Next Article in Journal
Nitrogen Cycling in Widgeongrass and Eelgrass Beds in the Lower Chesapeake Bay
Previous Article in Journal
Empirical Correlation between Electrical Conductivity and Nitrogen Content in Biochar as Influenced by Pyrolysis Temperature
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Nitrogen
Volume 5
Issue 2
10.3390/nitrogen5020020
nitrogen-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Whole Genome Analysis of Streptomyces spp. Strains Isolated from the Rhizosphere of Vitis vinifera L. Reveals Their Role in Nitrogen and Phosphorus Metabolism

by
Gustavo Montes-Montes
1,2,
Román González-Escobedo
2,*,
Laila N. Muñoz-Castellanos
1,
Graciela D. Avila-Quezada
3,
Obed Ramírez-Sánchez
4,
Alejandra Borrego-Loya
1,
Ismael Ortiz-Aguirre
2 and
Zilia Y. Muñoz-Ramírez
1,*
1
Facultad de Ciencias Químicas, Universidad Autónoma de Chihuahua, Campus II Circuito Universitario s/n, Chihuahua 31125, Mexico
2
Facultad de Zootecnia y Ecología, Universidad Autónoma de Chihuahua, Periférico Francisco R. Almada km 1, Chihuahua 31453, Mexico
3
Facultad de Ciencias Agrotecnológicas, Universidad Autónoma de Chihuahua, Campus I s/n, Chihuahua 31350, Mexico
4
Soil Genomics & Discovery Department, Solena Inc. Av. Olímpica 3020-D, Villas de San Juan, León 37295, Mexico
*
Authors to whom correspondence should be addressed.
Nitrogen 2024, 5(2), 301-314; https://doi.org/10.3390/nitrogen5020020
Submission received: 18 February 2024 / Revised: 24 March 2024 / Accepted: 15 April 2024 / Published: 16 April 2024
(This article belongs to the Special Issue Nitrogen Cycling and Bacterial Community)
Browse Figures
Versions Notes

Abstract

:
The rhizospheric microorganisms of agricultural crops play a crucial role in plant growth and nutrient cycling. In this study, we isolated two Streptomyces strains, Streptomyces sp. LM32 and Streptomyces sp. LM65, from the rhizosphere of Vitis vinifera L. We then conducted genomic analysis by assembling, annotating, and inferring phylogenomic information from the whole genome sequences. Streptomyces sp. strain LM32 had a genome size of 8.1 Mb and a GC content of 72.14%, while Streptomyces sp. strain LM65 had a genome size of 7.3 Mb and a GC content of 71%. Through ANI results, as well as phylogenomic, pan-, and core-genome analysis, we found that strain LM32 was closely related to the species S. coelicoflavus, while strain LM65 was closely related to the species S. achromogenes subsp. achromogenes. We annotated the functional categories of genes encoded in both strains, which revealed genes involved in nitrogen and phosphorus metabolism. This suggests that these strains have the potential to enhance nutrient availability in the soil, promoting agricultural sustainability. Additionally, we identified gene clusters associated with nitrate and nitrite ammonification, nitrosative stress, allantoin utilization, ammonia assimilation, denitrifying reductase gene clusters, high-affinity phosphate transporter and control of PHO regulon, polyphosphate, and phosphate metabolism. These findings highlight the ecological roles of these strains in sustainable agriculture, particularly in grapevine and other agricultural crop systems.

1. Introduction

The rhizosphere, which is the layer of soil surrounding plant roots, serves as an ecological niche where microorganisms interact with the host [1]. These interactions include bacteria that play a vital role in nitrogen and phosphorus metabolism, which are essential for soil health, productivity, and plant growth. These bacteria are involved in various processes such as denitrification, nitrate, and nitrite ammonification, as well as activities related to phosphate uptake, regulation, and utilization. They have the capability to efficiently utilize and recycle compounds, thereby enhancing plant development, nutrient uptake, and soil quality [2].
The grapevine (Vitis vinifera L.) is a perennial woody plant that plays a vital role in the global economy and society. Vineyards span approximately 7.5 million hectares worldwide and yield around 35.9 million tonnes of wine. In Mexico, viticulture and wine production have become a significant economic activity, resulting in an annual wine production of 36 million liters and involving the cultivation of approximately 73,000 tons of grapes [3].
Several studies have been conducted on the microbial communities associated with grapevines, specifically focusing on the microbial communities found in the endosphere, phyllosphere, and rhizosphere [4]. The rhizosphere is of particular importance due to the crucial role microorganisms play in soil biogeochemical processes, such as nitrogen and phosphorus metabolism [5,6]. The presence and activity of bacteria in the rhizosphere are essential for sustainable agriculture as they help reduce reliance on synthetic nitrogen and phosphorus fertilizers, thereby mitigating the negative environmental impacts associated with their production and usage, including water pollution and greenhouse gas emissions [7]. The Streptomyces bacterial genus plays a vital role in microbial dynamics and plant health. It promotes plant growth by participating in phosphorus and nitrogen metabolism [8]. Furthermore, it contributes to plant health by producing antimicrobial compounds [9,10] and other bioactive compounds involved in biological control [11,12,13]. Microbial nitrogen and phosphorus metabolism are crucial processes in the dynamics of biogeochemical cycles. Certain microorganisms possess the unique ability to convert nitrogen and phosphorus into chemically assimilable forms for living organisms. This process not only enhances plant growth but also influences soil fertility, thereby impacting the productivity and sustainability of terrestrial ecosystems [14,15].
In this context, genomic analyses applied to the study of biological nitrogen and phosphorus metabolism by Streptomyces spp. have provided a comprehensive understanding of the molecular mechanisms involved. These analyses have opened up new possibilities for enhancing agricultural sustainability and managing soil fertility [16,17,18]. These advanced tools enable a more precise comprehension of plant–microorganism interactions and offer opportunities to optimize the utilization of this crucial biological function for the benefit of agriculture and ecosystem health. Therefore, the objective of this study was to analyze the whole genome of two strains of Streptomyces spp. that were isolated from the rhizosphere of grapevines. To achieve this objective, we performed whole genome sequencing, assembly, annotation, phylogenomic analysis, as well as pan- and core-genome analysis to identify closely related species of Streptomyces. Additionally, gene functional annotation analysis was conducted to infer the functional capabilities of these Streptomyces strains in nitrogen and phosphorus metabolism.

2. Materials and Methods

2.1. Isolation of the Bacterial Streptomyces spp. Strains

The bacterial strains used in this study were obtained from a previous study, where a total of 122 bacteria were isolated from the rhizospheric soil of Vitis vinifera in Sacramento, Chihuahua, Mexico (28°50′04.9″ N, 106°15′25.1″ W). They were selected based on their demonstrated biological activities implicated in the production of agriculturally significant compounds. Soil samples were collected at a depth of 15–20 cm in the vineyard rhizosphere, and serial dilutions were streaked on Ashby mannitol agar. After 5–7 days of incubation at 30 ± 2 °C, individual bacterial colonies were further purified. The isolates were cryopreserved in 15% glycerol and stored at −80 °C.

