.Tang W, Wang Y, Zhang J, Cai Y, He Z. Biosynthetic pathway of carotenoids in Rhodotorula and strategies for enhanced their production. J Microbiol Biotechn. 2019;29:507–17.CAS 
Article 

Google Scholar 
101.Steven B, Briggs G, McKay CP, Pollard WH, Greer CW, Whyte LG. Characterization of the microbial diversity in a permafrost sample from the Canadian high Arctic using culture-dependent and culture-independent methods. FEMS Microbiol Ecol. 2007;59:513–23.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
102.Dozmorov MG, Giles CB, Koelsch KA, Wren JD. Systematic classification of non-coding RNAs by epigenomic similarity. BMC Bioinforma. 2013;14:S2.Article 

Google Scholar 
103.Sunkar R, Li Y-F, Jagadeeswaran G. Functions of microRNAs in plant stress responses. Trends Plant Sci. 2012;17:196–203.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
104.Ambros V. MicroRNA pathways in flies and worms: growth, death, fat, stress, and timing. Cell. 2003;113:673–6.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
105.Lau SK, Chow W-N, Wong AY, Yeung JM, Bao J, Zhang N, et al. Identification of microRNA-like RNAs in mycelial and yeast phases of the thermal dimorphic fungus Penicillium marneffei. Plos Negl Trop D. 2013;7:e2398.Article 
CAS 

Google Scholar 
106.Zhou Q, Wang Z, Zhang J, Meng H, Huang B. Genome-wide identification and profiling of microRNA-like RNAs from Metarhizium anisopliae during development. Fungal Biol UK. 2012;116:1156–62.CAS 
Article 

Google Scholar 
107.Lambert M, Benmoussa A, Provost P. Small non-coding RNAs derived from eukaryotic ribosomal RNA. Noncoding RNA 2019;5:16.CAS 
PubMed Central 

Google Scholar 
108.Thompson DM, Lu C, Green PJ, Parker R. tRNA cleavage is a conserved response to oxidative stress in eukaryotes. RNA. 2008;14:2095–103.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
109.Gebetsberger J, Wyss L, Mleczko AM, Reuther J, Polacek N. A tRNA-derived fragment competes with mRNA for ribosome binding and regulates translation during stress. RNA Biol. 2017;14:1364–73.PubMed 
Article 
PubMed Central 

Google Scholar 
110.Bąkowska-Żywicka K, Kasprzyk M, Twardowski T. tRNA-derived short RNAs bind to Saccharomyces cerevisiae ribosomes in a stress-dependent manner and inhibit protein synthesis in vitro. FEMS Yeast Res. 2016;16:fow077.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
111.McCool MA, Bryant CJ, Baserga SJ. MicroRNAs and long non-coding RNAs as novel regulators of ribosome biogenesis. Biochem Soc T. 2020;48:595–612.CAS 
Article 

Google Scholar 
112.Wei H, Zhou B, Zhang F, Tu Y, Hu Y, Zhang B, et al. Profiling and identification of small rDNA-derived RNAs and their potential biological functions. Plos One. 2013;8:e56842.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
113.Lee H-C, Chang S-S, Choudhary S, Aalto AP, Maiti M, Bamford DH, et al. qiRNA is a new type of small interfering RNA induced by DNA damage. Nature. 2009;459:274–7.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
114.Zhu C, Yan Q, Weng C, Hou X, Mao H, Liu D, et al. Erroneous ribosomal RNAs promote the generation of antisense ribosomal siRNA. P Natl Acad Sci USA. 2018;115:10082–7.CAS 
Article 

Google Scholar 
115.Zhou X, Chen X, Wang Y, Feng X, Guang S. A new layer of rRNA regulation by small interference RNAs and the nuclear RNAi pathway. RNA Biol. 2017;14:1492–8.PubMed 
PubMed Central 
Article 

Google Scholar 
116.Zhou X, Feng X, Mao H, Li M, Xu F, Hu K, et al. RdRP-synthesized antisense ribosomal siRNAs silence pre-rRNA via the nuclear RNAi pathway. Nat Struct Mol Biol. 2017;24:258.CAS 
PubMed 
Article 

