Suprabasin-derived bioactive peptides identified by plasma peptidomics

Size matters: the biochemical logic of ligand type in endocrine crosstalk

The endocrine system is a fundamental type of long-range cell-cell communication that is important for maintaining metabolism, physiology, and other aspects of organismal homeostasis. Endocrine signaling is mediated by diverse blood-borne ligands, also called hormones, including metabolites, lipids, steroids, peptides, and proteins. The size and structure of these hormones are fine-tuned to make them bioactive, responsive, and adaptable to meet the demands of changing environments. Why has nature selected such diverse ligand types to mediate communication in the endocrine system? What is the chemical, signaling, or physiologic logic of these ligands? What fundamental principles from our knowledge of endocrine communication can be applied as we continue as a field to uncover additional new circulating molecules that are claimed to mediate long-range cell and tissue crosstalk? This review provides a framework based on the biochemical logic behind this crosstalk with respect to their chemistry, temporal regulation in physiology, specificity, signaling actions, and evolutionary development.

Integrating a Multi-label Deep Learning Approach with Protein Information to Compare Bioactive Peptides in Brain and Plasma

Peptide therapeutics is gaining momentum. Advances in the field of peptidomics have enabled researchers to harvest vital information from various organisms and tissue types concerning peptide existence, expression and function. The development of mass spectrometry techniques for high-throughput peptide quantitation has paved the way for the identification and discovery of numerous known and novel peptides. Though much has been achieved, scientists are still facing difficulties when it comes to reducing the search space of the large mass spectrometry-generated peptidomics datasets and focusing on the subset of functionally relevant peptides. Moreover, there is currently no straightforward way to analytically compare the distributions of bioactive peptides in distinct biological samples, which may reveal much useful information when seeking to characterize tissue- or fluid-specific peptidomes. In this chapter, we demonstrate how to identify, rank, and compare predicted bioactive peptides and bioactivity distributions from extensive peptidomics datasets. To aid this task, we utilize MultiPep, a multi-label deep learning approach designed for classifying peptide bioactivities, to identify bioactive peptides. The predicted bioactivities are synergistically combined with protein information from the UniProt database, which assist in navigating through the jungle of putative therapeutic peptides and relevant peptide leads.

Hallmarks of the metabolic secretome

The identification of novel secreted factors is advancing at an unprecedented pace. However, there is a critical need to consolidate and integrate this knowledge to provide a framework of their diverse mechanisms, functional significance, and inter-relationships. Complicating this effort are challenges related to nonstandardized methods, discrepancies in sample handling, and inconsistencies in the annotation of unknown molecules. This Review aims to synthesize the rapidly expanding field of the metabolic secretome, encompassing the five major types of secreted factors: proteins, peptides, metabolites, lipids, and extracellular vesicles. By systematically defining the functions and detection of the components within the metabolic secretome, this Review provides a primer into the advances of the field, and how integration of the techniques discussed can provide a deeper understanding of the mechanisms underlying metabolic homeostasis and its disorders.

ANGT_HUMAN[448–462], an Anorexigenic Peptide Identified using Plasma Peptidomics

The discovery of bioactive peptides is an important research target that enables the elucidation of the pathophysiology of human diseases and provides seeds for drug discovery. Using a large number of native peptides previously identified using plasma peptidomics technology, we sequentially synthesized selected sequences and subjected them to functional screening using human cultured cells. A 15-amino-acid residue proangiotensinogen-derived peptide, designated ANGT_HUMAN[448–462], elicited cellular responses and bound to cultured human cells. Synthetic fluorescent-labeled and biotinylated ANGT_HUMAN[448–462] peptides were rendered to bind to cell- and tissue-derived proteins and peptide-cell protein complexes were retrieved and analyzed using liquid chromatography-tandem mass spectrometry, revealing the β-subunit of ATP synthase as its cell-surface binding protein. Because ATP synthase mediates the effects of anorexigenic peptides, the ability of ANGT_HUMAN[448–462] to modulate eating behavior in mice was investigated. Both intraperitoneal and intracerebroventricular injections of low doses of ANGT_HUMAN[448–462] suppressed spontaneous food and water intake throughout the dark phase of the diurnal cycle without affecting locomotor activity. Immunoreactive ANGT_HUMAN[448–462], distributed throughout human tissues and in human-derived cells, is mostly co-localized with angiotensin II and is occasionally present separately from angiotensin II. In this study, an anorexigenic peptide, ANGT_HUMAN[448–462], was identified by exploring cell surface target proteins of the human native peptides identified using plasma peptidomics.

SBSN drives bladder cancer metastasis via EGFR/SRC/STAT3 signalling

BACKGROUND

Patients with metastatic bladder cancer have very poor prognosis and predictive biomarkers are urgently needed for early clinical detection and intervention. In this study, we evaluate the effect and mechanism of Suprabasin (SBSN) on bladder cancer metastasis.

METHODS

A tissue array was used to detect SBSN expression by immunohistochemistry. A tumour-bearing mouse model was used for metastasis evaluation in vivo. Transwell and wound-healing assays were used for in vitro evaluation of migration and invasion. Comprehensive molecular screening was achieved by western blotting, immunofluorescence, luciferase reporter assay, and ELISA.

RESULTS

SBSN was found markedly overexpressed in bladder cancer, and indicated poor prognosis of patients. SBSN promoted invasion and metastasis of bladder cancer cells both in vivo and in vitro. The secreted SBSN exhibited identical biological function and regulation in bladder cancer metastasis, and the interaction of secreted SBSN and EGFR could play an essential role in activating the signalling in which SBSN enhanced the phosphorylation of EGFR and SRC kinase, followed with phosphorylation and nuclear location of STAT3.