2.2. Genomic DNA Extraction, Library Preparation, and Sequencing

Total DNA was extracted from each sample using a ZymoBIOMICSTM DNA Miniprep Kit (Zymo Research, Irvine, CA, USA) following the manufacturer’s instructions. The DNA quality and quantity were determined by using a NanoDrop spectrophotometer (Thermo Scientific, Wilmington, DE, USA) based on its A260/280 ratio, and observed in a 1.0% agarose gel electrophoresis. For genomic library preparation and sequencing, the total DNA was shipped to Illumina (San Diego, CA, USA). Briefly, 100 ng of total DNA was processed following instructions of Illumina DNA prep (M) tagmentation kit (#Cat. 20018705). Because many samples were run in the same flow cell, specific indexes were added to each DNA library with IDT for Illumina DNA/RNA UD Indexes Set A Tagmentation (#Cat. 20027213). Library concentration was quantified with Qubit dsDNA HS Assay kit (Thermo Fisher Scientific, Waltham, MA, USA) and the integrity of DNA libraries was assessed with the Bioanalyzer 2100 Agilent (NGS 1-6000 kit). Libraries were sequenced in an S4 flow cell in a 2 × 150 bp strategy on an Illumina NovaSeq 6000 sequencer (Illumina Inc., San Diego, CA, USA) [19].

2.3. Genome De Novo Assembly and Annotation

Illumina raw sequence reads of Streptomyces sp. strain LM32 and Streptomyces sp. strain LM65 were processed by trimming and filtering using a 4 bp Q20 sliding window in Trimmomatic v.0.39 [20]. To assess the quality of the obtained reads, FastQC v.0.11.9 [21] was employed and a de novo assembly was performed using SPAdes v.3.14.1 [22]. The assembled genomes were then evaluated using QUAST v.5.1.0rc1 [23]. To predict gene clusters related to nitrogen and phosphorus metabolism, the resulting complete genomes were annotated using Prokka v.1.14.5 [24] and RASTtk v.1.3.0 [25]. Finally, the gene function annotations were visualized as arrow diagrams using the ggplot2 library [26] and the gggenes library [27] in RStudio [28].

2.4. Genomes Selection and Phylogenomic Analysis of Streptomyces spp.

In order to conduct a comprehensive analysis of Streptomyces genomes, 1–4 genomes were downloaded for each species from the NCBI database (Table S1), resulting in a total of 307 genomes. Additionally, the genome of Escherichia coli str. K-12 substr. MG1655 (NC_000913.3) was downloaded as a reference outgroup for the phylogenetic tree. All statistical data (n = 308) were obtained using QUAST v.5.1.0rc1 [23]. Subsequently, a phylogenomic analysis was performed on 310 genome sequences, including the Streptomyces strains from this study, using the Virtual Analysis Method for Phylogenomic Fingerprint Estimation (VAMPhyRE v.2020; https://biomedbiotec.encb.ipn.mx/VAMPhyRE accessed on 5 January 2024), following the procedure described by Muñoz-Ramírez et al. [29]. Briefly, VAMPhyRE was used to determine Virtual Genome Fingerprints (VGF) from the bacterial genomes in our dataset, including both complete and draft forms. Virtual hybridization was conducted by identifying hybridization sites using a collection of 15,264 VAMPhyRE probes, each 13 nucleotides long (VPS-13), targeting both the positive (+) and negative (−) strands of the genomes, allowing for a single mismatch. Each genome’s VGF is constituted by the assemblage of hybridization sites. Genomic distances were calculated by comparing all pairs of VGF and determining the number of shared homologous sites, resulting in a distance matrix using the methodology described by Nei and Li [30]. To ensure that only homologous sites shared within the distance metrics were considered, an extending-match technique was employed, where seven bases at both ends of the sites were extended, and homologous sites were determined by a minimum criterion of 27 base matches. The phylogenomic tree was constructed using the Neighbor-Joining method with the software MEGA 11 [31], and further refined and annotated using iTOL v.3 [32].

2.5. Average Nucleotide Identity Analysis

To gain a deeper understanding of the similarities between whole genomes and to determine whether two genomes share genomic identities above or below the species threshold, an Average Nucleotide Identity (ANI) analysis was performed. This analysis involved comparing Streptomyces sp. strain LM32, Streptomyces sp. strain LM65, and other reference strains of Streptomyces available in the NCBI database. Correlation indexes of tetra-nucleotide signatures (Tetra), ANIm, and FastANI values were calculated using the JspeciesWS web service [33] and FastANI [34]. The representation of visual reciprocal mappings between two pairs of Streptomyces genomes was plotted with a Python script (visualize.py) [35]. The ANI value, based on whole genome sequences, has been widely accepted as a reliable method for determining whether organisms belong to the same species, with a typical threshold of ≥95% ANI [34].

2.6. Pan- and Core-Genome Analysis

An analysis of the pan- and core-genome was conducted for the genomes within the same cluster of strains LM32 and LM65. In the case of Streptomyces sp. strain LM32, the genomes of S. coelicoflavus strains DBR11, NBC_00465, NBRC 15399, and S3018 were included in the analysis. Conversely, for Streptomyces sp. strain LM65, the analysis was performed on the genome of S. achromogenes subsp. achromogenes NRRL B-2120 and included the genomes of S. achromogenes strains W4I19-2 and B2I10. Initially, annotation of the genomes was conducted using Prokka v.1.14.16 [24] using an e-value of 1 × 10−12. The resulting GFF files from this annotation were then employed for the pan-genome calculation, carried out using the Panaroo pipeline v.1.4.2 [36] in ‘strict’ mode, with a 90% identity threshold for protein sequences and a 75% coverage cut-off for gene length. Subsequently, the presence and absence files generated were used to calculate a Venn diagram utilizing the ggVennDiagram library [37] in RStudio [28].

3. Results and Discussion

3.1. Genome De Novo Assembly

A total of 21,874,730 and 41,761,631 paired-end reads were obtained for Streptomyces sp. strain LM32 and Streptomyces sp. strain LM65, respectively, after processing the high-quality reads. The coverage for strain LM32 was approximately 70×, while for strain LM65 it was approximately 148×. The genome size and GC content for strain LM32 were 8.1 Mb and 72.14%, respectively, while for strain LM65 they were 7.3 Mb and 71%. The quality of the assemblies was assessed using the QUAST software v.5.1.0rc1, resulting in an N50 value of 150,500 and L50 of 18 for strain LM32, and an N50 value of 164,678 and L50 of 16 for strain LM65. Streptomycetes have unique genomic characteristics, including a lengthy linear chromosome ranging from 6 to 12 Mb, and encoding 5300 to 11,000 proteins, which distinguishes them from other bacterial genera [38,39]. In terms of annotation, a total of 7251 coding sequences (CDS), 83 tRNA, and 8 rRNA were identified in strain LM32, whereas in strain LM65, a total of 6440 CDS, 99 tRNA, and 4 rRNA were identified. The genome characteristics of both LM32 and LM65 strains are summarized in Table 1.

