Esub8: A novel tool to predict protein subcellular localizations in eukaryotic organisms

Background

Subcellular localization of a new protein sequence is very important and fruitful for understanding its function. As the number of new genomes has dramatically increased over recent years, a reliable and efficient system to predict protein subcellular location is urgently needed.

Results

Esub8 was developed to predict protein subcellular localizations for eukaryotic proteins based on amino acid composition. In this research, the proteins are classified into the following eight groups: chloroplast, cytoplasm, extracellular, Golgi apparatus, lysosome, mitochondria, nucleus and peroxisome. We know subcellular localization is a typical classification problem; consequently, a one-against-one (1-v-1) multi-class support vector machine was introduced to construct the classifier. Unlike previous methods, ours considers the order information of protein sequences by a different method. Our method is tested in three subcellular localization predictions for prokaryotic proteins and four subcellular localization predictions for eukaryotic proteins on Reinhardt's dataset. The results are then compared to several other methods. The total prediction accuracies of two tests are both 100% by a self-consistency test, and are 92.9% and 84.14% by the jackknife test, respectively. Esub8 also provides excellent results: the total prediction accuracies are 100% by a self-consistency test and 87% by the jackknife test.

Conclusions

Our method represents a different approach for predicting protein subcellular localization and achieved a satisfactory result; furthermore, we believe Esub8 will be a useful tool for predicting protein subcellular localizations in eukaryotic organisms.

     

Targeting a heterologous protein to multiple plant organelles via rationally designed 5′ mRNA tags

Background

Plant bioengineers require simple genetic devices for predictable localization of heterologous proteins to multiple subcellular compartments.

Results

We designed novel hybrid signal sequences for multiple-compartment localization and characterize their function when fused to GFP in Nicotiana benthamiana leaf tissue. TriTag-1 and TriTag-2 use alternative splicing to generate differentially localized GFP isoforms, localizing it to the chloroplasts, peroxisomes and cytosol. TriTag-1 shows a bias for targeting the chloroplast envelope while TriTag-2 preferentially targets the peroxisomes. TriTag-3 embeds a conserved peroxisomal targeting signal within a chloroplast transit peptide, directing GFP to the chloroplasts and peroxisomes.

Conclusions

Our novel signal sequences can reduce the number of cloning steps and the amount of genetic material required to target a heterologous protein to multiple locations in plant cells. This work harnesses alternative splicing and signal embedding for engineering plants to express multi-functional proteins from single genetic constructs.

     

FGsub: <Emphasis Type="Italic">Fusarium graminearum</Emphasis> protein subcellular localizations predicted from primary structures

Background

The fungal pathogen Fusarium graminearum (telomorph Gibberella zeae) is the causal agent of several destructive crop diseases, where a set of genes usually work in concert to cause diseases to crops. To function appropriately, the F. graminearum proteins inside one cell should be assigned to different compartments, i.e. subcellular localizations. Therefore, the subcellular localizations of F. graminearum proteins can provide insights into protein functions and pathogenic mechanisms of this destructive pathogen fungus. Unfortunately, there are no subcellular localization information for F. graminearum proteins available now. Computational approaches provide an alternative way to predicting F. graminearum protein subcellular localizations due to the expensive and time-consuming biological experiments in lab.

Results

In this paper, we developed a novel predictor, namely FGsub, to predict F. graminearum protein subcellular localizations from the primary structures. First, a non-redundant fungi data set with subcellular localization annotation is collected from UniProtKB database and used as training set, where the subcellular locations are classified into 10 groups. Subsequently, Support Vector Machine (SVM) is trained on the training set and used to predict F. graminearum protein subcellular localizations for those proteins that do not have significant sequence similarity to those in training set. The performance of SVMs on training set with 10-fold cross-validation demonstrates the efficiency and effectiveness of the proposed method. In addition, for F. graminearum proteins that have significant sequence similarity to those in training set, BLAST is utilized to transfer annotations of homologous proteins to uncharacterized F. graminearum proteins so that the F. graminearum proteins are annotated more comprehensively.

Conclusions

In this work, we present FGsub to predict F. graminearum protein subcellular localizations in a comprehensive manner. We make four fold contributions to this filed. First, we present a new algorithm to cope with imbalance problem that arises in protein subcellular localization prediction, which can solve imbalance problem and avoid false positive results. Second, we design an ensemble classifier which employs feature selection to further improve prediction accuracy. Third, we use BLAST to complement machine learning based methods, which enlarges our prediction coverage. Last and most important, we predict the subcellular localizations of 12786 F. graminearum proteins, which provide insights into protein functions and pathogenic mechanisms of this destructive pathogen fungus.

