MtSSPdb: A New Database for the Small Secreted Peptide Research Community

Small secreted peptides (SSPs) are short peptides that function as messengers and regulate a variety of processes in plants (Lease and Walker, 2006). They are coded in small open reading frames that give rise to preproteins of 100 to 250 amino acids, which then undergo processing into active peptides of five to 50 amino acids. Their small size makes the detection of their open reading frame, expression, and peptide itself challenging. SSPs have been shown to regulate plant growth and development and biotic as well as abiotic interactions between the plant and its environment (Lease and Walker, 2006; Czyzewicz et al., 2013; Matsubayashi, 2014; Breiden and Simon, 2016; de Bang et al., 2017). They are involved in the regulation of nodulation that enables legumes to form symbiotic associations with rhizobia, which can fix nitrogen from the atmosphere and make it available for the plant (Djordjevic et al., 2015). Many crop plants are dependent on synthetic fertilizer, so understanding, and possibly modulating, SSPs is of great interest for the research community and eventually for agriculture.

In this issue of Plant Physiology, Boschiero et al. (2020) present their new SSP database for Medicago: MtSSPdb. This database follows the genome-wide identification and analysis of Medicago SSPs by the same group in 2017 (de Bang et al., 2017).

The authors scanned and reannotated the Medicago truncatula genome with special focus on the often overlooked SSP open reading frames. Using hidden Markov models (HMMs) of known SSP families for sequence alignments, they identified known and potential new SSP families. A BLAST tool helps to predict the presence of SSPs in a given sequence. Boschiero et al. (2020) also compiled available expression data to create a gene expression atlas for M. truncatula SSPs. Another section of the database collects Medicago phenotypes after the application of predicted and subsequently synthesized SSPs. While MtSSPdb is currently focused on Medicago, the SSP families previously identified by the same group in maize (Zea mays), tobacco (Nicotiana tabacum), and Arabidopsis (Arabidopsis thaliana) have been included as well (de Bang et al., 2017).

To enable access to their database, Boschiero et al. (2020) created a user interface accessible at https://mtsspdb.noble.org/.

Searching the database by entering the gene name, identifier, keywords, or the SSP family name will result in a gene card with information on location, sequence, protein properties, SSP family and alignments, or SSP family HMM profiles.

It is also possible to BLAST a sequence against the database and analyze it for the presence of potential SSPs. The analysis uses the protein length, the presence of a predicted signal peptide cleavage site, the similarity of HMM patterns or homology to known SSP families, and the absence of transmembrane helices to estimate if an SSP is encoded.

In the Gene Expression Atlas, users can search expression patterns of SSPs by keyword, identifier, or a specific expression pattern in the different RNA sequencing (RNA-seq) experiments included. Users can perform coexpression searches as well as Gene Ontology (GO) term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses to learn more about their SSP of interest.

A really exciting feature is the synthetic SSP database, where users can see which (hypothetical) SSPs have been synthesized and applied to Medicago to analyze their biological relevance. Aliquots of those synthesized peptides can be requested from the authors.

Boschiero et al. (2020) provide several case studies for the use of the database. For the example of CEP9 (C-Terminally Encoded Peptide9), they show how the database helps to identify SSP family members and sequence similarity. CEPs are a highly similar SSP family, and CEP1 had been previously shown to inhibit M. truncatula lateral root formation (Imin et al., 2013). The authors found that the CEP9 sequence is very similar to that of CEP1 and predicted that the same residues would require hydroxylation to render the peptide functional. They synthesized CEP1 and CEP9 with and without Pro hydroxylation and analyzed the effect on Medicago lateral root density. Whereas hydroxylated CEP1 and CEP9 decreased lateral root density, the nonhydroxylated versions did not show any effect compared with the control (Fig. 1).

Figure 1.

Case study using MtSSPdb: analysis of the importance of Pro hydroxylation in CEP peptides. A, CEP9 transcript, the two CEP9 domains CEP9-1 (red) and CEP9-2 (blue), and the synthetic peptides with and without Pro hydroxylation. B, Analysis of the synthetic CEP9 peptides without and with Pro hydroxylation. Treatment with both CEP9 peptides reduces lateral root density in Medicago if the Pro hydroxylation is present. Modified from Boschiero et al. (2020), figure 6, B and C.

Another case study shows the identification of a novel SSP in Medicago using the MtSSPdb. By looking closely at RNA-seq results, the authors found that a locus containing the PSY (Peptide Containing Sulfated Tyr) domain in Medicago actually has two transcripts with different expression patterns. They synthesized one of them, PSY7, and showed that its application affects Medicago primary root length and lateral root density.

The MtSSPdb is an exciting new tool for the SSP community. Other databases, such as PlantSSPdb, SPdb, and the Arabidopsis Small Secreted Peptide database, have been of great use but rely on either few and nonleguminous species, contain few SSPs, or are not up to date (Choo et al., 2005; Lease and Walker, 2006; Ghorbani et al., 2015). MtSSPdb is based on a manually curated genome, which helps to detect the small open reading frames, contains a vast number of known and predicted SSPs in Medicago, and includes most if not all available gene expression data for the Medicago SSPs. The SSP prediction tool is specialized for the rapidly evolving and short sequences of SSPs and will detect SSPs where other tools might not have been able to. While MtSSPdb is currently limited mostly to Medicago, the authors expect to broaden the database and include further species. Another way to expand the database is to include additional RNA-seq analyses in the Gene Expression Atlas as they are published. It will be interesting to see how MtSSPdb as a community resource grows with input from the community that it serves.