3.2. Phylogenomic Analysis of Streptomyces Genomes

The whole genome sequences (Table S1) were analyzed using VAMPHyRe software v.2020 to identify specific genomic fingerprints of Streptomyces species. The phylogenomic analyses placed strain LM32 within the species S. coelicoflavus, whose genomes were recovered from soil samples. Similarly, strain LM65 was classified within the species S. achromogenes subsp. achromogenes (Figure 1), whose genome was also recovered from a soil sample [40]. It has been demonstrated that analyzing whole genome sequences instead of MLST/16S rRNA can result in better clustering and taxonomic assignment of bacterial strains. This is primarily due to the complexity associated with the vast amount of biological information analyzed with the VGF [29].
The species identification was reinforced using both phylogenomic analysis and ANI results (Figure 2). Streptomyces sp. strain LM32 exhibited a FastANI value of 95.61%, an ANIm value of 95.62%, and a Tetra correlation of 0.99966 with S. coelicoflavus strain DBR11, which was isolated from soil samples in Assam, India. Based on the cutoff threshold of > 95%, the ANI values and phylogenomic analysis indicated a close relationship between strain LM32 and the species S. coelicoflavus. Similarly, Streptomyces sp. strain LM65 strain showed cutoff values >95% with FastANI (98.87%), ANIm (98.93%), and Tetra correlation (0.99983) with S. achromogenes subsp. achromogenes strain NRRL B-2120, which was isolated from soil samples in Tokyo, Japan [40]. This result suggests a close relationship between strain LM65 and the compared species [33,34]. It is noteworthy that in the case of S. achromogenes, within the results of the phylogenomic analysis, two of the deposited genomes (strains B2I10 and W4I19-2) clustered into a distinct clade from that of S. achromogenes subsp. achromogenes and Streptomyces sp. strain LM65. Upon proceeding with the ANI analyses, the results for these two strains exhibited ANI values significantly lower than 95% (Table S2). Upon further investigation into these two strains, their taxonomy check status on the NCBI page appears inconclusive, indicating a potential misassignment.

3.3. Pan- and Core-Genome

The pan- and core-genome analysis presented in the Venn diagram (Figure 3) reveals patterns of conservation and genetic diversity within the examined Streptomyces species. The core-genome, represented by the central intersections of the diagram, underscores a set of essential genes shared among the strains, reflecting their evolutionary heritage and fundamental biological functions, and the pan-genome is the set of all genes that are present in the analyzed dataset [41]. Instead, the strain-specific genes, located on the outer portions of the diagram, suggest genomic adaptability possibly linked to survival and specialization in various ecological niches [42]. Notably, the genome of Streptomyces sp. strain LM32 shares most of its genes with the other included genomes, exhibiting a core-genome of 53% (5,308 genes), which implies that Streptomyces sp. strain LM32 maintains a close evolutionary connection with its congeners.
On the other hand, the genome of Streptomyces sp. strain LM65 shares 25% of its genetic content with S. achromogenes subsp. achromogenes NRRL B-2120, indicative of a potential closer phylogenetic relationship. Conversely, the comparison of Streptomyces sp. strain LM65 with S. achromogenes W4I19-2 and S. achromogenes B2I10 shows less than 1% genetic overlap with each, suggesting a significant genetic divergence. Moreover, the comparison between S. achromogenes W4I19-2 and S. achromogenes B2I10 demonstrates a 36% shared genetic content, denoting a close relationship as corroborated by the phylogenetic tree (Figure 1), distinctly separate from the clade comprising Streptomyces sp. strain LM65 and S. achromogenes subsp. achromogenes NRRL B-2120. In addition, the pan and core genome analysis enabled the identification of genes detected as unique to the strains in this study, with some implicated in the metabolism of nitrogen. In the case of Streptomyces sp. strain LM32, a nitrite reductase [NAD(P)H] was detected as unique, while in Streptomyces sp. strain LM65, genes encoding the respiratory nitrate reductase alpha, beta, and delta chains were identified. These genes and their products are of significance in addressing extreme nutrient and energy limitations through the efficient utilization of nitrate as an energy source. This unique genetic advantage provides them with a competitive edge, particularly in environments rich in nitrate, maintaining their metabolic activity as a key survival strategy [43].
In general, there are no studies describing the involvement of S. coelicoflavus and S. achromogenes in nitrogen and phosphorus metabolism. Existing studies have instead focused on investigating their microbial capabilities in biocontrol through the production of secondary metabolites. For example, S. coelicoflavus has been utilized as a biological control agent [44], demonstrating its ability to inhibit quorum sensing [45] and produce extracellular enzymes such as peroxidase, laccase [46], and cellulases [47]. On the other hand, biosynthetic genes have been identified in the genome of S. achromogenes [48], some of which have been reported as potential sources of bioactive metabolites with antioxidant and anticancer activities [49]. Additionally, S. achromogenes has been shown to exhibit antifungal action against pathogens like Alternaria alternata, Mucor fragilis, and Fusarium brachygibbosum. Furthermore, it has been highlighted for its growth-stimulating activities when interacting with tomato plants [50].

