Exploring the function-location nexus: using multiple lines of evidence in defining the subcellular location of plant proteins

Genome-wide characterization and expression profiling of the TGA gene family in sweet orange (Citrus sinensis) reveal CsTGA7 responses to multiple phytohormones and abiotic stresses

Citrus is widely recognized as one of the most economically important fruit crops worldwide. However, citrus growth is frequently hindered by external environmental stresses, which severely limit its development and yield. The TGA (TGACG motif-binding factor) transcription factors (TFs) are members of the bZIP family and play essential roles in plant defense responses and organ development. Nevertheless, the systematic identification and functional analysis of the TGA family in citrus remains unreported. In this study, genome-wide analysis identified a total of seven CsTGA TFs in Citrus sinensis, which were classified into five subgroups. Phylogenetic and syntenic analysis revealed that the CsTGA genes are highly conserved, with no tandem or segmental duplication events among family members. Promoter sequence analysis identified numerous cis-acting elements associated with transcriptional regulation, phytohormone response, and environmental adaptation in the promoters of CsTGA genes. The expression patterns under five phytohormones and three abiotic stresses demonstrated significant responses of multiple CsTGA genes under various forms of adversity. Among all tested treatments, CsTGA7 showed the most robust response to multiple stresses. Tissue-specific expression pattern analysis revealed potential functional biases among CsTGA genes. In-depth analysis showed that CsTGA7 localized in the nucleus and possessed transcriptional activation activity, consistent with the typical characteristic of transcriptional regulators. In summary, our research systematically investigated the genomic signature of the TGA family in C. sinensis and unearthed CsTGA7 with potential functions in phytohormone signaling transduction and abiotic stress responses. Our study establishes a basis for further exploration of the function of CsTGA genes under abiotic stress.

Haplotype-resolved genome assembly and resequencing analysis provide insights into genome evolution and allelic imbalance in Pinus densiflora

Haplotype-level allelic characterization facilitates research on the functional, evolutionary and breeding-related features of extremely large and complex plant genomes. We report a 21.7-Gb chromosome-level haplotype-resolved assembly in Pinus densiflora. We found genome rearrangements involving translocations and inversions between chromosomes 1 and 3 of Pinus species and a proliferation of specific long terminal repeat (LTR) retrotransposons (LTR-RTs) in P. densiflora. Evolutionary analyses illustrated that tandem and LTR-RT-mediated duplications led to an increment of transcription factor (TF) genes in P. densiflora. The haplotype sequence comparison showed allelic imbalances, including presence-absence variations of genes (PAV genes) and their functional contributions to flowering and abiotic stress-related traits in P. densiflora. Allele-aware resequencing analysis revealed PAV gene diversity across P. densiflora accessions. Our study provides insights into key mechanisms underlying the evolution of genome structure, LTR-RTs and TFs within the Pinus lineage as well as allelic imbalances and diversity across P. densiflora.

PfERF106, a novel key transcription factor regulating the biosynthesis of floral terpenoids in Primula forbesii Franch

BACKGROUND

Flowers can be a source of essential oils used in the manufacture of substances with high economic value. The ethylene response factor (ERF) gene family plays a key role in regulating secondary metabolite biosynthesis in plants. However, until now, little has been known about the involvement of ERF transcription factors (TFs) in floral terpenoid biosynthesis.

RESULTS

In this study, an aromatic plant, Primula forbesii Franch., was used as research material to explore the key regulatory effects of PfERF106 on the biosynthesis of terpenoids. PfERF106, which encodes an IXb group ERF transcription factor, exhibited a consistent expression trend in the flowers of P. forbesii and was transcriptionally induced by exogenous ethylene. Transient silencing of PfERF106 in P. forbesii significantly decreased the relative contents of key floral terpenes, including (z)-β-ocimene, sabinene, β-pinene, γ-terpinene, linalool, eremophilene, α-ionone, and α-terpineol. In contrast, constitutive overexpression of PfERF106 in transgenic tobacco significantly increased the relative contents of key floral terpenes, including cis-3-hexen-1-ol, linalool, caryophyllene, cembrene, and sclareol. RNA sequencing of petals of PfERF106-silenced plants and empty-vector control plants revealed 52,711 expressed unigenes and 9,060 differentially expressed genes (DEGs). KEGG annotation analysis revealed that the DEGs were enriched for involvement in secondary metabolic biosynthetic pathways, including monoterpene and diterpene synthesis. Notably, 10 downregulated DEGs were determined to be the downstream target genes of PfERF106 affecting the biosynthesis of terpenoids in P. forbesii.

CONCLUSION

This study characterized the key positive regulatory effects of PfERF106 on the biosynthesis of terpenoids, indicating high-quality genetic resources for aroma improvement in P. forbesii. Thus, this study advances the artificial and precise directional regulation of metabolic engineering of aromatic substances.

Methods to analyze mitochondrial protein translocation in plant mitochondria

Complex processes have evolved in plants to import proteins into mitochondria. Investigating these processes in plants provides insights into the specialised machinery and pathways that have evolved to cope with; (1) the immobile nature of plants that results in exposure to environmental stresses, and (2) the more complex cell environment due to the presence of plastids, the most prevalent being chloropalst in leaves. In this chapter, we present detailed protocols for the isolation of respiratory competent, coupled mitochondria from Arabidopsis thaliana, conducting protein import assays, and analyzing protein assembly into large multi-subunit complexes. Additionally, we present straightforward protocols for examining the localization of fluorescently tagged proteins to organelles such as mitochondria through protoplast transfections.

PETA: evaluating the impact of protein transfer learning with sub-word tokenization on downstream applications

Protein language models (PLMs) play a dominant role in protein representation learning. Most existing PLMs regard proteins as sequences of 20 natural amino acids. The problem with this representation method is that it simply divides the protein sequence into sequences of individual amino acids, ignoring the fact that certain residues often occur together. Therefore, it is inappropriate to view amino acids as isolated tokens. Instead, the PLMs should recognize the frequently occurring combinations of amino acids as a single token. In this study, we use the byte-pair-encoding algorithm and unigram to construct advanced residue vocabularies for protein sequence tokenization, and we have shown that PLMs pre-trained using these advanced vocabularies exhibit superior performance on downstream tasks when compared to those trained with simple vocabularies. Furthermore, we introduce PETA, a comprehensive benchmark for systematically evaluating PLMs. We find that vocabularies comprising 50 and 200 elements achieve optimal performance. Our code, model weights, and datasets are available at https://github.com/ginnm/ProteinPretraining . SCIENTIFIC CONTRIBUTION: This study introduces advanced protein sequence tokenization analysis, leveraging the byte-pair-encoding algorithm and unigram. By recognizing frequently occurring combinations of amino acids as single tokens, our proposed method enhances the performance of PLMs on downstream tasks. Additionally, we present PETA, a new comprehensive benchmark for the systematic evaluation of PLMs, demonstrating that vocabularies of 50 and 200 elements offer optimal performance.

Genome-wide identification and comparative expression profiling of the WRKY transcription factor family in two Citrus species with different Candidatus Liberibacter asiaticus susceptibility

BACKGROUND

Salicylic Acid (SA) is a pivotal phytohormone in plant innate immunity enhancement of triggered by various pathogens, such as Candidatus Liberibacter asiaticus (CLas), the causal agent of Huanglongbing (HLB). WRKY is a plant specific transcription factor (TF) family, which plays crucial roles in plant response to biotic stresses. So far, the evolutionary history, functions, and expression patterns under SA treatment and CLas infection of WRKY family are poorly understood in Citrus, despite the release of the genome of several Citrus species. A comprehensive genomic and expressional analysis is worth to conduct for this family.