Google Scholar  More

Profftia and Vallotia are related to free-living bacteria and fungus-associated endosymbiontsPrevious 16S rRNA-based phylogenetic analyses suggested an affiliation of Profftia with free-living gammaproteobacteria and a close phylogenetic relationship between Vallotia and betaproteobacterial endosymbionts of Rhizopus fungi [14]. Biased nucleotide composition and accelerated sequence evolution of endosymbiont genomes [2, 3] often result in inconsistent phylogenies and may cause grouping of unrelated taxa [55, 56]. Thus, to further investigate the phylogenetic relationships of the A. laricis/tardus symbionts, we used conserved marker genes for maximum likelihood and Bayesian phylogenetic analyses.Phylogenetic analysis of 45 single-copy proteins demonstrated that Profftia opens up a novel insect symbiont lineage most similar to Hafnia species and an isolate from the human gastrointestinal tract within the Hafniaceae, which has been recently designated as a distinct family within the Enterobacteriales [57] (Fig. S2). Hafnia strains are frequently identified in the gastrointestinal tract of humans and animals and were also found in insects [58, 59]. The phylogenomic placement of Profftia in our analysis is in agreement with previous 16S rRNA-based analyses [14].Vallotia formed a monophyletic group with Mycetohabitans endofungorum and M. rhizoxinica, endosymbionts of Rhizopus fungi within the Burkholderiaceae [60, 61] with strong support in phylogenetic analyses based on a concatenated set of 108 proteins (Figs. 1 and S3; previous taxonomic assignments of the fungus-associated symbionts were as Burkholderia/Paraburkholderia endofungorum and rhizoxinica, respectively). Interestingly, Vallotia and M. endofungorum appeared as well-supported sister taxa within this clade. This implies a closer phylogenetic relationship between Vallotia and M. endofungorum and a common origin of adelgid endosymbionts from within a clade of fungus-associated bacterial symbionts. Lengths of branches leading to the fungus-associated endosymbionts were similar to those of free-living bacteria in the data set; however, Vallotia had a remarkably longer branch marking a rapid rate of sequence evolution characteristic of obligate intracellular bacteria [2, 3]. M. endofungorum and M. rhizoxinica have been identified in the cytosol of the zygomycete Rhizopus microsporus, best known as the causative agent of rice seedling blight [61, 62]. The necrotrophic fungus secretes potent toxins, rhizoxin and rhizonin, which are produced by the endosymbionts. The bacterial partners are obligatory for their host as they tightly control its sporulation, while they benefit from host nutrients and spread with the fungal spores [63, 64]. Additionally, related bacterial strains have also been found in association with Rhizopus fungi worldwide in a diverse set of environments, including other plant species, soil, food, and even human tissues [65, 66].Fig. 1: Phylogenomic analysis showing the affiliation of the adelgid endosymbiont “Candidatus Vallotia tarda” and its closest relatives, the fungus-associated endosymbionts M. rhizoxinica and M. endofungorum within the Burkholderiaceae.Selected members of Oxalobacteraceae (Janthinobacterium agaricidamnosum [HG322949], Collimonas pratensis [CP013234], and Herbaspirillum seropedicae [CP011930]) were used as outgroup. Maximum likelihood and Bayesian analyses were performed based on a concatenated alignment of 108 proteins. Maximum likelihood tree is shown. SH-aLRT support (%) and ultrafast bootstrap support (%) values based on 1000 replicates, and Bayesian posterior probabilities are indicated on the internal nodes. Asterisks stand for a maximal support in each analysis (100%/1).Full size imageTaken together, phylogenomic analyses support that Profftia and Vallotia open up novel insect symbionts lineages most closely related to free-living bacteria within the Hafniaceae and a clade of fungus-associated endosymbionts within the Burkholderiaceae, respectively. Given the well-supported phylogenetic positioning of “Candidatus Vallotia tarda” nested within a clade formed by Mycetohabitans species, we propose the transfer of “Candidatus Vallotia tarda” to the Mycetohabitans genus, as “Candidatus Mycetohabitans vallotii” (a detailed proposal for the re-classification is given in the Supplementary Material).
Vallotia and Profftia are evolutionary young symbionts of adelgidsThe complete sequence of the Profftia chromosome had a length of 1,225,795 bp and a G + C content of 31.9% (Table 1). It encoded for 645 proteins, one copy of each rRNA, 35 transfer RNAs (tRNAs), and 10 non-coding RNAs (ncRNAs). It had tRNAs and amino acid charging potential for all 20 standard amino acids. However, protein-coding sequences (CDSs) made up only 52.4% of the genome, and 21 pseudogenes indicated an ongoing gene inactivation.Table 1 Genomic features of Profftia and Vallotia.Full size tableThe Vallotia chromosome had a length of 1,123,864 bp. It had a G + C content and a coding density of 42.9 and 64.9%, respectively. However, a 72,431-bp-long contig showed a characteristically lower G + C content (36.1%) and contained only 46.2% putative CDSs. This contig had identical repeats at its ends, and genome annotation revealed neighboring genes for a plasmid replication initiation protein, and ParA/ParB partitioning proteins, which function in plasmid and chromosome segregation between daughter cells before cell division [67]. We thus assume that this contig corresponds to a circular plasmid of Vallotia. Vallotia has three rRNA operons, similarly to its close relative, M. rhizoxinica [68]. In total, the Vallotia genome encoded 780 proteins (29 on the putative plasmid), 41 tRNAs, and 52 predicted pseudogenes (5 on the putative plasmid).The host-restricted lifestyle has a profound influence on bacterial genomes. Relaxed purifying selection on many redundant functions and increased genetic drift can lead to the accumulation of slightly deleterious mutations and the proliferation of mobile genetic elements [69,70,71,72]. Disruption of DNA repair genes can increase mutation rates, which promote gene inactivation [73]. Non-functional genomic regions get subsequently lost, and ancient obligate endosymbionts typically have tiny (≪0.8 Mb), gene-dense genomes with AT-biased nucleotide composition [2, 74, 75]. Facultative symbionts also possess accelerated rates of sequence evolution but have larger genomes ( >2 Mb) with variable coding densities following the age of their host-restricted lifestyle [76]. The number of pseudogenes in Vallotia and Profftia is higher than in ancient intracellular symbionts, which suggests an intermediate state of genomic reduction [2]. The only moderately reduced size and AT bias together with the low protein-coding density of the Vallotia and Profftia genomes was most similar to those of evolutionary young co-obligate partners of insects [76], for instance, “Ca. Pseudomonas adelgestsugas” in A. tsugae [23], Serratia symbiotica in Cinara cedri [77, 78], and the Sodalis-like symbiont of Philaenus spumarius, the meadow spittlebug [79].The evolutionary link between Vallotia and fungus-associated endosymbiontsHigh level of genomic synteny between Vallotia and M. rhizoxinica
