Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics
ReviewBioinformatics tools for secretome analysis☆
Highlights
► Bioinformatics provides tools for in silico and experimental secretome profiling. ► The knowledge of secreted proteins brings clear insight into the basic biology. ► Bioinformatics integration of secretome in systemic knowledge is needed. ► Tools to recognize diagnostic and therapeutic opportunities are crucial.
Introduction
The term secretome refers to a set of proteins that includes extracellular matrix (ECM) proteins, proteins shed from the cell membrane, and vesicle proteins (e.g., from exosomes and microsomal vesicles) [1], [2]. These secreted proteins play important roles in homeostasis, immune response, development, proteolysis, adhesion, and extracellular matrix organization. The secretome is highly dynamic, and its composition changes in response to various pathologies and environmental stimuli. Intracellular pathway and network analyses can provide mechanistic insights by linking proteins to their underlying cellular functions and to other key players known to be involved in these events.
While the earliest secretome analyses were performed in bacteria and fungi [2], there have now been many investigations into the mammalian secretome. The Secreted Protein Database (SPD, http://spd.cbi.pku.edu.cn/) is a collection of over 18,000 secreted proteins from the human, mouse, and rat proteomes; it includes sequences from SwissProt, Trembl, Ensembl, and Refseq [3]. It is estimated that out of the total 20,500 human protein-coding genes, approximately 10% encode secreted proteins [4], [5], [6].
The majority of secretome studies are conducted in vitro using cell culture methods in which secreted proteins are obtained from conditioned media of serum-starved cultured cell lines [7]. These studies routinely employ high-resolution separation techniques, such as two-dimensional gel electrophoresis and/or liquid chromatography, in combination with advanced mass spectrometric methods for the unequivocal identification of peptides and proteins in samples [8]. Very recently, a method that combines click chemistry and pulsed stable isotope labeling with amino acids in cell culture has been successfully adopted to selectively enrich and quantify secreted proteins in a background of serum-containing media [9]. However, using different analytical approaches high-confidence proteins are identified using bioinformatics-based filters to remove from analyses all non-secreted proteins from broken and/or apoptotic cells that are present in the conditioned media of the cell lines. Such methodology typically involves interrogation of either primary sequence-based secretory pathway prediction algorithms or curated empirical subcellular localization databases (or a combination of both approaches).
An interesting fraction of secreted factors comprise cell surface receptor ligands, such as hormones, growth factors, and cytokines with important regulatory functions in biological processes [10], [11], [12]. Thus studying the cell secretome composition in mammals can enable identification of proteins released into host fluids, which could be candidates for use in developing new diagnostic tests and possibly new treatments in diseases and disorders [13], [14]. This emphasizes the need for large-scale and unbiased analysis of the cell secretome. In recent years, vastly improved bioinformatics analysis tools and technical advances in mass spectrometry have driven remarkable progress in secretome science. The present review provides an overview of the databases and software used for the data tracking, analysis, and interpretation of the secretome-describing the main functions and limitations of these tools.