3.4. Gene Functional Annotation

The functional categories of genes encoded in both Streptomyces strains LM32 and LM65 were annotated using the RAST server (Figure 4). This server predicted functional subsystems that included genes involved in various cellular activities, such as nitrogen and phosphorus metabolism. The nitrogen and phosphorus metabolism subsystem encompasses a range of biochemical pathways and enzymes that are responsible for the uptake, assimilation, and utilization of nitrogen and phosphorus compounds by microorganisms. Some of these pathways are involved in processes like nitrate and nitrite ammonification, nitrosative stress, allantoin utilization, ammonia assimilation, denitrifying reductase gene clusters, high-affinity phosphate transporter, and control of PHO regulon, polyphosphate, and phosphate metabolism. Among the Streptomyces genomes reported in the GenBank, the majority have been isolated from terrestrial environments, such as soil and land plants [39]. This preference for inhabiting terrestrial settings is due to Streptomyces’ significant role in nitrogen and phosphorus metabolism, which is facilitated by the metabolic pathways described above [51,52].
In order to identify the genes responsible for nitrogen and phosphorus metabolism, we conducted analyses using RAST software (Figure 5 and Table 2). The results showed that Streptomyces sp. strain LM32 and Streptomyces sp. strain LM65 encode gene clusters involved in denitrification. These gene clusters include the nitrate reductase complex, which consists of narG, narI, narX, and narH. In S. coelicolor, it has been demonstrated that this complex catalyzes the reduction of nitrate to nitrite, coupling this process to energy conservation under anoxic conditions, allowing the bacteria to survive and remain metabolically active [43,53]. Additionally, the activation of genes associated with pathways related to nitrate reduction has been observed in various bacterial groups. Among these genes, narH encodes a peptide with multiple sites for the binding of cofactors, narG is involved in the reduction in dinitrogen and ammonia, and nasD is involved in the reduction in nitrate in the cytoplasm [54].
Regarding the assimilation of ammonia, it plays a crucial role in the overall nitrogen metabolism of organisms. The process is regulated by the glutamine synthetase complex, specifically through the involvement of glnA and glnD, which control nitrogen metabolism transcriptionally in response to nitrogen concentration [55,57,58,64]. On the other hand, the utilization of allantoin is of significant importance in various organisms as it serves as a nitrogen and carbon source. Allantoin, derived from purine metabolism, is a valuable nutrient for bacteria like S. coelicolor. In this organism, the assimilation of purines follows a pathway regulated by genes such as alc and gcl, which are distributed across different regions. This suggests that both genes are involved in the regulation of purine catabolism and the production of antibiotics. Furthermore, it is possible that the enzymes generated by this pathway are also involved in the specific regulation of antioxidants and the disruption of oxidative homeostasis [59]. Additionally, in response to stress caused by reactive nitrogen species compounds, NsrR plays a regulatory role in reducing the concentration of nitric oxide. This mechanism is attributed to the loss of an iron–sulfur center in the structure [56,65].
In relation to phosphorus metabolism, genes that are known to have crucial roles in the uptake, regulation, and efficient utilization of phosphate through its metabolism have been identified. These genes include coding genes for both high- and low-affinity phosphate transporters, transcriptional regulators of the PHO regulon, enzymes involved in polyphosphate synthesis and degradation, as well as proteins involved in energy generation from inorganic pyrophosphate. Specifically, genes such as PhoR and PhoB in S. coelicolor have been found to be necessary for the full induction of the pho regulon, which is important for phosphate regulation. Additionally, these genes indirectly participate in nitrogen and carbon metabolism through interactions with their main regulators [61]. Other important genes that have been identified include those that encode for polyphosphate kinase, exopolyphosphatase, and inorganic pyrophosphatase. These genes have been reported in genera of the Actinomycetota phylum, such as S. albulus [62], Corynebacterium glutamicum [63], and Streptomyces alfalfae [66]. These genes are involved in assembly processes by catalyzing the synthesis of polyphosphate from inorganic phosphate or the reverse process, and they also play a role in recycling processes through the hydrolysis of polyphosphates and inorganic pyrophosphate. This leads to the release of inorganic phosphate, contributing to the overall functioning of phosphate cycling and nutrient dynamics in the soil.

4. Conclusions

In summary, this study has developed de novo complete genome assemblies for two strains of Streptomyces. This has allowed for a deeper understanding of the genomic composition, genetic similarity, and the genes related to nitrogen and phosphorus metabolism. Through phylogenomic analysis, ANI results, as well as pan- and core-genome analysis, it was determined that the LM32 strain was closely related to the species S. coelicoflavus, while the LM65 strain was closely related to the species S. achromogenes subsp. achromogenes. The functional annotation of genes in both strains revealed their involvement in nitrogen and phosphorus metabolism. Specifically, genes related to nitrate reduction, ammonia assimilation, and allantoin utilization were identified. Additionally, genes associated with phosphate metabolism, such as phosphate transporters and enzymes involved in polyphosphate synthesis and degradation, were also discovered. These findings are crucial for understanding the ecological roles of these strains in the rhizospheric soil of Vitis vinifera L.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/nitrogen5020020/s1, Table S1: Details and statistics of the 308 genome sequences downloaded. Table S2: ANI results.

Author Contributions

Conceptualization, R.G.-E. and Z.Y.M.-R.; methodology, G.M.-M., G.D.A.-Q., O.R.-S., I.O.-A., A.B.-L., L.N.M.-C. and Z.Y.M.-R.; validation, R.G.-E. and Z.Y.M.-R.; formal analysis, G.M.-M., R.G.-E. and Z.Y.M.-R.; investigation, R.G.-E. and Z.Y.M.-R.; resources, G.D.A.-Q. and L.N.M.-C.; data curation, G.M.-M. and Z.Y.M.-R.; writing—original draft preparation, R.G.-E. and Z.Y.M.-R.; writing—review and editing, G.M.-M., R.G.-E., G.D.A.-Q., O.R.-S., A.B.-L., I.O.-A., L.N.M.-C. and Z.Y.M.-R.; visualization, G.M.-M. and Z.Y.M.-R.; supervision, Z.Y.M.-R. and R.G.-E.; project administration, G.D.A.-Q. and L.N.M.-C. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The datasets generated and/or analyzed during the current study are available in the NCBI Bioproject database (PRJNA1075334) under the accession number SAMN39912921 (Streptomyces sp. strain LM32) and SAMN39912940 (Streptomyces sp. strain LM65).

Acknowledgments

We thank the National Council of Humanities, Sciences, and Technologies (CONAHCYT-Mexico) for the financial support provided to G.M.-M. throughout their master’s fellowship and to Z.Y.M.-R. throughout their postdoctoral fellowship. We thank the three anonymous reviewers for their valuable comments on the manuscript, which have helped enhance its clarity and quality.

Conflicts of Interest

The authors declare no conflicts of interest. Author O.R.-S. has been involved as a consultant and expert in bioinformatics.