     

Predicting essential proteins based on subcellular localization,orthology and PPI networks

Background

Essential proteins play an indispensable role in the cellular survival and development. There have been a series of biological experimental methods for finding essential proteins; however they are time-consuming, expensive and inefficient. In order to overcome the shortcomings of biological experimental methods, many computational methods have been proposed to predict essential proteins. The computational methods can be roughly divided into two categories, the topology-based methods and the sequence-based ones. The former use the topological features of protein-protein interaction (PPI) networks while the latter use the sequence features of proteins to predict essential proteins. Nevertheless, it is still challenging to improve the prediction accuracy of the computational methods.

Results

Comparing with nonessential proteins, essential proteins appear more frequently in certain subcellular locations and their evolution more conservative. By integrating the information of subcellular localization, orthologous proteins and PPI networks, we propose a novel essential protein prediction method, named SON, in this study. The experimental results on S.cerevisiae data show that the prediction accuracy of SON clearly exceeds that of nine competing methods: DC, BC, IC, CC, SC, EC, NC, PeC and ION.

Conclusions

We demonstrate that, by integrating the information of subcellular localization, orthologous proteins with PPI networks, the accuracy of predicting essential proteins can be improved. Our proposed method SON is effective for predicting essential proteins.

     

Prediction of essential proteins based on subcellular localization and gene expression correlation

Background

Essential proteins are indispensable to the survival and development process of living organisms. To understand the functional mechanisms of essential proteins, which can be applied to the analysis of disease and design of drugs, it is important to identify essential proteins from a set of proteins first. As traditional experimental methods designed to test out essential proteins are usually expensive and laborious, computational methods, which utilize biological and topological features of proteins, have attracted more attention in recent years. Protein-protein interaction networks, together with other biological data, have been explored to improve the performance of essential protein prediction.

Results

The proposed method SCP is evaluated on Saccharomyces cerevisiae datasets and compared with five other methods. The results show that our method SCP outperforms the other five methods in terms of accuracy of essential protein prediction.

Conclusions

In this paper, we propose a novel algorithm named SCP, which combines the ranking by a modified PageRank algorithm based on subcellular compartments information, with the ranking by Pearson correlation coefficient (PCC) calculated from gene expression data. Experiments show that subcellular localization information is promising in boosting essential protein prediction.

     

Tracking the elusive 5′ exonuclease activity of <Emphasis Type="Italic">Chlamydomonas reinhardtii</Emphasis> RNase J

Key message

Chlamydomonas RNase J is the first member of this enzyme family that has endo- but no intrinsic 5′ exoribonucleolytic activity. This questions its proposed role in chloroplast mRNA maturation.

Abstract

RNA maturation and stability in the chloroplast are controlled by nuclear-encoded ribonucleases and RNA binding proteins. Notably, mRNA 5′ end maturation is thought to be achieved by the combined action of a 5′ exoribonuclease and specific pentatricopeptide repeat proteins (PPR) that block the progression of the nuclease. In Arabidopsis the 5′ exo- and endoribonuclease RNase J has been implicated in this process. Here, we verified the chloroplast localization of the orthologous Chlamydomonas (Cr) RNase J and studied its activity, both in vitro and in vivo in a heterologous B. subtilis system. Our data show that Cr RNase J has endo- but no significant intrinsic 5′ exonuclease activity that would be compatible with its proposed role in mRNA maturation. This is the first example of an RNase J ortholog that does not possess a 5′ exonuclease activity. A yeast two-hybrid screen revealed a number of potential interaction partners but three of the most promising candidates tested, failed to induce the latent exonuclease activity of Cr RNase J. We still favor the hypothesis that Cr RNase J plays an important role in RNA metabolism, but our findings suggest that it rather acts as an endoribonuclease in the chloroplast.

     

High-Sensitivity and High-Resolution In Situ Hybridization of Coding and Long Non-coding RNAs in Vertebrate Ovaries and Testes

Background

Subcellular localization of coding and non-coding RNAs has emerged as major regulatory mechanisms of gene expression in various cell types and many organisms. However, techniques that enable detection of the subcellular distribution of these RNAs with high sensitivity and high resolution remain limited, particularly in vertebrate adult tissues and organs. In this study, we examined the expression and localization of mRNAs encoding Pou5f1/Oct4, Mos, Cyclin B1 and Deleted in Azoospermia-like (Dazl) in zebrafish and mouse ovaries by combining tyramide signal amplification (TSA)-based in situ hybridization with paraffin sections which can preserve cell morphology of tissues and organs at subcellular levels. In addition, the distribution of a long non-coding RNA (lncRNA), lncRNA-HSVIII, in mouse testes was examined by the same method.

