Stroke remains a leading cause of death and disability worldwide. Despite continuous advances, the identification of key molecular signatures in the hyper-acute phase of ischemic stroke is still a primary interest for translational research on stroke diagnosis, prognosis, and treatment. Data integration from high-throughput -omics techniques has become crucial to unraveling key interactions among different molecular elements in complex biological contexts, such as ischemic stroke. Thus, we used advanced data integration methods for a multi-level joint analysis of transcriptomics and proteomics data sets obtained from mouse brains at 2 h after cerebral ischemia. By modeling net-like correlation structures, we identified an integrated network of genes and proteins that are differentially expressed at a very early stage after stroke. We validated 10 of these deregulated elements in acute stroke, and changes in their expression pattern over time after cerebral ischemia were described. Of these, CLDN20, GADD45G, RGS2, BAG5, and CTNND2 were next evaluated as blood biomarkers of cerebral ischemia in mice and human blood samples, which were obtained from stroke patients and patients presenting stroke-mimicking conditions. Our findings indicate that CTNND2 levels in blood might potentially be useful for distinguishing ischemic strokes from stroke-mimicking conditions in the hyper-acute phase of the disease. Furthermore, circulating GADD45G content within the first 6 h after stroke could also play a key role in predicting poor outcomes in stroke patients. For the first time, we have used an integrative biostatistical approach to elucidate key molecules in the initial stages of stroke pathophysiology and highlight new notable molecules that might be further considered as blood biomarkers of ischemic stroke.

En France, environ 2 à 4 % de la population, souvent des femmes, est touchée par la fibromyalgie. Mais en réalité, qu’est-ce que la fibromyalgie ? Quelles sont les astuces naturelles permettant de soulager les patients qui présentent des symptômes de cette maladie ? Quels sont les symptômes de la fibromyalgie ? La fibromyalgie, ou syndrome fibromyalgique, […]

L’article Quelques remèdes de grand-mère pour soulager les symptômes de la fibromyalgie est apparu en premier sur Ortho Doc France.

Contrairement à ce qu’on pourrait croire, bien plus de femmes se préoccupent de leurs poids et y font attention. Pour ces femmes, comprendre ce qu’est un indice de masse corporelle (IMC) normal peut permettre de maintenir une bonne santé et savoir quand faire appel à un médecin pour leurs poids. C’est quoi l’IMC ou indice de masse […]

L’article Quel est l’IMC normal pour une femme ? est apparu en premier sur Ortho Doc France.

Le ventre est l’une des parties du corps où il est plus difficile de perdre de la graisse. Souvent, la meilleure solution pour arriver à perdre de la graisse à cet endroit précis du corps consiste à manger sainement et équilibré. Cependant, quels sont les aliments qui brûlent les graisses du ventre sans danger pour la […]

L’article Quels sont les aliments qui brûlent rapidement les graisses du ventre ? est apparu en premier sur Ortho Doc France.

Les troubles gastro-intestinaux peuvent avoir un impact significatif sur la qualité de vie des patients. Ils engendrent en effet des symptômes variés et inconfortables allant de douleurs abdominales et nausées à des troubles plus graves comme les maladies inflammatoires chroniques de l’intestin. Quelles sont les principales pathologies rencontrées dans cette spécialité de la médecine ? À quel […]

L’article Quelles sont les principales pathologies en gastroentérologie ? est apparu en premier sur Ortho Doc France.

Pour de nombreuses personnes, perte de poids rime obligatoirement avec privations. Et si vous appreniez aujourd’hui que vous pouvez perdre efficacement vos kilos en trop sans pour autant vous priver excessivement ? De nombreux programmes minceur et de rééquilibrage, notamment Croq’kilos offrent ce genre d’alternative. Mais de quoi s’agit-il réellement ? C’est quoi Croq’Kilos ? Croq’Kilos […]

L’article Croq’Kilos : programme minceur, rééquilibrage alimentaire est apparu en premier sur Ortho Doc France.

Colorectal cancer (CRC) arises as the consequence of progressive changes from normal epithelial cells through polyp to tumor, and thus is an useful model for studying metabolic shift. In the present study, we studied the metabolomic profiles using high analyte specific gas chromatography/mass spectrometry (GC/MS) and liquid chromatography tandem mass spectrometry (LC/MS/MS) to attain a systems-level view of the shift in metabolism in cells progressing along the path to CRC. Colonic tissues including tumor, polyps and adjacent matched normal mucosa from 26 patients with sporadic CRC from freshly isolated resections were used for this study. The metabolic profiles were obtained using GC/MS and LC/MS/MS. Our data suggest there was a distinct profile change of a wide range of metabolites from mucosa to tumor tissues. Various amino acids and lipids in the polyps and tumors were elevated, suggesting higher energy needs for increased cellular proliferation. In contrast, significant depletion of glucose and inositol in polyps revealed that glycolysis may be critical in early tumorigenesis. In addition, the accumulation of hypoxanthine and xanthine, and the decrease of uric acid concentration, suggest that the purine biosynthesis pathway could have been substituted by the salvage pathway in CRC. Further, there was a step-wise reduction of deoxycholic acid concentration from mucosa to tumors. It appears that to gain a growth advantage, cancer cells may adopt alternate metabolic pathways in tumorigenesis and this flexibility allows them to adapt and thrive in harsh environment.

