Skip to main content
Download PDF

Genomics-based identification of a cold adapted clade in Deinococcus

BMC Biology volume 22, Article number: 145 (2024) Cite this article

Abstract

Background

Microbes in the cold polar and alpine environments play a critical role in feedbacks that amplify the effects of climate change. Defining the cold adapted ecotype is one of the prerequisites for understanding the response of polar and alpine microbes to climate change.

Results

Here, we analysed 85 high-quality, de-duplicated genomes of Deinococcus, which can survive in a variety of harsh environments. By leveraging genomic and phenotypic traits with reverse ecology, we defined a cold adapted clade from eight Deinococcus strains isolated from Arctic, Antarctic and high alpine environments. Genome-wide optimization in amino acid composition and regulation and signalling enable the cold adapted clade to produce CO2 from organic matter and boost the bioavailability of mineral nitrogen.

Conclusions

Based primarily on in silico genomic analysis, we defined a potential cold adapted clade in Deinococcus and provided an updated view of the genomic traits and metabolic potential of Deinococcus. Our study would facilitate the understanding of microbial processes in the cold polar and alpine environments.

Background

Understanding the genetic make-up of microbes in a given environmental condition is one of the fundamental tasks of environmental microbiology and climate change microbiology in particular [1,2,3]. Polar and alpine environments are more sensitive to climate change, and microbes in these environments feedback and amplify the effects of climate change, for example, by producing and consuming greenhouse gases like CO2, CH4 and N2O [4, 5]. As important as polar and alpine microbes are, there is no unifying concept of polar and alpine microbiology yet. One challenge is to sort the diversity of polar and alpine microbes into ecologically meaningful units and to define the cold-adapted ecotypes.

The cold polar and alpine environments are colonised by or preserve a high diversity of microbes [6, 7]. These can be recovered by traditional pure-cultivation or obtained by direct sequencing from environmental samples, providing isolates and metagenomes [8, 9]. Individual genomes, MAGs and metagenomes have been used to understand the phenotypic and genetic characteristics of microbial adaptation to polar and alpine environments [10, 11].

Unlike temperate or thermal environments, the low temperature condition of polar and alpine environments selects psychrophiles; on the other hand, low temperature conditions are also ideal for the preservation of DNA and cells [12]. For example, most rDNA clones from the snow around Russian Antarctic stations were not alive and/or active [13]. And some microbes could also survive through phenotypic plasticity without genetic change; for example, snow-bacteria with different genetic backgrounds from glaciers on the Tibetan Plateau shifted their growth temperature downward to adapt to cold [14]. Although mesophiles could be phenotypically conserved to psychrophiles in a short time, phenotypic plasticity in general does not always facilitate genetic adaptation [15].

The bacterial genus Deinococcus (phylum Deinococcus-Thermus; class Deinococcus; order Deinococcales; family Deinococcaceae) is a coccoid or rod-shaped, non-sporulating bacterium characterised by a high tolerance to ionising radiation, UV radiation, desiccation, oxidants, low temperature and many other harsh environmental factors [16, 17]. Deinococcus have been isolated from a wide range of habitats such as processed meat, soil, desert, vegetation, freshwater, faeces and thermal springs, spanning from the mid-latitude regions to polar and high alpine reaches [18, 19]. Strains of Deinococcus derived from different sources are mixed in the phylogenetic tree, making it difficult to define its natural habitat ecotype. One case is for identification of the cold adapted Deinococcus ecotype, as the eight Deinococcus strains isolated from Arctic, Antarctic and high alpine environments located in three different positions in the phylogenetic tree. We speculate that with the well-established genomic proxy of cold adaptation and multiple genome sequences and the associated phenotypic data, it is now possible to define the polar and alpine-environment ecotype of Deinococcus [18,19,20].

In this study, we sequenced two genomes and utilised the existence of 83 Deinococcus genomes to identify the polar and alpine ecotype from Deinococcus. Combining genomic traits, sources of isolation and physiological information, we identified a clade that includes four Antarctic isolates and showed consistency in genomic and phenotypic traits based on reverse ecology theory.

Results

Phylogeny and general genomic features of Deinococcus

The phylogeny of Deinococcus showed that most of the isolates from different habitats formed mixed clusters with each other (Fig. 1, Additional file 1: Table S1). For example, isolates from polar and high alpine environments (i.e. the Arctic, Antarctic and Mount Evans) were distributed across the root, middle and top of the sorted phylogenomic tree (Fig. 1). The Arctic isolate, D. arcticus OD32 (referred to as polar 1 in Fig. 1), was in the middle of a clade containing strains from freshwater, evaporation cores and vegetation (Fig. 1 and Additional file 1: Table S1). The six Antarctic isolates with one high alpine derived strain in the root and middle parts of the tree, termed polar groups 2 and 3 in Fig. 1. The four isolates from polar 2 (D. marmoris PAMC 26562, D. marmoris DSM 12784, Deinococcus sp. AJ005AJ005, D. frigens DSM 12807) and the three from polar 3 (D. psychrotolerans S14-83, D. detaillensis H1 and D. alpinitundrae LMG 24283) were clustered together forming monophyletic clades (Fig. 1). Not only the polar and alpine strains were distantly distributed in the phylogenomic tree, the MAGs (Deinococcus sp. bin2 and Deinococcus sp. bin10) from the same sample were also distributed separately in the tree and had a large phylogenetic distance (Fig. 1). This situation paralleled the finding that Exiguobacterium MAGs from the New York subway system belonged to completely different ecotypes [21].

