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Genomic analyses reveal a low-temperature adapted clade in Halorubrum, a widespread haloarchaeon across global hypersaline environments

BMC Genomics volume 24, Article number: 508 (2023) Cite this article

Abstract

Background

Cold-adapted archaea have diverse ecological roles in a wide range of low-temperature environments. Improving our knowledge of the genomic features that enable psychrophiles to grow in cold environments helps us to understand their adaptive responses. However, samples from typical cold regions such as the remote Arctic and Antarctic are rare, and the limited number of high-quality genomes available leaves us with little data on genomic traits that are statistically associated with cold environmental conditions.

Results

In this study, we examined the haloarchaeal genus Halorubrum and defined a new clade that represents six isolates from polar and deep earth environments (‘PD group’ hereafter). The genomic G + C content and amino acid composition of this group distinguishes it from other Halorubrum and the trends are consistent with the established genomic optimization of psychrophiles. The cold adaptation of the PD group was further supported by observations of increased flexibility of proteins encoded across the genome and the findings of a growth test.

Conclusions

The PD group Halorubrum exhibited denser genome packing, which confers higher metabolic potential with constant genome size, relative to the reference group, resulting in significant differences in carbon, nitrogen and sulfur metabolic patterns. The most marked feature was the enrichment of genes involved in sulfur cycling, especially the production of sulfite from organic sulfur-containing compounds. Our study provides an updated view of the genomic traits and metabolic potential of Halorubrum and expands the range of sources of cold-adapted haloarchaea.

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Background

Habitats suitable for low-temperature adapted microorganisms represent a large proportion of the Earth’s biosphere as over 70% of the Earth’s biosphere has a temperature < 5 ℃ [1,2,3]. In such habitats, archaea are prevalent and are represented by a diverse array of taxa, which play critical roles in global biogeochemical cycles [2, 4]. The specific environmental conditions of low-temperature habitats also represent a treasure trove for the discovery of new adaptations and evolutionary mechanisms [5]. Given their importance, a series of studies have been conducted to uncover the ecologically significant genomic features of these cold-adapted microbes [2, 6,7,8].

One of the best-studied psychrophilic archaeon is Methanococcoides burtonii, a methylotrophic methanogen isolated from Ace Lake, Antarctica [9]. Comparative genomics has revealed that the cold-adapted archaea were characterized by higher Gln (glutamine) and Thr (threonine) content and lower Leu (leucine) content, as well as high genome plasticity, which induced the acquisition of adaptive genes from Proteobacteria [9, 10]. Proteomic analyses have indicated that Halorubrum lacusprofundi responded to low-temperature stressors with a number of synergistic changes, including higher abundance of proteins associated the formation of polyhydroxyalkanoate-like granules and the synthesis of high levels of Hsp20 chaperones [11]. Genomic and proteomic analysis of psychrophilic bacteria has also revealed amino acid composition bias and the presence of specific genes in response to cold temperatures. For example, in Pseudoalteromonas haloplanktis, a bias toward Asn (asparagine) has been observed; dioxygen scavenging genes have been enriched while whole pathways producing reactive oxygen species have been lost [12]. The optimization of genome-wide amino acid composition and the presence of specific genes have been observed in psychrophilic microbes such as Colwellia psychrerythraea [13], Psychromonas ingrahamii [14], Psychrobacter arcticus [15] and Planococcus halocryophilus [16]. These studies have established a basic understanding of the genomic and proteomic characteristics of the adaptation of microorganisms to cold environments [6, 8, 17].

However, such studies have been limited by the difficulty of collecting samples from remote polar and alpine regions (where the majority of psychrophilic microbes originate) and the high cost of sequencing in the early years, during which the sequencing of a new genome was a feat in itself. Thus, single genomes from taxa of interest have been used to identify genomic features, and these genomes have been compared to genetically distant counterparts (e.g. comparisons between Deltaproteobacteria Desulfotalea psychrophila and Gammaproteobacteria Pseudomonas putida; and Halobacteriota Methanococcoides burtonii and Methanobacteriota Methanocaldococcus jannaschii) [9, 18]. Thus, there is still a knowledge gap in the literature, and the identification of genomic traits that are statistically associated with cold-environment conditions based on multiple genomes is required, with the exclusion, as much as possible, of interference caused by genetic distance (i.e. phylogenetic noise) [19, 20]. This situation might be even more pronounced in Archaea, for which high-quality non-redundant microbial genomic data from polar and alpine regions are relatively rare and insufficient [21, 22].

The haloarchaeal genus Halorubrum (Halobacteriota; Halobacteria; Halobacteriales; Haloferacaceae) was established by transferring four species from the genus Halobacterium in 1995 [23]. In addition to the psychrophilic members of the genus that are abundant in Antarctica’s Deep Lake, Halorubrum strains have been isolated and detected in other saline and cold environments globally (e.g. deep salt mines, saline soils, solar salts and Canadian high Arctic permafrost) [11, 24,25,26]. Halorubrum is one of the largest genera (in terms of diversity) of haloarchaea, and more than 40 valid species have been recorded in the literature (https://lpsn.dsmz.de/text/introduction, accessed in Jan. 2022). Importantly, many Halorubrum strains have been isolated from deep subterranean salt mines [25]; such mines usually have a relatively low and constant temperature relative to solar salterns, another main habitat of Halorubrum [27, 28]. These observations suggest that cold-adapted Halorubrum species may not be exclusive to Antarctic lakes.

In this study, we analysed 70 high-quality non-redundant Halorubrum genomes derived from diverse hypersaline environments, including Antarctica’s Deep Lake and subterranean salt mines. We first present an updated summary of the general genomic features of Halorubrum. After identifying a clade characterized by their adaptation to cold environments, genomic traits that are statistically associated with cold adaptation are identified. Our findings illustrated the important role of Halorubrum in driving biogeochemical cycling within cold environments; and expanded the sources of cold-adapted haloarchaea to deep earth environments.