In quantitative proteomics work, the differences in expression of many separate proteins are routinely examined to test for significant differences between treatments. This leads to the multiple hypothesis testing problem: when many separate tests are performed many will be significant by chance and be false positive results. Statistical methods such as the false discovery rate (FDR) method that deal with this problem have been disseminated for more than one decade. However a survey of proteomics journals shows that such tests are not widely implemented in one commonly used technique, quantitative proteomics using two-dimensional electrophoresis (2-DE). We outline a selection of multiple hypothesis testing methods, including some that are well known and some lesser known, and present a simple strategy for their use by the experimental scientist in quantitative proteomics work generally. The strategy focuses on the desirability of simultaneous use of several different methods, the choice and emphasis dependent on research priorities and the results in hand. This approach is demonstrated using case scenarios with experimental and simulated model data.

Electrospray ionization is today the most widely used ionization technique in chemical and bio-chemical analysis. Interfaced with a mass spectrometer it allows to investigate the molecular composition of liquid samples. With electrospray a large variety of chemical substances can be ionized. There is no limitation in mass which enables even the investigation of large non-covalent protein complexes. Its high ionization efficiency profoundly changed bio-molecular sciences because proteins can be identified and quantified on trace amounts in a high throughput fashion. This review article focusses mainly on the exploration of the underlying ionization mechanism. Some ionization characteristics are discussed which are related to this mechanism. Typical spectra of peptides, proteins and non-covalent complexes are shown and the quantitative character of spectra is highlighted. Finally the possibilities and limitations in measuring the association constant of bivalent non-covalent complexes are described.

As the capability of mass spectrometry-based proteomics has matured, tens of thousands of peptides can be measured simultaneously, which has the benefit of offering a systems view of protein expression. However, a major challenge is that with an increase in throughput, protein quantification estimation from the native measured peptides has become a computational task. A limitation to existing computationally-driven protein quantification methods is that most ignore protein variation, such as alternate splicing of the RNA transcript and post-translational modifications or other possible proteoforms, which will affect a significant fraction of the proteome. The consequence of this assumption is that statistical inference at the protein level, and consequently downstream analyses, such as network and pathway modeling, have only limited power for biomarker discovery. Here, we describe a Bayesian model (BP-Quant) that uses statistically derived peptides signatures to identify peptides that are outside the dominant pattern, or the existence of multiple over-expressed patterns to improve relative protein abundance estimates. It is a research-driven approach that utilizes the objectives of the experiment, defined in the context of a standard statistical hypothesis, to identify a set of peptides exhibiting similar statistical behavior relating to a protein. This approach infers that changes in relative protein abundance can be used as a surrogate for changes in function, without necessarily taking into account the effect of differential post-translational modifications, processing, or splicing in altering protein function. We verify the approach using a dilution study from mouse plasma samples and demonstrate that BP-Quant achieves similar accuracy as the current state-of-the-art methods at proteoform identification with significantly better specificity. BP-Quant is available as a MatLab ® and R packages at https://github.com/PNNL-Comp-Mass-Spec/BP-Quant.

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Protein tyrosine kinases (PTKs) play key roles in cellular signal transduction, cell cycle regulation, cell division, and cell differentiation. Dysregulation of PTK-activated pathways, often by receptor overexpression, gene amplification, or genetic mutation, is a causal factor underlying numerous cancers. In this study, we have developed a parallel reaction monitoring (PRM)-based assay for quantitative profiling of 83 PTKs. The assay detects 308 proteotypic peptides from 54 receptor tyrosine kinases and 29 nonreceptor tyrosine kinases in a single run. Quantitative comparisons were based on the labeled reference peptide method. We implemented the assay in four cell models: 1) a comparison of proliferating versus epidermal growth factor (EGF)-stimulated A431 cells, 2) a comparison of SW480Null (mutant APC) and SW480APC (APC restored) colon tumor cell lines, and 3) a comparison of 10 colorectal cancer cell lines with different genomic abnormalities, and 4) lung cancer cell lines with either susceptibility (11-18) or acquired resistance (11-18R) to the epidermal growth factor receptor tyrosine kinase inhibitor erlotinib. We observed distinct PTK expression changes that were induced by stimuli, genomic features or drug resistance, which were consistent with previous reports. However, most of the measured expression differences were novel observations. For example, acquired resistance to erlotinib in the 11-18 cell model was associated not only with previously reported upregulation of MET, but also with upregulation of FLK2 and downregulation of LYN and PTK7. Immunoblot analyses and shotgun proteomics data were highly consistent with PRM data. Multiplexed PRM assays provide a targeted, systems-level profiling approach to evaluate cancer-related proteotypes and adaptations. Data are available through Proteome eXchange Accession PXD002706.