Fig. 1
figure 1

Deinococcus phylogeny and distribution of Arctic and Antarctic isolates in the tree rooted by Meiothermus cerbereus DSM 11376 and Meiothermus sp. QL-1. The tree was sorted with increasing node order; the Arctic isolate (D. arcticus OD32) is in the upper part of the tree; four Antarctic isolates are in the middle (D. marmoris PAMC 26562, D. marmoris DSM 12784, Deinococcus sp. AJ005AJ005, D. frigens DSM 12807); two Antarctic isolates and one alpine isolate at the bottom (D. psychrotolerans S14-83, D. detaillensis H1, D. alpinitundrae LMG 24283) of the tree. These three clades are referred to as the polar 1 (n = 1), polar 2 (n = 4) and polar 3 (n = 3) for brevity in the following sections. The four isolates from Polar 2 and the two from Polar 3 formed monophyletic clades. All the other genomes were set as non-polar group. The maximum likelihood phylogenomic tree was constructed using PhyloPhlAn 3.0. Dots at nodes indicate bootstrap. Bar 0.2 indicates accumulated changes per amino acid

Full size image

General genomic features and pan-genome of Deinococcus

Whole-genome similarity metric showed that the ANI (average nucleotide identity) and AAI (average amino acid identity) between the 85 genomes were 69.18 ~ 99.49 and 55.87 ~ 99.18, respectively (Fig. 2a). The genome sizes of Deinococcus ranged from 2.77 Mb (D. murrayi DSM 11303, isolated from a hot spring) to 6.65 Mb (D. hopiensis KR-140, isolated from desert soil), with a mean value of 4.15 Mb. Genome sizes did not differ significantly between the polar and non-polar groups (Fig. 2b). Although the metagenome assembled genome (Deinococcus sp. bin10) had the smallest calculated genome size of 2.46 Mb, it was not used to indicate the minimum genome size of Deinococcus, because genome completeness was estimated based on housekeeping marker genes by CheckM, which may not fully reflect the size of the non-pure culture derived genome [22].

Fig. 2
figure 2

Genetic relatedness and pangenome of Deinococcus. a Distribution and correlation of ANI and AAI. b Comparison of genome size. c Rarefaction curve for the accumulation of pan genes. d Rarefaction curve for the reduction of core genes. e Summary statistics of the 46,923 pan genes of Deinococcus – cloud genes: 0% ≦ strains < 15%; strict core genes: strains = 100%; shell genes: 15% ≦ strains < 95%; soft core genes: 95% ≦ strains < 99%. The curves were fitted to median values of 1000 permutations. The dark lines in panels a and b indicate median values and the shading indicates the 95% confidence interval. α = 0.443 ± 0.006 in a and α = 0.355 ± 0.018 in b, indicating an open pan- and core genome of Deinococcus

Full size image

The 85 Deinococcus genomes constituted an ‘open’ pan-genome, as indicated by the alpha value of 0.443 ± 0.006, which, being < 1, is indicative of this feature (Fig. 2c) [23]. It was predicted that approximately 325 genes will be found once a new genome has been added to the pan-genome. Meanwhile, around 3 core genes will be excluded following the addition of a new genome (Fig. 2d). Of the 46,923 genes in the pan-genome, most (89.69%) were shared by < 15% of isolates, forming the cloud genes (Fig. 2e). The shell genes (present in 15% ≦ isolates < 95%) made up 7.74% of the pan-genome, and the resting 2.57% was composed of the core genes (both strict and soft genes, present in ≥ 95% strains, Fig. 3c).

Fig. 3
figure 3

Cladogram and dot plots showing the trends of genome-wide GC content, intergenic GC content, rRNA GC content and Arg/Lys ration in Deinococcus. A clear drop in the Arg/Lys ration with respect to close neighbours (the six genomes, Deinococcus sp. D7000, D. radiopugnans DY59, D. radiopugnans ATCC 19172, D. humi DSM 27939, D. aerolatus JCM 15442 and D. aerophilus JCM 15443, in the same clade of polar 2 as shown in Fig. 1) of polar group 2 was indicated using an arrow

Full size image

The genome-wide GC (guanine + cytosine) contents of Deinococcus varied dramatically, with the lowest being 55.22% (D. misasensis DSM 22328, isolated from freshwater), the highest being 70.60% (D. actinosclerus BM2, isolated from soil) and the average being 66.94% (Fig. 3, Table S1). Genomes with the lowest GC content (including genomic DNA, intergenic DNA and rRNA DNA) were in the lower part of the phylogenomic cladogram, highlighting a trend that the variation in genome GC content converged from the root to the top of the tree, sorted by increasing node order (Fig. 3). Such a trend was not seen in the distribution of complete metabolic pathways along the phylogenomic cladogram (Additional file 2: Fig. S1). The predicted complete metabolic pathways of Deinococcus ranged from 206 to 300, and isolates that conducted more metabolic reactions were concentrated in the upper part of the tree (Fig. S1). In the upper part of the tree, the two main peaks in the number of metabolic pathways each contained four and six isolates derived from a diverse range of environments such as soil, sediment, vegetation and seawater (Additional file 2: Fig. S1). Apart from the fluctuation in GC content, there were troughs in GC content in polar group 2, and there is a clear decrease in Arg/Lys ratio in this group (Fig. 3).