Results

The mRNAs encoding Mos, Cyclin B1 and Dazl were found to assemble into distinct granules that were distributed in different subcellular regions of zebrafish and mouse oocytes, suggesting conserved and specific regulations of these mRNAs. The lncRNA-HSVIII was first detected in the nucleus of spermatocytes at prophase I of the meiotic cell cycle and was then found in the cytoplasm of round spermatids, revealing expression patterns of lncRNA during germ cell development. Collectively, the in situ hybridization method demonstrated in this study achieved the detection and comparison of precise distribution patterns of coding and non-coding RNAs at subcellular levels in single cells of adult tissues and organs.

Conclusions

This high-sensitivity and high-resolution in situ hybridization is applicable to many vertebrate species and to various tissues and organs and will be useful for studies on the subcellular regulation of gene expression at the level of RNA localization.

     

Comparative proteomic analysis between nitrogen supplemented and starved conditions in <Emphasis Type="Italic">Magnaporthe oryzae</Emphasis>

Background

Fungi are constantly exposed to nitrogen limiting environments, and thus the efficient regulation of nitrogen metabolism is essential for their survival, growth, development and pathogenicity. To understand how the rice blast pathogen Magnaporthe oryzae copes with limited nitrogen availability, a global proteome analysis under nitrogen supplemented and nitrogen starved conditions was completed.

Methods

M. oryzae strain 70–15 was cultivated in liquid minimal media and transferred to media with nitrate or without a nitrogen source. Proteins were isolated and subjected to unfractionated gel-free based liquid chromatography-tandem mass spectrometry (LC-MS/MS). The subcellular localization and function of the identified proteins were predicted using bioinformatics tools.

Results

A total of 5498 M. oryzae proteins were identified. Comparative analysis of protein expression showed 363 proteins and 266 proteins significantly induced or uniquely expressed under nitrogen starved or nitrogen supplemented conditions, respectively. A functional analysis of differentially expressed proteins revealed that during nitrogen starvation nitrogen catabolite repression, melanin biosynthesis, protein degradation and protein translation pathways underwent extensive alterations. In addition, nitrogen starvation induced accumulation of various extracellular proteins including small extracellular proteins consistent with observations of a link between nitrogen starvation and the development of pathogenicity in M. oryzae.

Conclusion

The results from this study provide a comprehensive understanding of fungal responses to nitrogen availability.

     

Fast subcellular localization by cascaded fusion of signal-based and homology-based methods

Background

The functions of proteins are closely related to their subcellular locations. In the post-genomics era, the amount of gene and protein data grows exponentially, which necessitates the prediction of subcellular localization by computational means.

Results

This paper proposes mitigating the computation burden of alignment-based approaches to subcellular localization prediction by a cascaded fusion of cleavage site prediction and profile alignment. Specifically, the informative segments of protein sequences are identified by a cleavage site predictor using the information in their N-terminal shorting signals. Then, the sequences are truncated at the cleavage site positions, and the shortened sequences are passed to PSI-BLAST for computing their profiles. Subcellular localization are subsequently predicted by a profile-to-profile alignment support-vector-machine (SVM) classifier. To further reduce the training and recognition time of the classifier, the SVM classifier is replaced by a new kernel method based on the perturbational discriminant analysis (PDA).

Conclusions

Experimental results on a new dataset based on Swiss-Prot Release 57.5 show that the method can make use of the best property of signal- and homology-based approaches and can attain an accuracy comparable to that achieved by using full-length sequences. Analysis of profile-alignment score matrices suggest that both profile creation time and profile alignment time can be reduced without significant reduction in subcellular localization accuracy. It was found that PDA enjoys a short training time as compared to the conventional SVM. We advocate that the method will be important for biologists to conduct large-scale protein annotation or for bioinformaticians to perform preliminary investigations on new algorithms that involve pairwise alignments.

