Proteomics Evidence of a Systemic Response to Desiccation in the Resurrection Plant Haberlea rhodopensis.

Global warming and drought stress are expected to have a negative impact on agricultural productivity. Desiccation-tolerant species, which are able to tolerate the almost complete desiccation of their vegetative tissues, are appropriate models to study extreme drought tolerance and identify novel approaches to improve the resistance of crops to drought stress. In the present study, to better understand what makes resurrection plants extremely tolerant to drought, we performed transmission electron microscopy and integrative large-scale proteomics, including organellar and phosphorylation proteomics, and combined these investigations with previously published transcriptomic and metabolomics data from the resurrection plant Haberlea rhodopensis. The results revealed new evidence about organelle and cell preservation, posttranscriptional and posttranslational regulation, photosynthesis, primary metabolism, autophagy, and cell death in response to desiccation in H. rhodopensis. Different protective intrinsically disordered proteins, such as late embryogenesis abundant (LEA) proteins, thaumatin-like proteins (TLPs), and heat shock proteins (HSPs), were detected. We also found a constitutively abundant dehydrin in H. rhodopensis whose phosphorylation levels increased under stress in the chloroplast fraction. This integrative multi-omics analysis revealed a systemic response to desiccation in H. rhodopensis and certain targets for further genomic and evolutionary studies on DT mechanisms and genetic engineering towards the improvement of drought tolerance in crops.

An innovative standard for LC-MS-based HCP profiling and accurate quantity assessment: Application to batch consistency in viral vaccine samples.

Biotherapeutics, molecules produced from biological systems, require rigorous purification steps to remove impurities including host cell proteins (HCPs). Regulatory guidelines require manufacturers to monitor process-related impurities along the purification workflow. Mass spectrometry (MS) has recently been considered as a complementary method to the well-established ELISA for HCPs quantification, since it has the advantage of unambiguously identifying individual HCP. In this study, we developed an innovative standard dedicated to MS-based HCP profiling analysis in order to monitor the consistency of viral vaccine intermediate purification samples. This standard, termed the HCP-PROFILER standard, is composed of a water-soluble bead (READYBEADS technology) which, after being added into the sample, releases unlabeled peptides in controlled amounts. The standard meets three desired criteria: (1) it is composed of multiple peptides, at different concentration levels, allowing construction of a calibration curve covering the dynamic range of HCPs present in the target sample, ensuring quantification accuracy; (2) it demonstrates high batch-to-batch reproducibility, ensuring quantification robustness and consistency over time; and (3) it is easy to use and avoids user-induced analytical biases. In this study, we present the use of the HCP-PROFILER standard for vaccine batches comparison and downstream process performance studies.

Mixotrophic growth of the extremophile Galdieria sulphuraria reveals the flexibility of its carbon assimilation metabolism.

Galdieria sulphuraria is a cosmopolitan microalga found in volcanic hot springs and calderas. It grows at low pH in photoautotrophic (use of light as a source of energy) or heterotrophic (respiration as a source of energy) conditions, using an unusually broad range of organic carbon sources. Previous data suggested that G. sulphuraria cannot grow mixotrophically (simultaneously exploiting light and organic carbon as energy sources), its photosynthetic machinery being repressed by organic carbon. Here, we show that G. sulphuraria SAG21.92 thrives in photoautotrophy, heterotrophy and mixotrophy. By comparing growth, biomass production, photosynthetic and respiratory performances in these three trophic modes, we show that addition of organic carbon to cultures (mixotrophy) relieves inorganic carbon limitation of photosynthesis thanks to increased CO2 supply through respiration. This synergistic effect is lost when inorganic carbon limitation is artificially overcome by saturating photosynthesis with added external CO2 . Proteomic and metabolic profiling corroborates this conclusion suggesting that mixotrophy is an opportunistic mechanism to increase intracellular CO2 concentration under physiological conditions, boosting photosynthesis by enhancing the carboxylation activity of Ribulose-1,5-bisphosphate carboxylase-oxygenase (Rubisco) and decreasing photorespiration. We discuss possible implications of these findings for the ecological success of Galdieria in extreme environments and for biotechnological applications.

Bdf1 Bromodomains Are Essential for Meiosis and the Expression of Meiotic-Specific Genes.