Signature of cold adaptation of Deinococcus isolates from polar environments

Compared with the non-polar group, a significant decrease in the genome-wide Arg/Lys ratio was identified in polar group 2 and 3 (Fig. 4a, Wilcoxon test, p < 0.05), but not in polar group 1. When compared with their closely related genomes in the same clade, the trend of increasing Arg/Lys ratio was still observed for polar group 2 (Fig. 4b, Wilcoxon test, p < 0.05), but not in polar group 3 (Fig. 4c, Wilcoxon test, p > 0.05). The decrease in genomic GC content was significant in polar group 2 compared to all non-polar genomes and their closely related genomes in the same clade. For the late case, the significance was cancelled out in polar group 3, similar to the situation for the Arg/Lys ratio (Additional file 2: Fig. S2). Except for polar group 1, both polar groups 2 and 3 had lower minimum, optimum and maximum growth temperatures than the reference group (Fig. 4d–f, Wilcoxon test, p < 0.05). Then, three groups of Deinococcus strains isolated from cold environments were featured out: polar group 1 had not processed plastic or genetic changes from ancestors; polar group 2 had processed plastic and genetic changes, or direct from Deinococcus ancestor via genetic selection; polar group 3 had processed plastic changes but not genetic changes for cold adaptation (Fig. 4g).

Fig. 4
figure 4

Comparison of Arg/Lys ratio and growth temperature profile between Deinococcus groups. a Comparison of Arg/Lys ratio between the polar groups and the reference group. b Comparison of Arg/Lys ratio between polar group 2 and six non-polar genomes in the same clade. c Comparison of Arg/Lys ratio between polar group 3 and four non-polar genomes in the same clade; comparison of d Minimum. e Optimum and f Maximum growth temperature profile between the polar groups and the non-polar group. ‡This non-polar group including six genomes, Deinococcus sp. D7000, D. radiopugnans DY59, D. radiopugnans ATCC 19172, D. humi DSM 27939, D. aerolatus JCM 15442 and D. aerophilus JCM 15443; †this non-polar group including four genomes, D. irradiatisoli 17bor-2, Deinococcus sp. bin10, and D. aquiradiocola JCM 14371; *p < 0.05, **p < 0.01, NS, not significant. g Three groups of Deinococcus strains isolated from cold environments: polar group 1 had not processed plastic (p) or genetic change from ancestors (o); polar group 2 had plastic and genetic change or evolved directly from ancestor (grey dotted line); polar group 3 had processed plastic change but not genetic change

Full size image

Biogeochemical cycle processes of Deinococcus

The polar isolates had a similar overall distribution of functional categories, with most of their genomes devoted to ‘carbohydrates metabolism’, ‘amino acids and derivatives metabolism’ and ‘protein metabolism’ (Fig. 5). The function category ‘regulation and cell signalling’ was significantly enriched in polar group 2 compared to other Deinococcus, with an average of 31 vs. 16 (Fig. 5).

Fig. 5
figure 5

Cladogram and heatmap showing the distribution of functional categories of Deinococcus. The left panel is the cladogram with grouping profile of Deinococcus, the upper panel is bar chart showing average genes in each category of Deinococcus and the middle panel is heatmap showing the distribution of functional categories of each Deinococcus genome

Full size image

Recently developed standardised tools for analysing genomic data allowed us to read the genomic blueprints of Deinococcus and reconstruct their roles in biogeochemical processes [24]. The carbon cycle scheme indicated that Deinococcus are typical heterotrophic microorganisms that use organic carbon, including acetate and ethanol, as their main energy source (Fig. 6a). Seven isolates, including one from polar group 2, were predicted to be able to oxidise CH4, four of which (D. apachensis DSM 19763, D. aerius TR0125, D. planocerae XY-FW106 and Deinococcus sp. SYSU M49105) were clustered together in the phylogenomic tree (Fig. 1 and Fig. 6a). Most of the reactions in the nitrogen cycle were absent in Deinococcus (shared less than 5% of isolates), but over 60% (53/85) of the isolates were predicted to be able to conduct nitrite ammonification, all the polar 2 isolates were positive in this reaction (Fig. 6b). In the sulphur cycle, Deinococcus were predicted to be able to oxidise SO32− to SO42− and redox SO42− back to SO32−, and nine isolates were likely to be able to conduct the oxidisation of S0 to SO32− (Fig. 6c). Of the other cycles besides carbon, nitrogen and sulphur, we found that Deinococcus can drive the transformation between As5+ and As3+, with ~ 80% of isolates conducting the reduction and ~ 40% the oxidisation (Fig. 6d).