Results

Phylogeny of Halorubrum

For phylogenomic clustering, Haloplanus rallus MBLA0036 (NZ_CP034345) and Haloplanus salinus JCM 18,368 (NZ_QPHM01000001) were chosen as the outgroup, as they are among the close relatives of Halorubrum [28] – species that are closely related to the in-group are more suitable for phylogenetic reconstruction than distantly related species [29]. Halorubrum spp. have been isolated from various saline environments globally, including Antarctica’s Deep Lake and salt mines hundreds of meters underground (Fig. 1a, Table S1). A clade in the middle of the tree harboured a higher percentage of isolates derived from deep-earth and Antarctic environments than the upper and lower clades (about 60% in the middle clade vs. < 3% in the upper clades and < 5% in the lower clades, Fig. 1b, please note that the tree has been sorted in increasing node order). This implies that the middle clade may represent a specific ecotype that is well adapted to the polar and deep-earth environments. The genomes belonging to this clade are referred to as the polar and deep-earth group (‘PD group’ hereafter). To investigate the genomic features shared by this group, we performed comparative genomic analysis by setting the remaining genomes as a control (i.e. reference group).

Fig. 1
figure 1

(a) Geographical locations of the Halorubrum isolates, and (b) Halorubrum phylogeny reconstructed by PhyloPhlAn 3.0 based on the 400 most universal markers using maximum likelihood algorithm. The central clade highlighted in blue starts with Halorubrum sp. DL18, which was isolated from Antarctica’s Deep Lake, and ends with Halorubrum sp. LN27, which was isolated from a deep salt column (350 m); this clade represents the polar and deep-earth group (in blue). The bar labelled ‘0.2’ indicates accumulated changes per amino acid. Dots at nodes indicate bootstrap proportions for the tree. The phylogenomic tree was sorted by increasing node order using FigTree, as described in the ‘Materials and methods’ section

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Low-temperature growth capacity of the PD group Halorubrum

The low-temperature growth pattern of the Antarctic isolates has already been well characterized [11]. To further evaluate the growth temperature response of the PD group Halorubrum, representative isolates from the PD group (Halorubrum sp. LN27) and the reference group (Halorubrum sp. T3) were grown at 4 °C on solid agar medium for 30 days. The isolate Halorubrum LN27 from the PD group exhibited a clearly enhanced rate of growth at 4 °C compared to Halorubrum T3 from the reference group (Fig. S1).

Pangenome and core genome of Halorubrum

The 70 Halorubrum genomes constituted an open pangenome with alpha value = 0.564 ± 0.004 (an alpha value < 1 is considered to indicate an ‘open’ pangenome, Fig. 2a) [30]. From the curves in Fig. 2a and b, it was predicted that about 130 more genes will be found once a new genome is added to the pangenome, and about three core genes will be excluded. The 70 genomes under study here were found to share 1,215 core genes (Fig. 2b). Of the 20,482 genes in the pangenome, most (78.63%) were present in < 15% strains, representing the cloud genes (Fig. 2c). The shell genes (present in 15% ≤ isolates < 95%) made up 13.09% of the pangenome, and the remaining 8.28% were therefore identified as core genes (both strict and soft core genes, present in ≥ 95% strains, Fig. 2c).

Fig. 2
figure 2

Rarefaction curves of pan and core gene numbers and summary statistics for the pangenome of Halorubrum. (a) Rarefaction curve for the accumulation of pan genes. (b) Rarefaction curve for the reduction of core genes. (c) Summary statistics for the 20,482 pan genes of Halorubrum. Cloud genes, 0% ≤ strains < 15%; shell genes, 15% ≤ strains < 95%; strict core genes, strains = 100%; soft core genes, 95% ≤ strains < 99%. The curves were fitted to median values of 1,000 permutations. The dark lines in (a) and (b) indicate median values, and the ‘shadows’ indicate the 95% confidence intervals. α = 0.564 ± 0.004 in (a) and α = 0.201 ± 0.011 in (b), indicating an open pangenome and core genome of Halorubrum. (d) Functional distribution of core genes (strict core genes plus soft core genes), shell genes and cloud genes for Halorubrum

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Based on the COG system, the core genes (both strict and soft core genes), shell genes and cloud genes of Halorubrum could be assigned to an equal number of nineteen functional categories (Fig. 2d). The proportion of genes assigned to each of the following categories showed a downward trend from core genes to shell and cloud genes: ‘energy production and conversion’, ‘cell cycle control, cell division, chromosome partitioning’, ‘coenzyme transport and metabolism’, ‘translation, ribosomal structure and biogenesis’, ‘posttranslational modification, protein turnover, chaperones’ and ‘intracellular trafficking, secretion, and vesicular transport’ (Fig. 2d). By contrast, functional categories related to ‘cell wall membrane envelope biogenesis’, ‘signal transduction mechanisms’, and ‘defence mechanisms’ accounted for increasing proportions from core genes to shell and cloud genes (Fig. 2d). About 31%, 43% and 42% of the core, shell and cloud genes, respectively, could not be assigned to categories with a known function (Fig. 2d).

Overview of the Halorubrum genomes

The Halorubrum genomes ranged in size from 2.77 Mb (Halorubrum sp. C191, isolated from an endorheic salt lake) to 3.93 Mb (Halorubrum sp. GN11GM_10 − 3_MGM, isolated from saturated brine), with a mean value of 3.40 Mb ± 0.24 Mb (roughly equivalent to the sizes of Hrr. lacusprofundi HLS1 and Hrr. lacusprofundi DL18, isolated from Deep Lake). The genomic G + C content of Halorubrum ranged from 62.90% (Hrr. vacuolatum DSM 8800, isolated from a saline lake) to 69.10% (Halorubrum sp. ATCC 700,873, isolated from a salt mine), and averaged 67.26% ± 1.23%. A comparison of genome size between the PD group and the reference group showed that there was no significant difference (Wilcoxon test; p > 0.05; Fig. 3a). However, the PD group had significantly higher numbers of coding sequences and higher coding density, indicating denser packing of genes (Wilcoxon test; p < 0.05; Fig. 3b and c). The higher coding density resulted in more metabolic pathways, with averages of 212 and 209 metabolic pathways in the PD group and the reference group, respectively (Wilcoxon test; p < 0.05; Fig. 3d; Table S2). The majority of the metabolic pathways that were overrepresented in the PD group were related to glycolysis and gluconeogenesis, the central carbon metabolic pathways that generate energy and mediate the synthesis of biomolecules [31].