RESULTS

Here, a genome-wide identification of WRKY TFs was performed in two Citrus species: Citrus sinensis (HLB-sensitive) and Poncirus trifoliata (HLB-tolerant). In total, 52 CsWRKYs and 51 PtrWRKYs were identified, whose physical and chemical properties, chromosome locations, phylogenetic relationships and structural characteristics were comparatively analyzed. Especially, expression patterns of these WRKY genes before and after SA treatment and CLas infection were compared. Based on this result, seven pairs of orthologous WRKY genes showing opposite expression patterns in two Citrus species were screened out. Moreover, two pairs of orthologous WRKY genes with significant differences in the number or type of stress-responsive cis-elements in the promoter regions were discovered. Subcellular localization and transcriptional activation activity assays revealed that these two pairs of orthologous genes are classic WRKY TFs localize in the nucleus and could function as transcriptional activators.

CONCLUSION

In this study, we systematically analyzed the genomic characterization of WRKY family in two Citrus species, together with the analyses of expression patterns under SA signaling and CLas infection. Our study laid a foundation for further study on the function of WRKY TFs in HLB response and SA signaling of Citrus.

Changes in the Spore Proteome of Bacillus cereus in Response to Introduction of Plasmids

Fluorescent fusion proteins were expressed in Bacillus cereus to visualize the germinosome by introducing a plasmid that carries fluorescent fusion proteins of germinant receptor GerR subunits or germinosome scaffold protein GerD. The effects of plasmid insertion and recombinant protein expression on the spore proteome were investigated. Proteomic analysis showed that overexpression of the target proteins had negligible effects on the spore proteome. However, plasmid-bearing spores displayed dramatic abundance changes in spore proteins involved in signaling and metabolism. Our findings indicate that the introduction of a plasmid alone alters the spore protein composition dramatically, with 993 proteins significantly down-regulated and 415 proteins significantly up-regulated among 3323 identified proteins. This shows that empty vector controls are more appropriate to compare proteome changes due to plasmid-encoded genes than is the wild-type strain, when using plasmid-based genetic tools. Therefore, researchers should keep in mind that molecular cloning techniques can alter more than their intended targets in a biological system, and interpret results with this in mind.

DeepLoc 2.0: multi-label subcellular localization prediction using protein language models

The prediction of protein subcellular localization is of great relevance for proteomics research. Here, we propose an update to the popular tool DeepLoc with multi-localization prediction and improvements in both performance and interpretability. For training and validation, we curate eukaryotic and human multi-location protein datasets with stringent homology partitioning and enriched with sorting signal information compiled from the literature. We achieve state-of-the-art performance in DeepLoc 2.0 by using a pre-trained protein language model. It has the further advantage that it uses sequence input rather than relying on slower protein profiles. We provide two means of better interpretability: an attention output along the sequence and highly accurate prediction of nine different types of protein sorting signals. We find that the attention output correlates well with the position of sorting signals. The webserver is available at services.healthtech.dtu.dk/service.php?DeepLoc-2.0.

Subcellular Proteomics as a Unified Approach of Experimental Localizations and Computed Prediction Data for Arabidopsis and Crop Plants

In eukaryotic organisms, subcellular protein location is critical in defining protein function and understanding sub-functionalization of gene families. Some proteins have defined locations, whereas others have low specificity targeting and complex accumulation patterns. There is no single approach that can be considered entirely adequate for defining the in vivo location of all proteins. By combining evidence from different approaches, the strengths and weaknesses of different technologies can be estimated, and a location consensus can be built. The Subcellular Location of Proteins in Arabidopsis database ( http://suba.live/ ) combines experimental data sets that have been reported in the literature and is analyzing these data to provide useful tools for biologists to interpret their own data. Foremost among these tools is a consensus classifier (SUBAcon) that computes a proposed location for all proteins based on balancing the experimental evidence and predictions. Further tools analyze sets of proteins to define the abundance of cellular structures. Extending these types of resources to plant crop species has been complex due to polyploidy, gene family expansion and contraction, and the movement of pathways and processes within cells across the plant kingdom. The Crop Proteins of Annotated Location database ( http://crop-pal.org/ ) has developed a range of subcellular location resources including a species-specific voting consensus for 12 plant crop species that offers collated evidence and filters for current crop proteomes akin to SUBA. Comprehensive cross-species comparison of these data shows that the sub-cellular proteomes (subcellulomes) depend only to some degree on phylogenetic relationship and are more conserved in major biosynthesis than in metabolic pathways. Together SUBA and cropPAL created reference subcellulomes for plants as well as species-specific subcellulomes for cross-species data mining. These data collections are increasingly used by the research community to provide a subcellular protein location layer, inform models of compartmented cell function and protein-protein interaction network, guide future molecular crop breeding strategies, or simply answer a specific question-where is my protein of interest inside the cell?

Wheat omics: Classical breeding to new breeding technologies

Wheat is an important cereal crop, and its significance is more due to compete for dietary products in the world. Many constraints facing by the wheat crop due to environmental hazardous, biotic, abiotic stress and heavy matters factors, as a result, decrease the yield. Understanding the molecular mechanism related to these factors is significant to figure out genes regulate under specific conditions. Classical breeding using hybridization has been used to increase the yield but not prospered at the desired level. With the development of newly emerging technologies in biological sciences i.e., marker assisted breeding (MAB), QTLs mapping, mutation breeding, proteomics, metabolomics, next-generation sequencing (NGS), RNA_sequencing, transcriptomics, differential expression genes (DEGs), computational resources and genome editing techniques i.e. (CRISPR cas9; Cas13) advances in the field of omics. Application of new breeding technologies develops huge data; considerable development is needed in bioinformatics science to interpret the data. However, combined omics application to address physiological questions linked with genetics is still a challenge. Moreover, viroid discovery opens the new direction for research, economics, and target specification. Comparative genomics important to figure gene of interest processes are further discussed about considering the identification of genes, genomic loci, and biochemical pathways linked with stress resilience in wheat. Furthermore, this review extensively discussed the omics approaches and their effective use. Integrated plant omics technologies have been used viroid genomes associated with CRISPR and CRISPR-associated Cas13a proteins system used for engineering of viroid interference along with high-performance multidimensional phenotyping as a significant limiting factor for increasing stress resistance in wheat.

Flow Cytometry and Sorting in Arabidopsis

Flow cytometry and sorting represents a valuable and mature experimental platform for the analysis of cellular populations. Applications involving higher plants started to emerge around 40 years ago and are now widely employed both to provide unique information regarding basic and applied questions in the biosciences and to advance agricultural productivity in practical ways. Further development of this platform is being actively pursued, and this promises additional progress in our understanding of the interactions of cells within complex tissues and organs. Higher plants offer unique challenges in terms of flow cytometric analysis, first since their organs and tissues are, almost without exception, three-dimensional assemblies of different cell types held together by tough cell walls, and, second, because individual plant cells are generally larger than those of mammals.This chapter, which updates work last reviewed in 2014 [Galbraith DW (2014) Flow cytometry and sorting in Arabidopsis. In: Sanchez Serrano JJ, Salinas J (eds) Arabidopsis Protocols, 3rd ed. Methods in molecular biology, vol 1062. Humana Press, Totowa, pp 509-537], describes the application of techniques of flow cytometry and sorting to the model plant species Arabidopsis thaliana, in particular emphasizing (a) fluorescence labeling in vivo of specific cell types and of subcellular components, (b) analysis using both conventional cytometers and spectral analyzers, (c) fluorescence-activated sorting of protoplasts and nuclei, and (d) transcriptome analyses using sorted protoplasts and nuclei, focusing on population analyses at the level of single protoplasts and nuclei. Since this is an update, details of new experimental methods are emphasized.