This article has been withdrawn by the authors. We discovered an error after this manuscript was published as a Paper in Press. Specifically, we learned that the structures of glycans presented for the PD-L1 peptide were drawn and labeled incorrectly. We wish to withdraw this article and submit a corrected version for review.

The histidine kinase Hik33 plays important roles in mediating cyanobacterial response to divergent types of abiotic stresses including cold, salt, high light (HL), and osmotic stresses. However, how these functions are regulated by Hik33 remains to be addressed. Using a hik33-deficient strain (hik33) of Synechocystis sp. PCC 6803 (Synechocystis) and quantitative proteomics, we found that Hik33 depletion induces differential protein expression highly similar to that induced by divergent types of stresses. This typically includes downregulation of proteins in photosynthesis and carbon assimilation that are necessary for cell propagation, and upregulation of heat shock proteins, chaperons, and proteases that are important for cell survival. This observation indicates that depletion of Hik33 alone mimics divergent types of abiotic stresses, and that Hik33 could be important for preventing abnormal stress response in the normal condition. Moreover, we found the majority of proteins of plasmid origin were significantly upregulated in hik33, though their biological significance remains to be addressed. Together, the systematically characterized Hik33-regulated cyanobacterial proteome, which is largely involved in stress responses, builds the molecular basis for Hik33 as a general regulator of stress responses.

This article has been withdrawn by the authors. This article did not comply with the editorial guidelines of MCP. Specifically, single peptide based protein identifications of 9-19% were included in the analysis and discussed in the results and conclusions. We wish to withdraw this article and resubmit a clarified, corrected manuscript for review.

Growing implications of glycosylation in physiological occurrences and human disease have prompted intensive focus on revealing glycomic perturbations through absolute and relative quantification. Empowered by seminal methodologies and increasing capacity for detection, identification, and characterization, the past decade has provided a significant increase in the number of suitable strategies for glycan and glycopeptide quantification. Mass spectrometry-based strategies for glycomic quantitation have grown to include metabolic incorporation of stable isotopes, deposition of mass difference and mass defect isotopic labels, and isobaric chemical labeling, providing researchers with ample tools for accurate and robust quantitation. Beyond this, workflows have been designed to harness instrument capability for label-free quantification and numerous software packages have been developed to facilitate reliable spectrum scoring. In this review, we present and highlight the most recent advances in chemical labeling and associated techniques for glycan and glycopeptide quantification.

Intact glycopeptide identification has long been known as a key and challenging barrier to the comprehensive and accurate understanding the role of glycosylation in an organism. Intact glycopeptide analysis is a blossoming field that has received increasing attention in recent years. Mass spectrometry (MS)-based strategies and relative software tools are major drivers that have greatly facilitated the analysis of intact glycopeptides, particularly intact N-glycopeptides. This manuscript provides a systematic review of the intact glycopeptide identification process using mass spectrometry data generated in shotgun proteomic experiments, which typically focus on N-glycopeptide analysis. Particular attention is paid to the software tools that have been recently developed in the last decade for the interpretation and quality control of glycopeptide spectra acquired using different MS strategies. The review also provides information about the characteristics and applications of these software tools, discusses their advantages and disadvantages, and concludes with a discussion of outstanding tools.

Mass spectrometry-based glycoproteomics has gone through some incredible developments over the last few years. Technological advances in glycopeptide enrichment, fragmentation methods, and data analysis workflows have enabled the transition of glycoproteomics from a niche application, mainly focused on the characterization of isolated glycoproteins, to a mature technology capable of profiling thousands of intact glycopeptides at once. In addition to numerous biological discoveries catalyzed by the technology, we are also observing an increase in studies focusing on global protein glycosylation and the relationship between multiple glycosylation sites on the same protein. It has become apparent that just describing protein glycosylation in terms of micro- and macro-heterogeneity, respectively the variation and occupancy of glycans at a given site, is not sufficient to describe the observed interactions between sites. In this perspective we propose a new term, meta-heterogeneity, to describe a higher level of glycan regulation: the variation in glycosylation across multiple sites of a given protein. We provide literature examples of extensive meta-heterogeneity on relevant proteins such as antibodies, erythropoietin, myeloperoxidase and a number of serum and plasma proteins. Furthermore, we postulate on the possible biological reasons and causes behind the intriguing meta-heterogeneity observed in glycoproteins.

Complex protein glycosylation occurs through biosynthetic steps in the secretory pathway that create macro- and microheterogeneity of structure and function.  Required for all life forms, glycosylation diversifies and adapts protein interactions with binding partners that underpin interactions at cell surfaces and pericellular and extracellular environments. Because these biological effects arise from heterogeneity of structure and function, it is necessary to measure their changes as part of the quest to understand nature.  Quite often, however, the assumption behind proteomics that post-translational modifications are discrete additions that can be modeled using the genome as a template does not apply to protein glycosylation.  Rather, it is necessary to quantify the glycosylation distribution at each glycosite and to aggregate this information into a population of mature glycoproteins that exist in a given biological system.  To date, mass spectrometric methods for assigning singly glycosylated peptides are well-established.  But it is necessary to quantify glycosylation heterogeneity accurately in order to gauge the alterations that occur during biological processes.  The task is to quantify the glycosylated peptide forms as accurately as possible and then apply appropriate bioinformatics algorithms to the calculation of micro- and macro-similarities.  In this review, we summarize current approaches for protein quantification as they apply to this glycoprotein similarity problem.