Fig. 3
figure 3

Comparison of the (a) genome size, (b) number of coding sequences, (c) coding density and (d) number of complete metabolic pathways between the polar and deep-earth group and the reference group. The polar and deep-earth group had a significantly higher number of coding sequences, higher coding density and more complete metabolic pathways than the reference group; no significant difference in genome size was found (Wilcoxon test, *, p < 0.05; **, p < 0.01; NS, not significant)

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Genome-wide and RNA G + C content of Halorubrum

The G + C content of genomic DNA, hypothetical sequences, and coding sequences was significantly lower in the PD group than the reference group (Wilcoxon test; p < 0.05; Fig. 4). We further calculated the G + C content at the three amino acid positions constituting a codon (GC1, GC2, GC3) for each of the coding sequences, and found that it was significantly lower at all three of the codon positions of the PD group’s coding sequences, compared to the reference group (Wilcoxon test; p < 0.05, Fig. 4). The decrease in G + C content could be detected when considering tRNA and rRNA as whole and tRNA only (Wilcoxon test; p < 0.05; Fig. 4), but it was not detectable for rRNA only (Wilcoxon test; p > 0.05; Fig. 4).

Fig. 4
figure 4

Comparison of the G + C content of genomic elements between the polar and deep-earth group and the reference group. Local G + C content of GC1, GC2 and GC3 (i.e. G + C composition at the first, second and third sites of genetic codons) (Wilcoxon test; *, p < 0.05; **, p < 0.01; ***, p < 0.005; NS, not significant; RNA including tRNA and rRNA)

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Amino acid composition in Halorubrum

Of the 20 standard amino acids, 11 were found to be present in significantly increased proportions in the PD group, relative to the reference group (lysine, glutamine, isoleucine, asparagine, tryptophane, histidine, cysteine, methionine, tyrosine, serine and glutamic acid) (Wilcoxon test; p < 0.05; Fig. 5a). Four amino acids (proline, arginine, valine and alanine) were found to be present in decreased proportions in the PD group (Wilcoxon test; p < 0.05, Fig. 5a). Two charged amino acids (Lys and Glu) were present in increased proportions in the PD group; one (Arg) was found to have decreased and one (aspartic acid) remained unchanged (Fig. 5a). Of the hydrophobic amino acids, three (Ile, Met and Tyr) were present in increased proportions and two (Pro, Val and Ala) in decreased proportions; three (phenylalanine, Leu and glycine) remained unchanged (Fig. 5a). All of the amphipathic amino acids (Trp, Met and Try) and the acidic amino acid Glu were found to be present in increased proportions in the PD group (Fig. 5a). This optimization of amino acid composition results in a significant increase in average flexibility, a good proxy of protein cold adaptation [17, 32] (Wilcoxon test; p < 0.05, Fig. 5b); however, it was not found to be associated with a significant change in isoelectric point between PD group and the reference group (Wilcoxon test; p > 0.05; Fig. 5c).

Fig. 5
figure 5

Optimization of genome-wide amino acid composition increased average flexibility but not the isoelectric point of the polar and deep-earth (PD) group Halorubrum. (a) Comparison of genome-wide amino acid composition of Halorubrum; the mean difference (colored circles) and standard deviation (vertical bars drawn through the circles) are plotted in the upper panel. p-values were obtained using the Wilcoxon test; the asterisks above the plot indicate significant differences between the polar and deep-earth group and the reference group. The lower panel shows the general chemical characteristics of each amino acid. (b, c) Comparison of protein flexibility (b) and protein isoelectric point (c) of the reference group and PD group Halorubrum at the genome scale. *, p < 0.05; **, p < 0.01; ***, p < 0.005; NS, not significant

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Functional potential of Halorubrum

The PD group was significantly different from the reference group in terms of gene content, specifically for genes related to CAZymes (carbohydrate-active enzymes), the nitrogen cycle and the sulfur cycle (PERMANOVA, p < 0.05, Fig. 6a). Analysis of the overall carbon cycle scheme indicated that Halorubrum are typical heterotrophic microorganisms which use organic carbon, including acetate and ethanol, as their main energy source (Fig. S2). For the carbohydrate-active enzymes, AA3_2 were depleted (Wilcoxon test; p < 0.05; Table S3). With regard to the nitrogen cycle, Halorubrum were predicted to be able to reduce NO3– to N2 or NH4+; the nitrogen cycle genes identified as being significantly enriched in the PD group were nirS/K (for reducing NO2– to NO), norB (for reducing NO to N2O) and nasA (for reducing NO3– to NO2–) (Fig. 6b). The absence of nifH and amoA indicates that Halorubrum may not be able to fix nitrogen or oxidize ammonia (Fig. 6b).

Fig. 6
figure 6

Comparison of functional capacity between the polar and deep-earth (PD) group Halorubrum and the reference group. (a) Nonmetric multidimensional scaling (NMDS) plots of CAZymes (carbohydrate-active enzymes) and nitrogen and sulfur cycling genes constructed based the Bray–Curtis dissimilarity. Analysis of the mean number of genes related to CAZymes, the nitrogen cycle and the sulfur cycle revealed a significant difference between the two groups (PERMANOVA; p < 0.05). The hulls indicate the 95% confidence intervals for a multivariate t-distribution for the respective groups of Halorubrum. (b) Summary of nitrogen cycling genes in Halorubrum; red up-arrows indicate genes that were enriched in the PD group. (c) The genes involved in organic and inorganic sulfur transformation pathways are shown in blue boxes; the red up-arrows denote the overrepresentation of these genes in the PD group Halorubrum. Modified from Yu et la., 2020. Note that the NMDS stress values of N and S cycling genes fall into the 0.2–0.3 category

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The reconstruction of the sulfur cycling pathway showed that the cold-adapted Halorubrum species encoded most of the key genes for both organic and inorganic sulfur transformation. The majority of the enriched genes in the PD group were involved in the production of sulfite from organic-sulfur-containing compounds – for example, in the production of sulfite from methanesulfonate, thiosulfate, alkanesulfonate, and sulfolactate by ssuDE, glpE, sseA, ssuDE and suyAB; and sulfite could be further reduced to sulfide by sir (Fig. 6c). Genes metC, metZ and cysK, which are involved in the production of sulfide from L-homocysteine, O-accetyl-L-serine and L-cysteine, were found to be enriched in the PD group Halorubrum (Fig. 6c).