Fig. 6
figure 6

Summary scheme of carbon, nitrogen, sulphur and arsenate cycling processes of Deinococcus. Each arrow represents a single transformation to step within a cycle; arrows in red indicate at least one isolate was positive for the reactions, and red parts of the pie charts indicate the percentage of isolates that can conduct these reactions. a Carbon cycle scheme showing that Deinococcus are typical heterotrophic bacteria characterised by being capable of utilizing organic carbon, fermenting ethanol and oxidising acetate, but not fixing CO2. Seven of the 85 isolates were predicted to be able to utilise CH4. b Nitrogen cycle scheme. c Sulphur cycle scheme. The chemical states of the nitrogen or sulphur atoms in b and c are indicated by the numbers in parentheses on the right of the nitrogen or sulphur-containing compounds. d Arsenate oxidation–reduction scheme showing that Deinococcus can drive the transformation between As5+ and As3+. Yellow, blue and green dots represent genomes from polar 1, 2 and 3 that were positive in the relevant reactions

Full size image

Discussion

Genomic features of Deinococcus and defining the cold adapted clade

In this study, we present an updated view of the genomic features of the genus Deinococcus. The first reported genome of Deinococcus was that of D. radiodurans R1, which is about 3.28 Mb in size and has a 66.94% GC content [25]. From the current dataset containing 85 nonredundant high-quality genomes contributed by the community worldwide, it was found that Deinococcus has an open pan-genome and a strict core genome with 733 genes. The genome size ranging from 2.77 Mb to 6.65 Mb and GC content ranging from 55.22 to 70.60%. Generally, in harsher environments (e.g. dry, cold and hot), the genome is expected to be smaller in size and more compact with functional genes [26, 27]. This trend was identified with strain D. murrayi DSM 11303 isolated from a hot spring, which had the smallest genome size of 2.77 Mb, but not in the polar and alpine genomes. Trends in genome size of cold adapted bacteria are not yet clear, and there is currently no general trend in genome GC content and indicator genes of cold adaptation [28, 29].

However, the optimisation of genome-wide amino acid composition to achieve low temperature activity (e.g. reduction of Arg and increase of Lys) seems to apply to all well-defined cold adapted bacteria and archaea across broad taxa [11, 21, 30]. This is reasonable because bacterial cells are at complete thermal equilibrium with their environment; whatever the other environmental conditions (nutritional status, salinity and pH, etc.) in which psychrophiles live, all the proteins must be adapted to the cold to enable an overall level of activity that is sufficient for growth at low temperature [29]. According to the principle of reverse ecology, the bacterial genome contains identifiable adaptation traits to its native environment. Using the Arg/Lys ratio as a proxy for cold adaptation, four genomes (D. marmoris PAMC 26562, D. marmoris DSM 12784, Deinococcus sp. AJ005AJ005, D. frigens DSM 12807) in polar 2 showed a clear signature of cold adaptation. The Arctic isolate D. arcticus OD32 showed no significant change in the Arg/Lys ratio. Although the polar group 3 (D. psychrotolerans S14-83, D. detaillensis H1, D. alpinitundrae LMG 24283) had a lower Arg/Lys ratio in comparison to all the non-polar genomes, this trend might only be caused by phylogenetic divergence [31], as the trend was not significant when comparing polar group 3 with its closely related genomes in the same clade. This is further supported by the fact that the distribution of genome-wide GC content also showed the same profile, although whether the decrease in genome GC content is a good proxy for cold adaption remains to be validated.

The cold adaptation revealed by the genomic proxy agrees well with the growth temperature profiles, as polar 2 strains had lower minimum, optimum and maximum growth temperatures than the non-polar genomes. With the exception of the Arctic isolate, the decrease in growth temperature was also observed in polar group 3, but the phenotype did not match the genotype. Both genetic change and phenotypic plasticity play important roles in the environmental adaptation of bacteria. From the evidence of genomic and phenotypic traits, it is suggested that polar group 2 represents the true cold-adapted ecological unit in Deinococcus, while phenotypic plasticity facilitates the survival of polar group 3 in cold environments. Indeed, the trend of a downward shift in the growth temperature range was observed for most of the glacial bacteria [9, 14]. A recent study found that temperature adaptation had a strong phylogenetic signal and was vertically inherited in Cryobacterium, suggesting that the cold adapted lineages would form monophyletic clade [32].

Based on the observed phenotypic and genomic clues and ecological principle, we propose that the polar group 2 (D. psychrotolerans S14-83, D. detaillensis H1, D. alpinitundrae LMG 24283) is the genetically cold adapted clade in Deinococcus. The origin of polar group 2 may have undergone phenotypic adaptation followed by genetic adaptation, and it is also possible that it evolved directly from a mesophilic ancestor via genetic selection; but the true evolutional trajectory of the cold adapted lineage remains to be verified. For polar group 3, the phenotypic plasticity of downshifting growth temperature was not a stepping stone to its genetic adaptation to low temperature conditions. Vertically inherited genetic material and environmental conditions jointly influence microbial phenotype, and our study presents a case for distinguishing between the two [33, 34].