This review covers recent developments in glycosaminoglycan (GAG) analysis via mass spectrometry (MS). GAGs participate in a variety of biological functions, including cellular communication, wound healing, and anticoagulation, and are important targets for structural characterization. GAGs exhibit a diverse range of structural features due to the variety of O- and N-sulfation modifications and uronic acid C-5 epimerization that can occur, making their analysis a challenging target. Mass spectrometry approaches to the structure assignment of GAGs have been widely investigated, and new methodologies remain the subject of development. Advances in sample preparation, tandem MS techniques (MS/MS), on-line separations and automated analysis software have advanced the field of GAG analysis. These recent developments have led to remarkable improvements in the precision and time efficiency for the structural characterization of GAGs.

Many cell surface and secreted proteins are modified by the covalent addition of glycans that play an important role in the development of multicellular organisms. These glycan modifications enable communication between cells and the extracellular matrix via interactions with specific glycan-binding lectins and the regulation of receptor-mediated signaling. Aberrant protein glycosylation has been associated with the development of several muscular diseases suggesting essential glycan- and lectin-mediated functions in myogenesis and muscle development but our molecular understanding of the precise glycans, catalytic enzymes and lectins involved remain only partially understood. Here, we quantified dynamic remodeling of the membrane-associated proteome during a time-course of myogenesis in cell culture. We observed wide-spread changes in the abundance of several important lectins and enzymes facilitating glycan biosynthesis. Glycomics-based quantification of released N-linked glycans confirmed remodeling of the glycome consistent with the regulation of glycosyltransferases and glycosidases responsible for their formation including a previously unknown di-galactose-to-sialic acid switch supporting a functional role of these glycoepitopes in myogenesis. Furthermore, dynamic quantitative glycoproteomic analysis with multiplexed stable isotope labelling and analysis of enriched glycopeptides with multiple fragmentation approaches identified glycoproteins modified by these regulated glycans including several integrins and growth factor receptors. Myogenesis was also associated with the regulation of several lectins most notably the up-regulation of galectin-1 (LGALS1). CRISPR/Cas9-mediated deletion of Lgals1 inhibited differentiation and myotube formation suggesting an early functional role of galectin-1 in the myogenic program. Importantly, similar changes in N-glycosylation and the up-regulation of galectin-1 during postnatal skeletal muscle development were observed in mice. Treatment of new-born mice with recombinant adeno-associated viruses to overexpress galectin-1 in the musculature resulted in enhanced muscle mass. Our data form a valuable resource to further understand the glycobiology of myogenesis and will aid the development of intervention strategies to promote healthy muscle development or regeneration.

CD4+ T cell responses are crucial for inducing and maintaining effective anti-cancer immunity, and the identification of human leukocyte antigen class II (HLA-II) cancer-specific epitopes is key to the development of potent cancer immunotherapies. In many tumor types, and especially in glioblastoma (GBM), HLA-II complexes are hardly ever naturally expressed. Hence, little is known about immunogenic HLA-II epitopes in GBM. With stable expression of the class II major histocompatibility complex transactivator (CIITA) coupled to a detailed and sensitive mass spectrometry based immunopeptidomics analysis, we here uncovered a remarkable breadth of the HLA-ligandome in HROG02, HROG17 and RA GBM cell lines. The effect of CIITA expression on the induction of the HLA-II presentation machinery was striking in each of the three cell lines, and it was significantly higher compared to interferon gamma (IFN) treatment. In total, we identified 16,123 unique HLA-I peptides and 32,690 unique HLA-II peptides. In order to genuinely define the identified peptides as true HLA ligands, we carefully characterized their association with the different HLA allotypes. In addition, we identified 138 and 279 HLA-I and HLA-II ligands, respectively, most of which are novel in GBM, derived from known GBM-associated tumor-antigens that have been used as source proteins for a variety of GBM vaccines. Our data further indicate that CIITA-expressing GBM cells acquired an antigen presenting cell-like phenotype as we found that they directly present external proteins as HLA-II ligands. Not only that CIITA-expressing GBM cells are attractive models for antigen discovery endeavors, but also such engineered cells have great therapeutic potential through massive presentation of a diverse antigenic repertoire.

Regulation of gene expression is essential for the functioning of all eukaryotic organisms. Understanding gene expression regulation requires determining which proteins interact with regulatory elements in chromatin. Mass spectrometry-based analysis of chromatin has emerged as a powerful tool to identify proteins associated with gene regulation, as it allows studying protein function and protein complex formation in their in vivo chromatin-bound context. Total chromatin isolated from cells can be directly analysed using mass spectrometry or further fractionated into transcriptionally active and inactive chromatin prior to MS-based analysis. Newly formed chromatin that is assembled during DNA replication can also be specifically isolated and analysed. Furthermore, capturing specific chromatin domains facilitates the identification of previously unknown transcription factors interacting with these domains. Finally, in recent years, advances have been made towards identifying proteins that interact with a single genomic locus of interest. In this review, we highlight the power of chromatin proteomics approaches and how these provide complementary alternatives compared to conventional affinity purification methods. Furthermore, we discuss the biochemical challenges that should be addressed to consolidate and expand the role of chromatin proteomics as a key technology in the context of gene expression regulation and epigenetics research in health and disease.