Discussion

A body of studies has revealed that Halorubrum harbours diverse species, and there are members of Halorubrum that are well adapted to low temperatures and are abundant in cold saline lakes such as Deep Lake [11, 33]. Halorubrum-related sequences were also found to be abundant in the 2 m sample of the permafrost from the Canadian high Arctic [24]. Despite the competitiveness of Halorubrum in different cold environments globally, it is challenging to grow them at temperatures lower than 4 °C in the laboratory, and Halorubrum can grow at temperatures higher than 40 °C [2, 25]. Indeed, it is difficult to ascertain how well-adapted and ecologically important a microorganism is based on laboratory temperature-dependent growth curve tests [34]. Thus, there is a lack of knowledge regarding the genetic boundary between the cold-adapted Halorubrum species and their counterparts that thrive at higher temperatures.

In our analysis of the pangenome of Halorubrum, we found that the core genes exhibited a similar degree of functional diversity to the shell genes and cloud genes. This may confer on each member of Halorubrum the functional capacity to colonize a wide range of habitats. Having an open pangenome also indicates that Halorubrum is beginning to expand in terms of function and exploring new ecological niches [35]. There is an emerging view that the ecological theory developed for animals and plants may apply to Bacteria and Archaea [36]. Thus, the ability to occupy diverse and geographically distant habitats may be one of the reasons why Halorubrum is one of the largest haloarchaeal genera and has rapidly changing variation of its populations [37].

With respect to the high intrageneric diversity of Halorubrum, a clade containing 60% isolates from deep subterranean salt mines and Deep Lake was identified in the phylogenomic tree (i.e. the PD group). Although the ambient temperatures (~ 15 ℃) of the deep salt mines are not as low as those of Deep Lake (< 15 ℃ all year and < 0 ℃ for ~ eight months of the year), they can be considered low-temperature environments relative to saline lakes and solar salterns, where temperatures are around 20–30 ℃ [27, 33]. We hypothesize that this clade represents a low-temperature-adapted ecotype in Halorubrum. This is generally supported by the growth tests described in this study and in a previous study by Williams et al., (2017), both of which found that isolates from the PD group grew well at low temperatures and outperformed the reference isolates.

To further test our hypothesis, we then analysed the DNA G + C content of the PD group. For Bacteria and Archaea, the G + C content of genomic elements, especially RNAs, is a good proxy for temperature adaptation [38]. The lower G + C content of genomic DNA and RNA genes in the PD group, relative to the reference group, corresponds well to the improved ability of members of the PD group to survive at lower temperatures. The PD group is located in the middle of the phylogenomic tree shown in Fig. 1, and both its genomic and RNA G + C content are lower than those of the upper and lower clades, further supporting the notion that the lower G + C content is correlated with low-temperature adaptation rather than phylogenetic divergence.

The optimization of amino acid composition in the PD group provided stronger evidence that the PD group represents a low-temperature adapted ecotype in Halorubrum. In a comparison of the amino acid composition of the PD and reference groups, significant changes in the proportions of 15 of the 20 standard amino acids were observed. Significant decreases in the proportions of Arg and Pro, and significant increases in Lys and Asn, all of which represent well-established signatures of cold adaptation, were identified in the PD group [6, 39, 40]. By clearly delineating the cold-adapted ecotype of Halorubrum, we are now able to summarize, in statistical terms, the overall amino acid optimization of Halorubrum in response to low-temperature environments. We identified a bias in amino acid composition toward Lys, Gln, Ile, Asn, Trp, His, Cys, Met, Tyr, Ser and Glu and against Pro, Arg, Val and Ala. Our findings in the PD group were consistent with trends identified in psychrophilic Arthrobacter, a genus of bacteria in the Actinomycetes family; which reported similar findings for eight of the eleven amino for which an increase was observed (Asn, Lys, Met, Ile, Ser, Gln, Trp and His) and three of the four for which a decrease was observed (Ala, Pro, and Arg) [41].

The optimization of protein amino acid composition would enhance the activity of enzymes at low temperatures via a reduction in the number and strength of salt bridges (i.e. Asp-Arg salt bridge to Asp-Lys salt bridge) and would confer conformational flexibility and reduce activation energy [17, 42]. A comparison of the average flexibility between the PD group and the reference group further supported the idea that amino acid optimization has enabled genome-scale cold-environment adaptation in the PD group [6, 39]. The substitution of Lys for Arg may also help to reduce the amount of nitrogen needed for cell replication, as Lys has lower nitrogen content [43]. It is worth noting that the amphipathic amino acid content (for all three – Trp, Met and Try) was higher in the PD group; this indicates that the amphipathic amino acids may present a novel signature of cold adaptation that has not been noted in previous studies. We also observed that the optimization of amino acid composition by the PD group had not resulted in an increase in isoelectric point, which was thought to be incompatible with cold adaptation [44]. We speculate that the PD group Halorubrum are adapted genetically to the cold but that other unknown growth requirements prevent them from growing at temperatures < 4 °C.

Although it is clear that the optimization of amino acid composition to increase protein flexibility is a good indicator of cold adaptation in both archaea and bacteria, there is no general trend when classifying the amino acids based on their chemical characteristics only. For example, the hydrophobic amino acids Ile and Met were increased while Pro and Ala content were decreased in the cold-adapted clades of Halorubrum and Arthrobacter; Lys and Arg both have positive charges at neutral pH values but showed opposite trends in adapted proteins [41]. The trend of decreased Leu content identified in previous studies was not seen in the cold-adapted clades of Halorubrum or Arthrobacter [9, 41]; thus, based on statistical analyses of multiple closely related genome data sets, Leu may not be the key amino acid in low-temperature adaptation. The contrasting trends observed for some amino acids in different cold-adapted taxa probably result from a balancing of the overall amino acid composition.

The PD group was found to have higher functional potential with constant genome size relative to the reference group. This suggests that the PD group had higher substrate- and energy-use efficiency, enabling these species to drive the biogeochemical cycle in the oligotrophic cold polar and deep-earth environments. We further compared the functional traits between the PD group and the reference group by dividing genes into different functional categories. The PD group was shown to differ from the other Halorubrum in terms of overall gene content and specific functional genes involved in carbohydrate metabolism, the nitrogen cycle and the sulfur cycle. Functional differentiation between the PD group and the reference group further supports the idea that the PD group represents a low-temperature adapted ecotype in Halorubrum. The denser packing of genes indicates that the PD group may have undergone stronger positive selection of related genes [35]. We can explore the specific biogeochemical role of this cold-adapted clade using the reverse ecology principle, which states that the genome of an organism includes identifiable adaptational features to its native environment [36].

