Evaluation of secretion prediction highlights differing approaches needed for oomycete and fungal effectors

© 2015 Sperschneider, Williams, Hane, Singh and Taylor. The steadily increasing number of sequenced fungal and oomycete genomes has enabled detailed studies of how these eukaryotic microbes infect plants and cause devastating losses in food crops. During infection, fungal and oomycete pathogens secrete effector molecules which manipulate host plant cell processes to the pathogen's advantage. Proteinaceous effectors are synthesized intracellularly and must be externalized to interact with host cells. Computational prediction of secreted proteins from genomic sequences is an important technique to narrow down the candidate effector repertoire for subsequent experimental validation. In this study, we benchmark secretion prediction tools on experimentally validated fungal and oomycete effectors. We observe that for a set of fungal SwissProt protein sequences, SignalP 4 and the neural network predictors of SignalP 3 (D-score) and SignalP 2 perform best. For effector prediction in particular, the use of a sensitive method can be desirable to obtain the most complete candidate effector set. We show that the neural network predictors of SignalP 2 and 3, as well as TargetP were the most sensitive tools for fungal effector secretion prediction, whereas the hidden Markov model predictors of SignalP 2 and 3 were the most sensitive tools for oomycete effectors. Thus, previous versions of SignalP retain value for oomycete effector prediction, as the current version, SignalP 4, was unable to reliably predict the signal peptide of the oomycete Crinkler effectors in the test set. Our assessment of subcellular localization predictors shows that cytoplasmic effectors are often predicted as not extracellular. This limits the reliability of secretion predictions that depend on these tools. We present our assessment with a view to informing future pathogenomics studies and suggest revised pipelines for secretion prediction to obtain optimal effector predictions in fungi and oomycetes

DeepSig: Deep learning improves signal peptide detection in proteins

Motivation: The identification of signal peptides in protein sequences is an important step toward protein localization and function characterization. Results: Here, we present DeepSig, an improved approach for signal peptide detection and cleavage-site prediction based on deep learning methods. Comparative benchmarks performed on an updated independent dataset of proteins show that DeepSig is the current best performing method, scoring better than other available state-of-the-art approaches on both signal peptide detection and precise cleavage-site identification. Availability and implementation: DeepSig is available as both standalone program and web server at https://deepsig.biocomp.unibo.it. All datasets used in this study can be obtained from the same website

A novel strategy for classifying the output from an in silico vaccine discovery pipeline for eukaryotic pathogens using machine learning algorithms

Background: An in silico vaccine discovery pipeline for eukaryotic pathogens typically consists of several computational tools to predict protein characteristics. The aim of the in silico approach to discovering subunit vaccines is to use predicted characteristics to identify proteins which are worthy of laboratory investigation. A major challenge is that these predictions are inherent with hidden inaccuracies and contradictions. This study focuses on how to reduce the number of false candidates using machine learning algorithms rather than relying on expensive laboratory validation. Proteins from Toxoplasma gondii, Plasmodium sp., and Caenorhabditis elegans were used as training and test datasets.Results: The results show that machine learning algorithms can effectively distinguish expected true from expected false vaccine candidates (with an average sensitivity and specificity of 0.97 and 0.98 respectively), for proteins observed to induce immune responses experimentally.Conclusions: Vaccine candidates from an in silico approach can only be truly validated in a laboratory. Given any in silico output and appropriate training data, the number of false candidates allocated for validation can be dramatically reduced using a pool of machine learning algorithms. This will ultimately save time and money in the laboratory. © 2013 Goodswen et al.; licensee BioMed Central Ltd

Designing novel construction for cell surface display of protein E on Escherichia coli using non-classical pathway based on Lpp-OmpA

Additional file 2. pNon-OmpA-PE. ab1. Sequencing result of pNon-OmpA-PE

Cellular interactions in the tumor microenvironment: the role of secretome

Over the past years, it has become evident that cancer initiation and progression depends on several components of the tumor microenvironment, including inflammatory and immune cells, fibroblasts, endothelial cells, adipocytes, and extracellular matrix. These components of the tumor microenvironment and the neoplastic cells interact with each other providing pro and antitumor signals. The tumor-stroma communication occurs directly between cells or via a variety of molecules secreted, such as growth factors, cytokines, chemokines and microRNAs. This secretome, which derives not only from tumor cells but also from cancer-associated stromal cells, is an important source of key regulators of the tumorigenic process. Their screening and characterization could provide useful biomarkers to improve cancer diagnosis, prognosis, and monitoring of treatment responses.Agência financiadora Fundação de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP) FAPESP 10/51168-0 12/06048-2 13/03839-1 National Council for Scientific and Technological Development (CNPq) CNPq 306216/2010-8 Fundacao para a Ciencia e a Tecnologia (FCT) UID/BIM/04773/2013 CBMR 1334info:eu-repo/semantics/publishedVersio

In vitro and in vivo screening for novel essential cell-envelope proteins in Pseudomonas aeruginosa