Organelle Visualization With Multicolored Fluorescent Markers in Bamboo

Bamboo is an important model plant to study the molecular mechanisms of rapid shoot growth and flowering once in a lifetime. However, bamboo research about protein functional characterization is largely lagged behind, mainly due to the lack of gene transformation platforms. In this study, a protoplast transient gene expression system in moso bamboo has been first established. Using this reliable and efficient system, we have generated a set of multicolored fluorescent markers based on the targeting sequences from endogenous proteins, which have been validated by their comparative localization with Arabidopsis organelle markers, in a combination with pharmaceutical treatments. Moreover, we further demonstrated the power of this multicolor marker set for rapid, combinatorial analysis of the subcellular localization of uncharacterized proteins, which may play potential functions in moso bamboo flowering and fast growth of shoots. Finally, this protoplast transient gene expression system has been elucidated for functional analysis in protein-protein interaction by fluorescence resonance energy transfer (FRET) and co-immunoprecipitation analysis. Taken together, in combination with the set of moso bamboo organelle markers, the protoplast transient gene expression system could be used for subcellular localization and functional study of unknown proteins in bamboo and will definitely promote rapid progress in diverse areas of research in bamboo plants.

Genome-wide investigation of WRKY transcription factors in sweet osmanthus and their potential regulation of aroma synthesis

WRKY transcription factors, one of the largest transcription factor families, play important roles in regulating the synthesis of secondary metabolites. In sweet osmanthus (Osmanthus fragrans), the monoterpenes have been demonstrated as the most important volatile compounds, and the W-box, which is the cognate binding site of WRKY transcription factors, could be identified in most of the terpene-synthesis-related genes' promoters. However, the role of the WRKY family in terpene synthesis in sweet osmanthus has rarely been examined. In this study, 154 WRKY genes with conserved WRKY domain were identified and classified into three groups. The group II was further divided into five subgroups, and almost all members of IId contained a plant zinc cluster domain. Eight OfWRKYs (OfWRKY7/19/36/38/42/84/95/139) were screened from 20 OfWRKYs for their flower-specific expression patterns in different tissues. Simultaneously, the expression patterns of OfWRKYs and emission patterns of volatile compounds during the flowering process were determined and gas chromatography-mass spectrometry results showed that monoterpenes, such as linalool and ocimene, accounted for the highest proportion, contributing to the floral scent of sweet osmanthus in two cultivars. In addition, correlation analysis revealed the expression patterns of OfWRKYs (OfWRKY7/19/36/139) were each correlated with distinct monoterpenes (linalool, linalool derivatives, ocimene and ocimene derivatives). Subcellular localization analysis showed that p35S::GFP-OfWRKY7/38/95/139 were localized in the nucleus and OfWRKY139 had very strong transactivation activity. Collectively, the results indicated potential roles of OfWRKY139 and OfWRKYs with plant zinc cluster domain in regulating synthesis of aromatic compounds in sweet osmanthus, laying the foundation for use of OfWRKYs to improve the aroma of ornamental plants.

A GDSL Esterase/Lipase Catalyzes the Esterification of Lutein in Bread Wheat

Xanthophylls are a class of carotenoids that are important micronutrients for humans. They are often found esterified with fatty acids in fruits, vegetables, and certain grains, including bread wheat (Triticum aestivum). Esterification promotes the sequestration and accumulation of carotenoids, thereby enhancing stability, particularly in tissues such as in harvested wheat grain. Here, we report on a plant xanthophyll acyltransferase (XAT) that is both necessary and sufficient for xanthophyll esterification in bread wheat grain. XAT contains a canonical Gly-Asp-Ser-Leu (GDSL) motif and is encoded by a member of the GDSL esterase/lipase gene family. Genetic evidence from allelic variants of wheat and transgenic rice (Oryza sativa) calli demonstrated that XAT catalyzes the formation of xanthophyll esters. XAT has broad substrate specificity and can esterify lutein, β-cryptoxanthin, and zeaxanthin using multiple acyl donors, yet it has a preference for triacylglycerides, indicating that the enzyme acts via transesterification. A conserved amino acid, Ser-37, is required for activity. Despite xanthophylls being synthesized in plastids, XAT accumulated in the apoplast. Based on analysis of substrate preferences and xanthophyll ester formation in vitro and in vivo using xanthophyll-accumulating rice callus, we propose that disintegration of the cellular structure during wheat grain desiccation facilitates access to lutein-promoting transesterification.plantcell;31/12/3092/FX1F1fx1.

Molecular Evolution and Functional Analysis of Rubredoxin-Like Proteins in Plants

Rubredoxins are a class of iron-containing proteins that play an important role in the reduction of superoxide in some anaerobic bacteria and also act as electron carriers in many biochemical processes. Unlike the more widely studied about rubredoxin proteins in anaerobic bacteria, very few researches about the function of rubredoxins have been proceeded in plants. Previous studies indicated that rubredoxins in A. thaliana may play a critical role in responding to oxidative stress. In order to identify more rubredoxins in plants that maybe have similar functions as the rubredoxin-like protein of A. thaliana, we identified and analyzed plant rubredoxin proteins using bioinformatics-based methods. Totally, 66 candidate rubredoxin proteins were identified based on public databases, exhibiting lengths of 187-360 amino acids with molecular weights of 19.856-37.117 kDa. The results of subcellular localization showed that these candidate rubredoxins were localized to the chloroplast, which might be consistent with the fact that rubredoxins were predominantly expressed in leaves. Analyses of conserved motifs indicated that these candidate rubredoxins contained rubredoxin and PDZ domains. The expression patterns of rubredoxins in glycophyte and halophytic plant under salt/drought stress revealed that rubredoxin is one of the important stress response proteins. Finally, the coexpression network of rubredoxin in Arabidopsis thaliana under abiotic was extracted from ATTED-II to explore the function and regulation relationship of rubredoxin in Arabidopsis thaliana. Our results showed that putative rubredoxin proteins containing PDZ and rubredoxin domains, localized to the chloroplast, may act with other proteins in chloroplast to responses to abiotic stress in higher plants. These findings might provide value inference to promote the development of plant tolerance to some abiotic stresses and other economically important crops.

Subcellular locations of potential cell wall proteins in plants: predictors, databases and cross-referencing

The cell wall is the most striking feature that distinguishes plant cells from animal cells. It plays an essential role in cell shape, stability, growth and protection. Despite being present in small amounts, cell wall proteins (CWPs) are crucial components of the cell wall. The cell wall proteome generally consists of sensu stricto CWPs, apoplast proteins and extracellular secreted proteins. Currently, there is a need for the bioinformatics analysis of a tremendous number of protein sequences that have been generated from genomic, transcriptomic and proteomics research. Compared with intracellular proteins, the location prediction of CWPs is challenging because many aspects of these proteins have not been experimentally characterized, and there are no CWP-trained, specific predictors available. By introducing the biological relevance (particularly molecular aspects) of the cell wall and CWPs, we critically evaluated the accuracy of 16 state-of-the-art predictors and classical predictors for the prediction of CWPs using an independent database of Arabidopsis and rice proteins. All experimentally verified CWPs and non-CWPs were retrieved from the UniProt Knowledgebase. Based on the evaluation, we currently recommend the predictors mGOASVM, HybridGO-Loc and FUEL-mLoc for CWPs. Furthermore, we outlined the public databases that can be used to cross-reference the subcellular location of CWPs. We illustrate a flowchart of the subcellular location prediction and a cross-reference of possible CWPs. Finally, we discuss challenges and perspectives in the bioinformatics analysis of CWPs. It is hoped that this article will provide practical guidance regarding CWPs for nonspecialists and provide insight for bioinformatics experts to develop computational tools for CWPs.

