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Communication

Metagenomic Analysis of Antarctic Biocrusts Unveils a Rich Range of Cold-Shock Proteins

by
Ekaterina Pushkareva
1,*,
Josef Elster
2,3 and
Burkhard Becker
1
1
Department of Biology, Botanical Institute, University of Cologne, Zulpicher Str. 47B, 50674 Cologne, Germany
2
Institute of Botany, Academy of Sciences of the Czech Republic, Dukelska 135, 37982 Trebon, Czech Republic
3
Centre for Polar Ecology, University of South Bohemia, Na Zlate Stoce 3, 37005 Ceske Budejovice, Czech Republic
*
Author to whom correspondence should be addressed.
Microorganisms 2023, 11(8), 1932; https://doi.org/10.3390/microorganisms11081932
Submission received: 24 May 2023 / Revised: 24 July 2023 / Accepted: 27 July 2023 / Published: 28 July 2023
(This article belongs to the Section Environmental Microbiology)

Abstract

:
Microorganisms inhabiting Antarctic biocrusts develop several strategies to survive extreme environmental conditions such as severe cold and drought. However, the knowledge about adaptations of biocrusts microorganisms are limited. Here, we applied metagenomic sequencing to study biocrusts from east Antarctica. Biocrusts were dominated by cyanobacteria, actinobacteria and proteobacteria. Furthermore, the results provided insights into the presence and abundance of cold shock proteins (Csp), cold shock domain A proteins (CsdA), and antifreeze proteins (AFP) in these extreme environments. The metagenomic analysis revealed a high number of CsdA across the samples. The majority of the Csp recorded in the studied biocrusts were Csp A, C, and E. In addition, CsdA, Csp, and AFP primarily originated from proteobacteria and actinobacteria.

1. Introduction

Biological soil crusts (biocrusts) are communities of organisms (microbial phototrophs, heterotrophic bacteria, archaea, fungi, lichens, and mosses) living in the uppermost layer of soil (around 5–10 mm). They contribute to the nutrient and carbon availability, soil formation, and establishment of other organisms. Biocrusts are particularly important in extreme environments where only a few species of vascular plants are present. In these habitats, they are often the only or main primary producers [1].
Microorganisms inhabiting Antarctic biocrusts are either cold tolerant or psychrophiles as they are capable to live below 0°C. They acquire special proteins that prevent cell damage during freezing [2]. For example, antifreeze proteins (AFP) lower the water’s freezing point and avoid the growth of ice crystal in the cell [3]. Similarly, cold shock proteins (Csp) enable efficient transcription and translation during the cold shock [4]. Furthermore, cold-tolerant microorganisms and psychrophiles produce extremozymes, which are able to catalyze chemical reactions under harsh environmental conditions.
The majority of studies investigating Csp, CsdA, and AFP in microorganisms primarily focus on bacteria [3,4], while knowledge about these proteins in algae is limited to a few studies [5,6]. Similarly, the role of these proteins in soil ecosystems, including biocrusts, has been largely overlooked.
Here, we characterized biocrust metagenomes from Enderby Land and Queen Maud Land, Antarctica. Additionally, we provide an overview of cold shock and antifreeze proteins in biocrusts from east Antarctica, which could serve as a baseline for future research on microorganism adaptation in extremely cold environments.

2. Materials and Methods

Biocrust samples from in Enderby Land and Queen Maud Land in east Antarctica were collected in austral summer of 2018/2019 during the Japanese Antarctic Research Expedition (JARE60). The coldest month in these regions is August with an average air temperature of around −19 °C and the warmest is January with an average air temperature of around −1 °C. Five biocrusts samples were collected from four different localities: Amundsen Bay (Amu8 and Amu14), Langhovde Hills (Lang37), Skarvsnes Foreland (Skar18), and Syowa Station (Syo6). The detailed description of the collected samples is presented in Supplementary Table S1.
The pH and conductivity of two technical replicates per each sample were evaluated in demineralized and distilled water, respectively. Total DNA was extracted from each sample using the DNeasy PowerSoil Pro Kit (QIAGEN, USA) according to the manufacturer’s instructions. The extracted DNAs were sent to Eurofins Genomic (Germany), where metagenomic sequencing on an Illumina MiSeq platform was performed. The raw reads were submitted to the Sequence Read Archive (SRA) under the project PRJNA945601.
Bioinformatic analysis was performed in the OmicsBox software (v. 3.0.30) using standard settings [7]. Preprocessing of FASTQ files was conducted in Trimmomatic [8] and the rRNAs were separated from the dataset using SortMeRNA [9]. The taxonomic assignments of the extracted rRNAs were performed using Kraken 2 (v2.1.2; [10]). The remaining reads were assembled de novo for each sample separately using MEGAHIT (v1.2.8; [11]). Gene prediction based on open reading frames (ORFs) was conducted using FragGeneScan [12]. Functional annotations of novel sequences were further performed using precomputed eggNOG-based orthology assignments [13]. The contigs were also aligned to the NCBI Blast searches (E−10) and Gene Ontology (GO) mapping and annotations were performed [14].
In addition, sequences annotated as cold-shock domain A, cold-shock proteins, and antifreeze proteins were extracted from the dataset and taxonomically assigned using Kraken 2.