Intracellular symbionts usually show a low level of genomic similarity to related bacteria. Rare examples of newly emerged bacteriocyte-associated symbionts of herbivorous insects pinpoint their source from plant-associated bacteria [4], gut bacteria [5], and other free-living bacteria [6].Genome alignments showed a low level of collinearity between the genomes of Profftia and its closest relatives. Among the relatives of Vallotia, a closed genome is available for M. rhizoxinica [68]. We therefore mostly focused on this fungus-associated symbiont as a reference for comparison with Vallotia.The Vallotia chromosome showed a surprisingly high level of synteny with the chromosome of M. rhizoxinica (Fig. 2A). However, its size was only ~40% of the fungus-associated symbiont chromosome. The putative plasmid of Vallotia was perfectly syntenic with the larger of the two plasmids of M. rhizoxinica (pBRH01), although the Vallotia plasmid was >90% smaller in size (72,431 bp versus 822,304 bp) [68]. Thus, the Vallotia plasmid showed a much higher level of reduction than the chromosome, which together with its lower G + C content and gene density suggests differential evolutionary constraints on these replicons.Fig. 2: High level of collinearity between the genomes of Vallotia and M. rhizoxinica.A Circos plot showing the synteny between the chromosome and plasmid of Vallotia and M. rhizoxinica, an endosymbiont of Rhizopus fungi. The outermost and the middle rings show genes in forward and reverse strand orientation, respectively. These include rRNA genes in red and tRNA genes in dark orange. The innermost ring indicates single-copy genes shared by M. rhizoxinica and Vallotia in black. Purple and dark yellow lines connect forward and reverse matches between the genomes, respectively. B Close up of the largest deletion on the chromosome of M. rhizoxinica and the syntenic region on the Vallotia chromosome. Genes are colored according to COG categories. Yellow: secondary metabolite biosynthesis; red: transposase; gray: unknown function; khaki: replication, recombination and repair; pink: lipid transport and metabolism; brown: protein turnover and chaperones; dark green: amino acid transport and metabolism; light green: cell envelope biogenesis; black: transcription. The figure was generated by Easyfig.Full size imageThe conservation of genome structure contrasts with the elevated number of transposases and inactive derivatives making up ~6% of the fungus-associated symbiont genome [68]. Transition to a host-restricted lifestyle is usually followed by a sharp proliferation of mobile genetic elements coupled with many genomic rearrangements [80,81,82]. However, mobile genetic elements get subsequently purged out of the genomes of strictly vertically transmitted symbionts via a mutational bias toward deletion and because of lack of opportunity for horizontal acquisition of novel genetic elements [71, 74]. Independent origins of the fungus and adelgid symbioses from free-living precursors would have likely resulted in extensive genome rearrangements due to the accumulation of mobile genetic elements, as seen, for instance, between different S. symbiotica strains in aphids [81]. In contrast to the fungus-associated symbiont, mobile elements are notably absent from the Vallotia genome, suggesting that they might have been lost early after the establishment of the adelgid symbiosis conserving high collinearity between the fungus- and adelgid-associated symbiont genomes. M. rhizoxinica is transmitted also horizontally among fungi and might have more exposure to foreign DNA, therefore at least part of the mobile elements could possibly be inserted into its genome after the host switch of the Vallotia precursor [61, 62].The observed high level of genome synteny between Vallotia and M. rhizoxinica genomes is consistent with the phylogenetic position of Vallotia interleaved within the clade of Rhizopus endosymbionts. This points toward a direct evolutionary link between these symbioses and a symbiont transition between the fungus and insect hosts.Shrinkage of the insect symbiont genomeDeletion of large genomic fragments—spanning many functionally unrelated genes—represents an important driving force of genome erosion especially at early stages of symbioses when selection on many functions is weak [3, 83]. Besides, gene loss also occurs individually and is ongoing, albeit at a much lower rate, even in ancient symbionts [75, 84, 85]. Both small and large deletions could be seen when comparing the Vallotia and M. rhizoxinica genomes. Several small deletions as small as one gene were observed sparsely in the entire length of the Vallotia genome within otherwise collinear regions. The largest genomic region missing from Vallotia encompassed 165 kbp on the M. rhizoxinica chromosome (Fig. 2B). The corresponding intergenic spacer was only 3843-bp long on the Vallotia genome between a phage shock protein and the Mfd transcription-repair-coupling factor, present both in Vallotia and M. rhizoxinica. Interestingly, this large genomic fragment included the large rhizoxin biosynthesis gene cluster (rhiIGBCDHEF), which is responsible for the production of rhizoxin, a potent antimitotic macrolide serving as a virulence factor for R. microsporus, the host of M. rhizoxinica [86]. A homologous gene cluster was also found in Pseudomonas fluorescens, and it has been suggested that it has been horizontally acquired by M. rhizoxinica [68, 86]. The rhi cluster is also present in M. endofungorum, therefore it was most likely already present in the genome of the common ancestor of the fungus- and adelgid-associated symbionts and got subsequently lost in Vallotia. Rhizoxin blocks microtubule formation in various types of eukaryotic cells [86, 87], thus the loss of this gene cluster in ancestral Vallotia could have contributed to the establishment of the adelgid symbiosis. However, this large deleted genomic region also contained several transposases and many other genes, such as argE and ilvA, coding for the final enzymes for ornithine and 2-oxobutanoate productions, which were located adjacent to each other at the beginning of this fragment. The largest deletion between the plasmids encompassed nearly 137 kbp of the megaplasmid of M. rhizoxinica and involved several non-ribosomal peptide synthetases (NRPS), insecticidal toxin complex (Tc) proteins, and a high number of transposases among others. M. rhizoxinica harbors 15 NRPS gene clusters [68] in total, all of which are absent in Vallotia. NRPSs are large multienzyme machineries that assemble various peptides, which might function as antibiotics, signal molecules, or virulence factors [88]. Insecticidal toxin complexes are bacterial protein toxins, which exhibit powerful insecticidal activity [89]. Two of such proteins are also present in the large deleted chromosomal region in close proximity to the rhizoxin biosynthesis gene cluster (Fig. 2B); however, their role in M. rhizoxinica