Esophageal squamous cell cancer (ESCC) is an aggressive malignancy with poor therapeutic outcomes. However, the alterations in proteins and post-translational modifications (PTMs) leading to the pathogenesis of ESCC remains unclear. Here, we provide the comprehensive characterization of the proteome, phosphorylome, lysine acetylome and succinylome for ESCC and matched control cells using quantitative proteomic approach. We identify abnormal protein and post-translational modification (PTM) pathways, including significantly downregulated lysine succinylation sites in cancer cells. Focusing on hyposuccinylation, we reveal that this altered PTM was enriched on enzymes of metabolic pathways inextricably linked with cancer metabolism. Importantly, ESCC malignant behaviors such as cell migration are inhibited once the level of succinylation was restored in vitro or in vivo. This effect was further verified by mutations to disrupt succinylation sites in candidate proteins. Meanwhile, we found that succinylation has a negative regulatory effect on histone methylation to promote cancer migration. Finally, hyposuccinylation is confirmed in primary ESCC specimens. Our findings together demonstrate that lysine succinylation may alter ESCC metabolism and migration, providing new insights into the functional significance of PTM in cancer biology.

The choice for adjuvant chemotherapy in stage II colorectal cancer (CRC) is controversial as many patients are cured by surgery alone and it is difficult to identify patients with high-risk of recurrence of the disease. There is a need for better stratification of this group of patients. Mass spectrometry imaging could identify patients at risk. We report here the N-glycosylation signatures of the different cell populations in a group of stage II CRC tissue samples. The cancer cells, compared to normal epithelial cells, have increased levels of sialylation and high-mannose glycans, as well as decreased levels of fucosylation and highly branched N-glycans. When looking at the interface between cancer and its microenvironment, it seems that the cancer N-glycosylation signature spreads into the surrounding stroma at the invasive front of the tumor. This finding was more outspoken in patients with a worse outcome within this sample group.

Paramphistomosis, caused by the rumen fluke, Calicophoron daubneyi, is a parasitic infection of ruminant livestock which has seen a rapid rise in prevalence throughout Western Europe in recent years. Following ingestion of metacercariae (parasite cysts) by the mammalian host, newly-excysted juveniles (NEJs) emerge and invade the duodenal submucosa which causes significant pathology in heavy infections. The immature larvae then migrate upwards, along the gastrointestinal tract, and enter the rumen where they mature and begin to produce eggs. Despite their emergence, and sporadic outbreaks of acute disease, we know little about the molecular mechanisms used by C. daubneyi to establish infection, acquire nutrients and to avoid the host immune response. Here, transcriptome analysis of four intra-mammalian life-cycle stages, integrated with secretome analysis of the NEJ and adult parasites (responsible for acute and chronic disease respectively), revealed how the expression and secretion of selected families of virulence factors and immunomodulators are regulated in accordance with fluke development and migration. Our data show that whilst a family of cathepsins B with varying S2 sub-site residues (indicating distinct substrate specificities) are differentially secreted by NEJs and adult flukes, cathepsins L and F are secreted in low abundance by NEJs only. We found that C. daubneyi has an expanded family of aspartic peptidases, which is up-regulated in adult worms, although they are underrepresented in the secretome. The most abundant proteins in adult fluke secretions were helminth defence molecules (HDMs) that likely establish an immune environment permissive to fluke survival and/or neutralise pathogen-associated molecular patterns (PAMPs) such as bacterial lipopolysaccharide in the microbiome-rich rumen. The distinct collection of molecules secreted by C. daubneyi allowed the development of the first coproantigen-based ELISA for paramphistomosis which, importantly, did not recognise antigens from other helminths commonly found as co-infections with rumen fluke.

Thermal proteome profiling (TPP) allows for the unbiased detection of drug – target protein engagements in vivo. Traditionally, one cell type is used for TPP studies, with the risk of missing important differentially expressed target proteins. The use of whole organisms would circumvent this problem. Zebrafish embryos are amenable to such an approach. Here, we used TPP on whole zebrafish embryo lysate to identify protein targets of napabucasin, a compound that may affect Signal transducer and activator of transcription 3 (Stat3) signaling through an ill-understood mechanism. In zebrafish embryos, napabucasin induced developmental defects consistent with inhibition of Stat3 signaling. TPP profiling showed no distinct shift in Stat3 upon napabucasin treatment, but effects were detected on the oxidoreductase, Pora, which might explain effects on Stat3 signaling. Interestingly, thermal stability of several aldehyde dehydrogenases (Aldhs) was affected. Moreover, napabucasin activated ALDH enzymatic activity in vitro. Aldhs have crucial roles in retinoic acid metabolism and functionally we validated napabucasin-mediated activation of the retinoic acid pathway in zebrafish in vivo. We conclude that TPP profiling in whole zebrafish embryo lysate is feasible and facilitates direct correlation of in vivo effects of small molecule drugs with their protein targets.