Ecology of the cold adapted Deinococcus clade

Universal pathways or cellular processes for cold adaptation have not yet been identified. However, the definition of cold adapted clade (only refer to the polar group 2 hereafter) would enable the determining the genomic traits of Deinococcus-specific pathways or functions. The cold adapted clade had a similar functional distribution which was constrained by its taxonomy [35]. However, this cold adapted clade was significantly enriched in genes that involved in regulation and cell signalling, suggesting that the regulation of cellular processes is also important while the low temperature protein activity been achieved via amino acid optimization [11, 21, 36].

We then constructed the genetic potentiality of carbon, nitrogen, sulphur and arsenate cycle driven by Deinococcus to investigate the role of the cold adapted clade in biogeochemical cycling. The carbon cycle scheme indicated that the cold adapted clade are typical heterotrophic microorganisms that use organic carbon, including acetate and ethanol, as their main energy source, and are not able to fix nitrogen. The cold adapted Deinococcus would mainly act as CO2 producers in the polar and alpine environments. In the nitrogen cycle, the cold adapted clade is characterised as all of the four strains being able to conduct nitrite ammonification, while this function was shared by about 60% of other Deinococcus, suggesting that the cold adapted Deinococcus were enhanced in increasing the bioavailability of nitrogen, a common good of the community [37]. In the sulphur cycle, Deinococcus were predicted to be able to conduct the transformation between SO32− and SO42−, and small proportion (10%, not including the cold adapted strains) were likely to be able to oxidise S0 to SO32−. In the arsenate cycle, Deinococcus were predicted to be more preferred to reduce As5+ to As3+, suggesting that the mesophilic and psychrophilic Deinococcus are the protentional contributor of acutely toxic arsenate in temperate and cold environments [38]. In addition to the clade-specific functions, the other main ecological signature of cold adapted Deinococcus is that they could perform the main function of this genus at low temperatures; otherwise, the function would be lost according to the black queen and genome streamlining theories [37, 39, 40].

Conclusion

In this study, we ordered the eight stains isolated from polar and alpine environment into three groups based on their genetic and phenotypic response to low temperature conditions: the polar group 1, refereeing to strain D. arcticus OD32, which processed no genetic chance and no phenotypic change; the polar group 2, refereeing to strains D. marmoris PAMC 26562, D. marmoris DSM 12784, Deinococcus sp. AJ005AJ005, D. frigens DSM 12807, which processed genetic change and downshifting growth temperature; the polar group 3, refereeing to strains D. psychrotolerans S14-83, D. detaillensis H1, D. alpinitundrae LMG 24283, which processed no genetic chance but downshifting growth temperature. Hence, we propose that polar group 2 represents the cold adapted ecotype of Deinococcus. Being able to cluster multiple isolates into cohesive ecological units facilitates the identification of genomic traits that are statistically associated with given environmental conditions [1, 21], which in turn will allow for a better understanding of how microbial communities respond to different environmental conditions in a changing world.

Methods

Strain isolation and genome sequencing

Strain D. psychrotolerans S14-83 was isolated from soil on the South Shetland Islands, Antarctica (62° 22′ 34″ S, 59° 42′ 34″ W) [17]. Strain D. rufus W37 was recovered from the type material deposited by Wang et al. [41] in CCTCC (China Center for Type Culture Collection). Genomic DNA was extracted from isolates using a TIANamp Bacteria DNA Kit (Tiangen, Beijing) following the manufacturer’s instructions. Using genomic DNA (extracted as described above) for the two isolates, paired-end libraries with an insert size of 500 bp were constructed and sequenced using an Illumina Hiseq 2000 platform. Filtered sequencing reads were subjected to assembly using SPAdes v3.11.1 with default options. The assembled genome sequences have been deposited in DDBJ/ENA/GenBank under the accessions PRJNA862670 [42] and PRJNA505982 [43].

Preparation of Deinococcus genomes for analysis

In November 2021, all genome sequences with the taxonomic identifier ‘Deinococcus’ were retrieved from GenBank, providing a total of 134 genomes. As the taxonomy of this group is not well resolved, the taxonomy of the raw genomes was reclassified using GTDB-Tk [44]. The completeness and contamination of each taxonomically conformed genome were calculated using CheckM v1.0.7 with default options [22]. Genomes composed of > 300 contigs, with an N50 of < 20 kb, completeness of < 95% and contamination of > 5%, were removed. Genomes were dereplicated to remove genomes with an average amino acid identity (AAI) of ≥ 99.5%. AAI values were calculated by CompareM with default options (https://github.com/dparks1134/CompareM). Ultimately, there were 85 genomes that met the quality requirements, including the two genomes sequenced in this study. Detailed information concerning the origin, biogeography and genomic quality of the data is provided in Table S1.

Phylogenetic and genomic analyses

Outgroup species that are closely related to the ingroup species are more suitable for phylogenetic reconstruction than distantly related species or ingroup species [45]. Thus, for phylogenomic clustering, complete genomes of Meiothermus cerbereus DSM 11376 (GCA_000620065.1) and Meiothermus sp. QL-1 (GCA_003351145.1) were chosen as the outgroup species as they are close relatives of Deinococcus [46]. With the two Meiothermus and 85 Deinococcus genomes, a maximum likelihood phylogenomic tree was constructed using PhyloPhlAn3 with default options [47]. As the phylogenomic tree can be drawn in multiple different equivalent appearances, to obtain a relatively fixed phylogenetic topology, the tree was sorted with increasing node order using FigTree 1.4.4 (https://github.com/rambaut/figtree/releases).