Section snippets
Computational methods for prediction of secreted proteins
Secreted proteins account for approximately 10% of the total proteins encoded by a genome. In the absence of experimental data, the secretome profile of a living cell can be generated with in silico approaches; many different but complementary bioinformatic tools can be used to predict prokaryotic and eukaryotic secreted proteins from genomic/transcriptomic annotations [15] (Fig. 1). Such predictions are possible because of the specific conserved features of secreted proteins. In eukaryotes,
Experimental profiling of cell line secreted proteins
Secreted and shed proteins that are released through classical and non-classical secretion pathways can be profiled using an in vitro system, where experimental and controlled conditions allow reproducible and quantifiable results (Fig. 1). Many cell types have been used in secretome studies. In mammalian cell cultures and in the majority of secretome analysis studies, cells are grown in bovine serum-free media. One alternative approach involves the supplementation of isotopically labeled amino
Secretome data interpretation
Bioinformatics tools (software and databases) are indispensable for data analysis and the construction of methodologies for interpreting secretome/proteome results (Fig. 1). Dependable data interpretation is necessary for the formulation and investigation of hypotheses relating to biological processes, and for the proposal of disease biomarkers and discovery of new drug targets (Fig. 2). It is possible to collect essential information about the proteins in a secretome using gene ontology (GO)
Bioinformatics-assisted standardization and sharing of datasets
Data sharing represents a new challenge in modern proteomics. There are several on-going international efforts to develop proteomics data standards to facilitate data sharing and reuse. The first obstacle to data sharing is the data format; each MS instrument generates raw data as files in proprietary formats (e.g., ABI/Sciex WIFF, Bruker FID/YEP/BAF, Thermo Scientific RAW, and Waters MassLynx file types). Recently, open source tools have been developed to convert proprietary formatted files to
Conclusions
The literature clearly contains an increasing number of publications on secretome identification and analysis, using both computational and experimental approaches. This wealth of studies provides an improved understanding of secretome biology in many types of organisms. Regarding fungal secreted proteins, such advancement can lead to greater development of various potential applications in bio-processing, environmental remediation industries, and in pathogenesis. Secreted proteins are also
Competing interests
The authors declare that they have no competing interests. All authors have read and approved the final manuscript.
Acknowledgements
This study was supported by grants from the Associazione Italiana per la Ricerca sul Cancro (AIRC n. 5896 and AIRC 5x1000 n. 12162) and the Italian Istituto Superiore di Sanità.
References (125)
- et al.
Secretome proteomics for discovery of cancer biomarkers
J. Proteome
(2010) - et al.
The cancer cell secretome: a good source for discovering biomarkers?
J. Proteome
(2010) - et al.
Progress in the development of a recombinant vaccine for human hookworm disease: the Human Hookworm Vaccine Initiative
Int. J. Parasitol.
(2003) Signal sequences. The limits of variation
J. Mol. Biol.
(1985)- et al.
EMBOSS: the European Molecular Biology Open Software Suite
Trends Genet.
(2000) - et al.
Basic local alignment search tool
J. Mol. Biol.
(1990) - et al.
A combined transmembrane topology and signal peptide prediction method
J. Mol. Biol.
(2004) - et al.
Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes
J. Mol. Biol.
(2001) - et al.
Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics
Mol. Cell Proteomics
(2002) - et al.
Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents
Mol. Cell Proteomics
(2004)
Proteomic patterns of tumour subsets in non-small-cell lung cancer
Lancet
Rapid identification of proteins by peptide-mass fingerprinting
Curr. Biol.
ZoomQuant: an application for the quantitation of stable isotope labeled peptides
J. Am. Soc. Mass Spectrom.
In situ proteomic analysis of human breast cancer epithelial cells using laser capture microdissection: annotation by protein set enrichment analysis and gene ontology
Mol. Cell Proteomics
A proteomic view on genome-based signal peptide predictions
Genome Res.
Secreted protein prediction system combining CJ-SPHMM, TMHMM, and PSORT
Mamm. Genome
Distinguishing protein-coding and noncoding genes in the human genome
Proc. Natl. Acad. Sci. U.S.A.
Mapping of the secretome of primary isolates of mammalian cells, stem cells and derived cell lines
Proteomics
Global secretome analysis identifies novel mediators of bone metastasis
Cell Res.
Secretome of the coprophilous fungus Doratomyces stemonitis C8, isolated from koala feces
Appl. Environ. Microbiol.
Selective enrichment of newly synthesized proteins for quantitative secretome analysis
Nat. Biotechnol.
Differential expression of CXCR3 targeting chemokines CXCL10, CXCL9, and CXCL11 in different types of skin inflammation
J. Pathol.
CD25 shedding by human natural occurring CD4 + CD25 + regulatory T cells does not inhibit the action of IL-2
Scand. J. Immunol.
Adipokines, myokines and cardiovascular disease
Circ. J.
Development of secreted proteins as biotherapeutic agents
Expert. Opin. Biol. Ther.
Gene profiling of human adipose tissue during evoked inflammation in vivo
Diabetes
The bacterial twin-arginine translocation pathway
Annu. Rev. Microbiol.