The increasing consumption of high-fat foods combined with a lack of exercise is a major contributor to the burden of obesity in humans. Aerobic exercise such as running is known to provide metabolic benefits, but how the over-consumption of a high fat diet (HFD) and exercise interact is not well characterized at the molecular level. Here, we examined the plasma proteome in mice for the effects of aerobic exercise as both a treatment and as a preventative regime for animals on either HFD or a healthy control diet. This analysis detected large changes in the plasma proteome induced by the HFD, such as increased abundance of SERPINA7, ALDOB, and down-regulation of SERPINA1E, CFD (adipsin). Some of these changes were significantly reverted using exercise as a preventative measure, but not as a treatment regime. To determine if either the intensity, or duration, of exercise influenced the outcome, we compared high-intensity interval training (HIIT) and endurance running. Endurance running slightly out-performed HIIT exercise, but overall, both provided similar reversion in abundance of plasma proteins modulated by the high-fat diet including SERPINA7, APOE, SERPINA1E, and CFD. Finally, we compared the changes induced by over-consumption of HFD to previous data from mice fed an isocaloric high saturated fat (SFA) or polyunsaturated fat (PUFA) diet. This identified several common changes including increased APOC2 and APOE, but also highlighted changes specific for either over-consumption of HFD (ALDOB, SERPINA7, CFD), SFA-based diets (SERPINA1E), or PUFA-based diets (Haptoglobin - Hp). Together, these data highlight the importance of early intervention with exercise to revert HFD-induced phenotypes and suggest some of the molecular mechanisms leading to the changes in the plasma proteome generated by high fat diet consumption. Web-based interactive visualizations are provided for this dataset (larancelab.com/hfd-exercise), which give insight into diet and exercise phenotypic interactions on the plasma proteome.

We developed a simple and rapid method to enrich protein N-terminal peptides, in which the protease TrypN is first employed to generate protein N-terminal peptides without Lys or Arg and internal peptides with two positive charges at their N-termini, and then the N-terminal peptides with or without N-acetylation are separated from the internal peptides by strong cation exchange chromatography according to a retention model based on the charge/orientation of peptides. This approach was applied to 20 μg of human HEK293T cell lysate proteins to profile the N-terminal proteome. On average, 1,550 acetylated and 200 unmodified protein N-terminal peptides were successfully identified in a single LC/MS/MS run with less than 3% contamination with internal peptides, even when we accepted only canonical protein N-termini registered in the Swiss-Prot database. Since this method involves only two steps, protein digestion and chromatographic separation, without the need for tedious chemical reactions, it should be useful for comprehensive profiling of protein N-termini, including proteoforms with neo-N-termini.

Malaria elimination is still pending on the development of novel tools that rely on a deep understanding of parasite biology. Proteins of all living cells undergo a myriad number of posttranslational modifications (PTMs) that are critical to multifarious life processes. An extensive proteome-wide dissection revealed a fine PTM map of most proteins in both Plasmodium falciparum, the causative agent of severe malaria, and the infected red blood cells. More than two-thirds of proteins of the parasite and its host cell underwent extensive and dynamic modification throughout the erythrocytic developmental stage. PTMs critically modulate the virulence factors involved in the host-parasite interaction and pathogenesis. Furthermore, P. falciparum stabilized the supporting proteins of erythrocyte origin by selective de-modification. Collectively, our multiple omic analyses, apart from having furthered a deep understanding of the systems biology of P. falciparum and malaria pathogenesis, provide a valuable resource for mining new antimalarial targets.

Biological functions emerge from complex and dynamic networks of protein-protein interactions. Because these protein-protein interaction networks, or interactomes, represent pairwise connections within a hierarchically organized system, it is often useful to identify higher-order associations embedded within them, such as multi-member protein complexes. Graph-based clustering techniques are widely used to accomplish this goal, and dozens of field-specific and general clustering algorithms exist. However, interactomes can be prone to errors, especially when inferred from high-throughput biochemical assays. Therefore, robustness to network-level noise is an important criterion for any clustering algorithm that aims to generate robust, reproducible clusters. Here, we tested the robustness of a range of graph-based clustering algorithms in the presence of noise, including algorithms common across domains and those specific to protein networks. Strikingly, we found that all of the clustering algorithms tested here markedly amplified noise within the underlying protein interaction network. Randomly rewiring only 1% of network edges yielded more than a 50% change in clustering results, indicating that clustering markedly amplified network-level noise. Moreover, we found the impact of network noise on individual clusters was not uniform: some clusters were consistently robust to injected noise while others were not. To assist in assessing this, we developed the clust.perturb R package and Shiny web application to measure the reproducibility of clusters by randomly perturbing the network. We show that clust.perturb results are predictive of real-world cluster stability: poorly reproducible clusters as identified by clust.perturb are significantly less likely to be reclustered across experiments. We conclude that graph-based clustering amplifies noise in protein interaction networks, but quantifying the robustness of a cluster to network noise can separate stable protein complexes from spurious associations.