The annotation of genes was standardised by annotating all genomes using PROKKA v1.14.5 with default options [48]. Genomic-scale reconstructions of metabolic pathways and biogeochemistry profiles were performed with gapseq v1.2 [49] and METABOLIC v4.0 [24]. The pan-genome of Deinococcus was constructed using PEPPAN v1.0.5 with default options and the gff files produced by PROKKA as the input. The result produced by the main program of PEPPAN was parsed using PEPPAN_parser with the arguments -t -c -a 95 and leaving others as their defaults [50]. Rarefaction curves of the pan and core genes were visualised with a custom made R script [51]. ANI was calculated using the ANI calculator (http://enve-omics.ce.gatech.edu/ani/). AAI values were calculated using CompareM with default options (https://github.com/dparks1134/CompareM).

Availability of data and materials

The assembled genome sequences have been deposited in DDBJ/ENA/GenBank under the accessions PRJNA862670 [42] and PRJNA505982 [43].

Abbreviations

MAGs:

Metagenome-assembled genomes

AAI:

Average amino acid identity

ANI:

Average nucleotide identity

References

  1. Kobras CM, Fenton AK, Sheppard SK. Next-generation microbiology: from comparative genomics to gene function. Genome Biol. 2021;22(1):123.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Edwards A, Cameron KA, Cook JM, Debbonaire AR, Furness E, Hay MC, et al. Microbial genomics amidst the Arctic crisis. Microb Genom. 2020;6(5):e000375.

    PubMed  PubMed Central  Google Scholar 

  3. Hutchins DA, Jansson JK, Remais JV, Rich VI, Singh BK, Trivedi P. Climate change microbiology-problems and perspectives. Nat Rev Microbiol. 2019;17(6):391–6.

    Article  CAS  PubMed  Google Scholar 

  4. Tiedje JM, Bruns MA, Casadevall A, Criddle CS, Eloe-Fadrosh E, Karl DM, et al. Microbes and climate change: a research prospectus for the future. mBio. 2022;13(3):e00800–22.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Hiis EG, Vick SHW, Molstad L, Røsdal K, Jonassen KR, Winiwarter W, et al. Unlocking bacterial potential to reduce farmland N2O emissions. Nature. 2024;630(8016):1–8.

    Article  Google Scholar 

  6. Kim S, Lee H, Hur SD, Sul WJ, Kim OS. Glaciers as microbial habitats: current knowledge and implication. J Microbiol. 2022;60(8):767–9.

    Article  PubMed  Google Scholar 

  7. Anesio AM, Laybourn-Parry J. Glaciers and ice sheets as a biome. Trends Ecol Evol. 2012;27(4):219–25.

    Article  PubMed  Google Scholar 

  8. Hultman J, Waldrop MP, Mackelprang R, David MM, McFarland J, Blazewicz SJ, et al. Multi-omics of permafrost, active layer and thermokarst bog soil microbiomes. Nature. 2015;521(7551):208–12.

    Article  CAS  PubMed  Google Scholar 

  9. Liu YQ, Priscu JC, Yao TD, Vick-Majors TJ, Michaud AB, Sheng L. Culturable bacteria isolated from seven high-altitude ice cores on the Tibetan Plateau. J Glaciol. 2019;65(249):29–38.

    Article  Google Scholar 

  10. Ji MK, Greening C, Vanwonterghem I, Carere CR, Bay SK, Steen JA, et al. Atmospheric trace gases support primary production in Antarctic desert surface soil. Nature. 2017;552(7685):400–3.

    Article  CAS  PubMed  Google Scholar 

  11. Shen L, Liu YQ, Allen MA, Xu BQ, Wang NL, Williams TJ, et al. Linking genomic and physiological characteristics of psychrophilic Arthrobacter to metagenomic data to explain global environmental distribution. Microbiome. 2021;9(1):136–50.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Jansson JK, Tas N. The microbial ecology of permafrost. Nat Rev Microbiol. 2014;12(6):414–25.

    Article  CAS  PubMed  Google Scholar 

  13. Lopatina A, Krylenkov V, Severinov K. Activity and bacterial diversity of snow around Russian Antarctic stations. Res Microbiol. 2013;164(9):949–58.

    Article  PubMed  Google Scholar 

  14. Shen L, Yao TD, Liu YQ, Jiao NZ, Kang SC, Xu BQ, et al. Downward-shifting temperature range for the growth of snow-bacteria on glaciers of the Tibetan Plateau. Geomicrobiol J. 2014;31(9):779–87.

    Article  Google Scholar 

  15. Ho WC, Zhang JZ. Evolutionary adaptations to new environments generally reverse plastic phenotypic changes. Nat Commun. 2018;9(1):350.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Lim S, Jung JH, Blanchard L, de Groot A. Conservation and diversity of radiation and oxidative stress resistance mechanisms in Deinococcus species. FEMS Microbiol Rev. 2019;43(1):19–52.