Bromodomain and Extra-terminal motif (BET) proteins play a central role in transcription regulation and chromatin signalling pathways. They are present in unicellular eukaryotes and in this study, the role of the BET protein Bdf1 has been explored in Saccharomyces cerevisiae. Mutation of Bdf1 bromodomains revealed defects on both the formation of spores and the meiotic progression, blocking cells at the exit from prophase, before the first meiotic division. This phenotype is associated with a massive deregulation of the transcription of meiotic genes and Bdf1 bromodomains are required for appropriate expression of the key meiotic transcription factor NDT80 and almost all the Ndt80-inducible genes, including APC complex components. Bdf1 notably accumulates on the promoter of Ndt80 and its recruitment is dependent on Bdf1 bromodomains. In addition, the ectopic expression of NDT80 during meiosis partially bypasses this dependency. Finally, purification of Bdf1 partners identified two independent complexes with Bdf2 or the SWR complex, neither of which was required to complete sporulation. Taken together, our results unveil a new role for Bdf1 -working independently from its predominant protein partners Bdf2 and the SWR1 complex-as a regulator of meiosis-specific genes.

DAPAR & ProStaR: software to perform statistical analyses in quantitative discovery proteomics.

DAPAR and ProStaR are software tools to perform the statistical analysis of label-free XIC-based quantitative discovery proteomics experiments. DAPAR contains procedures to filter, normalize, impute missing value, aggregate peptide intensities, perform null hypothesis significance tests and select the most likely differentially abundant proteins with a corresponding false discovery rate. ProStaR is a graphical user interface that allows friendly access to the DAPAR functionalities through a web browser. AVAILABILITY AND IMPLEMENTATION: DAPAR and ProStaR are implemented in the R language and are available on the website of the Bioconductor project (http://www.bioconductor.org/). A complete tutorial and a toy dataset are accompanying the packages. CONTACT: samuel.wieczorek@cea.fr, florence.combes@cea.fr, thomas.burger@cea.fr.

ChloroKB: A Web Application for the Integration of Knowledge Related to Chloroplast Metabolic Network.

Higher plants, as autotrophic organisms, are effective sources of molecules. They hold great promise for metabolic engineering, but the behavior of plant metabolism at the network level is still incompletely described. Although structural models (stoichiometry matrices) and pathway databases are extremely useful, they cannot describe the complexity of the metabolic context, and new tools are required to visually represent integrated biocurated knowledge for use by both humans and computers. Here, we describe ChloroKB, a Web application (http://chlorokb.fr/) for visual exploration and analysis of the Arabidopsis (Arabidopsis thaliana) metabolic network in the chloroplast and related cellular pathways. The network was manually reconstructed through extensive biocuration to provide transparent traceability of experimental data. Proteins and metabolites were placed in their biological context (spatial distribution within cells, connectivity in the network, participation in supramolecular complexes, and regulatory interactions) using CellDesigner software. The network contains 1,147 reviewed proteins (559 localized exclusively in plastids, 68 in at least one additional compartment, and 520 outside the plastid), 122 proteins awaiting biochemical/genetic characterization, and 228 proteins for which genes have not yet been identified. The visual presentation is intuitive and browsing is fluid, providing instant access to the graphical representation of integrated processes and to a wealth of refined qualitative and quantitative data. ChloroKB will be a significant support for structural and quantitative kinetic modeling, for biological reasoning, when comparing novel data with established knowledge, for computer analyses, and for educational purposes. ChloroKB will be enhanced by continuous updates following contributions from plant researchers.

A proteomics assay to detect eight CBRN-relevant toxins in food.

A proteomics assay was set up to analyze food substrates for eight toxins of the CBRN (chemical, biological, radiological and nuclear) threat, namely ricin, Clostridium perfringens epsilon toxin (ETX), Staphylococcus aureus enterotoxins (SEA, SEB and SED), shigatoxins from Shigella dysenteriae and entero-hemorragic Escherichia coli strains (STX1 and STX2) and Campylobacter jejuni cytolethal distending toxin (CDT). The assay developed was based on an antibody-free sample preparation followed by bottom-up LC-MS/MS analysis operated in targeted mode. Highly specific detection and absolute quantification were obtained using isotopically labeled proteins (PSAQ standards) spiked into the food matrix. The sensitivity of the assay for the eight toxins was lower than the oral LD50 which would likely be used in a criminal contamination of food supply. This assay should be useful in monitoring biological threats. In the public-health domain, it opens the way for multiplex investigation of food-borne toxins using targeted LC-MS/MS.

Accounting for the Multiple Natures of Missing Values in Label-Free Quantitative Proteomics Data Sets to Compare Imputation Strategies.