In our analysis of the nitrogen cycle, genomic data indicated that Halorubrum were able to reduce NO3– to N2 or NH4+ but were not able to fix nitrogen or oxidize ammonia; this is consistent with the physiology of Halorubrum [45]. The genes nirS/K, norB and nasA were significantly enriched in the PD group, suggesting enhanced reduction of NO3– by the cold-adapted Halorubrum species in polar and deep-earth hypersaline environments [45]. Our result is consistent with the findings that most of the genes involved in the denitrification pathway could be detected in Arctic permafrost, but the relative gene abundances for N2 production were low, leading to the accumulation of N2O, another greenhouse gas [46, 47].

The cold-adapted Halorubrum isolates encoded a number of key genes involved in both organic and inorganic sulfur transformation, and were especially enriched in genes involved in organic sulfur transformation. This suggests that the cold-adapted Halorubrum species prefer organic sulfur to generate energy for cellular activity and growth. Our results corroborated the findings of previous studies, in which strong psychrophilic adaptation of the sulfate reducers was identified in the Arctic sediment, and psychrophilic Arthrobacter were characterized as harbouring a complete mycothiol (MSH, a sulfur-containing compound) biosynthesis pathway [41, 48]. The capture of advantageous genes – such as those discussed here that confer on the PD group Halorubrum the ability to explore new ecological niches (i.e. deep subterranean salt mines and polar lakes) – can lead to the expansion of genomes. The fact that the genome sizes in the PD group Halorubrum remained constant suggests that the capture of new genes in this group might have overridden the selection for genome streamlining [35, 49]. This also implies that the genome content of the PD group Halorubrum is optimized such that maximum metabolic complexity is achieved without the cost of having increased the number of regulatory genes [50, 51].

Ordering genomes from geographically distant locations with similar low-temperature conditions into ecologically cohesive units helps to improve our understanding of the genomic features that are statistically associated with particular environmental conditions. However, it is difficult to identify a strict monophyletic group in which all isolates are from cold environments (e.g. polar, high alpine, and deep-earth environments) [41]. In this study, the PD group was found to harbour four isolates that were not from polar or deep-earth environments; however, these isolates formed a mixed clade with the polar and deep-earth isolates and shared conserved genomic traits. The benefits of defining the PD group were achieved at the expense of including the four non-cold-environment-derived isolates. However, there are in principle strict limits to what can be achieved by any simple system of classification; for example, in the classification of terrestrial climate, some locations may simultaneously satisfy the criteria for more than one category [52].

Conclusions

By adding four isolates from deep salt mines to a clade anchored by the well-studied psychrophilic Hrr. lacusprofundi strains HLS1 and DL18, we have expanded the range of sources of cold-adapted Halorubrum species – which were previously limited to Antarctica – to include deep-earth environments. We also analysed the genomes of new PD group Halorubrum isolated from subterranean salt mines and reconstructed their C, N and S cycling capacities. In comparison to the reference group, the PD group Halorubrum possessed distinct genomic signatures consistent with their representation in low-temperature environments, and more compact genomes.

In the era of next-generation microbiology, two advances will improve our understanding of the genetic basis of environmental adaptation: (i) increasing the availability of multiple genomes sharing similar environmental conditions [21]; and (ii) minimizing the phylogenetic distance between target groups and reference groups [35, 53]. In this study, we defined a cold-adapted clade in Halorubrum harbouring 10 non-redundant genomes, for which phylogenetic noise was reduced as much as possible by using all other Halorubrum genomes as a reference group. Thus, an important step has been taken towards achieving the two advances that will improve our understanding of the environmental adaptation of microbes.

Materials and methods

Strain isolation and genome sequencing

Five haloarchaeal strains (Halorubrum sp. T3, Y78, Y69, ZC67, and F4) were isolated from salt rocks collected from depths of 300–700 m in salt mines in Yunnan, China, as described previously [25]. Strain Halorubrum sp. LN27 was isolated from a salt mine in Anhui, China, at a depth of 350 m, as described previously [54]. JCM168 medium was used to cultivate the isolates (https://jcm.brc.riken.jp/en/).

Genomic DNA was extracted from the isolates using a TIANamp Bacteria DNA Kit (Tiangen, Beijing), following the manufacturer’s instructions. Using genomic DNA, paired-end libraries with an insert size of 500 bp were constructed and sequenced using an Illumina HiSeq 2000 platform. Prior to de novo sequence assembly, low-quality reads were filtered out using Fastp with the default options [55]. Filtered sequencing reads were assembled using SPAdes v3.13.1 with the default options [56]. The assembled genome sequences were deposited in the DDBJ/ENA/GenBank database with the assembly ID provided in Table S1.

Preparation of Halorubrum genomes for analysis

In September 2020, we retrieved all Halorubrum genome sequences from GenBank, obtaining 94 genomes. With the addition of the Halorubrum sp. F4 genome sequenced in this study and the five genomes sequenced by the authors in previous studies [54, 57], we obtained a total of 100 Halorubrum genomes. As the assignment of taxonomy in NCBI is relaxed, the taxonomy of the raw genomes was re-classified using the classify_wf workflow implemented in GTDB-Tk v1.4.0 with the database GTDB R95 prior to downstream analysis [58].

An organism’s genome contains all of its biological information; higher-quality genomes are therefore more informative. Thus, the 100 raw genomes were subjected to the following quality control and deduplication processes. QUAST v4.6.1 was used for contig and N50 calculations [59], and CheckM v1.0.7 was used for the genome quality estimation for each genome, using the default options [60]. Next, genomes with > 300 contigs, N50 < 20 kb, completeness < 95%, and contamination > 5% were removed. After quality filtering, we deduplicated the genomes by removing those with average amino acid identity (AAI) ≥ 99.5%. Genome quality filtering and deduplication were performed according to Parks et al., (2017) [61] and Shen et al., (2021) [41]. AAI values were calculated using CompareM with the default options (https://github.com/dparks1134/CompareM). A total of 70 genomes met the quality control requirements, including all six of the genomes contributed by the authors (Table S1).

