Predicting eukaryotic protein secretion without signals

Short peptidoglycan recognition protein 5 modulates immune response to bacteria in Indian major carp, Cirrhinusmrigala

Peptidoglycan recognition proteins (PGRPs) function in host antibacterial responses by recognizing bacterial peptidoglycan (PGN). In the present study, a short pgrp5 (named mpgrp5) was identified in Cirrhinus mrigala (mrigal). The full-length cDNA of the mpgrp5 gene was 1255 bp, containing an open reading frame of 746 bp encoding a protein of 248 amino acids. The predicted protein contained the typical Pgrp/amidase domain, conserved Zn2+, and PGN binding residues. The phylogenetic analysis revealed that the mpgrp5 is closely related to Pgrps reported in Labeo rohita, Cyrinus carpio, and Ctenopharyngodon idella. The ontogenetic expression of mpgrp5 was highest at 7 days post-hatching (dph) and its possible maternal transfer. mpgrp5 was constitutively expressed in all tissues examined, with the highest expression observed in the intestine. Furthermore, mpgrp5 was found upregulated in mrigal post-challenge in a time-dependent manner at 6hpi in the liver (3.16 folds, p < 0.05) and kidney (2.79 folds, p < 0.05) and at 12hpi in gill (1.90 folds, p < 0.01), skin (1.93 folds, p < 0.01), and intestine, (2.71 folds, p < 0.05) whereas at 24hpi in spleen (4.0 folds, p < 0.01). Our results suggest that mpgrp5 may play an important role in antibacterial immune response from early life stages in mrigal.

LSPpred Suite: Tools for Leaderless Secretory Protein Prediction in Plants

Plant proteins that are secreted without a classical signal peptide leader sequence are termed leaderless secretory proteins (LSPs) and are implicated in both plant development and (a)biotic stress responses. In plant proteomics experimental workflows, identification of LSPs is hindered by the possibility of contamination from other subcellar compartments upon purification of the secretome. Applying machine learning algorithms to predict LSPs in plants is also challenging due to the rarity of experimentally validated examples for training purposes. This work attempts to address this issue by establishing criteria for identifying potential plant LSPs based on experimental observations and training random forest classifiers on the putative datasets. The resultant plant protein database LSPDB and bioinformatic prediction tools LSPpred and SPLpred are available at lsppred.lspdb.org. The LSPpred and SPLpred modules are internally validated on the training dataset, with false positives controlled at 5%, and are also able to classify the limited number of established plant LSPs (SPLpred (3/4, LSPpred 4/4). Until such time as a larger set of bona fide (independently experimentally validated) LSPs is established using imaging technologies (light/fluorescence/electron microscopy) to confirm sub-cellular location, these tools represent a bridging method for predicting and identifying plant putative LSPs for subsequent experimental validation.

Secretomic Insights into the Pathophysiology of Venturia inaequalis: The Causative Agent of Scab, a Devastating Apple Tree Disease

Apple scab, caused by Venturia inaequalis, is one of the world's most commercially significant apple diseases. The fungi have a catastrophic impact on apples, causing considerable losses in fruit quality and productivity in many apple-growing locations despite numerous control agents. Fungi secrete various effectors and other virulence-associated proteins that suppress or alter the host's immune system, and several such proteins were discovered in this work. Using state-of-the-art bioinformatics techniques, we examined the V. inaequalis reference genome (EU-B04), resulting in the identification of 647 secreted proteins, of which 328 were classified as small secreted proteins (SSPs), with 76.52% of SSPs identified as anticipated effector proteins. The more prevalent CAZyme proteins were the enzymes engaged in plant cell wall disintegration (targeting pectin and xylanase), adhesion and penetration (Cutinases/acetyl xylan esterase), and reactive oxygen species formation (multicopper oxidases). Furthermore, members of the S9 prolyl oligopeptidase family were identified as the most abundant host defense peptidases. Several known effector proteins were discovered to be expressed during the V. inaequalis infection process on apple leaves. The present study provides valuable data that can be used to develop new strategies for controlling apple scab.

Proteomic study of mesothelial and endothelial cross-talk: key lessons

INTRODUCTION

The peritoneum, pleura, and pericardium are yet understudied multicellular systems where mesothelial cells (MCs) and endothelial cells (ECs) are in close proximity. Crosstalk between these cell types likely plays role in molecular transport, immunological reactions, and metabolic processes in health, disease, and therapeutic intervention.