remains elusive.The Vallotia genome encodes a subset of functions of the fungus-associated endosymbiontsThe number of protein-coding genes of Vallotia is less than one-third of those of M. rhizoxinica and M. endofungorum, although metabolic functions are already reduced in the fungus-associated endosymbionts compared to free-living Burkholderia species [68] (Figs. S4 and S5). When compared to the two genomes of the fungus-associated endosymbionts, only 53 proteins were specific to Vallotia (Fig. S6). All of these were short (on average 68 amino acid long) hypothetical proteins and most of them showed no significant similarity to other proteins in public databases. Whether these Vallotia-specific hypothetical proteins might be over-annotated/non-functional open reading frames or orphan genes with a yet unknown function [90, 91] needs further investigation. Four genes were present in Vallotia and M. rhizoxinica but were missing in M. endofungorum. These encoded for BioA and BioD in biotin biosynthesis, NagZ in cell wall recycling, and an MFS transporter. Fifteen genes, including, for instance, the MreB rod-shape-determining protein, glycosyltransferase and hit family proteins, genes in lipopolysaccharide, lipoate synthesis, and the oxidative pentose phosphate pathway, were shared between Vallotia and M. endofungorum only. The rest of the Vallotia genes, coding for 91% of all of its proteins, were shared among the fungus- and insect-associated endosymbionts.Comparing the genes present in both endosymbionts to those shared only by the fungus-associated endosymbionts (Fig. S7), we can infer selective functions maintained or lost during transition to insect endosymbiosis. Translation-related functions have been retained in the greatest measure in the group shared by all endosymbionts. Functions, where higher proportion of genes were specific to the fungus endosymbioses, were related to transcription, inorganic ion transport and metabolism, secondary metabolite biosynthesis, signal transduction, intracellular trafficking, secretion, vesicular transport, and defense mechanisms. Most of the proteins specific to either of the fungus-associated symbionts were homologous to transposases and integrases, transcriptional regulators, or had an unknown function.Fungus-associated endosymbionts encode a high number of transcriptional regulators (~5% of all genes in M. rhizoxinica) [68], but Vallotia has retained only a handful of such genes, which is a feature similar to other insect symbionts and might facilitate the overproduction of essential amino acids [75, 92].M. rhizoxinica is resistant against various β-lactams and has an arsenal of efflux pumps that might provide defense against antibacterial fungal molecules, the latter might also excrete virulence factors to the fungus cytosol (type I secretion) [68]. Besides, M. rhizoxinica encodes several genes for pilus formation; adhesion proteins; and type II, type III, and type IV secretion systems, which likely play a central role in host infection and manipulation in the bacteria–fungus symbiosis [68, 93, 94]. However, all of the corresponding genes are missing in Vallotia. Thus, neither of these mechanisms likely play a role in the adelgid symbiosis. Indeed, we could not even detect remnants of these genes in the Vallotia genome, except for a type II secretion system protein as a pseudogene. Loss of these functions is consistent with a strictly vertical transmission of Vallotia between host generations. Transovarial transmission likely does not require active infection mechanisms, and the endosymbionts rather move between the insect cells in a passive manner via an endocytic/exocytic vesicular route [12, 95]. In contrast, M. rhizoxinca is also able to spread horizontally among fungi and re-infect cured Rhizopus strains under laboratory conditions [61, 62].Differential reduction of metabolic pathways in Vallotia and Profftia
Although compared to their closest free-living relatives both Vallotia and Profftia have lost many genes in all functional categories, both retained the highest number of genes in translation-related functions (Fig. S4). Besides, functions related to cell division, nucleotide and coenzyme transport and metabolism, DNA replication and repair, posttranslational modification, and cell envelope biogenesis are reduced to a lesser extent in both endosymbionts. As a consequence, most of the genes of Vallotia and Profftia are devoted to translation and cell envelope biogenesis, which make up higher proportions of their genomes than in related bacteria (Fig. S5). Retention of a minimal set of genes involved in central cellular functions such as translation, transcription, and replication is a typical feature of reduced genomes, even extremely tiny ones of long-term symbionts [75]. However, ancient intracellular symbionts usually miss a substantial number of genes for the production of the cell envelope and might rely on host-derived membrane compounds [96,97,98].Based on pathway reconstructions, both Vallotia (Fig. S8) and Profftia (Fig. S9) have a complete gene set for peptidoglycan, fatty acid, and phospholipid biosynthesis and retained most of the genes for the production of lipid A, LPS core, and the Lpt LPS transport machinery. Besides, we found a partial set of genes for O antigen biosynthesis in the Vallotia genome. Regarding the membrane protein transport and assembly, both adelgid endosymbionts have the necessary genes for Sec and signal recognition particle translocation and the BAM outer membrane protein assembly complex. Profftia also has a complete Lol lipoprotein trafficking machinery (lolABCDE), which can deliver newly matured lipoproteins from the inner membrane to the outer membrane [99]. In addition, Profftia has a near-complete gene set for the Tol-Pal system; however, tolA has been pseudogenized suggesting an ongoing reduction of this complex. Further, both adelgid endosymbionts have retained mrdAB and mreBCD having a role in the maintenance of cell wall integrity and morphology [100, 101]. The observed well-preserved cellular functions for cell envelope biogenesis and integrity are consistent with the rod-shaped cell morphology of Profftia and Vallotia [14], contrasting the spherical/pleomorphic cell shape of ancient endosymbionts, such as Annandia in A. tsugae and Pineus species [10, 11, 15].Regarding the central metabolism, Vallotia lacks 6-phosphofructokinase but has a complete gene set for gluconeogenesis and the tricarboxylic acid (TCA) cycle. TCA cycle genes are typically lost in long-term symbionts but are present in facultative and evolutionarily recent obligate endosymbionts [79, 82, 102]. Interestingly, Vallotia does not have a recognized sugar transporter. Similarly to M. rhizoxinica [68], a glycerol kinase gene next to a putative glycerol uptake facilitator protein is present on its plasmid. However, the latter gene has a frameshift mutation and a premature stop codon in the first 40% of the sequence and whether it can still produce a functional protein remains unknown.Profftia can convert acetyl-CoA to acetate for energy but lacks TCA cycle genes, a