Missing values are a genuine issue in label-free quantitative proteomics. Recent works have surveyed the different statistical methods to conduct imputation and have compared them on real or simulated data sets and recommended a list of missing value imputation methods for proteomics application. Although insightful, these comparisons do not account for two important facts: (i) depending on the proteomics data set, the missingness mechanism may be of different natures and (ii) each imputation method is devoted to a specific type of missingness mechanism. As a result, we believe that the question at stake is not to find the most accurate imputation method in general but instead the most appropriate one. We describe a series of comparisons that support our views: For instance, we show that a supposedly "under-performing" method (i.e., giving baseline average results), if applied at the "appropriate" time in the data-processing pipeline (before or after peptide aggregation) on a data set with the "appropriate" nature of missing values, can outperform a blindly applied, supposedly "better-performing" method (i.e., the reference method from the state-of-the-art). This leads us to formulate few practical guidelines regarding the choice and the application of an imputation method in a proteomics context.

hEIDI: An Intuitive Application Tool To Organize and Treat Large-Scale Proteomics Data.

Advances in high-throughput proteomics have led to a rapid increase in the number, size, and complexity of the associated data sets. Managing and extracting reliable information from such large series of data sets require the use of dedicated software organized in a consistent pipeline to reduce, validate, exploit, and ultimately export data. The compilation of multiple mass-spectrometry-based identification and quantification results obtained in the context of a large-scale project represents a real challenge for developers of bioinformatics solutions. In response to this challenge, we developed a dedicated software suite called hEIDI to manage and combine both identifications and semiquantitative data related to multiple LC-MS/MS analyses. This paper describes how, through a user-friendly interface, hEIDI can be used to compile analyses and retrieve lists of nonredundant protein groups. Moreover, hEIDI allows direct comparison of series of analyses, on the basis of protein groups, while ensuring consistent protein inference and also computing spectral counts. hEIDI ensures that validated results are compliant with MIAPE guidelines as all information related to samples and results is stored in appropriate databases. Thanks to the database structure, validated results generated within hEIDI can be easily exported in the PRIDE XML format for subsequent publication. hEIDI can be downloaded from http://biodev.extra.cea.fr/docs/heidi .

Looking for Missing Proteins in the Proteome of Human Spermatozoa: An Update.

The Chromosome-Centric Human Proteome Project (C-HPP) aims to identify "missing" proteins in the neXtProt knowledgebase. We present an in-depth proteomics analysis of the human sperm proteome to identify testis-enriched missing proteins. Using protein extraction procedures and LC-MS/MS analysis, we detected 235 proteins (PE2-PE4) for which no previous evidence of protein expression was annotated. Through LC-MS/MS and LC-PRM analysis, data mining, and immunohistochemistry, we confirmed the expression of 206 missing proteins (PE2-PE4) in line with current HPP guidelines (version 2.0). Parallel reaction monitoring acquisition and sythetic heavy labeled peptides targeted 36 ≪one-hit wonder≫ candidates selected based on prior peptide spectrum match assessment. 24 were validated with additional predicted and specifically targeted peptides. Evidence was found for 16 more missing proteins using immunohistochemistry on human testis sections. The expression pattern for some of these proteins was specific to the testis, and they could possibly be valuable markers with fertility assessment applications. Strong evidence was also found of four "uncertain" proteins (PE5); their status should be re-examined. We show how using a range of sample preparation techniques combined with MS-based analysis, expert knowledge, and complementary antibody-based techniques can produce data of interest to the community. All MS/MS data are available via ProteomeXchange under identifier PXD003947. In addition to contributing to the C-HPP, we hope these data will stimulate continued exploration of the sperm proteome.

Deletion of FtsH11 protease has impact on chloroplast structure and function in Arabidopsis thaliana when grown under continuous light.

The membrane-integrated metalloprotease FtsH11 of Arabidopsis thaliana is proposed to be dual-targeted to mitochondria and chloroplasts. A bleached phenotype was observed in ftsh11 grown at long days or continuous light, pointing to disturbances in the chloroplast. Within the chloroplast, FtsH11 was found to be located exclusively in the envelope. Two chloroplast-located proteins of unknown function (Tic22-like protein and YGGT-A) showed significantly higher abundance in envelope membranes and intact chloroplasts of ftsh11 and therefore qualify as potential substrates for the FtsH11 protease. No proteomic changes were observed in the mitochondria of 6-week-old ftsh11 compared with wild type, and FtsH11 was not immunodetected in these organelles. The abundance of plastidic proteins, especially of photosynthetic proteins, was altered even during standard growth conditions in total leaves of ftsh11. At continuous light, the amount of photosystem I decreased relative to photosystem II, accompanied by a drastic change of the chloroplast morphology and a drop of non-photochemical quenching. FtsH11 is crucial for chloroplast structure and function during growth in prolonged photoperiod.

