An effect size filter improves the reproducibility in spectral counting-based comparative proteomics

Relative Abundance of Spermadhesin-1 in the Seminal Plasma of Young Nellore Bulls Is in Agreement with Reproductive Parameters

This study aimed to evaluate the proteomic profile of seminal plasma from young Nellore bulls. We used 20 bulls aged between 19.8 and 22.7 months, divided into two groups according to the results of the Breeding Soundness Evaluation (BSE): approved (FIT n = 10) and not approved (UNFIT n = 10). The scrotal perimeter was measured and a semen collection was performed through electroejaculation. The percentage of sperm motility, mass motility, and sperm vigor were calculated using conventional microscopy, and the percentage of sperm abnormalities was calculated using phase-contrast microscopy of all ejaculates. Seminal plasma was separated from spermatozoa using centrifugation and processed for proteomic analysis by LC-MS/MS. Seminal plasma proteins were identified using MASCOT Daemon software v.2.4.0 and label-free quantification analysis was carried out by SCAFFOLD Q+ software v.4.0 using the Exponentially Modified Protein Abundance Index (emPAI) method. Functional classification of proteins was performed based on their genetic ontology terms using KOG. Functional cluster analysis was performed on DAVID. There were no differences in scrotal perimeter and physical semen characteristics between FIT and UNFIT groups of bulls. The percentage of sperm abnormalities was higher (p < 0.05) in the UNFIT group of bulls. A total of 297 proteins were identified for the two groups. There were a total of 11 differentially abundant proteins (p < 0.05), two of them more abundant in FIT bulls (Spermadhesin-1 and Ig gamma-1 chain C region) and nine in UNFIT bulls (Vasoactive intestinal peptide, Metalloproteinase inhibitor 2, Ig lambda-1 chain C regions, Protein FAM3C, Hemoglobin beta, Seminal ribonuclease, Spermadhesin 2, Seminal plasma protein BSP-30kDa, and Spermadhesin Z13). Spermadhesin-1 was the protein with the highest relative abundance (36.7%) in the seminal plasma among all bulls, corresponding to 47.7% for the FIT bulls and 25,7% for the UNFIT bulls. Posttranslational modification, protein turnover, and chaperones were the functional categories with the highest number of classified proteins. Protein functional annotation clusters were related to Phospholipid efflux, ATP binding, and chaperonin-containing T-complex. The differentially abundant proteins in the group of FIT bulls were related to sperm capacitation and protection against reactive species of oxygen. In contrast, differentially expressed proteins in the group of UNFIT bulls were related to motility inhibition, intramembrane cholesterol removal and oxidative stress. In conclusion, the proteomic profile of the seminal plasma of FIT bulls presents proteins with participation in several biological processes favorable to fertilization, while the proteins of the seminal plasma of UNFIT bulls indicate a series of alterations that can compromise the fertilizing capacity of the spermatozoa. In addition, the relative abundance of spermadhesin-1 found in the seminal plasma of young Nellore bulls could be studied as a reproductive parameter for selection.

msmsEDA & msmsTests: Label-Free Differential Expression by Spectral Counts

msmsTests is an R/Bioconductor package providing functions for statistical tests in label-free LC-MS/MS data by spectral counts. These functions aim at discovering differentially expressed proteins between two biological conditions. Three tests are available: Poisson GLM regression, quasi-likelihood GLM regression, and the negative binomial of the edgeR package. The three models admit blocking factors to control for nuisance variables. To assure a good level of reproducibility a post-test filter is available, where (1) a minimum effect size considered biologically relevant, and (2) a minimum expression of the most abundant condition, may be set. A companion package, msmsEDA, proposes functions to explore datasets based on msms spectral counts. The provided graphics help in identifying outliers, the presence of eventual batch factors, and check the effects of different normalizing strategies. This protocol illustrates the use of both packages on two examples: A purely spike-in experiment of 48 human proteins in a standard yeast cell lysate; and a cancer cell-line secretome dataset requiring a biological normalization.