    Article  CAS  PubMed  Google Scholar 

  17. Tian J, Wang LQ, Liu P, Geng YC, Zhu GX, Zheng RC, et al. Deinococcus psychrotolerans sp. nov., isolated from soil on the South Shetland Islands Antarctica. Int J Syst Evol Micr. 2019;69(12):3696–701.

    Article  CAS  Google Scholar 

  18. Callegan RP, Nobre MF, McTernan PM, Battista JR, Navarro-González R, McKay CP, et al. Description of four novel psychrophilic, ionizing radiation-sensitive Deinococcus species from alpine environments. Int J Syst Evol. 2008;58(5):1252–8.

    Article  CAS  Google Scholar 

  19. Gerber E, Bernard R, Castang S, Chabot N, Coze F, Dreux-Zigha A, et al. Deinococcus as new chassis for industrial biotechnology: biology, physiology and tools. J Appl Microbiol. 2015;119(1):1–10.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Hirsch P, Gallikowski CA, Siebert J, Peissl K, Kroppenstedt R, Schumann P, et al. Deinococcus frigens sp. nov., Deinococcus saxicola sp. nov., and Deinococcus marmoris sp. nov., low temperature and draught-tolerating, UV-resistant bacteria from continental Antarctica. Syst Appl Microbiol. 2004;27(6):636–45.

    Article  CAS  PubMed  Google Scholar 

  21. Shen L, Liu YQ, Chen LZ, Lei TT, Ren P, Ji MK, et al. Genomic basis of environmental adaptation in the widespread poly-extremophilic Exiguobacterium group. ISME J. 2024;18(1):wrad020.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25(7):1043–55.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Maturana JL, Cardenas JP. Insights on the evolutionary genomics of the Blautia genus: potential new species and genetic content among lineages. Front Microbiol. 2021;12:660920.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Zhou ZC, Tran PQ, Breister AM, Liu Y, Kieft K, Cowley ES, et al. Metabolic: high-throughput profiling of microbial genomes for functional traits, metabolism, biogeochemistry, and community-scale functional networks. Microbiome. 2022;10(1):33.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. White O, Eisen JA, Heidelberg JF, Hickey EK, Peterson JD, Dodson RJ, et al. Genome sequence of the radioresistant bacterium Deinococcus radiodurans R1. Science. 1999;286(5444):1571–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Bradford MA, McCulley RL, Crowther TW, Oldfield EE, Wood SA, Fierer N. Cross-biome patterns in soil microbial respiration predictable from evolutionary theory on thermal adaptation. Nat Ecol Evol. 2019;3(2):223–31.

    Article  PubMed  Google Scholar 

  27. Chen LZ, Hong T, Wu ZR, Song WZ, Chen SX, Liu YQ, et al. Genomic analyses reveal a low-temperature adapted clade in Halorubrum, a widespread haloarchaeon across global hypersaline environments. BMC Genomics. 2023;24(1):508.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Meyer MM. Revisiting the relationships between genomic G + C content, RNA secondary structures, and optimal growth temperature. J Mol Evol. 2021;89(3):165–71.

    Article  CAS  PubMed  Google Scholar 

  29. Cavicchioli R. Cold-adapted archaea. Nat Rev Microbiol. 2006;4(5):331–43.

    Article  CAS  PubMed  Google Scholar 

  30. Siddiqui KS, Williams TJ, Wilkins D, Yau S, Allen MA, Brown MV, et al. Psychrophiles. Annu Rev Earth Planet Sci. 2013;41(1):87–115.

    Article  CAS  Google Scholar 

  31. 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(4):fiy023.

    Article  Google Scholar 

  32. Liu Q, Yang LL, Xin YH. Diversity of the genus Cryobacterium and proposal of 19 novel species isolated from glaciers. Front Microbiol. 2023;14:1115168.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Maistrenko OM, Mende DR, Luetge M, Hildebrand F, Schmidt TS, Li SS, et al. Disentangling the impact of environmental and phylogenetic constraints on prokaryotic within-species diversity. ISME J. 2020;14(5):1247–59.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Tamames J, Sánchez PD, Nikel PI, Pedrós-Alió C. Quantifying the relative importance of phylogeny and environmental preferences as drivers of gene content in prokaryotic microorganisms. Front Microbiol. 2016;7:182731.

    Article  Google Scholar 

  35. Konstantinidis KT, Tiedje JM. Genomic insights that advance the species definition for prokaryotes. Proc Natl Acad Sci USA. 2005;102(7):2567–72.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Feller G, Gerday C. Psychrophilic enzymes: hot topics in cold adaptation. Nat Rev Microbiol. 2003;1(3):200–8.

    Article  CAS  PubMed  Google Scholar 

  37. Mas A, Jamshidi S, Lagadeuc Y, Eveillard D, Vandenkoornhuyse P. Beyond the black queen hypothesis. ISME J. 2016;10(9):2085–91.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Zhu YG, Xue XM, Kappler A, Rosen BP, Meharg AA. Linking genes to microbial biogeochemical cycling: lessons from arsenic. Environ Sci Technol. 2017;51(13):7326–39.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Giovannoni SJ, Cameron Thrash J, Temperton B. Implications of streamlining theory for microbial ecology. ISME J. 2014;8(8):1553–65.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Morris JJ, Lenski RE, Zinser ER. The Black Queen Hypothesis: evolution of dependencies through adaptive gene loss. MBio. 2012;3(2):e00036–12.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Wang Q, Song YL, Choi L, Liu HY, Wang GJ, Li MS. Deinococcus rufus sp. nov., isolated from soil near an iron factory. Int J Syst Evol Microbiol. 2018;68(5):1622–6.