Mass spectrometry-based workflow for accurate quantification of Escherichia coli enzymes: how proteomics can play a key role in metabolic engineering.

Metabolic engineering aims to design high performance microbial strains producing compounds of interest. This requires systems-level understanding; genome-scale models have therefore been developed to predict metabolic fluxes. However, multi-omics data including genomics, transcriptomics, fluxomics, and proteomics may be required to model the metabolism of potential cell factories. Recent technological advances to quantitative proteomics have made mass spectrometry-based quantitative assays an interesting alternative to more traditional immuno-affinity based approaches. This has improved specificity and multiplexing capabilities. In this study, we developed a quantification workflow to analyze enzymes involved in central metabolism in Escherichia coli (E. coli). This workflow combined full-length isotopically labeled standards with selected reaction monitoring analysis. First, full-length (15)N labeled standards were produced and calibrated to ensure accurate measurements. Liquid chromatography conditions were then optimized for reproducibility and multiplexing capabilities over a single 30-min liquid chromatography-MS analysis. This workflow was used to accurately quantify 22 enzymes involved in E. coli central metabolism in a wild-type reference strain and two derived strains, optimized for higher NADPH production. In combination with measurements of metabolic fluxes, proteomics data can be used to assess different levels of regulation, in particular enzyme abundance and catalytic rate. This provides information that can be used to design specific strains used in biotechnology. In addition, accurate measurement of absolute enzyme concentrations is key to the development of predictive kinetic models in the context of metabolic engineering.

Proteomic analysis of the multimeric nuclear egress complex of human cytomegalovirus.

Herpesviral capsids are assembled in the host cell nucleus before being translocated into the cytoplasm for further maturation. The crossing of the nuclear envelope represents a major event that requires the formation of the nuclear egress complex (NEC). Previous studies demonstrated that human cytomegalovirus (HCMV) proteins pUL50 and pUL53, as well as their homologs in all members of Herpesviridae, interact with each other at the nuclear envelope and form the heterodimeric core of the NEC. In order to characterize further the viral and cellular protein content of the multimeric NEC, the native complex was isolated from HCMV-infected human primary fibroblasts at various time points and analyzed using quantitative proteomics. Previously postulated components of the HCMV-specific NEC, as well as novel potential NEC-associated proteins such as emerin, were identified. In this regard, interaction and colocalization between emerin and pUL50 were confirmed by coimmunoprecipitation and confocal microscopy analyses, respectively. A functional validation of viral and cellular NEC constituents was achieved through siRNA-mediated knockdown experiments. The important role of emerin in NEC functionality was demonstrated by a reduction of viral replication when emerin expression was down-regulated. Moreover, under such conditions, reduced production of viral proteins and deregulation of viral late cytoplasmic maturation were observed. Combined, these data prove the functional importance of emerin as an NEC component, associated with pUL50, pUL53, pUL97, p32/gC1qR, and further regulatory proteins. Summarized, our findings provide the first proteomics-based characterization and functional validation of the HCMV-specific multimeric NEC.

A foundation for reliable spatial proteomics data analysis.

Quantitative mass-spectrometry-based spatial proteomics involves elaborate, expensive, and time-consuming experimental procedures, and considerable effort is invested in the generation of such data. Multiple research groups have described a variety of approaches for establishing high-quality proteome-wide datasets. However, data analysis is as critical as data production for reliable and insightful biological interpretation, and no consistent and robust solutions have been offered to the community so far. Here, we introduce the requirements for rigorous spatial proteomics data analysis, as well as the statistical machine learning methodologies needed to address them, including supervised and semi-supervised machine learning, clustering, and novelty detection. We present freely available software solutions that implement innovative state-of-the-art analysis pipelines and illustrate the use of these tools through several case studies involving multiple organisms, experimental designs, mass spectrometry platforms, and quantitation techniques. We also propose sound analysis strategies for identifying dynamic changes in subcellular localization by comparing and contrasting data describing different biological conditions. We conclude by discussing future needs and developments in spatial proteomics data analysis.

Deciphering thylakoid sub-compartments using a mass spectrometry-based approach.