Advances in the Prediction of Protein Subcellular Locations with Machine Learning

Background:

Revealing the subcellular location of a newly discovered protein can bring insight into their function and guide research at the cellular level. The experimental methods currently used to identify the protein subcellular locations are both time-consuming and expensive. Thus, it is highly desired to develop computational methods for efficiently and effectively identifying the protein subcellular locations. Especially, the rapidly increasing number of protein sequences entering the genome databases has called for the development of automated analysis methods.

Methods:

In this review, we will describe the recent advances in predicting the protein subcellular locations with machine learning from the following aspects: i) Protein subcellular location benchmark dataset construction, ii) Protein feature representation and feature descriptors, iii) Common machine learning algorithms, iv) Cross-validation test methods and assessment metrics, v) Web servers.

Result & Conclusion:

Concomitant with a large number of protein sequences generated by highthroughput technologies, four future directions for predicting protein subcellular locations with machine learning should be paid attention. One direction is the selection of novel and effective features (e.g., statistics, physical-chemical, evolutional) from the sequences and structures of proteins. Another is the feature fusion strategy. The third is the design of a powerful predictor and the fourth one is the protein multiple location sites prediction.

The Chloroplast Envelope Protease FTSH11 - Interaction With CPN60 and Identification of Potential Substrates

FTSH proteases are membrane-bound, ATP-dependent metalloproteases found in bacteria, mitochondria and chloroplasts. The product of one of the 12 genes encoding FTSH proteases in Arabidopsis, FTSH11, has been previously shown to be essential for acquired thermotolerance. However, the substrates of this protease, as well as the mechanism linking it to thermotolerance are largely unknown. To get insight into these, the FTSH11 knockout mutant was complemented with proteolytically active or inactive variants of this protease, tagged with HA-tag, under the control of the native promoter. Using these plants in thermotolerance assay demonstrated that the proteolytic activity, and not only the ATPase one, is essential for conferring thermotolerance. Immunoblot analyses of leaf extracts, isolated organelles and sub-fractionated chloroplast membranes localized FTSH11 mostly to chloroplast envelopes. Affinity purification followed by mass spectrometry analysis revealed interaction between FTSH11 and different components of the CPN60 chaperonin. In affinity enrichment assays, CPN60s as well as a number of envelope, stroma and thylakoid proteins were found associated with proteolytically inactive FTSH11. Comparative proteomic analysis of WT and knockout plants, grown at 20°C or exposed to 30°C for 6 h, revealed a plethora of upregulated chloroplast proteins in the knockout, some of them might be candidate substrates. Among these stood out TIC40, which was stabilized in the knockout line after recovery from heat stress, and three proteins that were found trapped in the affinity enrichment assay: the nucleotide antiporter PAPST2, the fatty acid binding protein FAP1 and the chaperone HSP70. The consistent behavior of these four proteins in different assays suggest that they are potential FTSH11 substrates.

Overexpression of SmbHLH10 enhances tanshinones biosynthesis in Salvia miltiorrhiza hairy roots

The bHLH transcription factors have important role in regulation of plant growth, development, and secondary metabolism. Tanshinones are the major pharmaceutical components present in Salvia miltiorrhiza Bunge. It has been reported that bHLHs have functions in terpenoids biosynthesis. Here, we got a bHLH family member named SmbHLH10 which could positively regulate tanshinones biosynthesis in S. miltiorrhiza hairy roots. In the SmbHLH10-overexpressing line 6, four major tanshinones contents were reaching 2.51-fold (dihydrotanshinone I), 2.84-fold (cryptotanshinone), 2.89- fold (tanshinone I), 2.68-fold (tanshinone II A) of WT, respectively. The variation in tanshinones biosynthetic pathway gene transcription was generally consistent with tanshinones content. DXS2, DXS3 and DXR of MEP pathway were induced substantially, reaching 10-fold, 3-fold, 5.74-fold higher of the WT, respectively. The downstream pathway genes CPS1, CPS5 and CYP76AH1 were highest in line OE-SmbHLH10-6, reached 4.93, 16.29 and 3.27-fold of the WT, respectively, while KSL1's expression was highest in line OE-SmbHLH10-4, 4.64-fold of WT. Yeast one-hybrid assays results showed that SmbHLH10 could binds the predicted G-box motifs within the promoters of DXS2, CPS1 and CPS5. These findings indicated that SmbHLH10 could directly binds to G-box in the pathway genes' promotor, activate their expression and then upregulate tanshinones biosynthesis.

Isolation and Respiratory Measurements of Mitochondria from Arabidopsis thaliana

Mitochondria are essential organelles involved in numerous metabolic pathways in plants, most notably the production of adenosine triphosphate (ATP) from the oxidation of reduced compounds such as nicotinamide adenine dinucleotide (NADH) and flavin adenine dinucleotide (FADH2). The complete annotation of the Arabidopsis thaliana genome has established it as the most widely used plant model system, and thus the need to purify mitochondria from a variety of organs (leaf, root, or flower) is necessary to fully utilize the tools that are now available for Arabidopsis to study mitochondrial biology. Mitochondria are isolated by homogenization of the tissue using a variety of approaches, followed by a series of differential centrifugation steps producing a crude mitochondrial pellet that is further purified using continuous colloidal density gradient centrifugation. The colloidal density material is subsequently removed by multiple centrifugation steps. Starting from 100 g of fresh leaf tissue, 2 - 3 mg of mitochondria can be routinely obtained. Respiratory experiments on these mitochondria display typical rates of 100 - 250 nmol O2 min^-1 mg total mitochondrial protein^-1 (NADH-dependent rate) with the ability to use various substrates and inhibitors to determine which substrates are being oxidized and the capacity of the alternative and cytochrome terminal oxidases. This protocol describes an isolation method of mitochondria from Arabidopsis thaliana leaves using continuous colloidal density gradients and an efficient respiratory measurements of purified plant mitochondria.

HPSLPred: An Ensemble Multi-Label Classifier for Human Protein Subcellular Location Prediction with Imbalanced Source

Predicting the subcellular localization of proteins is an important and challenging problem. Traditional experimental approaches are often expensive and time-consuming. Consequently, a growing number of research efforts employ a series of machine learning approaches to predict the subcellular location of proteins. There are two main challenges among the state-of-the-art prediction methods. First, most of the existing techniques are designed to deal with multi-class rather than multi-label classification, which ignores connections between multiple labels. In reality, multiple locations of particular proteins imply that there are vital and unique biological significances that deserve special focus and cannot be ignored. Second, techniques for handling imbalanced data in multi-label classification problems are necessary, but never employed. For solving these two issues, we have developed an ensemble multi-label classifier called HPSLPred, which can be applied for multi-label classification with an imbalanced protein source. For convenience, a user-friendly webserver has been established at http://server.malab.cn/HPSLPred.

SmMYB36, a Novel R2R3-MYB Transcription Factor, Enhances Tanshinone Accumulation and Decreases Phenolic Acid Content in Salvia miltiorrhiza Hairy Roots

Phenolic acids and tanshinones are two major bioactive components in Salvia miltiorrhiza Bunge. A novel endogenous R2R3-MYB transcription factor, SmMYB36, was identified in this research. This transcript factor can simultaneously influence the content of two types of components in SmMYB36 overexpression hairy roots. SmMYB36 was mainly localized in the nucleus of onion epidermis and it has transactivation activity. The overexpression of SmMYB36 promoted tanshinone accumulation but inhibited phenolic acid and flavonoid biosynthesis in Salvia miltiorrhiza hairy roots. The altered metabolite content was due to changed metabolic flow which was regulated by transcript expression of metabolic pathway genes. The gene transcription levels of the phenylpropanoid general pathway, tyrosine derived pathway, methylerythritol phosphate pathway and downstream tanshinone biosynthetic pathway changed significantly due to the overexpression of SmMYB36. The wide distribution of MYB binding elements (MBS, MRE, MBSI and MBSII) and electrophoretic mobility shift assay results indicated that SmMYB36 may be an effective tool to regulate metabolic flux shifts.