     

Subcellular localization defines modification and production of Δ<Superscript>9</Superscript>-tetrahydrocannabinolic acid synthase in transiently transformed <Emphasis Type="Italic">Nicotiana benthamiana</Emphasis>

Objective

Through heterologous expression of the tetrahydrocannabinolic acid synthase (THCAS) coding sequence from Cannabis sativa L. in Nicotiana benthamiana, we evaluated a transient plant-based expression system for the production of enzymes involved in cannabinoid biosynthesis.

Results

Thcas was modularized according to the GoldenBraid grammar and its expression tested upon alternative subcellular localization of the encoded catalyst with and without fusion to a fluorescent protein. THCAS was detected only when ER targeting was used; cytosolic and plastidal localization resulted in no detectable protein. Moreover, THCAS seems to be glycosylated in N. benthamiana, suggesting that this modification might have an influence on the stability of the protein. Activity assays with cannabigerolic acid as a substrate showed that the recombinant enzyme produced not only THCA (123 ± 12 fkat g _FW ^?1 activity towards THCA production) but also cannabichromenic acid (CBCA; 31 ± 2.6 fkat g _FW ^?1 activity towards CBCA production).

Conclusion

Nicotiana benthamiana is a suitable host for the generation of cannabinoid producing enzymes. To attain whole pathway integration, careful analysis of subcellular localization is necessary.

     

The distinctive cell division interactome of <Emphasis Type="Italic">Neisseria gonorrhoeae</Emphasis>

Background

Bacterial cell division is an essential process driven by the formation of a Z-ring structure, as a cytoskeletal scaffold at the mid-cell, followed by the recruitment of various proteins which form the divisome. The cell division interactome reflects the complement of different interactions between all divisome proteins. To date, only two cell division interactomes have been characterized, in Escherichia coli and in Streptococcus pneumoniae. The cell divison proteins encoded by Neisseria gonorrhoeae include FtsZ, FtsA, ZipA, FtsK, FtsQ, FtsI, FtsW, and FtsN. The purpose of the present study was to characterize the cell division interactome of N. gonorrhoeae using several different methods to identify protein-protein interactions. We also characterized the specific subdomains of FtsA implicated in interactions with FtsZ, FtsQ, FtsN and FtsW.

Results

Using a combination of bacterial two-hybrid (B2H), glutathione S-transferase (GST) pull-down assays, and surface plasmon resonance (SPR), nine interactions were observed among the eight gonococcal cell division proteins tested. ZipA did not interact with any other cell division proteins. Comparisons of the N. gonorrhoeae cell division interactome with the published interactomes from E. coli and S. pneumoniae indicated that FtsA-FtsZ and FtsZ-FtsK interactions were common to all three species. FtsA-FtsW and FtsK-FtsN interactions were only present in N. gonorrhoeae. The 2A and 2B subdomains of FtsA_Ng were involved in interactions with FtsQ, FtsZ, and FtsN, and the 2A subdomain was involved in interaction with FtsW.

Conclusions

Results from this research indicate that N. gonorrhoeae has a distinctive cell division interactome as compared with other microorganisms.

     

CITRIC: cold-inducible translational readthrough in the chloroplast of <Emphasis Type="Italic">Chlamydomonas reinhardtii</Emphasis> using a novel temperature-sensitive transfer RNA

Background

The chloroplast of eukaryotic microalgae such as Chlamydomonas reinhardtii is a potential platform for metabolic engineering and the production of recombinant proteins. In industrial biotechnology, inducible expression is often used so that the translation or function of the heterologous protein does not interfere with biomass accumulation during the growth stage. However, the existing systems used in bacterial or fungal platforms do not transfer well to the microalgal chloroplast. We sought to develop a simple inducible expression system for the microalgal chloroplast, exploiting an unused stop codon (TGA) in the plastid genome. We have previously shown that this codon can be translated as tryptophan when we introduce into the chloroplast genome a trnW_UCA gene encoding a plastidial transfer RNA with a modified anticodon sequence, UCA.

Results

A mutated version of our trnW_UCA gene was developed that encodes a temperature-sensitive variant of the tRNA. This allows transgenes that have been modified to contain one or more internal TGA codons to be translated differentially according to the culture temperature, with a gradient of recombinant protein accumulation from 35 °C (low/off) to 15 °C (high). We have named this the CITRIC system, an acronym for cold-inducible translational readthrough in chloroplasts. The exact induction behaviour can be tailored by altering the number of TGA codons within the transgene.