Circulating SOD2 Is a Candidate Response Biomarker for Neoadjuvant Therapy in Breast Cancer

There is a great need for non-invasive tools that inform of an early molecular response to cancer therapeutic treatment. Here, we tested the hypothesis that proteolytically resistant proteins could be candidate circulating tumor biomarkers for cancer therapy. Proteins resistant to proteolysis are drastically under-sampled by current proteomic workflows. These proteins could be reliable sensors for the response to therapy since they are likely to stay longer in circulation. We selected manganese superoxide dismutase (SOD2), a mitochondrial redox enzyme, from a screening of proteolytic resistant proteins in breast cancer (BC). First, we confirmed the robustness of SOD2 and determined that its proteolytic resistance is mediated by its quaternary protein structure. We also proved that the release of SOD2 upon chemotherapy treatment correlates with cell death in BC cells. Then, after confirming that SOD2 is very stable in human serum, we sought to measure its circulating levels in a cohort of BC patients undergoing neoadjuvant therapy. The results showed that circulating levels of SOD2 increased when patients responded to the treatment according to the tumor shrinkage during neoadjuvant chemotherapy. Therefore, the measurement of SOD2 levels in plasma could improve the non-invasive monitoring of the therapeutic treatment in breast cancer patients. The identification of circulating biomarkers linked to the tumor cell death induced by treatment could be useful for monitoring the action of the large number of cancer drugs currently used in clinics. We envision that our approach could help uncover candidate tumor biomarkers to measure a tumor’s response to cancer therapy in real time by sampling the tumor throughout the course of treatment.

Lignin induced iron reduction by novel sp., Tolumonas lignolytic BRL6-1

Lignin is the second most abundant carbon polymer on earth and despite having more fuel value than cellulose, it currently is considered a waste byproduct in many industrial lignocellulose applications. Valorization of lignin relies on effective and green methods of de-lignification, with a growing interest in the use of microbes. Here we investigate the physiology and molecular response of the novel facultative anaerobic bacterium, Tolumonas lignolytica BRL6-1, to lignin under anoxic conditions. Physiological and biochemical changes were compared between cells grown anaerobically in either lignin-amended or unamended conditions. In the presence of lignin, BRL6-1 accumulates higher biomass and has a shorter lag phase compared to unamended conditions, and 14% of the proteins determined to be significantly higher in abundance by log2 fold-change of 2 or greater were related to Fe(II) transport in late logarithmic phase. Ferrozine assays of the supernatant confirmed that Fe(III) was bound to lignin and reduced to Fe(II) only in the presence of BRL6-1, suggesting redox activity by the cells. LC-MS/MS analysis of the secretome showed an extra band at 20 kDa in lignin-amended conditions. Protein sequencing of this band identified a protein of unknown function with homology to enzymes in the radical SAM superfamily. Expression of this protein in lignin-amended conditions suggests its role in radical formation. From our findings, we suggest that BRL6-1 is using a protein in the radical SAM superfamily to interact with the Fe(III) bound to lignin and reducing it to Fe(II) for cellular use, increasing BRL6-1 yield under lignin-amended conditions. This interaction potentially generates organic free radicals and causes a radical cascade which could modify and depolymerize lignin. Further research should clarify the extent to which this mechanism is similar to previously described aerobic chelator-mediated Fenton chemistry or radical producing lignolytic enzymes, such as lignin peroxidases, but under anoxic conditions.

Clinical Implications of Extracellular HMGA1 in Breast Cancer

The unconventional secretion of proteins is generally caused by cellular stress. During the tumorigenesis, tumor cells experience high levels of stress, and the secretion of some theoretically intracellular proteins is activated. Once in the extracellular space, these proteins play different paracrine and autocrine roles and could represent a vulnerability of cancer. One of these proteins is the high mobility group A1 (HMGA1), which is frequently overexpressed in tumors and presents a low expression in normal adult tissues. We have recently described that HMGA1 establishes an autocrine loop in invasive triple-negative breast cancer (TNBC) cells. The secretion of HMGA1 and its binding to the receptor for advanced glycation end products (RAGE) mediates the migration, invasion, and metastasis of TNBC cells and predicts the onset of metastasis in these patients. In this review, we summarized different strategies to exploit the novel tumorigenic phenotype mediated by extracellular HMGA1. We envisioned future clinical applications where the association between its change in subcellular localization and breast cancer progression could be used to predict tumor aggressiveness and guide treatment decisions. Furthermore, we proposed that targeting extracellular HMGA1 as monotherapy using monoclonal antibodies, or in combination with chemotherapy and other targeted therapies, could bring new therapeutic options for TNBC patients.