Photosynthesis has shaped atmospheric and ocean chemistries and probably changed the climate as well, as oxygen is released from water as part of the photosynthetic process. In photosynthetic eukaryotes, this process occurs in the chloroplast, an organelle containing the most abundant biological membrane, the thylakoids. The thylakoids of plants and some green algae are structurally inhomogeneous, consisting of two main domains: the grana, which are piles of membranes gathered by stacking forces, and the stroma-lamellae, which are unstacked thylakoids connecting the grana. The major photosynthetic complexes are unevenly distributed within these compartments because of steric and electrostatic constraints. Although proteomic analysis of thylakoids has been instrumental to define its protein components, no extensive proteomic study of subthylakoid localization of proteins in the BBY (grana) and the stroma-lamellae fractions has been achieved so far. To fill this gap, we performed a complete survey of the protein composition of these thylakoid subcompartments using thylakoid membrane fractionations. We employed semiquantitative proteomics coupled with a data analysis pipeline and manual annotation to differentiate genuine BBY and stroma-lamellae proteins from possible contaminants. About 300 thylakoid (or potentially thylakoid) proteins were shown to be enriched in either the BBY or the stroma-lamellae fractions. Overall, present findings corroborate previous observations obtained for photosynthetic proteins that used nonproteomic approaches. The originality of the present proteomic relies in the identification of photosynthetic proteins whose differential distribution in the thylakoid subcompartments might explain already observed phenomenon such as LHCII docking. Besides, from the present localization results we can suggest new molecular actors for photosynthesis-linked activities. For instance, most PsbP-like subunits being differently localized in stroma-lamellae, these proteins could be linked to the PSI-NDH complex in the context of cyclic electron flow around PSI. In addition, we could identify about a hundred new likely minor thylakoid (or chloroplast) proteins, some of them being potential regulators of the chloroplast physiology.

DIGESTIF: a universal quality standard for the control of bottom-up proteomics experiments.

In bottom-up mass spectrometry-based proteomics analyses, variability at any step of the process, particularly during sample proteolysis, directly affects the sensitivity, accuracy, and precision of peptide detection and quantification. Currently, no generic internal standards are available to control the quality of sample processing steps. This makes it difficult to assess the comparability of MS proteomic data obtained under different experimental conditions. Here, we describe the design, synthesis, and validation of a universal protein standard, called DIGESTIF, that can be added to any biological sample. The DIGESTIF standard consists of a soluble recombinant protein scaffold to which a set of 11 artificial peptides (iRT peptides) with good ionization properties has been incorporated. In the protein scaffold, the amino acids flanking iRT peptide cleavage sites were selected either to favor or hinder protease cleavage. After sample processing, the retention time and relative intensity pattern of the released iRT peptides can be used to assess the quality of sample workup, the extent of digestion, and the performance of the LC-MS system. Thus, DIGESTIF can be used to standardize a broad spectrum of applications, ranging from simple replicate measurements to large-scale biomarker screening in biomedical applications.

Multiplex quantification of protein toxins in human biofluids and food matrices using immunoextraction and high-resolution targeted mass spectrometry.

The development of rapid methods for unambiguous identification and precise quantification of protein toxins in various matrices is essential for public health surveillance. Nowadays, analytical strategies classically rely on sensitive immunological assays, but mass spectrometry constitutes an attractive complementary approach thanks to direct measurement and protein characterization ability. We developed here an innovative multiplex immuno-LC-MS/MS method for the simultaneous and specific quantification of the three potential biological warfare agents, ricin, staphylococcal enterotoxin B, and epsilon toxin, in complex human biofluids and food matrices. At least 7 peptides were targeted for each toxin (43 peptides in total) with a quadrupole-Orbitrap high-resolution instrument for exquisite detection specificity. Quantification was performed using stable isotope-labeled toxin standards spiked early in the sample. Lower limits of quantification were determined at or close to 1 ng·mL(-1). The whole process was successfully applied to the quantitative analysis of toxins in complex samples such as milk, human urine, and plasma. Finally, we report new data on toxin stability with no evidence of toxin degradation in milk in a 48 h time frame, allowing relevant quantitative toxin analysis for samples collected in this time range.

Characterization of chloroplastic fructose 1,6-bisphosphate aldolases as lysine-methylated proteins in plants.