Ancient Plant Glyoxylate/Succinic Semialdehyde Reductases: GLYR1s Are Cytosolic, Whereas GLYR2s Are Localized to Both Mitochondria and Plastids

Plant NADPH-dependent glyoxylate/succinic semialdehyde reductases 1 and 2 (GLYR1 and GLYR2) are considered to be involved in detoxifying harmful aldehydes, thereby preserving plant health during exposure to various abiotic stresses. Phylogenetic analysis revealed that the two GLYR isoforms appeared in the plant lineage prior to the divergence of the Chlorophyta and Streptophyta, which occurred approximately 750 million years ago. Green fluorescent protein fusions of apple (Malus x domestica Borkh.), rice (Oryza sativa L.) and Arabidopsis thaliana [L.] Heynh GLYRs were transiently expressed in tobacco (Nicotiana tabaccum L.) suspension cells or Arabidopsis protoplasts, as well in methoxyfenozide-induced, stably transformed Arabidopsis seedlings. The localization of apple GLYR1 confirmed that this isoform is cytosolic, whereas apple, rice and Arabidopsis GLYR2s were localized to both mitochondria and plastids. These findings highlight the potential involvement of GLYRs within distinct compartments of the plant cell.

Plant-Specific Preprotein and Amino Acid Transporter Proteins Are Required for tRNA Import into Mitochondria

A variety of eukaryotes, in particular plants, do not contain the required number of tRNAs to support the translation of mitochondria-encoded genes and thus need to import tRNAs from the cytosol. This study identified two Arabidopsis (Arabidopsis thaliana) proteins, Tric1 and Tric2 (for tRNA import component), which on simultaneous inactivation by T-DNA insertion lines displayed a severely delayed and chlorotic growth phenotype and significantly reduced tRNA import capacity into isolated mitochondria. The predicted tRNA-binding domain of Tric1 and Tric2, a sterile-α-motif at the C-terminal end of the protein, was required to restore tRNA uptake ability in mitochondria of complemented plants. The purified predicted tRNA-binding domain binds the T-arm of the tRNA for alanine with conserved lysine residues required for binding. T-DNA inactivation of both Tric proteins further resulted in an increase in the in vitro rate of in organello protein synthesis, which was mediated by a reorganization of the nuclear transcriptome, in particular of genes encoding a variety of proteins required for mitochondrial gene expression at both the transcriptional and translational levels. The characterization of Tric1/2 provides mechanistic insight into the process of tRNA import into mitochondria and supports the theory that the tRNA import pathway resulted from the repurposing of a preexisting protein import apparatus.

Deciphering the roles of acyl-CoA-binding proteins in plant cells

Lipid trafficking is vital for metabolite exchange and signal communications between organelles and endomembranes. Acyl-CoA-binding proteins (ACBPs) are involved in the intracellular transport, protection, and pool formation of acyl-CoA esters, which are important intermediates and regulators in lipid metabolism and cellular signaling. In this review, we highlight recent advances in our understanding of plant ACBP families from a cellular and developmental perspective. Plant ACBPs have been extensively studied in Arabidopsis thaliana (a dicot) and to a lesser extent in Oryza sativa (a monocot). Thus far, they have been detected in the plasma membrane, vesicles, endoplasmic reticulum, Golgi apparatus, apoplast, cytosol, nuclear periphery, and peroxisomes. In combination with biochemical and molecular genetic tools, the widespread subcellular distribution of respective ACBP members has been explicitly linked to their functions in lipid metabolism during development and in response to stresses. At the cellular level, strong expression of specific ACBP homologs in specialized cells, such as embryos, stem epidermis, guard cells, male gametophytes, and phloem sap, is of relevance to their corresponding distinct roles in organ development and stress responses. Other interesting patterns in their subcellular localization and spatial expression that prompt new directions in future investigations are discussed.

Molecular and biochemical characterizations of the monoacylglycerol lipase gene family of Arabidopsis thaliana

Monoacylglycerol lipase (MAGL) catalyzes the last step of triacylglycerol breakdown, which is the hydrolysis of monoacylglycerol (MAG) to fatty acid and glycerol. Arabidopsis harbors over 270 genes annotated as 'lipase', the largest class of acyl lipid metabolism genes that have not been characterized experimentally. In this study, computational modeling suggested that 16 Arabidopsis putative MAGLs (AtMAGLs) have a three-dimensional structure that is similar to a human MAGL. Heterologous expression and enzyme assays indicated that 11 of the 16 encoded proteins indeed possess MAG lipase activity. Additionally, AtMAGL4 displayed hydrolase activity with lysophosphatidylcholine and lysophosphatidylethanolamine (LPE) substrates and AtMAGL1 and 2 utilized LPE as a substrate. All recombinant AtMAGLs preferred MAG substrates with unsaturated fatty acids over saturated fatty acids and AtMAGL8 exhibited the highest hydrolase activities with MAG containing 20:1 fatty acids. Except for AtMAGL4, -14 and -16, all AtMAGLs showed similar activity with both sn-1 and sn-2 MAG isomers. Spatial, temporal and stress-induced expression of the 16 AtMAGL genes was analyzed by transcriptome analyses. AtMAGL:eYFP fusion proteins provided initial evidence that AtMAGL1, -3, -6, -7, -8, -11, -13, -14 and -16 are targeted to the endoplasmic reticulum and/or Golgi network, AtMAGL10, -12 and -15 to the cytosol and AtMAGL2, -4 and -5 to the chloroplasts. Furthermore, AtMAGL8 was associated with the surface of oil bodies in germinating seeds and leaves accumulating oil bodies. This study provides the broad characterization of one of the least well-understood groups of Arabidopsis lipid-related enzymes and will be useful for better understanding their roles in planta.

Sparse regressions for predicting and interpreting subcellular localization of multi-label proteins

Background

Predicting protein subcellular localization is indispensable for inferring protein functions. Recent studies have been focusing on predicting not only single-location proteins, but also multi-location proteins. Almost all of the high performing predictors proposed recently use gene ontology (GO) terms to construct feature vectors for classification. Despite their high performance, their prediction decisions are difficult to interpret because of the large number of GO terms involved.

Results

This paper proposes using sparse regressions to exploit GO information for both predicting and interpreting subcellular localization of single- and multi-location proteins. Specifically, we compared two multi-label sparse regression algorithms, namely multi-label LASSO (mLASSO) and multi-label elastic net (mEN), for large-scale predictions of protein subcellular localization. Both algorithms can yield sparse and interpretable solutions. By using the one-vs-rest strategy, mLASSO and mEN identified 87 and 429 out of more than 8,000 GO terms, respectively, which play essential roles in determining subcellular localization. More interestingly, many of the GO terms selected by mEN are from the biological process and molecular function categories, suggesting that the GO terms of these categories also play vital roles in the prediction. With these essential GO terms, not only where a protein locates can be decided, but also why it resides there can be revealed.

Conclusions

Experimental results show that the output of both mEN and mLASSO are interpretable and they perform significantly better than existing state-of-the-art predictors. Moreover, mEN selects more features and performs better than mLASSO on a stringent human benchmark dataset. For readers’ convenience, an online server called SpaPredictor for both mLASSO and mEN is available at http://bioinfo.eie.polyu.edu.hk/SpaPredictorServer/.

A set of GFP-based organelle marker lines combined with DsRed-based gateway vectors for subcellular localization study in rice (Oryza sativa L.)