References

  1. Kumawat, K.C.; Razdan, N.; Saharan, K. Rhizospheric Microbiome: Bio-Based Emerging Strategies for Sustainable Agriculture Development and Future Perspectives. Microbiol. Res. 2022, 254, 126901. [Google Scholar] [CrossRef] [PubMed]
  2. Oleńska, E.; Małek, W.; Wójcik, M.; Swiecicka, I.; Thijs, S.; Vangronsveld, J. Beneficial Features of Plant Growth-Promoting Rhizobacteria for Improving Plant Growth and Health in Challenging Conditions: A Methodical Review. Sci. Total Environ. 2020, 743, 140682. [Google Scholar] [CrossRef]
  3. SADER Vino Mexicano Igual a Excelencia. Available online: https://www.gob.mx/agricultura/articulos/vino-mexicano-igual-a-excelencia?idiom=es (accessed on 16 February 2024).
  4. Bettenfeld, P.; Cadena i Canals, J.; Jacquens, L.; Fernandez, O.; Fontaine, F.; van Schaik, E.; Courty, P.E.; Trouvelot, S. The Microbiota of the Grapevine Holobiont: A Key Component of Plant Health. J. Adv. Res. 2022, 40, 1–15. [Google Scholar] [CrossRef] [PubMed]
  5. Yadav, A.; Yadav, K.; Yadav, A.; Yadav, K. Regulation of Plant-Microbe Interactions in the Rhizosphere for Plant Growth and Metabolism: Role of Soil Phosphorus. In Phosphorus Soils Plants; IntechOpen: London, UK, 2024. [Google Scholar] [CrossRef]
  6. Shanthi, V. Actinomycetes: Implications and Prospects in Sustainable Agriculture. In Biofertilizers: Study and Impact; Scrivener Publishing LLC: Beverly, MA, USA, 2021; pp. 335–370. [Google Scholar] [CrossRef]
  7. Nwachukwu, B.C.; Ayangbenro, A.S.; Babalola, O.O. Elucidating the Rhizosphere Associated Bacteria for Environmental Sustainability. Agriculture 2021, 11, 75. [Google Scholar] [CrossRef]
  8. Gamalero, E.; Bona, E.; Novello, G.; Boatti, L.; Mignone, F.; Massa, N.; Cesaro, P.; Berta, G.; Lingua, G. Discovering the Bacteriome of Vitis vinifera Cv. Pinot Noir in a Conventionally Managed Vineyard. Sci. Rep. 2020, 10, 6453. [Google Scholar] [CrossRef]
  9. Kanjanamaneesathian, P.; Shah, A.; Ridgway, H.; Jones, E.E. Diversity and Bioactivity of Endophytic Actinobacteria Associated with Grapevines. Curr. Microbiol. 2022, 79, 390. [Google Scholar] [CrossRef]
  10. Santamaría, R.I.; Martínez-Carrasco, A.; Martín, J.; Tormo, J.R.; Pérez-Victoria, I.; González, I.; Genilloud, O.; Reyes, F.; Díaz, M. Grapevine Xylem Sap Is a Potent Elicitor of Antibiotic Production in Streptomyces spp. Antibiotics 2022, 11, 672. [Google Scholar] [CrossRef]
  11. Abdalla, M.A.; Win, H.Y.; Islam, M.T.; Von Tiedemann, A.; Schüffler, A.; Laatsch, H. Khatmiamycin, a Motility Inhibitor and Zoosporicide against the Grapevine Downy Mildew Pathogen Plasmopara viticola from Streptomyces sp. ANK313. J. Antibiot. 2011, 64, 655–659. [Google Scholar] [CrossRef] [PubMed]
  12. del Pilar Martínez-Diz, M.; Díaz-Losada, E.; Andrés-Sodupe, M.; Bujanda, R.; Maldonado-González, M.M.; Ojeda, S.; Yacoub, A.; Rey, P.; Gramaje, D. Field Evaluation of Biocontrol Agents against Black-Foot and Petri Diseases of Grapevine. Pest Manag. Sci. 2021, 77, 697–708. [Google Scholar] [CrossRef]
  13. Buzón-Durán, L.; Sánchez-Hernández, E.; Sánchez-Báscones, M.; García-González, M.C.; Hernández-Navarro, S.; Correa-Guimarães, A.; Martín-Ramos, P. A Coating Based on Bioactive Compounds from Streptomyces spp. and Chitosan Oligomers to Control Botrytis cinerea Preserves the Quality and Improves the Shelf Life of Table Grapes. Plants 2023, 12, 577. [Google Scholar] [CrossRef]
  14. Rashid, M.H.; Krehenbrink, M.; Akhtar, M.S. Nitrogen-Fixing Plant-Microbe Symbioses. Sustain. Agric. Rev. 2015, 15, 193–234. [Google Scholar] [CrossRef]
  15. Negi, S.; Kumar, P.; Kumar, J.; Singh, A.; Dubey, R.C. Indigenous Nitrogen Fixing Microbes Engineer Rhizosphere and Enhance Nutrient Availability and Plant Growth. In Rhizosphere Engineering; Academic Press: Cambridge, MA, USA, 2022; pp. 19–43. [Google Scholar] [CrossRef]
  16. Pullan, S.T.; Chandra, G.; Bibb, M.J.; Merrick, M. Genome-Wide Analysis of the Role of GlnR in Streptomyces venezuelae Provides New Insights into Global Nitrogen Regulation in Actinomycetes. BMC Genom. 2011, 12, 175. [Google Scholar] [CrossRef] [PubMed]
  17. Liu, D.; Yan, R.; Fu, Y.; Wang, X.; Zhang, J.; Xiang, W. Antifungal, Plant Growth-Promoting, and Genomic Properties of an Endophytic Actinobacterium Streptomyces sp. NEAU-S7GS2. Front. Microbiol. 2019, 10, 2077. [Google Scholar] [CrossRef]
  18. Nonthakaew, N.; Panbangred, W.; Songnuan, W.; Intra, B. Plant Growth-Promoting Properties of Streptomyces spp. Isolates and Their Impact on Mung Bean Plantlets’ Rhizosphere Microbiome. Front. Microbiol. 2022, 13, 967415. [Google Scholar] [CrossRef] [PubMed]
  19. Muñoz-Ramírez, Z.Y.; González-Escobedo, R.; Avila-Quezada, G.D.; Ramírez-Sánchez, O.; Higareda-Alvear, V.M.; Zapata-Chávez, E.; Borrego-Loya, A.; Muñoz-Castellanos, L.N. Exploring Microbial Rhizosphere Communities in Asymptomatic and Symptomatic Apple Trees Using Amplicon Sequencing and Shotgun Metagenomics. Agronomy 2024, 14, 357. [Google Scholar] [CrossRef]
  20. Bolger, A.M.; Lohse, M.; Usadel, B. Trimmomatic: A Flexible Trimmer for Illumina Sequence Data. Bioinformatics 2014, 30, 2114–2120. [Google Scholar] [CrossRef]
  21. Andrews, S.; Krueger, F.; Segonds-Pichon, A.; Biggins, L.; Krueger, C.; Wingett, S. FastQC: A Quality Control Tool for High Throughput Sequence Data; Babraham Bioinformatics, Babraham Institute: Cambridge, UK, 2010. [Google Scholar]
  22. Prjibelski, A.; Antipov, D.; Meleshko, D.; Lapidus, A.; Korobeynikov, A. Using SPAdes De Novo Assembler. Curr. Protoc. Bioinform. 2020, 70, e102. [Google Scholar] [CrossRef]
  23. Gurevich, A.; Saveliev, V.; Vyahhi, N.; Tesler, G. Genome Analysis QUAST: Quality Assessment Tool for Genome Assemblies. Bioinformatics 2013, 29, 1072–1075. [Google Scholar] [CrossRef]
  24. Seemann, T. Prokka: Rapid Prokaryotic Genome Annotation. Bioinformatics 2014, 30, 2068–2069. [Google Scholar] [CrossRef] [PubMed]