AREAS COVERED

In this review, we discuss recent proteomic efforts to characterize the crosstalk between MC and EC. We describe the proteomic methods necessary for investigation of crosstalk between MC and EC, as well as the in-vitro models that can be employed. Potential experimental approaches range from conditioned medium, via co-culture on semi-permeable membranes, to 3D cell culture based organoid models. While the biological and clinical relevance of the models may increase with their ability to mimic close cell communication, the practicality of these complex experiments corresponds vice versa, making standardization more difficult and expensive.

EXPERT OPINION

Currently, data and reports on mesothelial-to-endothelial crosstalk are still very scarce. In our opinion, the in-vitro model using semi-permeable cell culture inserts will allow to establish a basic understanding of cellular crosstalk that may occur between those cell types. Later-on, more sophisticated 3D cell cultures may be better able to simulate the transport dynamics within the peritoneal membrane.

Recent Advances in the Prediction of Subcellular Localization of Proteins and Related Topics

Prediction of subcellular localization of proteins from their amino acid sequences has a long history in bioinformatics and is still actively developing, incorporating the latest advances in machine learning and proteomics. Notably, deep learning-based methods for natural language processing have made great contributions. Here, we review recent advances in the field as well as its related fields, such as subcellular proteomics and the prediction/recognition of subcellular localization from image data.

Secretomics—A Key to a Comprehensive Picture of Unconventional Protein Secretion

For a long time, leaderless secreted proteins (LLSP) were neglected as artifacts derived from dying cells. It is now generally accepted that secretion of LLSP–as a part of the collective term unconventional protein secretion (UPS) - is an evolutionarily conserved process and that these LLSP are actively and selectively secreted from living cells bypassing the classical endoplasmic reticulum-Golgi pathway. However, the mechanism of UPS pathways, as well as the number of LLSP and which part of a protein is involved in the selection of LLSPs for secretion, are still enigmatic and await clarification. Secretomics-a proteomics-based approach to identify and quantify all proteins secreted by a cell-is inherently unbiased toward a particular secretion pathway and offers the opportunity to shed light on the UPS. Here, we will evaluate and present recent results of proteomic workflows allowing to obtain high-confident secretome data. Additionally, we address that cell culture conditions largely affect the composition of the secretome. This has to be kept in mind to control cell culture induced artifacts and adaptation stress in serum free conditions. Evaluation of click chemistry for secretome analysis of cells under serum-containing conditions showed a significant change in the cellular proteome with longer incubation time upon treatment with non-canonical amino acid azidohomoalanine. Finally, we showed that the number of LLSP far exceeds the number of secreted proteins annotated in Uniprot and ProteinAtlas. Thus, secretomics in combination with sophisticated microbioanalytical and sample preparation methods is well suited to provide a comprehensive picture of UPS.

Diversity and conservation of plant small secreted proteins associated with arbuscular mycorrhizal symbiosis

Arbuscular mycorrhizal symbiosis (AMS) is widespread mutualistic association between plants and fungi, which plays an essential role in nutrient exchange, enhancement in plant stress resistance, development of host, and ecosystem sustainability. Previous studies have shown that plant small secreted proteins (SSPs) are involved in beneficial symbiotic interactions. However, the role of SSPs in the evolution of AMS has not been well studied yet. In this study, we performed computational analysis of SSPs in 60 plant species and identified three AMS-specific ortholog groups containing SSPs only from at least 30% of the AMS species in this study and three AMS-preferential ortholog groups containing SSPs from both AMS and non-AMS species, with AMS species containing significantly more SSPs than non-AMS species. We found that independent lineages of monocot and eudicot plants contained genes in the AMS-specific ortholog groups and had significant expansion in the AMS-preferential ortholog groups. Also, two AMS-preferential ortholog groups showed convergent changes, between monocot and eudicot species, in gene expression in response to arbuscular mycorrhizal fungus Rhizophagus irregularis. Furthermore, conserved cis-elements were identified in the promoter regions of the genes showing convergent gene expression. We found that the SSPs, and their closely related homologs, in each of three AMS-preferential ortholog groups, had some local variations in the protein structural alignment. We also identified genes co-expressed with the Populus trichocarpa SSP genes in the AMS-preferential ortholog groups. This first plant kingdom-wide analysis on SSP provides insights on plant-AMS convergent evolution with specific SSP gene expression and local diversification of protein structures.