feature characteristic to more reduced genomes, such as, for instance, Annandia in A. tsugae [23]. Profftia has import systems for a variety of organic compounds, such as murein tripeptides, phospholipids, thiamine, spermidine and putrescine, 3-phenylpropionate, and a complete phosphotransferase system for the uptake of sugars.NADH dehydrogenase, ATP synthase, and cytochrome oxidases (bo/bd-1) are encoded on both adelgid symbiont genomes. However, Vallotia is not able to produce ubiquinone and six pseudogenes in its genome indicate a recent inactivation of this pathway (Fig. S10).Profftia retained more functions in inorganic ion transport and metabolism, while Vallotia had a characteristically higher number of genes related to amino acid biosynthesis (see its function below) and nucleotide transport and metabolism (Fig. S4). For instance, Profftia can take up sulfate and use it for assimilatory sulfate reduction and cysteine production, and it has also retained many genes for heme biosynthesis (Fig. S9). However, it cannot produce inosine-5-phosphate and uridine 5’-monophosphate precursors for the de novo synthesis of purine and pyrimidine nucleotides and thus would need to import these compounds.Notably, although core genes in DNA replication and repair [70] are well preserved, multiple pseudogenes may indicate an ongoing erosion of DNA repair functions in the genomes. These include genes for the UvrABC nucleotide excision repair complex in both adelgid symbionts, helicases (recG, recQ), mismatch repair genes (mutL, mutS; the MutHLS complex is also missing in Profftia), and alkA encoding a DNA glycosylase in Vallotia.Taken together, their moderately reduced, gene-sparse genomes but still versatile metabolic capabilities support that Vallotia and Profftia are evolutionarily recently acquired endosymbionts. This is following their occurrence in lineages of adelgids, which likely diversified relatively recently, ~60 and ~47 million years ago, respectively, from the remaining clades of Adelgidae [8].
Vallotia and Profftia are both obligatory nutritional symbiontsComplementary functions in essential amino acid provisionVallotia and Profftia complement each other’s role in the essential amino acid synthesis, thus have a co-obligatory status in the A. laricis/A. tardus symbiosis (Fig. 3). Although Vallotia likely generates most essential amino acids, solely Profftia can produce chorismate, a key precursor for the synthesis of phenylalanine and tryptophan. Profftia is likely responsible for the complete biosynthesis of phenylalanine as it has a full set of genes for this pathway. It can also convert chorismate to anthranilate; however, further genes for tryptophan biosynthesis are only present in the Vallotia genome. Thus, Vallotia likely takes up anthranilate for tryptophan biosynthesis. Anthranilate synthase (trpEG), is subject to negative feedback regulation by tryptophan [103], thus partition of this rate-limiting step between the co-symbionts can enhance overproduction of the amino acid and might stabilize dual symbiotic partnerships at an early stage of coexistence. The production of tryptophan is partitioned between Vallotia and Profftia similarly as seen in other insect symbioses [77, 78, 104], and it is also shared but is more redundant between the Annandia and Pseudomonas symbionts of A. tsugae [23]. The Vallotia–Profftia system generally shows a lower level of functional overlap between the symbionts and is more unbalanced than the Annandia–Pseudomonas association. In the latter, redundant genes are present also in the synthesis of phenylalanine, threonine, lysine, and arginine, and Annandia can produce seven and the Pseudomonas partner five essential amino acids with the contribution of host genes [23].Fig. 3: Division of labor in amino acid biosynthesis and transport between Vallotia and Profftia showing co-obligatory status of endosymbionts of A. laricis/tardus.Amino acids produced by Vallotia and Profftia are shown in blue and red, respectively. Bolded texts indicate essential amino acids. The insect host likely supplies ornithine, homocysteine, 2-oxobutanoate, and glutamine. Other compounds that cannot be synthesized by the symbionts are shown in gray italics.Full size imageThe Vallotia genome encodes for all the enzymes for the synthesis of five essential amino acids (histidine, leucine, valine, lysine, threonine). ArgG and tyrB among the essential amino acid synthesis-related genes are only present on the plasmid of Vallotia, which might be a reason that the plasmid is still part of its genome. However, neither of the endosymbionts can produce ornithine, 2-oxobutanoate, and homocysteine de novo, which are key for the biosynthesis of arginine, isoleucine, and methionine, respectively. The corresponding functions are also missing from the Annandia–Pseudomonas system [23]. These compounds are thus likely supplied by the insect host, as seen for instance in aphids, mealybugs, and psyllids, where the respective genes are present in the insect genomes and are typically overexpressed within the bacteriome [97, 105, 106]. The metC and argA genes are still present as pseudogenes in Vallotia suggesting a recent loss of these functions in methionine and arginine biosynthesis, respectively.In most plant sap-feeding insects harboring a dual symbiotic system, typically the more ancient symbiont provides most of the essential amino acids [77, 107]. Given its prominent role in nutrient provision and its presence in both larch- and Douglas fir-associated adelgids, Vallotia might be the older symbiont. Loss of functions in chorismate and anthranilate biosynthesis might have led to the fixation of Profftia in the system.Vallotia and Profftia have more redundant functions in non-essential amino acid production (Fig. 3). Only Profftia can produce cysteine and tyrosine, while none of the symbionts can build up glutamine, thus this latter amino acid is likely supplied by the insect bacteriocytes.The presence of relevant transporters can complement missing functions in amino acid synthesis (Fig. 3). For instance, Profftia has a high-affinity glutamine ABC transporter and three symporters (BrnQ, Mtr, TdcC), which can import five among the essential amino acids that can be produced by Vallotia. Vallotia might excrete isoleucine, valine, and leucine via AzICD, a putative branched-chain amino acid efflux pump [108], and these amino acids could be taken up by Profftia via BrnQ and would be readily available also for the insect host.B vitamin provision by Vallotia
Regarding the B vitamin synthesis, Vallotia is likely able to produce thiamine (B1), riboflavin (B2), pantothenate (B5), pyridoxine (B6), biotin (B7), and folic acid (B9) (Fig. S11). Although Vallotia misses some genes of the canonical pathways, alternative enzymes and host-derived compounds might bypass these reactions, as detailed in the Supplementary Material. Profftia has only a few genes related to B vitamin biosynthesis. Three pseudogenes (ribAEC) in the riboflavin synthesis pathway indicate that these functions might have been lost recently in this symbiont (Fig. S11). More