Protease activity has been associated with pathological processes that can lead to cancer development and progression. However, understanding the pathological unbalance in proteolysis is challenging since changes can occur simultaneously at protease, their inhibitor and substrate levels. Here, we present a pipeline that combines peptidomics, proteomics and peptidase predictions for studying proteolytic events in the saliva of seventy-nine patients and their association with oral squamous cell carcinoma (OSCC) prognosis. Our findings revealed differences in the saliva peptidome of patients with (pN+) or without (pN0) lymph node metastasis and delivered a panel of ten endogenous peptides correlated with poor prognostic factors plus five molecules able to classify pN0 and pN+ patients (ROC-AUC>0.85). In addition, endo- and exopeptidases putatively implicated in the processing of differential peptides were investigated using cancer tissue gene expression data from publicly repositories reinforcing their association with poorer survival rates and prognosis in oral cancer. The dynamics of the OSCC-related proteolysis was further explored via the proteomic profiling of saliva. This revealed that peptidase/endopeptidase inhibitors exhibited reduced levels in the saliva of pN+ patients, as confirmed by SRM-MS, whilst minor changes were detected in the level of saliva proteases. Taken together, our results indicated that proteolytic activity is accentuated in the saliva of OSCC patients with lymph node metastasis and, at least in part, this is modulated by reduced levels of salivary peptidase inhibitors. Therefore, this integrated pipeline provided better comprehension and discovery of molecular features with implications in the oral cancer metastasis prognosis.

Hirschsprung disease (HSCR) is a heterogeneous group of neurocristopathy characterized by the absence of the enteric ganglia along a variable length of the intestine. Genetic defects play a major role in the pathogenesis of HSCR while family studies of pathogenic variants in all the known genes (loci) only demonstrate incomplete penetrance and variable expressivity for unknown reasons. Here, we applied large-scale, quantitative proteomics of human colon tissues from 21 patients using iTRAQ method followed by bioinformatics analysis. Selected findings were confirmed by parallel reaction monitoring (PRM) verification. At last the interesting differentially expressed proteins were confirmed by western blot. A total of 5341 proteins in human colon tissues were identified. Among them, 664 proteins with >1.2-fold difference were identified in 6 groups: groups A1 and A2 pooled protein from the ganglionic and aganglionic colon of male, long-segment HSCR patients (L-HSCR, n=7); groups B1 and B2 pooled protein from the ganglionic and aganglionic colon of male, short-segment HSCR patients (S-HSCR, n=7); and groups C1 and C2 pooled protein from the ganglionic and aganglionic colon of female, S-HSCR patients (n=7). Based on these analyses, 49 proteins from 5 pathways were selected for PRM verification, including ribosome, endocytosis, spliceosome, oxidative phosphorylation and cell adhesion. The downregulation of three neuron projection development genes ARF4, KIF5B and RAB8A in the aganglionic part of the colon were verified in 15 paired colon samples using WB. The findings of this study will shed new light on the pathogenesis of HSCR and facilitate the development of therapeutic targets.

Thyroglobulin (Tg) is a secreted iodoglycoprotein serving as the precursor for T3 and T4 hormones. Many characterized Tg gene mutations produce secretion-defective variants resulting in congenital hypothyroidism (CH). Tg processing and secretion is controlled by extensive interactions with chaperone, trafficking, and degradation factors comprising the secretory proteostasis network. While dependencies on individual proteostasis network components are known, the integration of proteostasis pathways mediating Tg protein quality control and the molecular basis of mutant Tg misprocessing remain poorly understood. We employ a multiplexed quantitative affinity purification–mass spectrometry approach to define the Tg proteostasis interactome and changes between WT and several CH-variants. Mutant Tg processing is associated with common imbalances in proteostasis engagement including increased chaperoning, oxidative folding, and engagement by targeting factors for ER-associated degradation (ERAD). Furthermore, we reveal mutation-specific changes in engagement with N-glycosylation components, suggesting distinct requirements for one Tg variant on dual engagement of both oligosaccharyltransferase complex isoforms for degradation. Modulating dysregulated proteostasis components and pathways may serve as a therapeutic strategy to restore Tg secretion and thyroid hormone biosynthesis.