Phylogenomic and genomic analysis

The Halorubrum phylogenomic tree was constructed using PhyloPhlAn3 [62]. The phylogenomic tree can be drawn in multiple different but equivalent forms, so to obtain a relatively fixed phylogenetic topology, the phylogenomic tree was sorted with increasing node order using FigTree 1.4.4 (https://github.com/rambaut/figtree/releases). The annotation of the genes was standardized by annotating all genomes using PROKKA v1.14.5 [63]. Genome-scale reconstruction of metabolic pathways and analysis of the biogeochemical profiles were performed using gapseq v1.2 [64] and METABOLIC v4.0 [65]. Genome-scale calculation of protein flexibility and their isoelectric points were performed using ProtScale (https://web.expasy.org/protscale/) and ipc v1.0 [66] .The Halorubrum pangenome was constructed using PEPPAN v1.0.5 with the gff files produced by PROKKA used as input; the result produced by the main program of PEPPAN was parsed using PEPPAN_parser with the arguments -t -c -a 95 [67]. Rarefaction curves of the pan- and core-gene numbers were visualized with a custom-made R script [68]. The assignment of COGs (Cluster of Orthologous Groups of proteins) was performed with eggNOG-mapper v2.1.9 (http://eggnog-mapper.embl.de/). In addition to the general functional annotations, we used more specific tools for an in-depth exploration of the carbon, nitrogen and sulfur metabolism potential of Halorubrum. Carbohydrate-active enzymes were predicted using dbCAN2 [69]. Genes involved in nitrogen and sulfur cycling were predicted using NCycDB and SCycDB and the accompanying scripts [70, 71]. All parameters were set as default for the genomic tools and scripts used above, except where noted. R v4.2.1 and ggplot2 v3.4.2 were used for statistical analysis and plotting [68, 72]. For mapping strain isolation sites, we used the following R packages: pacman v0.5.1, leaflet v2.1.2, ggmap v3.0.2, sp v1.6-0, maptools v1.1-6, maps v3.4.1 and tidyverse v2.0.0. Additionally, we used ggalluvial v0.12.5 and ggsci v3.0.0 to show the functional distribution of core genes, cloud genes, and shell genes in Halorubrum. We used R package ggsignif v0.6.4 to add significance markers. For comparing the amino genome-wide acid composition of the of Halorubrum, we used R packages reshape2 v0.9.3 and ggpubr v0.6.0. We also used R packages vegan v2.6-4 and dplyr v1.1.2 to calculate PERMANOVA and NMDS. p-values were adjusted for multiple testing where required with Benjamini-Hochberg [73].

Data Availability

The genome sequences of strains T3, Y78, Y69, ZC67, and F4 have been deposited at GenBank under the accessions GCA_000296615, GCA_007671725, GCA_007671685, GCA_004114995 and JAPDFS000000000, respectively.

References

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

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  3. Rodrigues DF, Tiedje JM. Coping with our cold planet. Appl Environ Microbiol. 2008;74(6):1677–86.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Boetius A, Anesio MA, Deming WJ, Mikucki AJ, Rapp ZJ. Microbial ecology of the cryosphere: sea ice and glacial habitats. Nat Rev Microbiol. 2015;13:677–90.

    Article  CAS  PubMed  Google Scholar 

  5. Hamm JN, Erdmann S, Eloe-Fadrosh EA, Angeloni A, Zhong L, Brownlee C, et al. Unexpected host dependency of Antarctic Nanohaloarchaeota. Proc Natl Acad Sci USA. 2019;116(29):14661–70.

  6. Siddiqui KS, Williams TJ, Wilkins D, Yau S, Allen MA, Brown MV, Lauro FM, Cavicchioli R. Psychrophiles. Annu Rev Earth Planet Sci. 2013;41(1):87–115.

    Article  CAS  Google Scholar 

  7. Kim S, Lee H, Hur SD, Sul WJ, Kim OS. Glaciers as microbial habitats: current knowledge and implication. J Microbiol. 2022;60(8):767–79.

    Article  PubMed  Google Scholar 

  8. De Maayer P, Anderson D, Cary C, Cowan DA. Some like it cold: understanding the survival strategies of psychrophiles. Embo Rep. 2014;15(5):508–17.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Saunders NF, Thomas T, Curmi PM, Mattick JS, Kuczek E, Slade R, et al. Mechanisms of thermal adaptation revealed from the genomes of the Antarctic Archaea Methanogenium frigidum and Methanococcoides burtonii. Genome Res. 2003;13(7):1580–8.

  10. Allen MA, Lauro FM, Williams TJ, Burg D, Siddiqui KS, De Francisci D, et al. The genome sequence of the psychrophilic archaeon, Methanococcoides burtonii: the role of genome evolution in cold adaptation. ISME J. 2009;3(9):1012–35.

  11. Williams TJ, Liao Y, Ye J, Kuchel RP, Poljak A, Raftery MJ, et al. Cold adaptation of the Antarctic haloarchaea Halohasta litchfieldiae and Halorubrum lacusprofundi. Environ Microbiol. 2017;19(6):2210–27.

  12. Medigue C, Krin E, Pascal G, Barbe V, Bernsel A, Bertin PN, et al. Coping with cold: the genome of the versatile marine Antarctica bacterium Pseudoalteromonas haloplanktis TAC125. Genome Res. 2005;15(10):1325–35.

  13. Methé BA, Nelson KE, Deming JW, Momen B, Melamud E, Zhang XJ, et al. The psychrophilic lifestyle as revealed by the genome sequence of Colwellia psychrerythraea 34H through genomic and proteomic analyses. Proc Natl Acad Sci USA. 2005;102(31):10913–8.

  14. Riley M, Staley JT, Danchin A, Wang TZ, Brettin TS, Hauser LJ, Land ML, Thompson LS. Genomics of an extreme psychrophile, Psychromonas ingrahamii. BMC Genom. 2008;9:210.