  25. Brettin, T.; Davis, J.J.; Disz, T.; Edwards, R.A.; Gerdes, S.; Olsen, G.J.; Olson, R.; Overbeek, R.; Parrello, B.; Pusch, G.D.; et al. RASTtk: A Modular and Extensible Implementation of the RAST Algorithm for Building Custom Annotation Pipelines and Annotating Batches of Genomes. Sci. Rep. 2015, 5, 8365. [Google Scholar] [CrossRef]
  26. Wickham, H. Data Analysis; Springer International Publishing: Berlin/Heidelberg, Germany, 2016; pp. 189–201. [Google Scholar] [CrossRef]
  27. Wilkins, D. Gggenes: Draw Gene Arrow Maps in “Ggplot2”. Available online: https://rdrr.io/cran/gggenes/ (accessed on 10 January 2024).
  28. R Core Team. R: A Language and Environment for Statistical Computing. Available online: https://www.R-project.org/ (accessed on 6 January 2024).
  29. Muñoz-Ramírez, Z.Y.; Mendez-Tenorio, A.; Kato, I.; Bravo, M.M.; Rizzato, C.; Thorell, K.; Torres, R.; Aviles-Jimenez, F.; Camorlinga, M.; Canzian, F.; et al. Whole Genome Sequence and Phylogenetic Analysis Show Helicobacter pylori strains from Latin America Have Followed a Unique Evolution Pathway. Front. Cell Infect. Microbiol. 2017, 7, 245288. [Google Scholar] [CrossRef] [PubMed]
  30. Nei, M.; Li, W.H. Mathematical Model for Studying Genetic Variation in Terms of Restriction Endonucleases. Proc. Natl. Acad. Sci. USA 1979, 76, 5269–5273. [Google Scholar] [CrossRef] [PubMed]
  31. Tamura, K.; Stecher, G.; Kumar, S. MEGA11: Molecular Evolutionary Genetics Analysis Version 11. Mol. Biol. Evol. 2021, 38, 3022–3027. [Google Scholar] [CrossRef]
  32. Letunic, I.; Bork, P. Interactive Tree Of Life (ITOL) v5: An Online Tool for Phylogenetic Tree Display and Annotation. Nucleic Acids Res. 2021, 49, W293–W296. [Google Scholar] [CrossRef] [PubMed]
  33. Richter, M.; Rosselló-Móra, R.; Oliver Glöckner, F.; Peplies, J. JSpeciesWS: A Web Server for Prokaryotic Species Circumscription Based on Pairwise Genome Comparison. Bioinformatics 2016, 32, 929–931. [Google Scholar] [CrossRef] [PubMed]
  34. Jain, C.; Rodriguez-R, L.M.; Phillippy, A.M.; Konstantinidis, K.T.; Aluru, S. High Throughput ANI Analysis of 90K Prokaryotic Genomes Reveals Clear Species Boundaries. Nat. Commun. 2018, 9, 5114. [Google Scholar] [CrossRef] [PubMed]
  35. Shimoyama, Y. pyGenomeViz: A Genome Visualization Python Package for Comparative Genomics. Available online: https://github.com/moshi4/pyGenomeViz (accessed on 10 January 2024).
  36. Tonkin-Hill, G.; MacAlasdair, N.; Ruis, C.; Weimann, A.; Horesh, G.; Lees, J.A.; Gladstone, R.A.; Lo, S.; Beaudoin, C.; Floto, R.A.; et al. Producing Polished Prokaryotic Pangenomes with the Panaroo Pipeline. Genome Biol. 2020, 21, 1–21. [Google Scholar] [CrossRef]
  37. Gao, C.H.; Yu, G.; Cai, P. GgVennDiagram: An Intuitive, Easy-to-Use, and Highly Customizable R Package to Generate Venn Diagram. Front. Genet. 2021, 12, 706907. [Google Scholar] [CrossRef] [PubMed]
  38. Bury-Moné, S.; Thibessard, A.; Lioy, V.S.; Leblond, P. Dynamics of the Streptomyces chromosome: Chance and necessity. Trends in Genetics 2023, 39, 873–887. [Google Scholar] [CrossRef]
  39. Nikolaidis, M.; Hesketh, A.; Frangou, N.; Mossialos, D.; Van de Peer, Y.; Oliver, S.G.; Amoutzias, G.D. A Panoramic View of the Genomic Landscape of the Genus Streptomyces. Microb. Genom. 2023, 9, 001028. [Google Scholar] [CrossRef]
  40. Ju, K.S.; Gao, J.; Doroghazi, J.R.; Wang, K.K.A.; Thibodeaux, C.J.; Li, S.; Metzger, E.; Fudala, J.; Su, J.; Zhang, J.K.; et al. Discovery of Phosphonic Acid Natural Products by Mining the Genomes of 10,000 Actinomycetes. Proc. Natl. Acad. Sci. USA 2015, 112, 12175–12180. [Google Scholar] [CrossRef]
  41. Tettelin, H.; Masignani, V.; Cieslewicz, M.J.; Donati, C.; Medini, D.; Ward, N.L.; Angiuoli, S.V.; Crabtree, J.; Jones, A.L.; Durkin, A.S.; et al. Genome Analysis of Multiple Pathogenic Isolates of Streptococcus agalactiae: Implications for the Microbial “Pan-Genome”. Proc. Natl. Acad. Sci. USA 2005, 102, 13950–13955. [Google Scholar] [CrossRef] [PubMed]
  42. D’Auria, G.; Jiménez-Hernández, N.; Peris-Bondia, F.; Moya, A.; Latorre, A. Legionella pneumophila Pangenome Reveals Strain-Specific Virulence Factors. BMC Genom. 2010, 11, 181. [Google Scholar] [CrossRef] [PubMed]
  43. Sawers, R.G.; Fischer, M.; Falke, D. Anaerobic Nitrate Respiration in the Aerobe Streptomyces coelicolor A3(2): Helping Maintain a Proton Gradient during Dormancy. Environ. Microbiol. Rep. 2019, 11, 645–650. [Google Scholar] [CrossRef] [PubMed]
  44. Raghava Rao, K.V.; Mani, P.; Satyanarayana, B.; Raghava Rao, T. Purification and Structural Elucidation of Three Bioactive Compounds Isolated from Streptomyces coelicoflavus BC 01 and Their Biological Activity. 3 Biotech 2017, 7, 24. [Google Scholar] [CrossRef] [PubMed]
  45. Hassan, R.; Shaaban, M.I.; Abdel Bar, F.M.; El-Mahdy, A.M.; Shokralla, S. Quorum Sensing Inhibiting Activity of Streptomyces coelicoflavus Isolated from Soil. Front. Microbiol. 2016, 7, 188534. [Google Scholar] [CrossRef] [PubMed]
  46. Mon, W.P.; Jantaharn, P.; Boonlue, S.; McCloskey, S.; Kanokmedhakul, S.; Mongkolthanaruk, W. Enzymatic Degradation of Azo Bonds and Other Functional Groups on Commercial Silk Dyes by Streptomyces coelicoflavus CS-29. Environ. Nat. Resour. J. 2022, 20, 19–28. [Google Scholar] [CrossRef]
  47. Waheeb, M.S.; Elkhatib, W.F.; Yassien, M.A.; Hassouna, N.A. Production of Cellulase by Soil Isolated Streptomyces sp. Arch. Pharm. Sci. Ain Shams Univ. 2021, 5, 225–233. [Google Scholar] [CrossRef]
  48. Kaweewan, I.; Ohnishi-Kameyama, M.; Kodani, S. Isolation of a New Antibacterial Peptide Achromosin from Streptomyces achromogenes subsp. achromogenes Based on Genome Mining. J. Antibiot. 2016, 70, 208–211. [Google Scholar] [CrossRef]
  49. Tangjitjaroenkun, J.; Pluempanupat, W.; Tangchitcharoenkhul, R.; Yahayo, W.; Supabphol, R. Antibacterial, Antioxidant, Cytotoxic Effects and GC-MS Analysis of Mangrove-Derived Streptomyces achromogenes TCH4 Extract. Arch. Biol. Sci. 2021, 73, 223–235. [Google Scholar] [CrossRef]