In the post-genomic era, many useful tools have been developed to accelerate the investigation of gene functions. Fluorescent proteins have been widely used as protein tags for studying the subcellular localization of proteins in plants. Several fluorescent organelle marker lines have been generated in dicot plants; however, useful and reliable fluorescent organelle marker lines are lacking in the monocot model rice. Here, we developed eight different GFP-based organelle markers in transgenic rice and created a set of DsRed-based gateway vectors for combining with the marker lines. Two mitochondrial-localized rice ascorbate peroxidase genes fused to DsRed and successfully co-localized with mitochondrial-targeted marker lines verified the practical use of this system. The co-localization of GFP-fusion marker lines and DsRed-fusion proteins provide a convenient platform for in vivo or in vitro analysis of subcellular localization of rice proteins.

Crop epigenetics and the molecular hardware of genotype × environment interactions

Crop plants encounter thermal environments which fluctuate on a diurnal and seasonal basis. Future climate resilient cultivars will need to respond to thermal profiles reflecting more variable conditions, and harness plasticity that involves regulation of epigenetic processes and complex genomic regulatory networks. Compartmentalization within plant cells insulates the genomic central processing unit within the interphase nucleus. This review addresses the properties of the chromatin hardware in which the genome is embedded, focusing on the biophysical and thermodynamic properties of DNA, histones and nucleosomes. It explores the consequences of thermal and ionic variation on the biophysical behavior of epigenetic marks such as DNA cytosine methylation (5mC), and histone variants such as H2A.Z, and how these contribute to maintenance of chromatin integrity in the nucleus, while enabling specific subsets of genes to be regulated. Information is drawn from theoretical molecular in vitro studies as well as model and crop plants and incorporates recent insights into the role epigenetic processes play in mediating between environmental signals and genomic regulation. A preliminary speculative framework is outlined, based on the evidence of what appears to be a cohesive set of interactions at molecular, biophysical and electrostatic level between the various components contributing to chromatin conformation and dynamics. It proposes that within plant nuclei, general and localized ionic homeostasis plays an important role in maintaining chromatin conformation, whilst maintaining complex genomic regulation that involves specific patterns of epigenetic marks. More generally, reversible changes in DNA methylation appear to be consistent with the ability of nuclear chromatin to manage variation in external ionic and temperature environment. Whilst tentative, this framework provides scope to develop experimental approaches to understand in greater detail the internal environment of plant nuclei. It is hoped that this will generate a deeper understanding of the molecular mechanisms underlying genotype × environment interactions that may be beneficial for long-term improvement of crop performance in less predictable climates.

Frataxin Is Localized to Both the Chloroplast and Mitochondrion and Is Involved in Chloroplast Fe-S Protein Function in Arabidopsis

Frataxin plays a key role in eukaryotic cellular iron metabolism, particularly in mitochondrial heme and iron-sulfur (Fe-S) cluster biosynthesis. However, its precise role has yet to be elucidated. In this work, we studied the subcellular localization of Arabidopsis frataxin, AtFH, using confocal microscopy, and found a novel dual localization for this protein. We demonstrate that plant frataxin is targeted to both the mitochondria and the chloroplast, where it may play a role in Fe-S cluster metabolism as suggested by functional studies on nitrite reductase (NIR) and ferredoxin (Fd), two Fe-S containing chloroplast proteins, in AtFH deficient plants. Our results indicate that frataxin deficiency alters the normal functioning of chloroplasts by affecting the levels of Fe, chlorophyll, and the photosynthetic electron transport chain in this organelle.

Predict subcellular locations of singleplex and multiplex proteins by semi-supervised learning and dimension-reducing general mode of Chou's PseAAC

Predicting protein subcellular location is one of major challenges in Bioinformatics area since such knowledge helps us understand protein functions and enables us to select the targeted proteins during drug discovery process. While many computational techniques have been proposed to improve predictive performance for protein subcellular location, they have several shortcomings. In this work, we propose a method to solve three main issues in such techniques; i) manipulation of multiplex proteins which may exist or move between multiple cellular compartments, ii) handling of high dimensionality in input and output spaces and iii) requirement of sufficient labeled data for model training. Towards these issues, this work presents a new computational method for predicting proteins which have either single or multiple locations. The proposed technique, namely iFLAST-CORE, incorporates the dimensionality reduction in the feature and label spaces with co-training paradigm for semi-supervised multi-label classification. For this purpose, the Singular Value Decomposition (SVD) is applied to transform the high-dimensional feature space and label space into the lower-dimensional spaces. After that, due to limitation of labeled data, the co-training regression makes use of unlabeled data by predicting the target values in the lower-dimensional spaces of unlabeled data. In the last step, the component of SVD is used to project labels in the lower-dimensional space back to those in the original space and an adaptive threshold is used to map a numeric value to a binary value for label determination. A set of experiments on viral proteins and gram-negative bacterial proteins evidence that our proposed method improve the classification performance in terms of various evaluation metrics such as Aiming (or Precision), Coverage (or Recall) and macro F-measure, compared to the traditional method that uses only labeled data.

Transport of rice cyclobutane pyrimidine dimer photolyase into mitochondria relies on a targeting sequence located in its C-terminal internal region

The cyclobutane pyrimidine dimer (CPD), which represents a major type of DNA damage induced by ultraviolet-B (UVB) radiation, is a principal cause of UVB-induced growth inhibition in plants. CPD photolyase is the primary enzyme for repairing CPDs and is crucial for determining the sensitivity of Oryza sativa (rice) to UVB radiation. CPD photolyase is widely distributed among species ranging from eubacteria to eukaryotes, and is classified into class I or II based on its primary structure. We previously demonstrated that rice CPD photolyase (OsPHR), which belongs to class II and is encoded by a single-copy gene, is a unique nuclear/mitochondrial/chloroplast triple-targeting protein; however, the location and nature of the organellar targeting information contained within OsPHR are unknown. Here, the nuclear and mitochondrial targeting signal sequences of OsPHR were identified by systematic deletion analysis. The nuclear and mitochondrial targeting sequences are harbored within residues 487-489 and 391-401 in the C-terminal region of OsPHR (506 amino acid residues), respectively. The mitochondrial targeting signal represents a distinct topogenic sequence that differs structurally and functionally from classical N-terminal pre-sequences, and this region, in addition to its role in localization to the mitochondria, is essential for the proper functioning of the CPD photolyase. Furthermore, the mitochondrial targeting sequence, which is characteristic of class-II CPD photolyases, was acquired before the divergence of class-II CPD photolyases in eukaryotes. These results indicate that rice plants have evolved a CPD photolyase that functions in mitochondria to protect cells from the harmful effects of UVB radiation.

SUBAcon: a consensus algorithm for unifying the subcellular localization data of the Arabidopsis proteome

MOTIVATION

Knowing the subcellular location of proteins is critical for understanding their function and developing accurate networks representing eukaryotic biological processes. Many computational tools have been developed to predict proteome-wide subcellular location, and abundant experimental data from green fluorescent protein (GFP) tagging or mass spectrometry (MS) are available in the model plant, Arabidopsis. None of these approaches is error-free, and thus, results are often contradictory.

RESULTS

To help unify these multiple data sources, we have developed the SUBcellular Arabidopsis consensus (SUBAcon) algorithm, a naive Bayes classifier that integrates 22 computational prediction algorithms, experimental GFP and MS localizations, protein-protein interaction and co-expression data to derive a consensus call and probability. SUBAcon classifies protein location in Arabidopsis more accurately than single predictors.