3. Results and Discussion

Metagenomic sequencing produced around 744 M of quality filtered reads. Of these, 0.2–0.3% were rRNAs. Around 4 M ORFs per sample were predicted with an average length of 369–462 bp (Table 1). Taxonomic analysis of the metagenomic data using Kraken2 revealed the presence of bacteria (64–83% of reads), eukaryotes (0.5–6% of reads), archaea (up to 0.3% of reads), and viruses (up to 0.03% of reads), and 16–42% of reads were not classified (Supplementary Figure S1). Furthermore, the dominant bacterial phylum in the Lang37 and Syo6 samples was cyanobacteria, which is consistent with the results of 16 S rRNA sequencing Contrary to the results of amplicon sequencing, the samples Amu8 and Amu14, analyzed by metagenomic sequencing, exhibited a dominance of actinobacteria, while Skar18 had a higher number of reads assigned to proteobacteria. Previous comparative analysis between these two sequencing techniques demonstrated that metagenomics is a more preferable tool for evaluating microbial community structure when compared to amplicon sequencing [15].
Only 4% of the assembled contigs was annotated to GO by EggNOG (Table 1). The sequences were annotated into four categories: Information Storage and Processing (21% in average), Cellular Processes and Signaling (22% in average), Metabolism (38% in average), and Poorly Characterized (19% in average). No major differences in the proportions of these categories were observed among the sites. In contrast, between 29 and 30% of contigs were annotated to GO with Blast2GO implemented in OmicsBox software. Even though EggNOG-mapper performs fast functional annotation, the database is limited, especially for psychrophilic organisms. For example, antifreeze proteins, which will be discussed later, were annotated by Blast2GO, but were not found in the EggNOG dataset. Therefore, we used Blast2GO annotations for further results and discussions.
Cold-shock proteins (Csp) are known to help organisms to survive a rapid temperature drop and maintain physiological performance during a cold stress episode. Here, we recorded between 194 to 439 genes assigned to different Csp subfamilies in the studied biocrusts (Figure 1, Supplementary Table S2), which represents a higher number of genes compared to previous studies on Antarctic soil [16]. CspA, C, and E were the dominant subfamilies within all cold-shock proteins. CspA facilitates translation at low temperature by destabilizing mRNA structures and is the major Csp in some bacteria [17,18]. In the studied biocrusts, CspA was found to be the most abundant Csp (Figure 1). Furthermore, the majority of reads annotated as CspA were identified by Kraken2 as originating from bacteria. Three specific genera, namely Hymenobacter (Bacteroidota), Polymorphobacter, and Sphingomonas (proteobacteria), were consistently detected across all the analyzed samples. The presence of CspA has been already recorded in the available genomes of these genera documented at the UniProt database. Nevertheless, 25–38% of the CspA reads could not be classified to any known taxa. This may suggest the presence of either yet-to-be-described bacterial taxa or under-represented eukaryotic organisms in the Kraken2 database. In addition, CspC and CspE are involved in regulation of expression of stress response proteins such as RpoS and UspA [4], which were also observed in the studied samples. Most of the reads annotated as CspC and E were classified under the phylum Pseudomonadota, which is consistent with previous findings of the presence of cold shock proteins in the available genome of Pseudomonas [19]. In contrast to bacteria, eukaryotic microalgae, particularly Chlamydomonas, contain only a single cold shock protein, NAB1 [5], and we did not observe it in the metagenomic datasets.