  50. Chávez-Avila, S.; Valencia-Marin, M.F.; Guzmán-Guzmán, P.; Kumar, A.; Babalola, O.O.; Orozco-Mosqueda, M.d.C.; de los Santos-Villalobos, S.; Santoyo, G. Deciphering the Antifungal and Plant Growth-Stimulating Traits of the Stress-Tolerant Streptomyces achromogenes subsp. achromogenes strain UMAF16, a Bacterium Isolated from Soils Affected by Underground Fires. Biocatal. Agric. Biotechnol. 2023, 53, 102859. [Google Scholar] [CrossRef]
  51. Bhatti, A.A.; Haq, S.; Bhat, R.A. Actinomycetes Benefaction Role in Soil and Plant Health. Microb. Pathog. 2017, 111, 458–467. [Google Scholar] [CrossRef]
  52. Gopalakrishnan, S.; Srinivas, V.; Prasanna, S.L. Streptomyces. In Beneficial Microbes in Agro-Ecology: Bacteria and Fungi; Academic Press: Cambridge, MA, USA, 2020; pp. 55–71. [Google Scholar] [CrossRef]
  53. Fischer, M.; Alderson, J.; Van Keulen, G.; White, J.; Sawers, R.G. The Obligate Aerobe Streptomyces coelicolor A3(2) Synthesizes Three Active Respiratory Nitrate Reductases. Microbiology 2010, 156, 3166–3179. [Google Scholar] [CrossRef]
  54. Stolz, J.F.; Basu, P. Evolution of Nitrate Reductase: Molecular and Structural Variations on a Common Function. ChemBioChem 2002, 3, 198–206. [Google Scholar] [CrossRef]
  55. Fisher, S.H.; Wray, L.V. Regulation of Glutamine Synthetase in Streptomyces coelicolor. J. Bacteriol. 1989, 171, 2378–2383. [Google Scholar] [CrossRef]
  56. Crack, J.C.; Munnoch, J.; Dodd, E.L.; Knowles, F.; Al Bassam, M.M.; Kamali, S.; Holland, A.A.; Cramer, S.P.; Hamilton, C.J.; Johnson, M.K.; et al. NsrR from Streptomyces coelicolor Is a Nitric Oxide-Sensing [4Fe-4S] Cluster Protein with a Specialized Regulatory Function. J. Biol. Chem. 2015, 290, 12689–12704. [Google Scholar] [CrossRef]
  57. Reuther, J.; Wohlleben, W. Nitrogen Metabolism in Streptomyces coelicolor: Transcriptional and Post-Translational Regulation. J. Mol. Microbiol. Biotechnol. 2007, 12, 139–146. [Google Scholar] [CrossRef]
  58. Mast, Y.; Wohlleben, W.; Reuther, J. Regulation of Nitrogen Assimilation in Streptomyces and Other Actinobacteria. Regulation of Nitrogen Assimilation in Streptomycetes and Other Actinobacteria; Caister Academic Press: Norfolk, UK, 2011; pp. 125–136. [Google Scholar]
  59. Navone, L.; Macagno, J.P.; Licona-Cassani, C.; Marcellin, E.; Nielsen, L.K.; Gramajo, H.; Rodriguez, E. AllR Controls the Expression of Streptomyces coelicolor Allantoin Pathway Genes. Appl. Environ. Microbiol. 2015, 81, 6649–6659. [Google Scholar] [CrossRef] [PubMed]
  60. Ordóñez-Robles, M.; Santos-Beneit, F.; Rodríguez-García, A.; Martín, J.F. Analysis of the Pho Regulon in Streptomyces Tsukubaensis. Microbiol. Res. 2017, 205, 80–87. [Google Scholar] [CrossRef]
  61. Fernández-Martínez, L.T.; Santos-Beneit, F.; Martín, J.F. Is PhoR-PhoP Partner Fidelity Strict? PhoR Is Required for the Activation of the Pho Regulon in Streptomyces coelicolor. Mol. Genet. Genom. 2012, 287, 565–573. [Google Scholar] [CrossRef]
  62. Wang, L.; Yang, H.; Wu, M.; Zhang, J.; Zhang, H.; Mao, Z.; Chen, X. Integrative Transcriptome and Proteome Revealed High-Yielding Mechanisms of Epsilon-Poly-L-Lysine by Streptomyces albulus. Front. Microbiol. 2023, 14, 1123050. [Google Scholar] [CrossRef] [PubMed]
  63. Lindner, S.N.; Knebel, S.; Wesseling, H.; Schoberth, S.M.; Wendisch, V.F. Exopolyphosphatases PPX1 and PPX2 from Corynebacterium glutamicum. Appl. Environ. Microbiol. 2009, 75, 3161–3170. [Google Scholar] [CrossRef] [PubMed]
  64. Krysenko, S. Impact of Nitrogen-Containing Compounds on Secondary Metabolism in Streptomyces spp.—A Source of Metabolic Engineering Strategies. SynBio 2023, 1, 204–225. [Google Scholar] [CrossRef]
  65. Tucker, N.P.; Hicks, M.G.; Clarke, T.A.; Crack, J.C.; Chandra, G.; Le Brun, N.E.; Dixon, R.; Hutchings, M.I. The Transcriptional Repressor Protein NsrR Senses Nitric Oxide Directly via a [2Fe-2S] Cluster. PLoS ONE 2008, 3, e3623. [Google Scholar] [CrossRef]
  66. Chen, J.; Hu, L.; Chen, N.; Jia, R.; Ma, Q.; Wang, Y. The Biocontrol and Plant Growth-Promoting Properties of Streptomyces alfalfae XN-04 Revealed by Functional and Genomic Analysis. Front. Microbiol. 2021, 12, 745766. [Google Scholar] [CrossRef]
Nitrogen 05 00020 g001
Figure 1. Phylogenomic analyses of 309 Streptomyces strains using whole-genome sequences analyzed with VAMPhyRE. The genome of the E. coli str. K-12 substr. MG1655 (NC_000913.3) was used to root the tree. The labels in blue indicate genomes that cluster within the same clade as the Streptomyces strains from this study. The labels in red identify the genomes of Streptomyces sp. strain LM32 and Streptomyces sp. strain LM65.
Figure 1. Phylogenomic analyses of 309 Streptomyces strains using whole-genome sequences analyzed with VAMPhyRE. The genome of the E. coli str. K-12 substr. MG1655 (NC_000913.3) was used to root the tree. The labels in blue indicate genomes that cluster within the same clade as the Streptomyces strains from this study. The labels in red identify the genomes of Streptomyces sp. strain LM32 and Streptomyces sp. strain LM65.
Nitrogen 05 00020 g001
Nitrogen 05 00020 g002
Figure 2. Detailed visualization of reciprocal mappings between two pairs of Streptomyces genomes calculated with FastANI. (a) Comparison between Streptomyces sp. strain LM32 and Streptomyces coelicoflavus DBR11. (b) Comparison between Streptomyces sp. strain LM65 and Streptomyces achromogenes subsp. achromogenes. The red lines denote conserved genomic regions with the color intensity indicating a high value of ANI.
Figure 2. Detailed visualization of reciprocal mappings between two pairs of Streptomyces genomes calculated with FastANI. (a) Comparison between Streptomyces sp. strain LM32 and Streptomyces coelicoflavus DBR11. (b) Comparison between Streptomyces sp. strain LM65 and Streptomyces achromogenes subsp. achromogenes. The red lines denote conserved genomic regions with the color intensity indicating a high value of ANI.
