Subcellular Phenotyping: Using Proteomics to Quantitatively Link Subcellular Leaf Protein and Organelle Distribution Analyses of Pisum sativum Cultivars

A giant virus infecting the amoeboflagellate Naegleria

Giant viruses (Nucleocytoviricota) are significant lethality agents of various eukaryotic hosts. Although metagenomics indicates their ubiquitous distribution, available giant virus isolates are restricted to a very small number of protist and algal hosts. Here we report on the first viral isolate that replicates in the amoeboflagellate Naegleria. This genus comprises the notorious human pathogen Naegleria fowleri, the causative agent of the rare but fatal primary amoebic meningoencephalitis. We have elucidated the structure and infection cycle of this giant virus, Catovirus naegleriensis (a.k.a. Naegleriavirus, NiV), and show its unique adaptations to its Naegleria host using fluorescence in situ hybridization, electron microscopy, genomics, and proteomics. Naegleriavirus is only the fourth isolate of the highly diverse subfamily Klosneuvirinae, and like its relatives the NiV genome contains a large number of translation genes, but lacks transfer RNAs (tRNAs). NiV has acquired genes from its Naegleria host, which code for heat shock proteins and apoptosis inhibiting factors, presumably for host interactions. Notably, NiV infection was lethal to all Naegleria species tested, including the human pathogen N. fowleri. This study expands our experimental framework for investigating giant viruses and may help to better understand the basic biology of the human pathogen N. fowleri.

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?

CropPAL for discovering divergence in protein subcellular location in crops to support strategies for molecular crop breeding

Agriculture faces increasing demand for yield, higher plant-derived protein content and diversity while facing pressure to achieve sustainability. Although the genomes of many of the important crops have been sequenced, the subcellular locations of most of the encoded proteins remain unknown or are only predicted. Protein subcellular location is crucial in determining protein function and accumulation patterns in plants, and is critical for targeted improvements in yield and resilience. Integrating location data from over 800 studies for 12 major crop species into the cropPAL2020 data collection showed that while >80% of proteins in most species are not localised by experimental data, combining species data or integrating predictions can help bridge gaps at similar accuracy. The collation and integration of over 61 505 experimental localisations and more than 6 million predictions showed that the relative sizes of the protein catalogues located in different subcellular compartments are comparable between crops and Arabidopsis. A comprehensive cross-species comparison showed that between 50% and 80% of the subcellulomes are conserved across species and that conservation only depends to some degree on the phylogenetic relationship of the species. Protein subcellular locations in major biosynthesis pathways are more often conserved than in metabolic pathways. Underlying this conservation is a clear potential for subcellular diversity in protein location between species by means of gene duplication and alternative splicing. Our cropPAL data set and search platform (https://crop-pal.org) provide a comprehensive subcellular proteomics resource to drive compartmentation-based approaches for improving yield, protein composition and resilience in future crop varieties.

What Is New in (Plant) Proteomics Methods and Protocols: The 2015-2019 Quinquennium

The third edition of "Plant Proteomics Methods and Protocols," with the title "Advances in Proteomics Techniques, Data Validation, and Integration with Other Classic and -Omics Approaches in the Systems Biology Direction," was conceived as being based on the success of the previous editions, and the continuous advances and improvements in proteomic techniques, equipment, and bioinformatics tools, and their uses in basic and translational plant biology research that has occurred in the past 5 years (in round figures, of around 22,000 publications referenced in WoS, 2000 were devoted to plants).The monograph contains 29 chapters with detailed proteomics protocols commonly employed in plant biology research. They present recent advances at all workflow stages, starting from the laboratory (tissue and cell fractionation, protein extraction, depletion, purification, separation, MS analysis, quantification) and ending on the computer (algorithms for protein identification and quantification, bioinformatics tools for data analysis, databases and repositories). At the end of each chapter there are enough explanatory notes and comments to make the protocols easily applicable to other biological systems and/or studies, discussing limitations, artifacts, or pitfalls. For that reason, as with the previous editions, it would be especially useful for beginners or novices.Out of the 29 chapters, six are devoted to descriptive proteomics, with a special emphasis on subcellular protein profiling (Chapters 5 - 10 ), six to PTMs (Chapters 11 , and 14 - 18 ), three to protein interactions (Chapters 19 - 21 ), and two to specific proteins, peroxidases (Chapter 24 ) and proteases and protease inhibitors (Chapter 26 ). The book reflects the new trajectory in MS-based protein identification and quantification, moving from the classic gel-based approaches to the most recent labeling (Chapters 10 , 11 , 29 ), shotgun (Chapters 5 , 7 , 12 , 15 ), parallel reaction monitoring (Chapter 16 ), and targeted data acquisition (Chapter 13 ). MS imaging (Chapter 25 ), the only in vivo MS-based proteomics strategy, is far from being fully optimized and exploited in plant biology research. A confident protein identification and quantitation, especially in orphan species, of low-abundance proteins, is still a challenging task (Chapters 4 , 28 ).What is really new is the use of different techniques for proteomics data validation and their integration into other classic and -omics approaches in the systems biology direction. Chapter 2 reports on multiple extractions in a single experiment of the different biomolecules, nucleic acids, proteins, and metabolites. Chapter 27 describes how metabolic pathways can be reconstructed from multiple -omics data, and Chapter 3 network building. Finally, Chapters 22 and 23 deal with, respectively, the search for allele-specific proteins and proteogenomics.Around 200 groups were, almost 1 year ago, invited to take part in this edition. Unfortunately, only 10% of them kindly accepted. My gratitude to those who accepted our invitation but also to those who did not, as all of them have contributed to the plant proteomics field. I will enlist, in this introductory chapter, following my own judgment, some of the relevant papers published in the past 5 years, those that have shown us how to enhance and exploit the potential of proteomics in plant biology research, without aiming at giving a too exhaustive list.