1.Lomolino, M. V. Elevation gradients of species-density: historical and prospective views. Glob. Ecol. Biogeogr. 10, 3–13 (2001).Article 

Google Scholar 
2.McCain, C. M. Global analysis of reptile elevational diversity. Glob. Ecol. Biogeogr. 19, 541–553 (2010).
Google Scholar 
3.Quintero, I. & Jetz, W. Global elevational diversity and diversification of birds. Nature 555, 246–250 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
4.Orme, C. D. L. et al. Global hotspots of species richness are not congruent with endemism or threat. Nature 436, 1016–1019 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
5.Rahbek, C. et al. Humboldt’s enigma: what causes global patterns of mountain biodiversity? Science 365, 1108–1113 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
6.Wiens, J. J., Parra-Olea, G., García-París, M. & Wake, D. B. Phylogenetic history underlies elevational biodiversity patterns in tropical salamanders. Proc. R. Soc. B 274, 919–928 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
7.Pigot, A. L., Trisos, C. H. & Tobias, J. A. Functional traits reveal the expansion and packing of ecological niche space underlying an elevational diversity gradient in passerine birds. Proc. R. Soc. B 283, 20152013 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
8.Körner, C. & Spehn, E. M. (eds) Mountain Biodiversity: A Global Assessment (CRC Press, 2002).9.Merckx, V. S. F. T. et al. Evolution of endemism on a young tropical mountain. Nature 524, 347–350 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
10.Fjeldsa, J. Geographical patterns for relict and young species of birds in Africa and South America and implications for conservation priorities. Biodivers. Conserv. 3, 207–226 (1994).Article 