AVAILABILITY

SUBAcon is a useful tool for recovering proteome-wide subcellular locations of Arabidopsis proteins and is displayed in the SUBA3 database (http://suba.plantenergy.uwa.edu.au). The source code and input data is available through the SUBA3 server (http://suba.plantenergy.uwa.edu.au//SUBAcon.html) and the Arabidopsis SUbproteome REference (ASURE) training set can be accessed using the ASURE web portal (http://suba.plantenergy.uwa.edu.au/ASURE).

The plant glycosyltransferase clone collection for functional genomics

The glycosyltransferases (GTs) are an important and functionally diverse family of enzymes involved in glycan and glycoside biosynthesis. Plants have evolved large families of GTs which undertake the array of glycosylation reactions that occur during plant development and growth. Based on the Carbohydrate-Active enZymes (CAZy) database, the genome of the reference plant Arabidopsis thaliana codes for over 450 GTs, while the rice genome (Oryza sativa) contains over 600 members. Collectively, GTs from these reference plants can be classified into over 40 distinct GT families. Although these enzymes are involved in many important plant specific processes such as cell-wall and secondary metabolite biosynthesis, few have been functionally characterized. We have sought to develop a plant GTs clone resource that will enable functional genomic approaches to be undertaken by the plant research community. In total, 403 (88%) of CAZy defined Arabidopsis GTs have been cloned, while 96 (15%) of the GTs coded by rice have been cloned. The collection resulted in the update of a number of Arabidopsis GT gene models. The clones represent full-length coding sequences without termination codons and are Gateway® compatible. To demonstrate the utility of this JBEI GT Collection, a set of efficient particle bombardment plasmids (pBullet) was also constructed with markers for the endomembrane. The utility of the pBullet collection was demonstrated by localizing all members of the Arabidopsis GT14 family to the Golgi apparatus or the endoplasmic reticulum (ER). Updates to these resources are available at the JBEI GT Collection website http://www.addgene.org/.

R3P-Loc: a compact multi-label predictor using ridge regression and random projection for protein subcellular localization

Locating proteins within cellular contexts is of paramount significance in elucidating their biological functions. Computational methods based on knowledge databases (such as gene ontology annotation (GOA) database) are known to be more efficient than sequence-based methods. However, the predominant scenarios of knowledge-based methods are that (1) knowledge databases typically have enormous size and are growing exponentially, (2) knowledge databases contain redundant information, and (3) the number of extracted features from knowledge databases is much larger than the number of data samples with ground-truth labels. These properties render the extracted features liable to redundant or irrelevant information, causing the prediction systems suffer from overfitting. To address these problems, this paper proposes an efficient multi-label predictor, namely R3P-Loc, which uses two compact databases for feature extraction and applies random projection (RP) to reduce the feature dimensions of an ensemble ridge regression (RR) classifier. Two new compact databases are created from Swiss-Prot and GOA databases. These databases possess almost the same amount of information as their full-size counterparts but with much smaller size. Experimental results on two recent datasets (eukaryote and plant) suggest that R3P-Loc can reduce the dimensions by seven-folds and significantly outperforms state-of-the-art predictors. This paper also demonstrates that the compact databases reduce the memory consumption by 39 times without causing degradation in prediction accuracy. For readers׳ convenience, the R3P-Loc server is available online at url:http://bioinfo.eie.polyu.edu.hk/R3PLocServer/.

Flow cytometry and sorting in Arabidopsis

Flow cytometry, and the accompanying technology of cell sorting, represents an established and valuable experimental platform for the analysis of cellular populations. Applications involving higher plants, which started to emerge around 30 years ago, are now widely employed both to provide unique information regarding fundamental questions in basic and applied bioscience and to advance agricultural productivity in practical ways. Further developments of this platform are being actively pursued, promising additional advances in our understanding of the interactions of cells within the complex tissues and organs. Higher plants offer unique challenges in terms of flow cytometric analysis, first since their organs and tissues are, almost without exception, three-dimensional assemblies of different cell types and second that their individual cells are generally larger than those of mammals. This chapter focuses on the use of flow cytometry and cell sorting with the model species Arabidopsis thaliana, in particular addressing (1) fluorescence in vivo labeling of specific cell types, (2) fluorescence-activated sorting of protoplasts and nuclei, and (3) transcriptome analyses using sorted protoplasts and nuclei.

Protein (multi-)location prediction: using location inter-dependencies in a probabilistic framework

Motivation

Knowing the location of a protein within the cell is important for understanding its function, role in biological processes, and potential use as a drug target. Much progress has been made in developing computational methods that predict single locations for proteins. Most such methods are based on the over-simplifying assumption that proteins localize to a single location. However, it has been shown that proteins localize to multiple locations. While a few recent systems attempt to predict multiple locations of proteins, their performance leaves much room for improvement. Moreover, they typically treat locations as independent and do not attempt to utilize possible inter-dependencies among locations. Our hypothesis is that directly incorporating inter-dependencies among locations into both the classifier-learning and the prediction process can improve location prediction performance.

Results

We present a new method and a preliminary system we have developed that directly incorporates inter-dependencies among locations into the location-prediction process of multiply-localized proteins. Our method is based on a collection of Bayesian network classifiers, where each classifier is used to predict a single location. Learning the structure of each Bayesian network classifier takes into account inter-dependencies among locations, and the prediction process uses estimates involving multiple locations. We evaluate our system on a dataset of single- and multi-localized proteins (the most comprehensive protein multi-localization dataset currently available, derived from the DBMLoc dataset). Our results, obtained by incorporating inter-dependencies, are significantly higher than those obtained by classifiers that do not use inter-dependencies. The performance of our system on multi-localized proteins is comparable to a top performing system (YLoc⁺), without being restricted only to location-combinations present in the training set.

The Arabidopsis cytosolic proteome: the metabolic heart of the cell

The plant cytosol is the major intracellular fluid that acts as the medium for inter-organellar crosstalk and where a plethora of important biological reactions take place. These include its involvement in protein synthesis and degradation, stress response signaling, carbon metabolism, biosynthesis of secondary metabolites, and accumulation of enzymes for defense and detoxification. This central role is highlighted by estimates indicating that the majority of eukaryotic proteins are cytosolic. Arabidopsis thaliana has been the subject of numerous proteomic studies on its different subcellular compartments. However, a detailed study of enriched cytosolic fractions from Arabidopsis cell culture has been performed only recently, with over 1,000 proteins reproducibly identified by mass spectrometry. The number of proteins allocated to the cytosol nearly doubles to 1,802 if a series of targeted proteomic characterizations of complexes is included. Despite this, few groups are currently applying advanced proteomic approaches to this important metabolic space. This review will highlight the current state of the Arabidopsis cytosolic proteome since its initial characterization a few years ago.

Using the SUBcellular database for Arabidopsis proteins to localize the Deg protease family

Sub-functionalization during the expansion of gene families in eukaryotes has occurred in part through specific subcellular localization of different family members. To better understand this process in plants, compiled records of large-scale proteomic and fluorescent protein localization datasets can be explored and bioinformatic predictions for protein localization can be used to predict the gaps in experimental data. This process can be followed by targeted experiments to test predictions. The SUBA3 database is a free web-service at http://suba.plantenergy.uwa.edu.au that helps users to explore reported experimental data and predictions concerning proteins encoded by gene families and to define the experiments required to locate these homologous sets of proteins. Here we show how SUBA3 can be used to explore the subcellular location of the Deg protease family of ATP-independent serine endopeptidases (Deg1-Deg16). Combined data integration and new experiments refined location information for Deg1 and Deg9, confirmed Deg2, Deg5, and Deg8 in plastids and Deg 15 in peroxisomes and provide substantial experimental evidence for mitochondrial localized Deg proteases. Two of these, Deg3 and Deg10, additionally localized to the plastid, revealing novel dual-targeted Deg proteases in the plastid and the mitochondrion. SUBA3 is continually updated to ensure that researchers can use the latest published data when planning the experimental steps remaining to localize gene family functions.