Cold shock DEAD-box protein A (CsdA) is essential for cold-tolerant and psychrophilic microorganisms as it prevent the formation of double-stranded mRNA stabilized at low temperatures and, subsequently, allows the translation of the mRNA by the ribosomal complex [20]. Between 299 and 653 CsdA were detected in the Antarctic biocrusts, and the majority originated from Actinomycetes (Figure 1; Supplementary Table S3). CsdA has been previously observed in the genomes of actinobacteria taxa [21].
Antifreeze proteins (AFP) are ice-binding proteins that inhibit ice crystal growth and, thus, protect the microorganism cell damage due to freezing. We reported 6–48 AFP in the collected biocrust samples (Figure 1). Actinomycetes were the only bacterial class associated with the recorded AFP (Supplementary Table S4). A few strains of Actinomycetes, specifically from the genera Subtercola and Cryobacterium, isolated from cryoconite holes in Svalbard, were previously observed to exhibit AFP activity in the culture medium [22]. However, the majority of the reads assigned as AFP were not identified by Kraken 2. These proteins were previously reported in Antarctic eukaryotic microalgae, such as Chlamydomonas and Chlorominima [6,23]. Chlamydomonas, as a typical representative of polar biocrusts [1], could potentially be the source of the unclassified AFP in this study.
Overall, the composition Csp, CsdA, and AFP in the studied biocrusts did not differ pronouncedly among the different sites. However, the biocrust sample with penguin feather (Amu14) exhibited a higher number of CsdA. Despite having a lower abundance of bacteria, Amu14 had a higher number of eukaryotic reads compared to the other samples. Considering that the amplicon sequencing showed the dominance of Chloroplastida within the eukaryotic community in this sample , it could be suggested that microalgae might be the main source of the CsdA in the Antarctic biocrusts. The presence of CsdA was confirmed in Chlamydomonas [5], but unfortunately, there has been a limited number of studies investigating CsdA genes in other eukaryotic microalgae. In addition, the similarity of the majority of the sequences to the NCBI database was below 90%, suggesting the possible occurrence of novel Csp, CsdA, and AFP in the studied biocrusts (Supplementary Table S5).
In conclusion, this study provides valuable insights into the metagenomic profile of biocrusts in Antarctica. Moreover, it lays the foundation for future investigations on the adaptation of microorganisms in extreme cold environments and highlights the importance of understanding the role of cold shock and antifreeze proteins in Antarctic biocrusts.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/microorganisms11081932/s1; Figure S1: Relative abundance of the Antarctic biocrust microorganisms assessed by metagenomic sequencing; Table S1: Description of the biocrusts collected in Antarctic.; Table S2: List of the taxa associated with cold shock proteins (Csp) recorded in the Antarctic biocrusts.; Table S3: List of the taxa associated with cold shock domain A (CsdA) recorded in the Antarctic biocrusts.; Table S4: List of the taxa associated with antifreeze proteins (Afp) recorded in the Antarctic biocrusts.; Table S5: BLAST top hit with similarity (%) of the sequences assigned as cold-shock proteins (Csp), cold-shock domain (CsdA) and antifreeze proteins (AFP) in the Antarctic biocrusts.