Google Scholar 
11.Jetz, W., Rahbek, C. & Colwell, R. K. The coincidence of rarity and richness and the potential signature of history in centres of endemism. Ecol. Lett. 7, 1180–1191 (2004).Article 

Google Scholar 
12.Weir, J. T. Divergent timing and patterns of species accumulation in lowland and highland Neotropical birds. Evolution 60, 842–855 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
13.Hughes, C. & Eastwood, R. Island radiation on a continental scale: exceptional rates of plant diversification after uplift of the Andes. Proc. Natl Acad. Sci. USA 103, 10334–10339 (2006).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
14.Cozzarolo, C.-S. et al. Biogeography and ecological diversification of a mayfly clade in New Guinea.Front. Ecol. Evol. 7, 233 (2019).Article 

Google Scholar 
15.Davies, T. J., Savolainen, V., Chase, M. W., Moat, J. & Barracloug, T. G. Environmental energy and evolutionary rates in flowering plants. Proc. R. Soc. B 271, 2195–2200 (2004).PubMed 
PubMed Central 
Article 

Google Scholar 
16.Graves, G. R. Linearity of geographic range and its possible effect on the population structure of andean birds. Auk 105, 47–52 (1988).Article 

Google Scholar 
17.Janzen, D. H. Why mountain passes are higher in the tropics. Am. Nat. 101, 233–249 (1967).Article 

Google Scholar 
18.Cai, T. et al. What makes the Sino-Himalayan mountains the major diversity hotspots for pheasants? J. Biogeogr. 45, 640–651 (2018).Article 

Google Scholar 
19.Rana, S. K., Gross, K. & Price, T. D. Drivers of elevational richness peaks, evaluated for trees in the east Himalaya. Ecology 100, e02548 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
20.Rahbek, C. et al. Building mountain biodiversity: geological and evolutionary processes. Science 365, 1114–1119 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
21.Ribas, C. C., Moyle, R. G., Miyaki, C. Y. & Cracraft, J. The assembly of montane biotas: linking Andean tectonics and climatic oscillations to independent regimes of diversification in Pionus parrots. Proc. R. Soc. B 274, 2399–2408 (2007).PubMed 
PubMed Central 
Article 

Google Scholar 
22.Schwery, O. et al. As old as the mountains: the radiations of the Ericaceae. N. Phytologist 207, 355–367 (2015).Article 

Google Scholar 
23.Bates, J. M. & Zink, R. M. Evolution into the Andes: molecular evidence for species relationships in the genus Leptopogon. Auk 111, 507–515 (1994).
Google Scholar 
24.Roy, M. S. Recent diversification in African greenbuls (Pycnonotidae: Andropadus) supports a montane speciation model. Proc. R. Soc. B 264, 1337–1344 (1997).PubMed Central 
Article 

Google Scholar 
25.Garcia-Moreno, J. et al. Pre-Pleistocene differentiation among chat-tyrants. Condor 100, 629–640 (1998).Article 

Google Scholar 
26.Oliveros, C. H. et al. Earth history and the passerine superradiation. Proc. Natl Acad. Sci. USA 116, 7916–7925 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
27.Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444–448 (2012).CAS 
Article 

Google Scholar 
28.Title, P. O. & Rabosky, D. L. Tip rates, phylogenies and diversification: what are we estimating, and how good are the estimates? Methods Ecol. Evol. 10, 821–834 (2019).Article 