Advances in membranous vesicle and exosome proteomics improving biological understanding and biomarker discovery
Proteomics
A new method for predicting signal sequence cleavage sites
Nucleic Acids Res.
PrediSi: prediction of signal peptides and their cleavage positions
Nucleic Acids Res.
Gapped BLAST and PSI-BLAST: a new generation of protein database search programs
Nucleic Acids Res.
High-performance signal peptide prediction based on sequence alignment techniques
Bioinformatics
Computational comparative study of tuberculosis proteomes using a model learned from signal peptide structures
PLoS One
Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites
Protein Eng.
Prediction of signal peptides and signal anchors by a hidden Markov model
Proc. Int. Conf. Intell. Syst. Mol. Biol.
Improved prediction of signal peptides: SignalP 3.0
J. Mol. Biol.
A comprehensive assessment of N-terminal signal peptides prediction methods
BMC Bioinforma.
SPOCTOPUS: a combined predictor of signal peptides and membrane protein topology
Bioinformatics
Transmembrane topology and signal peptide prediction using dynamic Bayesian networks
PLoS Comput. Biol.
Improving the accuracy of transmembrane protein topology prediction using evolutionary information
Bioinformatics
Transmembrane protein topology prediction using support vector machines
BMC Bioinforma.
SignalP 4.0: discriminating signal peptides from transmembrane regions
Nat. Methods
A hidden Markov model for predicting transmembrane helices in protein sequences
Proc. Int. Conf. Intell. Syst. Mol. Biol.
Adaptation of protein secretion to extremely high-salt conditions by extensive use of the twin-arginine translocation pathway
Mol. Microbiol.
Prediction of twin-arginine signal peptides
BMC Bioinforma.
Combined prediction of Tat and Sec signal peptides with hidden Markov models
Bioinformatics
Prediction of lipoprotein signal peptides in Gram-negative bacteria
Protein Sci.
Prediction of lipoprotein signal peptides in Gram-positive bacteria with a Hidden Markov Model
J. Proteome Res.
Feature-based prediction of non-classical and leaderless protein secretion
Protein Eng. Des. Sel.
Non-classical protein secretion in bacteria
BMC Microbiol.
Cited by (82)
Trypanosoma evansi secretome carries potential biomarkers for Surra diagnosis
2023, Journal of ProteomicsCitation Excerpt :One potential source of biomarkers is the organism's secretome, where a set of proteins are at a given time and under certain conditions [17,18]. The analysis of secretomes by mass spectrometry combined with bioinformatics [19] has been an important strategy used to reveal experimental data on secreted proteins, including non-conventional secretory pathways [20], which has helped to identify potential biomarkers [20–22] as well as to discover new pathological mechanisms [23]. The first study that analyzed the secretome of trypanosomes infecting animals used T. congolense and T. evansi [24].
Exploring the role of secretory proteins in the human infectious diseases diagnosis and therapeutics
2023, Advances in Protein Chemistry and Structural BiologyCitation Excerpt :Constitutive cells include the liver cells, fibroblast, macrophages, B-lymphocytes while neurons, endocrine, exocrine, neutrophils, mast cells, and such are included within the regulated cells (Kelly, 1985). Besides the classical pathway marked by the N-terminal signal peptide, alternative pathways involving cell-surface shedding and SP inclusion within the secretory vesicles are also adopted by several proteins (Caccia et al., 2013). The dynamic nature of the human secretome conforms it to pathological and environmental-stimuli-induced alterations, having differential expression levels that can be significant markers in the diagnosis and prognosis of diseases.
Proteomic changes in the extracellular environment of sea bass thymocytes exposed to 17α-ethinylestradiol in vitro
2021, Comparative Biochemistry and Physiology - Part D: Genomics and ProteomicsEmergence of the Stem Cell Secretome in Regenerative Engineering
2020, Trends in BiotechnologyProgresses on bacterial secretomes enlighten research on Mycoplasma secretome
2020, Microbial Pathogenesis
- ☆
This article is part of a Special Issue entitled: An Updated Secretome.