The Gram-negative bacterium Pseudomonas aeruginosa represents a prototype of multi-drug resistant opportunistic pathogens for which novel therapeutic options are urgently required. In order to identify new candidates as potential drug targets, we combined large-scale transposon mutagenesis data analysis and bioinformatics predictions to retrieve a set of putative essential genes which are conserved in P. aeruginosa and predicted to encode cell envelope or secreted proteins. By generating unmarked deletion or conditional mutants, we confirmed the in vitro essentiality of two periplasmic proteins, LptH and LolA, responsible for lipopolysaccharide and lipoproteins transport to the outer membrane respectively, and confirmed that they are important for cell envelope stability. LptH was also found to be essential for P. aeruginosa ability to cause infection in different animal models. Conversely, LolA-depleted cells appeared only partially impaired in pathogenicity, indicating that this protein likely plays a less relevant role during bacterial infection. Finally, we ruled out any involvement of the other six proteins under investigation in P. aeruginosa growth, cell envelope stability and virulence. Besides proposing LptH as a very promising drug target in P. aeruginosa, this study confirms the importance of in vitro and in vivo validation of potential essential genes identified through random transposon mutagenesis

Evolutionary interplay between symbiotic relationships and patterns of signal peptide gain and loss

Can orthologous proteins differ in terms of their ability to be secreted? To answer this question, we investigated the distribution of signal peptides within the orthologous groups of Enterobacterales. Parsimony analysis and sequence comparisons revealed a large number of signal peptide gain and loss events, in which signal peptides emerge or disappear in the course of evolution. Signal peptide losses prevail over gains, an effect which is especially pronounced in the transition from the free-living or commensal to the endosymbiotic lifestyle. The disproportionate decline in the number of signal peptide-containing proteins in endosymbionts cannot be explained by the overall reduction of their genomes. Signal peptides can be gained and lost either by acquisition/elimination of the corresponding N-terminal regions or by gradual accumulation of mutations. The evolutionary dynamics of signal peptides in bacterial proteins represents a powerful mechanism of functional diversification

Unusual Metabolism and Hypervariation in the Genome of a Gracilibacterium (BD1-5) from an Oil-Degrading Community.

The candidate phyla radiation (CPR) comprises a large monophyletic group of bacterial lineages known almost exclusively based on genomes obtained using cultivation-independent methods. Within the CPR, Gracilibacteria (BD1-5) are particularly poorly understood due to undersampling and the inherent fragmented nature of available genomes. Here, we report the first closed, curated genome of a gracilibacterium from an enrichment experiment inoculated from the Gulf of Mexico and designed to investigate hydrocarbon degradation. The gracilibacterium rose in abundance after the community switched to dominance by Colwellia Notably, we predict that this gracilibacterium completely lacks glycolysis, the pentose phosphate and Entner-Doudoroff pathways. It appears to acquire pyruvate, acetyl coenzyme A (acetyl-CoA), and oxaloacetate via degradation of externally derived citrate, malate, and amino acids and may use compound interconversion and oxidoreductases to generate and recycle reductive power. The initial genome assembly was fragmented in an unusual gene that is hypervariable within a repeat region. Such extreme local variation is rare but characteristic of genes that confer traits under pressure to diversify within a population. Notably, the four major repeated 9-mer nucleotide sequences all generate a proline-threonine-aspartic acid (PTD) repeat. The genome of an abundant Colwellia psychrerythraea population has a large extracellular protein that also contains the repeated PTD motif. Although we do not know the host for the BD1-5 cell, the high relative abundance of the C. psychrerythraea population and the shared surface protein repeat may indicate an association between these bacteria.IMPORTANCE CPR bacteria are generally predicted to be symbionts due to their extensive biosynthetic deficits. Although monophyletic, they are not monolithic in terms of their lifestyles. The organism described here appears to have evolved an unusual metabolic platform not reliant on glucose or pentose sugars. Its biology appears to be centered around bacterial host-derived compounds and/or cell detritus. Amino acids likely provide building blocks for nucleic acids, peptidoglycan, and protein synthesis. We resolved an unusual repeat region that would be invisible without genome curation. The nucleotide sequence is apparently under strong diversifying selection, but the amino acid sequence is under stabilizing selection. The amino acid repeat also occurs in a surface protein of a coexisting bacterium, suggesting colocation and possibly interdependence

Genome-wide analyses of Liberibacter species provides insights into evolution, phylogenetic relationships, and virulence factors.

'Candidatus Liberibacter' species are insect-transmitted, phloem-limited α-Proteobacteria in the order of Rhizobiales. The citrus industry is facing significant challenges due to huanglongbing, associated with infection from 'Candidatus Liberibacter asiaticus' (Las). In order to gain greater insight into 'Ca. Liberibacter' biology and genetic diversity, we have performed genome sequencing and comparative analyses of diverse 'Ca. Liberibacter' species, including those that can infect citrus. Our phylogenetic analysis differentiates 'Ca. Liberibacter' species and Rhizobiales in separate clades and suggests stepwise evolution from a common ancestor splitting first into nonpathogenic Liberibacter crescens followed by diversification of pathogenic 'Ca. Liberibacter' species. Further analysis of Las genomes from different geographical locations revealed diversity among isolates from the United States. Our phylogenetic study also indicates multiple Las introduction events in California and spread of the pathogen from Florida to Texas. Texan Las isolates were closely related, while Florida and Asian isolates exhibited the most genetic variation. We have identified conserved Sec translocon (SEC)-dependent effectors likely involved in bacterial survival and virulence of Las and analysed their expression in their plant host (citrus) and insect vector (Diaphorina citri). Individual SEC-dependent effectors exhibited differential expression patterns between host and vector, indicating that Las uses its effector repertoire to differentially modulate diverse organisms. Collectively, this work provides insights into the evolution of 'Ca. Liberibacter' species, the introduction of Las in the United States and identifies promising Las targets for disease management