    Article  CAS  PubMed  Google Scholar 

  42. Shen L. Environmental genome project. 2022. GeneBank https://www.ncbi.nlm.nih.gov/datasets/genome/GCF_034440735.1/.

  43. Tian J. A new bacteria genome data sequencing. 2018. GeneBank https://www.ncbi.nlm.nih.gov/datasets/genome/GCA_003860465.1/.

  44. Chaumeil PA, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the genome taxonomy database. Bioinformatics. 2020;36(6):1925–7.

    Article  CAS  Google Scholar 

  45. Yang Z, Rannala B. Molecular phylogenetics: principles and practice. Nat Rev Genet. 2012;13(5):303–14.

    Article  CAS  PubMed  Google Scholar 

  46. Ten LN, Cho H, Cho YJ, Jung HY. Deinococcus terrigena sp. nov., a novel member of the family Deinococcaceae. Anton Leeuw Int J G. 2019;112(3):389–99.

    Article  CAS  Google Scholar 

  47. Asnicar F, Thomas AM, Beghini F, Mengoni C, Manara S, Manghi P, et al. Precise phylogenetic analysis of microbial isolates and genomes from metagenomes using PhyloPhlAn 3.0. Nat Commun. 2020;11(1):2500.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014;30(14):2068–9.

    Article  CAS  PubMed  Google Scholar 

  49. Zimmermann J, Kaleta C, Waschina S. Gapseq: informed prediction of bacterial metabolic pathways and reconstruction of accurate metabolic models. Genome Biol. 2021;22(1):81.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Zhou ZM, Charlesworth J, Achtman M. Accurate reconstruction of bacterial pan- and core genomes with PEPPAN. Genome Res. 2020;30(11):1667–79.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Ihaka R, Gentleman R. R: a language for data analysis and graphics. J Comput Graph Statist. 1996;5(3):299–314.

    Article  Google Scholar 

Download references

Acknowledgements

Dr. Fabrizio Pucci from Université Libre de Bruxelles and Dr. Sheng Wang from Shanghai Zelixir Biotech Company Ltd. are thanked for their help in interpreting the genomic data. Hefei Advanced Computing Center is thanked for providing computer resources.

Funding

This work was supported by the Joint Funds of the National Natural Science Foundation of China (U21A20176), the Second Tibetan Plateau Scientific Expedition and Research (STEP) program (No. 2019QZKK0503), the National Natural Science Foundation of China (No. 41701085), the National Science and Technology Fundamental Resources Investigation Program of China (2021FY100900), the Outstanding Innovative Research Team for Molecular Enzymology and Detection in Anhui Provincial Universities (2022AH010012), and the Key Project of Anhui Province Higher Education Research Program (Natural Science) (2022AH050186).

Author information

Authors and Affiliations

  1. College of Life Sciences, Anhui Normal University, Wuhu, 241000, China

    Liang Shen, Jiayu Hu, Luyao Zhang, Zirui Wu, Liangzhong Chen & Shaoxing Chen

  2. Anhui Provincial Key Laboratory of Molecular Enzymology and Mechanism of Major Diseases, and Auhui Provincial Engineering Research Centre for Molecular Detection and Diagnostics, Anhui Normal University, Wuhu, 241000, China

    Liang Shen

  3. State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, 100101, China

    Namita Paudel Adhikari & Yongqin Liu

  4. Center for the Pan-Third Pole Environment, Lanzhou University, Lanzhou, 730000, China

    Namita Paudel Adhikari, Mukan Ji & Yongqin Liu

  5. China Center for Type Culture Collection (CCTCC), College of Life Sciences, Wuhan University, Wuhan, 430072, People’s Republic of China

    Fang Peng

Authors
  1. Liang Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jiayu Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Luyao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zirui Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Liangzhong Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Namita Paudel Adhikari
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Mukan Ji
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Shaoxing Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Fang Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Yongqin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

LS, FP and YQL designed the study; LS, ZRW, JYH, LYZ and LZC performed the data analysis; LS, NPA, SXC, FP, MKJ and YQL interpreted the data and wrote the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Liang Shen or Fang Peng.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Handling editor: Graham Bell

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: Table S1. Information of Deinococcus genomes used in this study.

12915_2024_1944_MOESM2_ESM.docx

Additional file 2: Figures S1-S2. Fig. S1. Cladogram and dot plots showing the genome-wide GC content, genome size, and predicted complete metabolic pathways in Deinococcus. Fig. S2. Comparison of GC contents between Deinococcus groups.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shen, L., Hu, J., Zhang, L. et al. Genomics-based identification of a cold adapted clade in Deinococcus. BMC Biol 22, 145 (2024). https://doi.org/10.1186/s12915-024-01944-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12915-024-01944-8

Keywords

BMC Biology

ISSN: 1741-7007

Contact us