Green targeting predictor and ambiguous targeting predictor 2: the pitfalls of plant protein targeting prediction and of transient protein expression in heterologous systems

The challenges of plant protein targeting prediction are the existence of dual subcellular targets and the bias of experimentally confirmed data towards few and mostly nonplant model species. To assess whether training with proteins from evolutionarily distant species has a negative impact on prediction accuracy, we developed the Green Targeting Predictor tool, which was trained with a species-specific data set for Physcomitrella patens. Its performance was compared with that of the same tool trained with a mixed data set. In addition, we updated the Ambiguous Targeting Predictor. We found that predictions deviated from in vivo observations predominantly for proteins diverging within the green lineage, as well as for dual targeted proteins. To evaluate the usefulness of heterologous expression systems, selected proteins were subjected to localization studies in P. patens, Arabidopsis thaliana and Nicotiana tabacum. Four out of six proteins that show dual targeting in the original plant system were located only in a single compartment in one or both heterologous systems. We conclude that targeting signals of divergent plant species exhibit differences, calling for custom in silico and in vivo approaches when aiming to unravel the actual distribution patterns of proteins within a plant cell.

The dual targeting ability of type II NAD(P)H dehydrogenases arose early in land plant evolution

BACKGROUND

Type II NAD(PH) dehydrogenases are located on the inner mitochondrial membrane of plants, fungi, protists and some primitive animals. However, recent observations have been made which identify several Arabidopsis type II dehydrogenases as dual targeted proteins. Targeting either mitochondria and peroxisomes or mitochondria and chloroplasts.

RESULTS

Members of the ND protein family were identified in various plant species. Phylogenetic analyses and subcellular targeting predictions were carried out for all proteins. All ND proteins from three model plant species Arabidopsis, rice and Physcomitrella were cloned as N- and C-terminal GFP fusions and subcellular localisations were determined. Dual targeting of plant type II dehydrogenases was observed to have evolved early in plant evolution and to be widespread throughout different plant species. In all three species tested dual targeting to both mitochondria and peroxisomes was found for at least one NDA and NDB type protein. In addition two NDB type proteins from Physcomitrella were also found to target chloroplasts. The dual targeting of NDC type proteins was found to have evolved later in plant evolution.

CONCLUSIONS

The functions of type II dehydrogenases within plant cells will have to be re-evaluated in light of this newly identified subcellular targeting information.

Emission spectra profiling of fluorescent proteins in living plant cells

BACKGROUND

Fluorescence imaging at high spectral resolution allows the simultaneous recording of multiple fluorophores without switching optical filters, which is especially useful for time-lapse analysis of living cells. The collected emission spectra can be used to distinguish fluorophores by a computation analysis called linear unmixing. The availability of accurate reference spectra for different fluorophores is crucial for this type of analysis. The reference spectra used by plant cell biologists are in most cases derived from the analysis of fluorescent proteins in solution or produced in animal cells, although these spectra are influenced by both the cellular environment and the components of the optical system. For instance, plant cells contain various autofluorescent compounds, such as cell wall polymers and chlorophyll, that affect the spectral detection of some fluorophores. Therefore, it is important to acquire both reference and experimental spectra under the same biological conditions and through the same imaging systems.

RESULTS

Entry clones (pENTR) of fluorescent proteins (FPs) were constructed in order to create C- or N-terminal protein fusions with the MultiSite Gateway recombination technology. The emission spectra for eight FPs, fused C-terminally to the A- or B-type cyclin dependent kinases (CDKA;1 and CDKB1;1) and transiently expressed in epidermal cells of tobacco (Nicotiana benthamiana), were determined by using the Olympus FluoView™ FV1000 Confocal Laser Scanning Microscope. These experimental spectra were then used in unmixing experiments in order to separate the emission of fluorophores with overlapping spectral properties in living plant cells.

CONCLUSIONS

Spectral imaging and linear unmixing have a great potential for efficient multicolor detection in living plant cells. The emission spectra for eight of the most commonly used FPs were obtained in epidermal cells of tobacco leaves and used in unmixing experiments. The generated set of FP Gateway entry vectors represents a valuable resource for plant cell biologists.

Multilabel learning for protein subcellular location prediction

Protein subcellular localization aims at predicting the location of a protein within a cell using computational methods. Knowledge of subcellular localization of proteins indicates protein functions and helps in identifying drug targets. Prediction of protein subcellular localization is an important but challenging problem, particularly when proteins may simultaneously exist at, or move between, two or more different subcellular location sites. Most of the existing protein subcellular localization methods are only used to deal with the single-location proteins. To better reflect the characteristics of multiplex proteins, we formulate prediction of subcellular localization of multiplex proteins as a multilabel learning problem. We present and compare two multilabel learning approaches, which exploit correlations between labels and leverage label-specific features, respectively, to induce a high quality prediction model. Experimental results on six protein data sets under various organisms show that our described methods achieve significantly higher performance than any of the existing methods. Among the different multilabel learning methods, we find that methods exploiting label correlations performs better than those leveraging label-specific features.

Acquisition, conservation, and loss of dual-targeted proteins in land plants

The dual-targeting ability of a variety of proteins from Physcomitrella patens, rice (Oryza sativa), and Arabidopsis (Arabidopsis thaliana) was tested to determine when dual targeting arose and to what extent it was conserved in land plants. Overall, the targeting ability of over 80 different proteins from rice and P. patens, representing 42 dual-targeted proteins in Arabidopsis, was tested. We found that dual targeting arose early in land plant evolution, as it was evident in many cases with P. patens proteins that were conserved in rice and Arabidopsis. Furthermore, we found that the acquisition of dual-targeting ability is still occurring, evident in P. patens as well as rice and Arabidopsis. The loss of dual-targeting ability appears to be rare, but does occur. Ascorbate peroxidase represents such an example. After gene duplication in rice, individual genes encode proteins that are targeted to a single organelle. Although we found that dual targeting was generally conserved, the ability to detect dual-targeted proteins differed depending on the cell types used. Furthermore, it appears that small changes in the targeting signal can result in a loss (or gain) of dual-targeting ability. Overall, examination of the targeting signals within this study did not reveal any clear patterns that would predict dual-targeting ability. The acquisition of dual-targeting ability also appears to be coordinated between proteins. Mitochondrial intermembrane space import and assembly protein40, a protein involved in oxidative folding in mitochondria and peroxisomes, provides an example where acquisition of dual targeting is accompanied by the dual targeting of substrate proteins.

Protein immunolocalization in maize tissues

The analysis of gene expression at transcript and at protein level is of outstanding importance in plant developmental biology. Proteins can be localized with subcellular resolution by immunolocalization using specific antibodies or generating reporter lines carrying the specific protein fused to a fluorescent protein. Immunolocalization is particularly suitable to confirm the expression pattern of transgenic reporter lines. It also represents a valid alternative, especially for plants such as maize, for which transformation is time consuming and still often unsuccessful, by-passing also some side-effects of fusion proteins. Indeed, the availability of specific antibodies for immunolocalizations and observations of maize tissues under a confocal microscope is a powerful tool for studying protein targeting to different cellular compartments.In the following chapter we describe the complete procedure for the localization of proteins in different maize tissues both at cellular and sub-cellular level, using polyclonal or monoclonal antibodies. Tissues can be embedded in different substrates, such as paraplast, PEG400 and agarose, depending on the tissue and the desired use: epifluorescence or confocal microscope observations. An additional protocol for the analysis of the nuclear distribution of modified histones in squashed maize root apexes is also presented.