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. 2021 May 21;19(6):286.
doi: 10.3390/md19060286.

An Integrative Bioinformatic Analysis for Keratinase Detection in Marine-Derived Streptomyces

Affiliations

An Integrative Bioinformatic Analysis for Keratinase Detection in Marine-Derived Streptomyces

Ricardo Valencia et al. Mar Drugs. .

Abstract

Keratinases present promising biotechnological applications, due to their ability to degrade keratin. Streptomyces appears as one of the main sources of these enzymes, but complete genome sequences of keratinolytic bacteria are still limited. This article reports the complete genomes of three marine-derived streptomycetes that show different levels of feather keratin degradation, with high (strain G11C), low (strain CHD11), and no (strain Vc74B-19) keratinolytic activity. A multi-step bioinformatics approach is described to explore genes encoding putative keratinases in these genomes. Despite their differential keratinolytic activity, multiplatform annotation reveals similar quantities of ORFs encoding putative proteases in strains G11C, CHD11, and Vc74B-19. Comparative genomics classified these putative proteases into 140 orthologous groups and 17 unassigned orthogroup peptidases belonging to strain G11C. Similarity network analysis revealed three network communities of putative peptidases related to known keratinases of the peptidase families S01, S08, and M04. When combined with the prediction of cellular localization and phylogenetic reconstruction, seven putative keratinases from the highly keratinolytic strain Streptomyces sp. G11C are identified. To our knowledge, this is the first multi-step bioinformatics analysis that complements comparative genomics with phylogeny and cellular localization prediction, for the prediction of genes encoding putative keratinases in streptomycetes.

Keywords: genomic comparison; keratinases; keratinolytic proteases; marine-derived Streptomyces.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Phylogenomic tree of selected Streptomyces strains using 634 identified single-copy orthogroups via Orthofinder and subsequent Fasttree inference. Genomes of type (T) and reference (R) strains belonging to the Streptomyces genus were retrieved from the PATRIC database. Catenulispora acidiphila DSM44928 and Kitasatospora setae KM-6054 were used as outgroups, rooting the tree at the Catenulispora node. Bootstrap support values (b = 1000) are depicted for each branch. Strains sequenced in this study are displayed in bold font. The following abbreviations apply: S., Streptomyces; K., Kitasatospora; C., Catenulispora.
Figure 2
Figure 2
Venn diagram of common p-orthogroup representatives between streptomycete strains G11C, CHD11, and Vc74B-19. Numbers indicate the number of p-orthogroups found for each strain or between strains.
Figure 3
Figure 3
Protease similarity network, including the three-strain (584 sequences), keratinase (61 sequences), and non-keratinase (50 sequences) datasets. The E-value threshold of the blast alignment for the network is 1 × 10−40 Each node represents an identified putative protease, and the color fill indicates the origin of the sequence: Red, strain G11C; green, strain CHD11; blue, strain Vc74B-19; black, functionally known keratinases; yellow, putative non-keratinases. Edge transparency was adjusted to represent E-value difference: Darker edges correspond to smaller E-values. (A) entire network, (BD) zoom into particular network clusters possessing known keratinases: Community 1, 4, and 41, respectively.
Figure 4
Figure 4
(A) Two-dimensional PCA using localization features for proteases. Protease sequences were retrieved from genomes of strain G11C, Vc74B-19, and CHD11. The x- and y-axis explain 41% and 18% of the observed variance of the data, respectively. Functional keratinases are depicted in red, putative non-keratinases in yellow, sequences from our three streptomycete genomes (i.e., three-strain category) in green, sequences without assigned p-orthogroup in magenta and keratinase-linked proteins, according to the network, are depicted in blue. Loadings represent the cellular localization features: Wall, membrane, cytoplasmic, and extracellular of the software CELLO and PSORTb, in addition to putative signal peptides: LIPO(Sec/SPII), SP(Sec/SPI), TAT(Tat/SPI) predicted with SignalP. (B) Two-dimensional t-SNE using localization features for the same group of proteases. Defined t-SNE group 0, 1, and 2 are enclosed by dashed polygons.
Figure 5
Figure 5
Maximum likelihood trees from filtered MSA of t-SNE group 0 (A) and t-SNE group 1 (B) sequences. Ancestral state probabilities inferred in internal nodes for each category (functional keratinase, keratinase-linked protein, three-strain, and non-keratinase) are depicted as pie charts, with a total of 1 for each pie chart. Support values based on bootstrapping are indicated for each node. Selected clades under stipulated criteria are enclosed by light red boxes, while discarded clades are enclosed by light yellow boxes.
Figure 6
Figure 6
Bioinformatic pipeline to predict potential keratinases in Streptomyces sp. G11C. This analysis integrates a series of steps, including comparative genomics with network similarities, cellular localization prediction, and phylogeny to provide a set of genes considered to encode putative keratinases.

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