Nitrogen 05 00020 g002
Nitrogen 05 00020 g003
Figure 3. Comparative pan- and core-genome analysis of Streptomyces species. (a) Comparison between Streptomyces sp. strain LM32 and Streptomyces coelicoflavus. (b) Comparison between Streptomyces sp. strain LM65 and Streptomyces achromogenes. Each ellipse shows in sum the total number of genes of one strain. Intersections indicate predicted shared content. The color gradient represents the density of shared genes across the genomes.
Figure 3. Comparative pan- and core-genome analysis of Streptomyces species. (a) Comparison between Streptomyces sp. strain LM32 and Streptomyces coelicoflavus. (b) Comparison between Streptomyces sp. strain LM65 and Streptomyces achromogenes. Each ellipse shows in sum the total number of genes of one strain. Intersections indicate predicted shared content. The color gradient represents the density of shared genes across the genomes.
Nitrogen 05 00020 g003
Nitrogen 05 00020 g004
Figure 4. Pie charts showing an overview of RAST subsystems assigned to genes in (a) Streptomyces sp. strain LM32 and (b) Streptomyces sp. strain LM65.
Figure 4. Pie charts showing an overview of RAST subsystems assigned to genes in (a) Streptomyces sp. strain LM32 and (b) Streptomyces sp. strain LM65.
Nitrogen 05 00020 g004
Nitrogen 05 00020 g005
Figure 5. Predicted gene clusters of nitrogen and phosphorus metabolism in (a) Streptomyces sp. strain LM32 and (b) Streptomyces sp. strain LM65.
Figure 5. Predicted gene clusters of nitrogen and phosphorus metabolism in (a) Streptomyces sp. strain LM32 and (b) Streptomyces sp. strain LM65.
Nitrogen 05 00020 g005
Table 1. Genome characteristics of Streptomyces sp. strain LM32 and Streptomyces sp. strain LM65.
Table 1. Genome characteristics of Streptomyces sp. strain LM32 and Streptomyces sp. strain LM65.
Genome CharacteristicsLM32LM65
Genome size (Mb)8.17.3
Contigs > 500 bp11292
G + C content (%)72.1471
N50150,500164,678
L501816
CDS72516440
rRNA genes5,1,2 (5S,16S,23S)2,1,1 (5S,16S,23S)
tRNA genes8399
Table 2. Predicted genes related to nitrogen and phosphorus metabolism activities in Streptomyces spp.
Table 2. Predicted genes related to nitrogen and phosphorus metabolism activities in Streptomyces spp.
Nitrogen MetabolismGene NameGene AnnotationReference
Denitrifying reductase gene clustersnarGRespiratory nitrate reductase alpha chain[55]
narIRespiratory nitrate reductase gamma chain
narXRespiratory nitrate reductase delta chain
narHRespiratory nitrate reductase beta chain
Nitrosative stressNsrRNitrite-sensitive transcriptional repressor[56]
Ammonia assimilationgln-1Glutamine synthetase type II[55,57,58]
gltDGlutamate synthase [NADPH] small chain
glnBNitrogen regulatory protein P-II
gltBGlutamate synthase [NADPH] large chain
glnD[Protein-PII] uridylyltransferase
amtBAmmonium transporter
glnEGlutamate-ammonia-ligase adenylyltransferase
glnAGlutamine synthetase type I
Nitrate and nitrite ammonificationnarGRespiratory nitrate reductase alpha chain[43,54]
narXRespiratory nitrate reductase delta chain
narHRespiratory nitrate reductase beta chain
nasDNitrite reductase [NAD(P)H] large subunit
nasENitrite reductase [NAD(P)H] small subunit
narIRespiratory nitrate reductase gamma chain
alcAllantoicase[59]
gclGlyoxylate carboligase
Allantoin utilizationglxKGlycerate kinase
allBAllantoinase
garR2-hydroxy-3-oxopropionate reductase
Phosphorus metabolismGene nameGene annotation
High-affinity phosphate transporter and control of PHO regulonphoUPhosphate transport system regulatory protein[60]
phoRPhosphate regulon sensor protein
phoBPhosphate regulon transcriptional regulatory protein
ppk1Polyphosphate kinase
PolyphosphateppgkPolyphosphate glucokinase[61]
ppxExopolyphosphatase
ppk1Polyphosphate kinase
Phosphate metabolismphoHPhosphate starvation-inducible protein PhoH, predicted ATPase[61,62,63]
pitBProbable low-affinity inorganic phosphate transporter
PhoUPhosphate transport system regulatory protein
HWU94Phosphate transport regulator (distant homolog of PhoU)
phoLPredicted ATPase related to phosphate starvation-inducible protein
ppxExopolyphosphatase
ppk1Polyphosphate kinase
hppAPyrophosphate-energized proton pump
phoBPhosphate regulon transcriptional regulatory protein
pntBNAD(P) transhydrogenase subunit beta
PhoRPhosphate regulon sensor protein
ppaInorganic pyrophosphatase
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Montes-Montes, G.; González-Escobedo, R.; Muñoz-Castellanos, L.N.; Avila-Quezada, G.D.; Ramírez-Sánchez, O.; Borrego-Loya, A.; Ortiz-Aguirre, I.; Muñoz-Ramírez, Z.Y. Whole Genome Analysis of Streptomyces spp. Strains Isolated from the Rhizosphere of Vitis vinifera L. Reveals Their Role in Nitrogen and Phosphorus Metabolism. Nitrogen 2024, 5, 301-314. https://doi.org/10.3390/nitrogen5020020

AMA Style

Montes-Montes G, González-Escobedo R, Muñoz-Castellanos LN, Avila-Quezada GD, Ramírez-Sánchez O, Borrego-Loya A, Ortiz-Aguirre I, Muñoz-Ramírez ZY. Whole Genome Analysis of Streptomyces spp. Strains Isolated from the Rhizosphere of Vitis vinifera L. Reveals Their Role in Nitrogen and Phosphorus Metabolism. Nitrogen. 2024; 5(2):301-314. https://doi.org/10.3390/nitrogen5020020

Chicago/Turabian Style

Montes-Montes, Gustavo, Román González-Escobedo, Laila N. Muñoz-Castellanos, Graciela D. Avila-Quezada, Obed Ramírez-Sánchez, Alejandra Borrego-Loya, Ismael Ortiz-Aguirre, and Zilia Y. Muñoz-Ramírez. 2024. "Whole Genome Analysis of Streptomyces spp. Strains Isolated from the Rhizosphere of Vitis vinifera L. Reveals Their Role in Nitrogen and Phosphorus Metabolism" Nitrogen 5, no. 2: 301-314. https://doi.org/10.3390/nitrogen5020020

Article Metrics

Back to TopTop