Google Scholar 
29.Herrera-Alsina, L., van Els, P. & Etienne, R. S. Detecting the dependence of diversification on multiple traits from phylogenetic trees and trait data. Syst. Biol. 68, 317–328 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
30.Weir, J. T. & Schluter, D. The latitudinal gradient in recent speciation and extinction rates of birds and mammals. Science 315, 1574–1576 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
31.Derryberry, E. P. et al. Lineage diversification and morphological evolution in a large-scale continental radiation: the Neotropical ovenbirds and woodcreepers (Aves: Furnariidae). Evolution 65, 2973–2986 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
32.Fjeldså, J., Bowie, R. C. K. & Rahbek, C. The role of mountain ranges in the diversification of birds. Annu. Rev. Ecol. Evol. Syst. 43, 249–265 (2012).Article 

Google Scholar 
33.Chazot, N. et al. Into the Andes: multiple independent colonizations drive montane diversity in the Neotropical clearwing butterflies Godyridina. Mol. Ecol. 25, 5765–5784 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
34.Elias, M. et al. Out of the Andes: oatterns of diversification in clearwing butterflies. Mol. Ecol. 18, 1716–1729 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
35.McGuire, J. A., Witt, C. C., Altshuler, D. L. & Remsen, J. V. Phylogenetic systematics and biogeography of hummingbirds: Bayesian and maximum likelihood analyses of partitioned data and selection of an appropriate partitioning strategy. Syst. Biol. 56, 837–856 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
36.Brumfield, R. T. & Edwards, S. V. Evolution into and out of the Andes: a Bayesian analysis of historical diversification in Thamnophilus antshrikes. Evolution 61, 346–367 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
37.Zhou, C. et al. Genome-wide analysis sheds light on the high-altitude adaptation of the buff-throated partridge (Tetraophasis szechenyii). Mol. Genet. Genom. 295, 31–46 (2020).CAS 
Article 

Google Scholar 
38.Xu, Z., He, J. & Wang, J. Hypoxia affects the resistance of Scylla paramamosain to Vibrio alginolyticus via changes of energy metabolism. Aquac. Rep. 19, 100565 (2021).Article 

Google Scholar 
39.Storz, J. F., Scott, G. R. & Cheviron, Z. A. Phenotypic plasticity and genetic adaptation to high-altitude hypoxia in vertebrates. J. Exp. Biol. 213, 4125–4136 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
40.Scott, G. R. Elevated performance: the unique physiology of birds that fly at high altitudes. J. Exp. Biol. 214, 2455–2462 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
41.Projecto-Garcia, J. et al. Repeated elevational transitions in hemoglobin function during the evolution of Andean hummingbirds. Proc. Natl Acad. Sci. USA 110, 20669–20674 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
42.Scott, G. R. et al. Molecular evolution of cytochrome C oxidase underlies high-altitude adaptation in the bar-headed goose. Mol. Biol. Evol. 28, 351–363 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
43.Schumm, M., White, A. E., Supriya, K. & Price, T. D. Ecological limits as the driver of bird species richness patterns along the east Himalayan elevational gradient. Am. Nat. 195, 802–817 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
44.Malpica, A., Covarrubias, S., Villegas-Patraca, R. & Herrera-Alsina, L. Ecomorphological structure of avian communities changes upon arrival of wintering species. Basic Appl. Ecol. 24, 60–67 (2017).Article 

Google Scholar 
45.Etienne, R. S. et al. A minimal model for the latitudinal diversity gradient suggests a dominant role for ecological limits. Am. Nat. 194, E122–E133 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
46.Freeman, B. G., Scholer, M. N., Ruiz-Gutierrez, V. & Fitzpatrick, J. W. Climate change causes upslope shifts and mountaintop extirpations in a tropical bird community. Proc. Natl Acad. Sci. USA 115, 11982–11987 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
47.Bouckaert, R. et al. BEAST 2: a software platform for Bayesian evolutionary analysis. PLoS Comput. Biol. 10, e1003537 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
48.Braun, E. L., Cracraft, J. & Houde, P. in Avian Genomics in Ecology and Evolution (ed. Kraus, R. H. S.) 151–210 (Springer, 2019).49.del Hoyo, J., Elliott, A., Sargatal, J., Christie, D. A. & Kirwan, G. Handbook of the Birds of the World (Lynx Edicions, 2016).50.Chapman, F. M. et al. The distribution of bird life in Ecuador: a contribution to a study of the origin of Andean bird-life. Bull. Am. Mus. Nat. Hist. 55, 1–784 (1926).
Google Scholar 
51.Maddison, W. P., Midford, P. E. & Otto, S. P. Estimating a binary character’s effect on speciation and extinction. Syst. Biol. 56, 701–710 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
52.Beaulieu, J. M. & O’Meara, B. C. Detecting hidden diversification shifts in models of trait-dependent speciation and extinction. Syst. Biol. 65, 583–601 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
53.Harmon, L. J., Weir, J. T., Brock, C. D., Glor, R. E. & Challenger, W. GEIGER: investigating evolutionary radiations. Bioinformatics 24, 129–131 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
54.Daru, B., Karunarathne, P. & Schliep, K. phyloregion: R package for biogeographic regionalization and spatial conservation. Methods Ecol. Evol. 11, 1483–1491 (2020).Article 

Google Scholar  More