  15. Ayala-del-Río HL, Chain PS, Grzymski JJ, Ponder MA, Ivanova N, Bergholz PW, et al. The genome sequence of Psychrobacter arcticus 273-4, a psychroactive siberian permafrost bacterium, reveals mechanisms for adaptation to low-temperature growth. Appl Environ Microbiol. 2010;76(7):2304–12.

  16. Mykytczuk NCS, 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(6):1211–26.

  17. Feller G. Psychrophilic enzymes: from folding to function and biotechnology. Scientifica 2013, 2013:512840.

  18. Rabus R, Ruepp A, Frickey T, Rattei T, Fartmann B, Stark M, et al. The genome of Desulfotalea psychrophila, a sulfate-reducing bacterium from permanently cold Arctic sediments. Environ Microbiol. 2004;6(9):887–902.

    Article  CAS  PubMed  Google Scholar 

  19. Raymond-Bouchard I, Goordial J, Zolotarov Y, Ronholm J, Stromvik M, Bakermans C, Whyte LG. 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 

  20. Xu BL, Dai M, Chen Y, Meng D, Wang Y, Fang N, et al. Improving the thermostability and activity of a thermophilic subtilase by incorporating structural elements of its psychrophilic counterpart. Appl Environ Microbiol. 2015;81(18):6302–13.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

  22. Ruuskanen MO, Colby G, St.Pierre KA, St.Louis VL, Aris-Brosou S, Poulain AJ. Microbial genomes retrieved from high Arctic lake sediments encode for adaptation to cold and oligotrophic environments. Limnol Oceanogr. 2020;65(S1):233–S247.

    Article  CAS  Google Scholar 

  23. McGenity TJ, Grant WD. Transfer of Halobacterium saccharovorum, Halobacterium sodomense, Halobacterium trapanicum NRC 34021 and Halobacterium lacusprofundi to the genus Halorubrum gen. nov., as Halorubrum saccharovorum comb. nov., Halorubrum sodomense comb. nov., Halorubrum trapanicum comb. nov., and Halorubrum lacusprofundi comb. nov. Syst Appl Microbiol. 1995;18(2):237–43.

    Article  Google Scholar 

  24. Steven B, Pollard WH, Greer CW, Whyte LG. Microbial diversity and activity through a permafrost/ground ice core profile from the Canadian high Arctic. Environ Microbiol. 2008;10(12):3388–403.

    Article  CAS  PubMed  Google Scholar 

  25. Chen S, Liu HC, Zhao D, Yang J, Zhou J, Xiang H. Halorubrum yunnanense sp. nov., isolated from a subterranean salt mine. Int J Syst Evol Micr. 2015;65(12):4526–32.

  26. Ventosa A, Gutiérrez MC, Kamekura M, Zvyagintseva IS, Oren A. Taxonomic study of Halorubrum distributum and proposal of Halorubrum terrestre sp. nov. Int J Syst Evol Micr. 2004;54(Pt 2):389–92.

  27. Megaw J, Kelly SA, Thompson TP, Skvortsov T, Gilmore BF. Profiling the microbial community of a triassic halite deposit in Northern Ireland: an environment with significant potential for biodiscovery. FEMS Microbiol Lett. 2019;366(22):fnz242.

    Article  CAS  PubMed  Google Scholar 

  28. Zhang WJ, Cui HL. Halorubrum salinum sp. nov., isolated from a marine solar saltern. Arch Microbiol. 2014;196(6):395–400.

    Article  CAS  PubMed  Google Scholar 

  29. Yang ZH. Computational molecular evolution. Great Britain: Oxford University Press; 2006.

    Book  Google Scholar 

  30. 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 

  31. Gupta R, Gupta N. Glycolysis and Gluconeogenesis. In: Fundamentals of Bacterial Physiology and Metabolism Edited by Gupta R, Gupta N. Singapore: Springer Singapore; 2021: 267–287.

  32. De Wijn R, Rollet K, Ernst FGM, Wellner K, Betat H, Morl M, et al. CCA-addition in the cold: structural characterization of the psychrophilic CCA-adding enzyme from the permafrost bacterium Planococcus halocryophilus. Comput Struct Biotec. 2021;19:5845–55.

  33. Cavicchioli R. Microbial ecology of Antarctic aquatic systems. Nat Rev Microbiol. 2015;13(11):691–706.

    Article  CAS  PubMed  Google Scholar 

  34. Cavicchioli R. On the concept of a psychrophile. ISME J. 2016;10(4):793–5.

    Article  PubMed  Google Scholar 

  35. Koonin EV, Makarova KS, Wolf YI. Evolution of microbial genomics: conceptual shifts over a quarter century. Trends Microbiol. 2021;29(7):582–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Arevalo P, VanInsberghe D, Elsherbini J, Gore J, Polz MF. A reverse ecology approach based on a biological definition of microbial populations. Cell. 2019;178(4):820–834e814.

    Article  CAS  PubMed  Google Scholar 

  37. Ram Mohan N, Fullmer MS, Makkay AM, Wheeler R, Ventosa A, Naor A, et al. Evidence from phylogenetic and genome fingerprinting analyses suggests rapidly changing variation in Halorubrum and Haloarcula populations. Front Microbiol. 2014;5:143.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Galtier N, Lobry JR. Relationships between genomic G + C content, RNA secondary structures, and optimal growth temperature in prokaryotes. J Mol Evol. 1997;44(6):632–6.

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  40. Casanueva A, Tuffin M, Cary C, Cowan DA. Molecular adaptations to psychrophily: the impact of ‘omic’ technologies. Trends Microbiol. 2010;18(8):374–81.

    Article  CAS  PubMed  Google Scholar 

  41. Shen L, Liu Y, Allen MA, Xu B, Wang N, 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Ã…qvist J, Isaksen GV, Brandsdal BO. Computation of enzyme cold adaptation. Nat Rev Chem. 2017;1(7):0051.

    Article  Google Scholar 

  43. 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 

  44. Piszkin L, Bowman J. Extremophile enzyme optimization for low temperature and high salinity are fundamentally incompatible. Extremophiles. 2021;26(1):5.

    Article  PubMed  Google Scholar 

  45. Solchaga JI, Busalmen JP, Nercessian D. Unraveling anaerobic metabolisms in a hypersaline sediment. Front Microbiol. 2022;13:811432.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Yergeau E, Hogues H, Whyte LG, Greer CW. The functional potential of high Arctic permafrost revealed by metagenomic sequencing, qPCR and microarray analyses. ISME J. 2010;4(9):1206–14.