PncsHub: a platform for annotating and analyzing non-classically secreted proteins in Gram-positive bacteria

From industry to food to health, bacteria play an important role in all facets of life. Some of the most important bacteria have been purposely engineered to produce commercial quantities of antibiotics and therapeutics, and non-classical secretion systems are at the forefront of these technologies. Unlike the classical Sec or Tat pathways, non-classically secreted proteins share few common characteristics and use much more diverse secretion pathways for protein transport. Systematically categorizing and investigating the non-classically secreted proteins will enable a deeper understanding of their associated secretion mechanisms and provide a landscape of the Gram-positive secretion pathway distribution. We therefore developed PncsHub (https://pncshub.erc.monash.edu/), the first universal platform for comprehensively annotating and analyzing Gram-positive bacterial non-classically secreted proteins. PncsHub catalogs 4,914 non-classically secreted proteins, which are delicately categorized into 8 subtypes (including the 'unknown' subtype) and annotated with data compiled from up to 26 resources and visualisation tools. It incorporates state-of-the-art predictors to identify new and homologous non-classically secreted proteins and includes three analytical modules to visualise the relationships between known and putative non-classically secreted proteins. As such, PncsHub aims to provide integrated services for investigating, predicting and identifying non-classically secreted proteins to promote hypothesis-driven laboratory-based experiments.

Proteomic Analysis of the Promastigote Secretome of Seven Leishmania Species

Leishmaniasis is one of the most impactful parasitic diseases worldwide, endangering the lives of 1 billion people every year. There are 20 different species of Leishmania able to infect humans, causing cutaneous (CL), visceral (VL), and/or mucocutaneous leishmaniasis (MCL). Leishmania parasites are known to secrete a plethora of proteins to establish infection and modulate the host's immune system. In this study, we analyzed using tandem mass spectrometry the total protein content of the secretomes produced by promastigote forms from seven Leishmania species grown in serum-free in vitro cultures. The core secretome shared by all seven Leishmania species corresponds to up to one-third of total secreted proteins, suggesting conserved mechanisms of adaptation to the vertebrate host. The relative abundance confirms the importance of known virulence factors and some proteins uniquely present in CL- or VL-causing species and may provide further insight regarding their pathogenesis. Bioinformatic analysis showed that most proteins were secreted via unconventional mechanisms, with an important role for vesicle-based secretion for all species. Gene Ontology annotation and enrichment analyses showed a high level of functional conservation among species. This study contributes to the current knowledge on the biological significance of differently secreted proteins and provides new information on the correlation of Leishmania secretome to clinical outcomes and species-specific pathogenesis.

A chromosomal assembly of the soybean cyst nematode genome

The soybean cyst nematode (Heterodera glycines) is a sedentary plant parasite that exceeds billion USD annually in yield losses. This problem is exacerbated by H. glycines populations overcoming the limited sources of natural resistance in soybean and by the lack of effective and safe alternative treatments. Although there are genetic determinants that render soybeans resistant to nematode genotypes, resistant soybeans are increasingly ineffective because their multiyear usage has selected for virulent H. glycines populations. Successful H. glycines infection relies on the comprehensive re-engineering of soybean root cells into a syncytium, as well as the long-term suppression of host defences to ensure syncytial viability. At the forefront of these complex molecular interactions are effectors, the proteins secreted by H. glycines into host root tissues. The mechanisms that control genomic effector acquisition, diversification, and selection are important insights needed for the development of essential novel control strategies. As a foundation to obtain this understanding, we created a nine-scaffold, 158 Mb pseudomolecule assembly of the H. glycines genome using PacBio, Chicago, and Hi-C sequencing. A Mikado consensus gene prediction produced an annotation of 22,465 genes using short- and long-read expression data. To evaluate assembly and annotation quality, we cross-examined synteny among H. glycines assemblies, and compared BUSCO across related species. To describe the predicted proteins involved in H. glycines' secretory pathway, we contrasted expression between preparasitic and parasitic stages with functional gene information. Here, we present the results from our assembly and annotation of the H. glycines genome and contribute this resource to the scientific community.

Prediction of unconventional protein secretion by exosomes

MOTIVATION

In eukaryotes, proteins targeted for secretion contain a signal peptide, which allows them to proceed through the conventional ER/Golgi-dependent pathway. However, an important number of proteins lacking a signal peptide can be secreted through unconventional routes, including that mediated by exosomes. Currently, no method is available to predict protein secretion via exosomes.