Author Contributions

E.P. and B.B. designed the study. J.E. collected the samples. E.P. performed the laboratory work and processed the data. E.P. wrote the manuscript. B.B. and J.E performed the revision. All authors have read and agreed to the published version of the manuscript.

Funding

E.P. and B.B. were supported by the Deutsche Forschungsgemeinschaft (DFG) within the project BE1779/23-1 which is part of the Priority Program 1158 ‘Antarctic Research’. J.E. was funded by the Czech Science foundation [project 22–08680 L] and by the Czech Academy of Sciences [long-term research development project No. RVO 67985939]. We acknowledge support for the Article Processing Charge from the DFG (German Research Foundation, 491454339).

Data Availability Statement

The raw reads were submitted to the Sequence Read Archive (SRA) under the project PRJNA945601.

Acknowledgments

We would like to thank Sakae Kudoh, Satoshi Imura, Tomotake Wada, Masahiro Otani, Sho Shimada, Takuhei Shiozaki, Fumino Maruo, and all JARE60 expedition members for assisting in the collecting the samples.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Büdel, B.; Colesie, C. Biological Soil Crusts. In Antarctic Terrestrial Microbiology: Physical and Biological Properties of Antarctic Soil Habitats; Cowan, D., Ed.; Springer: Berlin/Heidelberg, Germany, 2014; pp. 131–161. ISBN 978-3-642-45212-3. [Google Scholar]
  2. D’Amico, S.; Collins, T.; Marx, J.C.; Feller, G.; Gerday, C. Psychrophilic Microorganisms: Challenges for Life. EMBO Rep. 2006, 7, 385–389. [Google Scholar] [CrossRef] [Green Version]
  3. Eskandari, A.; Leow, T.C.; Rahman, M.B.A.; Oslan, S.N. Antifreeze Proteins and Their Practical Utilization in Industry, Medicine, and Agriculture. Biomolecules 2020, 10, 1649. [Google Scholar] [CrossRef] [PubMed]
  4. Keto-Timonen, R.; Hietala, N.; Palonen, E.; Hakakorpi, A.; Lindström, M.; Korkeala, H. Cold Shock Proteins: A Minireview with Special Emphasis on Csp-Family of Enteropathogenic Yersinia. Front. Microbiol. 2016, 7, 1151. [Google Scholar] [CrossRef] [Green Version]
  5. Ermilova, E. Cold Stress Response: An Overview in Chlamydomonas. Front. Plant Sci. 2020, 11, 569437. [Google Scholar] [CrossRef] [PubMed]
  6. Gálvez, F.E.; Saldarriaga-Córdoba, M.; Huovinen, P.; Silva, A.X.; Gómez, I. Revealing the Characteristics of the Antarctic Snow Alga Chlorominima Collina Gen. et Sp. Nov. Through Taxonomy, Physiology, and Transcriptomics. Front. Plant Sci. 2021, 12, 1050. [Google Scholar] [CrossRef]
  7. OmicsBox—Bioinformatics Made Easy (Version 3.0.30). BioBam Bioinformatics. 2019. Available online: https://www.biobam.com/omicsbox (accessed on 24 May 2023).
  8. Bolger, A.M.; Lohse, M.; Usadel, B. Trimmomatic: A Flexible Trimmer for Illumina Sequence Data. Bioinformatics 2014, 30, 2114–2120. [Google Scholar] [CrossRef] [Green Version]
  9. Kopylova, E.; Noé, L.; Touzet, H. SortMeRNA: Fast and Accurate Filtering of Ribosomal RNAs in Metatranscriptomic Data. Bioinformatics 2012, 28, 3211–3217. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  10. Wood, D.E.; Lu, J.; Langmead, B. Improved Metagenomic Analysis with Kraken 2. Genome Biol. 2019, 20, 257. [Google Scholar] [CrossRef] [Green Version]
  11. 1Li, D.; Liu, C.M.; Luo, R.; Sadakane, K.; Lam, T.W. MEGAHIT: An Ultra-Fast Single-Node Solution for Large and Complex Metagenomics Assembly via Succinct de Bruijn Graph. Bioinformatics 2015, 31, 1674–1676. [Google Scholar] [CrossRef] [Green Version]
  12. Rho, M.; Tang, H.; Ye, Y. FragGeneScan: Predicting Genes in Short and Error-Prone Reads. Nucleic Acids Res. 2010, 38, e191. [Google Scholar] [CrossRef]
  13. Huerta-Cepas, J.; Forslund, K.; Coelho, L.P.; Szklarczyk, D.; Jensen, L.J.; von Mering, C.; Bork, P. Fast Genome-Wide Functional Annotation through Orthology Assignment by EggNOG-Mapper. Mol. Biol. Evol. 2017, 34, 2115–2122. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Götz, S.; García-Gómez, J.M.; Terol, J.; Williams, T.D.; Nagaraj, S.H.; Nueda, M.J.; Robles, M.; Talón, M.; Dopazo, J.; Conesa, A. High-Throughput Functional Annotation and Data Mining with the Blast2GO Suite. Nucleic Acids Res. 2008, 36, 3420–3435. [Google Scholar] [CrossRef] [PubMed]