    Article  CAS  PubMed  Google Scholar 

  47. Taş N, Prestat E, McFarland JW, Wickland KP, Knight R, Berhe AA, et al. Impact of fire on active layer and permafrost microbial communities and metagenomes in an upland alaskan boreal forest. ISME J. 2014;8(9):1904–19.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Scholze C, Jørgensen BB, Røy H. Psychrophilic properties of sulfate-reducing bacteria in Arctic marine sediments. Limnol Oceanogr. 2020;66:293–S302.

    Google Scholar 

  49. Morris JJ, Lenski RE, Zinser ER. The black queen hypothesis: evolution of dependencies through adaptive gene loss. mBio. 2012;3(2):e00036–00012.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Ranea JA, Grant A, Thornton JM, Orengo CA. Microeconomic principles explain an optimal genome size in bacteria. Trends Genet. 2005;21(1):21–5.

    Article  CAS  PubMed  Google Scholar 

  51. Konstantinidis KT, Tiedje JM. Trends between gene content and genome size in prokaryotic species with larger genomes. Proc Natl Acad Sci USA. 2004;101(9):3160–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Peel MC, Finlayson BL, Mcmahon TA. Updated world map of the Köppen-Geiger climate classification. Hydrol Earth Syst Sci. 2007;11(3):259–63.

    Google Scholar 

  53. 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 

  54. Chen S, Dai Y, Liu J, Zhang S, Chen F, Jin F, et al. VOLN27B: a new head-tailed halovirus isolated from an underground salt crystal and infecting Halorubrum. Archaea. 2021;2021:8271899.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Chen S, Zhou Y, Chen Y, Gu J. Fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018;34(17):i884–90.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, et al. Spades: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 2012;19(5):455–77.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Chen S, Sun S, Wang R, Feng H, Xiang H. Haloferax mediterranei R4 confers antagonistic and defensive capabilities. Appl Environ Microbiol. 2021;87(8):e02889–02820.

  58. Chaumeil PA, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the genome taxonomy database. Bioinformatics. 2019;36(6):1925–7.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Gurevich A, Saveliev V, Vyahhi N, Tesler G. QUAST: quality assessment tool for genome assemblies. Bioinformatics. 2013;29(8):1072–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Parks DH, Rinke C, Chuvochina M, Chaumeil P-A, Woodcroft BJ, Evans PN, et al. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat Microbiol. 2017;2(11):1533–42.

    Article  CAS  PubMed  Google Scholar 

  62. 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 

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

    Article  CAS  PubMed  Google Scholar 

  64. 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 

  65. Zhou Z, 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 

  66. Kozlowski LP. IPC - isoelectric point calculator. Biol Direct. 2016;11(1):55.

    Article  PubMed  PubMed Central  Google Scholar 

  67. Zhou Z, 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 

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

    Google Scholar 

  69. Zhang H, Yohe T, Huang L, Entwistle S, Wu P, Yang Z, Busk PK, et al. dbCAN2: a meta server for automated carbohydrate-active enzyme annotation. Nucleic Acids Res. 2018;46(W1):W95–W101.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Tu Q, Lin L, Cheng L, Deng Y, He Z. NCycDB: a curated integrative database for fast and accurate metagenomic profiling of nitrogen cycling genes. Bioinformatics. 2019;35(6):1040–8.

    Article  CAS  PubMed  Google Scholar 

  71. Yu X, Zhou J, Song W, Xu M, He Q, Peng Y, et al. SCycDB: a curated functional gene database for metagenomic profiling of sulphur cycling pathways. Appl Environ Microbiol. 2021;21(3):924–40.

    CAS  Google Scholar 

  72. Villanueva RA, Chen Z. ggplot2: elegant graphics for data analysis. Measurement: Interdisciplinary Research and Perspectives(2nd ed). 2016;17:160–7.

    Google Scholar 

  73. Yoav B, Daniel Y. The control of the false discovery rate in multiple testing under dependency. The Annals of Statistics. 2001;29(4):1165–88.

    Google Scholar 

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Acknowledgements

We thank Prof. Xiaobing Wu and Guoping Zhu for providing experimental platform. Dr. Fabrizio Pucci from Université Libre de Bruxelles is thanked for his help in revising this manuscript. We acknowledge the substantial value that the reviewers added to this study during the review process.

Funding

This work was supported by the National Natural Science Foundation of China (no. 41701085), the Innovation and Entrepreneurship Training Program of Anhui Normal University for Undergraduates (no. 202110370090), the Open Project Fund of Anhui Provincial Key Laboratory of Protection and Utilization of Important Biological Resources (no. swzy202008, swzy202011), the Anhui Provincial Engineering Research Centre for Molecular Detection and Diagnostics (2022AH010012) and the Natural Science Foundation of Anhui Province (2208085MC39).

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  1. Yongqin Liu

    Present address: Center for the Pan-third Pole Environment, Lanzhou University, Lanzhou, 730000, China

Authors and Affiliations

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

    Liangzhong Chen, Tao Hong, Zirui Wu, Shaoxing X. Chen & Liang Shen

  2. Anhui Provincial Key Laboratory of Conservation and Exploitation of Biological Resources, Anhui Normal University, Wuhu, 241000, China

    Liangzhong Chen & Liang Shen

  3. 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

    Liangzhong Chen

  4. Centre for Marine Bio-Innovation, University of New South Wales, Sydney, NSW, 2052, Australia

    Weizhi Song

  5. State Key Laboratory of Tibetan Plateau Earth System Science, Environment and Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, 100085, Beijing, China

    Yongqin Liu & Liang Shen

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Liang Shen designed the study. Shaoxing Chen collected the samples. Tao Hong performed the experiments. Liangzhong Chen, Zirui Wu and Weizhi Song analysed the data. Liang Shen, Yongqin Liu and Shaoxing Chen interpreted the data and wrote the paper. The authors read and approved the final manuscript.

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Chen, L., Hong, T., Wu, Z. et al. Genomic analyses reveal a low-temperature adapted clade in Halorubrum, a widespread haloarchaeon across global hypersaline environments. BMC Genomics 24, 508 (2023). https://doi.org/10.1186/s12864-023-09597-7

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