In pea (Pisum sativum), the protein-lysine methyltransferase (PsLSMT) catalyzes the trimethylation of Lys-14 in the large subunit (LS) of ribulose 1,5-bisphosphate carboxylase/oxygenase (Rubisco), the enzyme catalyzing the CO(2) fixation step during photosynthesis. Homologs of PsLSMT, herein referred to as LSMT-like enzymes, are found in all plant genomes, but methylation of LS Rubisco is not universal in the plant kingdom, suggesting a species-specific protein substrate specificity of the methyltransferase. In this study, we report the biochemical characterization of the LSMT-like enzyme from Arabidopsis thaliana (AtLSMT-L), with a focus on its substrate specificity. We show that, in Arabidopsis, LS Rubisco is not naturally methylated and that the physiological substrates of AtLSMT-L are chloroplastic fructose 1,6-bisphosphate aldolase isoforms. These enzymes, which are involved in the assimilation of CO(2) through the Calvin cycle and in chloroplastic glycolysis, are trimethylated at a conserved lysyl residue located close to the C terminus. Both AtLSMT-L and PsLSMT are able to methylate aldolases with similar kinetic parameters and product specificity. Thus, the divergent substrate specificity of LSMT-like enzymes from pea and Arabidopsis concerns only Rubisco. AtLSMT-L is able to interact with unmethylated Rubisco, but the complex is catalytically unproductive. Trimethylation does not modify the kinetic properties and tetrameric organization of aldolases in vitro. The identification of aldolases as methyl proteins in Arabidopsis and other species like pea suggests a role of protein lysine methylation in carbon metabolism in chloroplasts.

PredAlgo: a new subcellular localization prediction tool dedicated to green algae.

The unicellular green alga Chlamydomonas reinhardtii is a prime model for deciphering processes occurring in the intracellular compartments of the photosynthetic cell. Organelle-specific proteomic studies have started to delineate its various subproteomes, but sequence-based prediction software is necessary to assign proteins subcellular localizations at whole genome scale. Unfortunately, existing tools are oriented toward land plants and tend to mispredict the localization of nuclear-encoded algal proteins, predicting many chloroplast proteins as mitochondrion targeted. We thus developed a new tool called PredAlgo that predicts intracellular localization of those proteins to one of three intracellular compartments in green algae: the mitochondrion, the chloroplast, and the secretory pathway. At its core, a neural network, trained using carefully curated sets of C. reinhardtii proteins, divides the N-terminal sequence into overlapping 19-residue windows and scores the probability that they belong to a cleavable targeting sequence for one of the aforementioned organelles. A targeting prediction is then deduced for the protein, and a likely cleavage site is predicted based on the shape of the scoring function along the N-terminal sequence. When assessed on an independent benchmarking set of C. reinhardtii sequences, PredAlgo showed a highly improved discrimination capacity between chloroplast- and mitochondrion-localized proteins. Its predictions matched well the results of chloroplast proteomics studies. When tested on other green algae, it gave good results with Chlorophyceae and Trebouxiophyceae but tended to underpredict mitochondrial proteins in Prasinophyceae. Approximately 18% of the nuclear-encoded C. reinhardtii proteome was predicted to be targeted to the chloroplast and 15% to the mitochondrion.

Proteomic strategy for the identification of critical actors in reorganization of the post-meiotic male genome.

After meiosis, during the final stages of spermatogenesis, the haploid male genome undergoes major structural changes, resulting in a shift from a nucleosome-based genome organization to the sperm-specific, highly compacted nucleoprotamine structure. Recent data support the idea that region-specific programming of the haploid male genome is of high importance for the post-fertilization events and for successful embryo development. Although these events constitute a unique and essential step in reproduction, the mechanisms by which they occur have remained completely obscure and the factors involved have mostly remained uncharacterized. Here, we sought a strategy to significantly increase our understanding of proteins controlling the haploid male genome reprogramming, based on the identification of proteins in two specific pools: those with the potential to bind nucleic acids (basic proteins) and proteins capable of binding basic proteins (acidic proteins). For the identification of acidic proteins, we developed an approach involving a transition-protein (TP)-based chromatography, which has the advantage of retaining not only acidic proteins due to the charge interactions, but also potential TP-interacting factors. A second strategy, based on an in-depth bioinformatic analysis of the identified proteins, was then applied to pinpoint within the lists obtained, male germ cells expressed factors relevant to the post-meiotic genome organization. This approach reveals a functional network of DNA-packaging proteins and their putative chaperones and sheds a new light on the way the critical transitions in genome organizations could take place. This work also points to a new area of research in male infertility and sperm quality assessments.