Conclusions

CITRIC adds to the suite of genetic engineering tools available for the microalgal chloroplast, allowing a greater degree of control over the timing of heterologous protein expression. It could also be used as a heat-repressible system for studying the function of essential native genes in the chloroplast. The genetic components of CITRIC are entirely plastid-based, so no engineering of the nuclear genome is required.

     

Functional characterization of chloroplast-targeted RbgA GTPase in higher plants

Key message

Plant RbgA GTPase is targeted to chloroplasts and co-fractionated with chloroplast ribosomes, and plays a role in chloroplast rRNA processing and/or ribosome biogenesis.

Abstract

Ribosome Biogenesis GTPase A (RbgA) homologs are evolutionarily conserved GTPases that are widely distributed in both prokaryotes and eukaryotes. In this study, we investigated functions of chloroplast-targeted RbgA. Nicotiana benthamiana RbgA (NbRbgA) and Arabidopsis thaliana RbgA (AtRbgA) contained a conserved GTP-binding domain and a plant-specific C-terminal domain. NbRbgA and AtRbgA were mainly localized in chloroplasts, and possessed GTPase activity. Since Arabidopsis rbgA null mutants exhibited an embryonic lethal phenotype, virus-induced gene silencing (VIGS) of NbRbgA was performed in N. benthamiana. NbRbgA VIGS resulted in a leaf-yellowing phenotype caused by disrupted chloroplast development. NbRbgA was mainly co-fractionated with 50S/70S ribosomes and interacted with the chloroplast ribosomal proteins cpRPL6 and cpRPL35. NbRbgA deficiency lowered the levels of mature 23S and 16S rRNAs in chloroplasts and caused processing defects. Sucrose density gradient sedimentation revealed that NbRbgA-deficient chloroplasts contained reduced levels of mature 23S and 16S rRNAs and diverse plastid-encoded mRNAs in the polysomal fractions, suggesting decreased protein translation activity in the chloroplasts. Interestingly, NbRbgA protein was highly unstable under high light stress, suggesting its possible involvement in the control of chloroplast ribosome biogenesis under environmental stresses. Collectively, these results suggest a role for RbgA GTPase in chloroplast rRNA processing/ribosome biogenesis, affecting chloroplast protein translation in higher plants.

     

Molecular and Functional Characterization of a Rice Thioredoxin m Isoform and Its Interaction Proteins

Although subcellular localization and substrate specificity of thioredoxin isoforms have been characterized, there is little information on the specific functions of mtype plant thioredoxins or their interaction targets. Here, we describe the functional characterization of an Oryza sativa thioredoxin m (OsTrxm). We undertook yeast twohybrid screening using OsTrxm as a bait and found three interaction proteins, Pex14 and two Pex5 variants. Furthermore, two cysteines of OsTrxm were sufficient for the interaction between OsTrxm and these peroxisome proteins. To verify whether OsTrxm and the target proteins can be co-localized in vivo, we examined subcellular localization of OsTrxm-GFP and a peroxisomal marker RFP-SKL in Arabidopsis protoplast cells. Surprisingly, we detected OsTrxm localization in the cytosol and chloroplast. We confirmed these results by 2-D PAGE and Western blot analysis. Our results indicate that OsTrxm may play important roles in the cytoplasm for peroxisome biogenesis as well as in redox regulation of chloroplast proteins.     

Matrix metalloproteinases 2 and 9 increase permeability of sheep pleura in vitro

Background

Matrix metalloproteinases (MMPs) 2 and 9 are two gelatinase members which have been found elevated in exudative pleural effusions. In endothelial cells these MMPs increase paracellular permeability via the disruption of tight junction (TJ) proteins occludin and claudin. In the present study it was investigated if MMP2 and MMP9 alter permeability properties of the pleura tissue by degradation of TJ proteins in pleural mesothelium.

Results

In the present study the transmesothelial resistance (R_TM) of sheep pleura tissue was recorded in Ussing chambers after the addition of MMP2 or MMP9. Both enzymes reduced RTM of the pleura, implying an increase in pleural permeability. The localization and expression of TJ proteins, occludin and claudin-1, were assessed after incubation with MMPs by indirect immunofluorescence and western blot analysis. Our results revealed that incubation with MMPs did not alter neither proteins localization at cell periphery nor their expression.

Conclusions

MMP2 and MMP9 increase the permeability of sheep pleura and this finding suggests a role for MMPs in pleural fluid formation. Tight junction proteins remain intact after incubation with MMPs, contrary to previous studies which have shown TJ degradation by MMPs. Probably MMP2 and MMP9 augment pleural permeability via other mechanisms.