RESULTS

Here, we first assembled a dataset including the sequences of 2992 proteins secreted by exosomes and 2961 proteins that are not secreted by exosomes. Subsequently, we trained different random forests models on feature vectors derived from the sequences in this dataset. In tenfold cross-validation, the best model was trained on dipeptide composition, reaching an accuracy of 69.88% ± 2.08 and an area under the curve (AUC) of 0.76 ± 0.03. In an independent dataset, this model reached an accuracy of 75.73% and an AUC of 0.840. After these results, we developed ExoPred, a web-based tool that uses random forests to predict protein secretion by exosomes.

CONCLUSION

ExoPred is available for free public use at http://imath.med.ucm.es/exopred/ . Datasets are available at http://imath.med.ucm.es/exopred/datasets/ .

Advances and perspectives in discovery and functional analysis of small secreted proteins in plants

Small secreted proteins (SSPs) are less than 250 amino acids in length and are actively transported out of cells through conventional protein secretion pathways or unconventional protein secretion pathways. In plants, SSPs have been found to play important roles in various processes, including plant growth and development, plant response to abiotic and biotic stresses, and beneficial plant-microbe interactions. Over the past 10 years, substantial progress has been made in the identification and functional characterization of SSPs in several plant species relevant to agriculture, bioenergy, and horticulture. Yet, there are potentially a lot of SSPs that have not been discovered in plant genomes, which is largely due to limitations of existing computational algorithms. Recent advances in genomics, transcriptomics, and proteomics research, as well as the development of new computational algorithms based on machine learning, provide unprecedented capabilities for genome-wide discovery of novel SSPs in plants. In this review, we summarize known SSPs and their functions in various plant species. Then we provide an update on the computational and experimental approaches that can be used to discover new SSPs. Finally, we discuss strategies for elucidating the biological functions of SSPs in plants.

Comparative Secretomics Gives Access to High Confident Secretome Data: Evaluation of Different Methods for the Determination of Bona Fide Secreted Proteins

Secretome analysis is broadly applied to understand the interplay between cells and their microenvironment. In particular, the unbiased analysis by mass spectrometry-based proteomics of conditioned medium has been successfully applied. In this context, several approaches have been developed allowing to distinguish proteins actively secreted by cells from proteins derived from culture medium or proteins released from dying cells. Here, three different methods comparing conditioned medium and lysate by quantitative mass spectrometry-based proteomics to identify bona fide secreted proteins are evaluated. Evaluation in three different human cell lines reveals that all three methods give access to a similar set of bona fide secreted proteins covering a broad abundance range. In the analyzed primary cells, that is, mesenchymal stromal cells and normal human dermal fibroblasts, more than 70% of the identified proteins are linked to classical secretion pathways. Furthermore, 4-12% are predicted to be released by unconventional secretion pathways. Interestingly, evidence of release by ectodomain shedding in a large number of the remaining candidate proteins is found. In summary, it is convinced that comparative secretomics is currently the method of choice to obtain high-confident secretome data and to identify novel candidates for unconventional protein secretion which have been neglected so far.

Glucose Restriction Plus Refeeding in Vitro Induce Changes of the Human Adipocyte Secretome with an Impact on Complement Factors and Cathepsins

Adipose tissue is a major endocrine organ capable of secreting adipokines with a role in whole-body metabolism. Changes in the secretome profile during the development of obesity is suspected to contribute to the risk of health complications such as those associated with weight regain after weight loss. However, the number of studies on weight regain is limited and secretome changes during weight regain have hardly been investigated. In an attempt to generate leads for in vivo studies, we have subjected human Simpson Golabi Behmel Syndrome adipocytes to glucose restriction (GR) followed by refeeding (RF) as an in vitro surrogate for weight regain after weight loss. Using LC-MS/MS, we compared the secreted protein profile after GR plus RF with that of normal feeding (NF) to assess the consequences of GR plus RF. We identified 338 secreted proteins of which 49 were described for the first time as being secreted by adipocytes. In addition, comparison between NF and GR plus RF showed 39 differentially secreted proteins. Functional classification revealed GR plus RF-induced changes of enzymes for extracellular matrix modification, complement system factors, cathepsins, and several proteins related to Alzheimer’s disease. These observations can be used as clues to investigate metabolic consequences of weight regain, weight cycling or intermittent fasting.