Mass spectrometric approaches for profiling protein folding and stability

Protein stability reports on protein homeostasis, function, and binding interactions, such as to other proteins, metabolites and drugs. As such, there is a pressing need for technologies that can report on protein stability. The ideal technique could be applied in vitro or in vivo systems, proteome-wide, independently of matrix, under native conditions, with residue-level resolution, and on protein at endogenous levels. Mass spectrometry has rapidly become a preferred technology for identifying and quantifying proteins. As such, it has been increasingly incorporated into methodologies for interrogating protein stability and folding. Although no single technology can satisfy all desired applications, several emerging approaches have shown outstanding success at providing biological insight into the stability of the proteome. This chapter outlines some of these recent emerging technologies.

Determination of protein-only RNase P interactome in Arabidopsis mitochondria and chloroplasts identifies a complex between PRORP1 and another NYN domain nuclease

The essential type of endonuclease that removes 5' leader sequences from transfer RNA precursors is called RNase P. While ribonucleoprotein RNase P enzymes containing a ribozyme are found in all domains of life, another type of RNase P called 'PRORP', for 'PROtein-only RNase P', is composed of protein that occurs only in a wide variety of eukaryotes, in organelles and in the nucleus. Here, to find how PRORP functions integrate with other cell processes, we explored the protein interaction network of PRORP1 in Arabidopsis mitochondria and chloroplasts. Although PRORP proteins function as single subunit enzymes in vitro, we found that PRORP1 occurs in protein complexes and is present in high-molecular-weight fractions that contain mitochondrial ribosomes. The analysis of immunoprecipitated protein complexes identified proteins involved in organellar gene expression processes. In particular, direct interaction was established between PRORP1 and MNU2 a mitochondrial nuclease. A specific domain of MNU2 and a conserved signature of PRORP1 were found to be directly accountable for this protein interaction. Altogether, results revealed the existence of an RNA maturation complex in Arabidopsis mitochondria and suggested that PRORP proteins cooperated with other gene expression factors for RNA maturation in vivo.

Targeting PML in triple negative breast cancer elicits growth suppression and senescence

Oncogene addiction postulates that the survival and growth of certain tumor cells is dependent upon the activity of one oncogene, despite their multiple genetic and epigenetic abnormalities. This phenomenon provides a foundation for molecular targeted therapy and a rationale for oncogene-based stratification. We have previously reported that the Promyelocytic Leukemia protein (PML) is upregulated in triple negative breast cancer (TNBC) and it regulates cancer-initiating cell function, thus suggesting that this protein can be therapeutically targeted in combination with PML-based stratification. However, the effects of PML perturbation on the bulk of tumor cells remained poorly understood. Here we demonstrate that TNBC cells are addicted to the expression of this nuclear protein. PML inhibition led to a remarkable growth arrest combined with features of senescence in vitro and in vivo. Mechanistically, the growth arrest and senescence were associated to a decrease in MYC and PIM1 kinase levels, with the subsequent accumulation of CDKN1B (p27), a trigger of senescence. In line with this notion, we found that PML is associated to the promoter regions of MYC and PIM1, consistent with their direct correlation in breast cancer specimens. Altogether, our results provide a feasible explanation for the functional similarities of MYC, PIM1, and PML in TNBC and encourage further study of PML targeting strategies for the treatment of this breast cancer subtype.

Small is big in Arabidopsis mitochondrial ribosome

Mitochondria are responsible for energy production through aerobic respiration, and represent the powerhouse of eukaryotic cells. Their metabolism and gene expression processes combine bacterial-like features and traits that evolved in eukaryotes. Among mitochondrial gene expression processes, translation remains the most elusive. In plants, while numerous pentatricopeptide repeat (PPR) proteins are involved in all steps of gene expression, their function in mitochondrial translation remains unclear. Here we present the biochemical characterization of Arabidopsis mitochondrial ribosomes and identify their protein subunit composition. Complementary biochemical approaches identified 19 plant-specific mitoribosome proteins, of which ten are PPR proteins. The knockout mutations of ribosomal PPR (rPPR) genes result in distinct macroscopic phenotypes, including lethality and severe growth delay. The molecular analysis of rppr1 mutants using ribosome profiling, as well as the analysis of mitochondrial protein levels, demonstrate rPPR1 to be a generic translation factor that is a novel function for PPR proteins. Finally, single-particle cryo-electron microscopy (cryo-EM) reveals the unique structural architecture of Arabidopsis mitoribosomes, characterized by a very large small ribosomal subunit, larger than the large subunit, bearing an additional RNA domain grafted onto the head. Overall, our results show that Arabidopsis mitoribosomes are substantially divergent from bacterial and other eukaryote mitoribosomes, in terms of both structure and protein content.