  15. Becker, B.; Pushkareva, E. Metagenomics Provides a Deeper Assessment of the Diversity of Bacterial Communities in Polar Soils Than Metabarcoding. Genes 2023, 14, 812. [Google Scholar] [CrossRef]
  16. Koo, H.; Hakim, J.A.; Morrow, C.D.; Crowley, M.R.; Andersen, D.T.; Bej, A.K. Metagenomic Analysis of Microbial Community Compositions and Cold-Responsive Stress Genes in Selected Antarctic Lacustrine and Soil Ecosystems. Life 2018, 8, 29. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  17. Jiang, W.; Hou, Y.; Inouye, M. CspA, the Major Cold-Shock Protein of Escherichia coli, Is an RNA Chaperone. J. Biol. Chem. 1997, 272, 196–202. [Google Scholar] [CrossRef] [Green Version]
  18. Muchaamba, F.; Stephanand, R.; Tasara, T. Listeria Monocytogenes Cold Shock Proteins Small Proteins with A Huge Impact. Microorganisms 2021, 9, 1061. [Google Scholar] [CrossRef] [PubMed]
  19. Cabanás, C.G.L.; Legarda, G.; Ruano-Rosa, D.; Pizarro-Tobías, P.; Valverde-Corredor, A.; Niqui, J.L.; Triviño, J.C.; Roca, A.; Mercado-Blanco, J. Indigenous Pseudomonas Spp. Strains from the Olive (Olea Europaea L.) Rhizosphere as Effective Biocontrol Agents against Verticillium Dahliae: From the Host Roots to the Bacterial Genomes. Front. Microbiol. 2018, 9, 277. [Google Scholar] [CrossRef] [Green Version]
  20. Kuhn, E. Toward Understanding Life under Subzero Conditions: The Significance of Exploring Psychrophilic “Cold-Shock” Proteins. Astrobiology 2012, 12, 1078–1086. [Google Scholar] [CrossRef] [PubMed]
  21. López-Ramírez, V.; Alcaraz, L.D.; Moreno-Hagelsieb, G.; Olmedo-Alvarez, G. Phylogenetic Distribution and Evolutionary History of Bacterial DEAD-Box Proteins. J. Mol. Evol. 2011, 72, 413–431. [Google Scholar] [CrossRef] [Green Version]
  22. Singh, P.; Hanada, Y.; Singh, S.M.; Tsuda, S. Antifreeze Protein Activity in Arctic Cryoconite Bacteria. FEMS Microbiol. Lett. 2014, 351, 14–22. [Google Scholar] [CrossRef] [Green Version]
  23. Raymond, J.A.; Janech, M.G.; Fritsen, C.H. Novel Ice-Binding Proteins from a Psychrophilic Antarctic Alga (Chlamydomonadaceae, Chlorophyceae). J. Phycol. 2009, 45, 130–136. [Google Scholar] [CrossRef] [PubMed]
Figure 1. Metagenomic profile of cold-shock proteins (Csp), cold-shock domain (CsdA), and antifreeze proteins (AFP) in the biocrusts from east Antarctica. White color indicates no genes recorded in the sample.
Figure 1. Metagenomic profile of cold-shock proteins (Csp), cold-shock domain (CsdA), and antifreeze proteins (AFP) in the biocrusts from east Antarctica. White color indicates no genes recorded in the sample.
Microorganisms 11 01932 g001
Table 1. General overview on metagenomic sequencing of the Antarctic biocrusts.
Table 1. General overview on metagenomic sequencing of the Antarctic biocrusts.
Amu8Amu14Lang37Skar18Syo6
AssemblyNumber of contigs4,077,6853,483,8272,978,3912,810,5693,662,403
N5012001228152416851307
GO annotationBlast, mapped and annotated contigs1,206,0141,008,266865,031823,4421,107,021
%29.628.929.029.330.2
EggNOG GO annotated contigs163,852154,537114,524109,335147,282
%4.04.43.83.94.0
ORFsPredicted ORFs4,873,8004,214,6313,651,8263,427,9334,430,369
Avg. gene length (nt)369462384379373
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Pushkareva, E.; Elster, J.; Becker, B. Metagenomic Analysis of Antarctic Biocrusts Unveils a Rich Range of Cold-Shock Proteins. Microorganisms 2023, 11, 1932. https://doi.org/10.3390/microorganisms11081932

AMA Style

Pushkareva E, Elster J, Becker B. Metagenomic Analysis of Antarctic Biocrusts Unveils a Rich Range of Cold-Shock Proteins. Microorganisms. 2023; 11(8):1932. https://doi.org/10.3390/microorganisms11081932

Chicago/Turabian Style

Pushkareva, Ekaterina, Josef Elster, and Burkhard Becker. 2023. "Metagenomic Analysis of Antarctic Biocrusts Unveils a Rich Range of Cold-Shock Proteins" Microorganisms 11, no. 8: 1932. https://doi.org/10.3390/microorganisms11081932

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