CONCLUSIONS

Our findings highlight that SBSN, and secreted SBSN, promote bladder cancer metastasis through activation of EGFR/SRC/STAT3 pathway and identify SBSN as a potential diagnostic and therapeutic target for bladder cancer.

ACPNet: A Deep Learning Network to Identify Anticancer Peptides by Hybrid Sequence Information

Cancer is one of the most dangerous threats to human health. One of the issues is drug resistance action, which leads to side effects after drug treatment. Numerous therapies have endeavored to relieve the drug resistance action. Recently, anticancer peptides could be a novel and promising anticancer candidate, which can inhibit tumor cell proliferation, migration, and suppress the formation of tumor blood vessels, with fewer side effects. However, it is costly, laborious and time consuming to identify anticancer peptides by biological experiments with a high throughput. Therefore, accurately identifying anti-cancer peptides becomes a key and indispensable step for anticancer peptides therapy. Although some existing computer methods have been developed to predict anticancer peptides, the accuracy still needs to be improved. Thus, in this study, we propose a deep learning-based model, called ACPNet, to distinguish anticancer peptides from non-anticancer peptides (non-ACPs). ACPNet employs three different types of peptide sequence information, peptide physicochemical properties and auto-encoding features linking the training process. ACPNet is a hybrid deep learning network, which fuses fully connected networks and recurrent neural networks. The comparison with other existing methods on ACPs82 datasets shows that ACPNet not only achieves the improvement of 1.2% Accuracy, 2.0% F1-score, and 7.2% Recall, but also gets balanced performance on the Matthews correlation coefficient. Meanwhile, ACPNet is verified on an independent dataset, with 20 proven anticancer peptides, and only one anticancer peptide is predicted as non-ACPs. The comparison and independent validation experiment indicate that ACPNet can accurately distinguish anticancer peptides from non-ACPs.

GIP_HUMAN[22-51] is a new proatherogenic peptide identified by native plasma peptidomics

We recently established a new plasma peptidomic technique and comprehensively identified a large number of low-molecular weight and low-abundance native peptides using a single drop of human plasma. To discover a novel polypeptide that potently modulates the cardiovascular system, we performed a bioinformatics analysis of the large-scale identification results, sequentially synthesized the selected peptide sequences, tested their biological activities, and identified a 30-amino-acid proatherogenic peptide, GIP_HUMAN[22-51], as a potent proatherosclerotic peptide hormone. GIP_HUMAN[22-51] has a common precursor with the glucose-dependent insulinotropic polypeptide (GIP) and is located immediately N-terminal to GIP. Chronic infusion of GIP_HUMAN[22-51] into ApoE-/- mice accelerated the development of aortic atherosclerotic lesions, which were inhibited by co-infusions with an anti-GIP_HUMAN[22-51] antibody. GIP_HUMAN[22-51] increased the serum concentrations of many inflammatory and proatherogenic proteins, whereas neutralising antibodies reduced their levels. GIP_HUMAN[22-51] induced IκB-α degradation and nuclear translocation of NF-κB in human vascular endothelial cells and macrophages. Immunoreactive GIP_HUMAN[22-51] was detected in human tissues but there was no colocalization with the GIP. The plasma GIP_HUMAN[22-51] concentration in healthy humans determined using a stable-isotope tagged peptide was approximately 0.6 nM. This study discovered a novel endogenous proatherogenic peptide by using a human plasma native peptidomic resource.

Circulating prorenin: its molecular forms and plasma concentrations

The renin-angiotensin-aldosterone system plays pivotal roles in the maintenance of fluid homeostasis and in the pathophysiology of major human diseases. However, the molecular forms of plasma renin/prorenin have not been fully elucidated, and measurements of plasma prorenin levels are still unavailable for clinical practice. We attempted to evaluate the molecular forms of human plasma prorenin and to directly measure its concentration without converting it to renin to determine its activity. Polyacrylamide gel electrophoresis and subsequent immunoblotting using antibodies that specifically recognise prosegment sequences were used to analyse its molecular forms in plasma. We also created a sandwich enzyme-linked immunosorbent assay suitable for directly quantifying the plasma concentration. The plasma level in healthy people was 3.0-13.4 μg/mL, which is from 3 to 4 orders of magnitude higher than the levels reported thus far. Plasma immunoreactive prorenin consists of three major distinct components: a posttranslationally modified full-length protein, an albumin-bound form and a smaller protein truncated at the common C-terminal renin/prorenin portion. In contrast to plasma renin activity, plasma prorenin concentrations were not affected by the postural changes of the donor. Hence, plasma prorenin molecules may be posttranslationally modified/processed or bound to albumin and are present in far higher concentrations than previously thought.

A highly efficient method for extracting peptides from a single mouse hypothalamus

Living organisms contain a variety of endogenous peptides that function as significant regulators of many biological processes. Endogenous peptides are typically analyzed using liquid chromatography-mass spectrometry (LC-MS). However, due to the low efficiency of peptide extraction and low abundance of peptides in a single animal, LC-MS-based peptidomics studies have not facilitated an understanding of the individual differences and tissue specificity of peptide abundance. In this study, we developed a peptide extraction method followed by nano-flow LC-MS/MS analysis. This method enabled highly efficient and reproducible peptide extraction from sub-milligram quantities of hypothalamus dissected from a single animal. Diverse bioactive and authentic peptides were detected from a sample volume equivalent to 135 μg of hypothalamus. This method may be useful for elucidating individual differences and tissue specificity, as well as for facilitating the discovery of novel bioactive peptides and biomarkers and developing peptide therapeutics.