Tag-Count Analysis of Large-Scale Proteomic Data

Label-free quantitative methods are advantageous in bottom-up (shotgun) proteomics because they are robust and can easily be applied to different workflows without additional cost. Both label-based and label-free approaches are routinely applied to discovery-based proteomics experiments and are widely accepted as semiquantitative. Label-free quantitation approaches are segregated into two distinct approaches: peak-abundance-based approaches and spectral counting (SpC). Peak abundance approaches like MaxLFQ, which is integrated into the MaxQuant environment, require precursor peak alignment that is computationally intensive and cannot be routinely applied to low-resolution data. Not limited by these constraints, SpC approaches simply use the number of peptide identifications corresponding to a given protein as a measurement of protein abundance. We show here that spectral counts from multidimensional proteomic data sets have a mean-dispersion relationship that can be modeled in edgeR. Furthermore, by simulating spectral counts, we show that this approach can routinely be applied to large-scale discovery proteomics data sets to determine differential protein expression.

Proteomic analysis of the Simkania-containing vacuole: the central role of retrograde transport

Simkania negevensis is an obligate intracellular bacterial pathogen that grows in amoeba or human cells within a membrane-bound vacuole forming endoplasmic reticulum (ER) contact sites. The membrane of this Simkania-containing vacuole (SnCV) is a critical host-pathogen interface whose origin and molecular interactions with cellular organelles remain poorly defined. We performed proteomic analysis of purified ER-SnCV-membranes using label free LC-MS(2) to define the pathogen-containing organelle composition. Of the 1,178 proteins of human and 302 proteins of Simkania origin identified by this strategy, 51 host cell proteins were enriched or depleted by infection and 57 proteins were associated with host endosomal transport pathways. Chemical inhibitors that selectively interfere with trafficking at the early endosome-to-trans-Golgi network (TGN) interface (retrograde transport) affected SnCV formation, morphology and lipid transport. Our data demonstrate that Simkania exploits early endosome-to-TGN transport for nutrient acquisition and growth.

Label-Free LC-MS/MS Proteomic Analysis of Cerebrospinal Fluid Identifies Protein/Pathway Alterations and Candidate Biomarkers for Amyotrophic Lateral Sclerosis

Analysis of the cerebrospinal fluid (CSF) proteome has proven valuable to the study of neurodegenerative disorders. To identify new protein/pathway alterations and candidate biomarkers for amyotrophic lateral sclerosis (ALS), we performed comparative proteomic profiling of CSF from sporadic ALS (sALS), healthy control (HC), and other neurological disease (OND) subjects using label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS). A total of 1712 CSF proteins were detected and relatively quantified by spectral counting. Levels of several proteins with diverse biological functions were significantly altered in sALS samples. Enrichment analysis was used to link these alterations to biological pathways, which were predominantly related to inflammation, neuronal activity, and extracellular matrix regulation. We then used our CSF proteomic profiles to create a support vector machines classifier capable of discriminating training set ALS from non-ALS (HC and OND) samples. Four classifier proteins, WD repeat-containing protein 63, amyloid-like protein 1, SPARC-like protein 1, and cell adhesion molecule 3, were identified by feature selection and externally validated. The resultant classifier distinguished ALS from non-ALS samples with 83% sensitivity and 100% specificity in an independent test set. Collectively, our results illustrate the utility of CSF proteomic profiling for identifying ALS protein/pathway alterations and candidate disease biomarkers.

Enhancing the Biological Relevance of Secretome-Based Proteomics by Linking Tumor Cell Proliferation and Protein Secretion

Secretome profiling has become a methodology of choice for the identification of tumor biomarkers. We hypothesized that due to the dynamic nature of secretomes cellular perturbations could affect their composition but also change the global amount of protein secreted per cell. We confirmed our hypothesis by measuring the levels of secreted proteins taking into account the amount of proteome produced per cell. Then, we established a correlation between cell proliferation and protein secretion that explained the observed changes in global protein secretion. Next, we implemented a normalization correcting the statistical results of secretome studies by the global protein secretion of cells into a generalized linear model (GLM). The application of the normalization to two biological perturbations on tumor cells resulted in drastic changes in the list of statistically significant proteins. Furthermore, we found that known epithelial-to-mesenchymal transition (EMT) effectors were only statistically significant when the normalization was applied. Therefore, the normalization proposed here increases the sensitivity of statistical tests by increasing the number of true-positives. From an oncology perspective, the correlation between protein secretion and cellular proliferation suggests that slow-growing tumors could have high-protein secretion rates and consequently contribute strongly to tumor paracrine signaling.