The Rhizobium-legume symbiosis is a beneficial interaction in which the bacterium converts atmospheric nitrogen into ammonia and delivers it to the plant in exchange for carbon compounds. This symbiosis implies the adaptation of bacteria to live inside host plant cells. In this work we apply RP-LC-MS/MS and  iTRAQ techniques to study the proteomic profile of endosymbiotic cells (bacteroids) induced by Rhizobium leguminosarum bv viciae strain UPM791 in legume nodules. Nitrogenase subunits, tricarboxylic acid cycle enzymes, and stress response proteins are amongst the most abundant from over one thousand rhizobial proteins identified in pea (Pisum sativum) bacteroids. Comparative analysis of bacteroids induced in pea and in lentil (Lens culinaris)nodules revealed the existence of a significant host-specific differential response affecting dozens of bacterial proteins, including stress-related proteins, transcriptional regulators, and proteins involved in the carbon and nitrogen metabolisms. A mutant affected in one of these proteins, homologous to a GntR-like transcriptional regulator, showed a symbiotic performance significantly  impaired in symbiosis with pea, but not with lentil plants. Analysis of the proteomes of bacteroids isolated from both hosts also revealed the presence of different sets of plant-derived nodule-specific cysteine rich (NCR) peptides, indicating that the endosymbiotic bacteria find a host-specific cocktail of chemical stressors inside the nodule. By studying variations of the bacterial response to different plant cell environments we will be able to identify specific limitations imposed by the host that might give us clues for the improvement of rhizobial performance.

The early detection of pancreatic ductal adenocarcinoma is a complex clinical obstacle yet is key to improving the overall likelihood of patient survival. Current and prospective carbohydrate biomarkers CA19-9 and sTRA are sufficient for surveilling disease progression yet are not approved for delineating PDAC from other abdominal cancers and non-cancerous pancreatic pathologies. To further understand these glycan epitopes, an imaging mass spectrometry approach was utilized to assess the N-glycome of the human pancreas and pancreatic cancer in a cohort of PDAC patients represented by tissue microarrays and whole tissue sections. Orthogonally, these same tissues were characterized by multi-round immunofluorescence which defined expression of CA19-9 and sTRA as well as other lectins towards carbohydrate epitopes with the potential to improve PDAC diagnosis. These analyses revealed distinct differences not only in N-glycan spatial localization across both healthy and diseased tissues but importantly between different biomarker-categorized tissue samples. Unique sulfated bi-antennary N-glycans were detected specifically in normal pancreatic islets. N-glycans from CA19-9 expressing tissues tended to be bi-, tri- and tetra-antennary structures with both core and terminal fucose residues and bisecting N-acetylglucosamines. These N-glycans were detected in less abundance in sTRA-expressing tumor tissues, which favored tri- and tetra-antennary structures with polylactosamine extensions. Increased sialylation of N-glycans was detected in all tumor tissues. A candidate new biomarker derived from IMS was further explored by fluorescence staining with selected lectins on the same tissues. The lectins confirmed the expression of the epitopes in cancer cells and revealed different tumor-associated staining patterns between glycans with bisecting GlcNAc and those with terminal GlcNAc. Thus, the combination of lectin-IHC and IMS techniques produces more complete information for tumor classification than the individual analyses alone. These findings potentiate the development of early assessment technologies to rapidly and specifically identify PDAC in the clinic that may directly impact patient outcomes.

Aspergillus flavus (A. flavus), a pathogenic fungus, can produce carcinogenic and toxic aflatoxins that are a serious agricultural and medical threat worldwide. Attempts to decipher the aflatoxin biosynthetic pathway have been hampered by the lack of a high-quality genome annotation for A. flavus. To address this gap, we performed a comprehensive proteogenomic analysis using high-accuracy mass spectrometry data for this pathogen. The resulting high-quality dataset confirmed the translation of 8,724 previously-predicted genes, and identified 732 novel proteins, 269 splice variants, 447 single amino acid variants, 188 revised genes. A subset of novel proteins was experimentally validated by RT-PCR and synthetic peptides. Further functional annotation suggested that a number of the identified novel proteins may play roles in aflatoxin biosynthesis and stress responses in A. flavus. This comprehensive strategy also identified a wide range of post-translational modifications (PTMs), including 3,461 modification sites from 1,765 proteins. Functional analysis suggested the involvement of these modified proteins in the regulation of cellular metabolic and aflatoxin biosynthetic pathways. Together, we provided a high quality annotation of A. flavus genome and revealed novel insights into the mechanisms of aflatoxin production and pathogenicity in this pathogen.

The molecular mechanism associated with mammalian meiosis has yet to be fully explored, and one of the main reasons for this lack of exploration is that some meiosis-essential genes are still unknown. The profiling of gene expression during spermatogenesis has been performed in previous studies, yet few studies have aimed to find new functional genes. Since there is a huge gap between the number of genes that are able to be quantified and the number of genes that can be characterized by phenotype screening in one assay, an efficient method to rank quantified genes according to phenotypic relevance is of great importance. We proposed to rank genes by the probability of their function in mammalian meiosis based on global protein abundance using machine learning. Here, nine types of germ cells focusing on continual substages of meiosis prophase I were isolated, and the corresponding proteomes were quantified by high-resolution mass spectrometry. By combining meiotic labels annotated from the MGI mouse knockout database and the spermatogenesis proteomics dataset, a supervised machine learning package, FuncProFinder, was developed to rank meiosis-essential candidates. Of the candidates whose functions were unannotated, four of ten genes with the top prediction scores, Zcwpw1, Tesmin, 1700102P08Rik and Kctd19, were validated as meiosis-essential genes by knockout mouse models. Therefore,  mammalian meiosis-essential genes could be efficiently predicted based on the protein abundance dataset, which provides a paradigm for other functional gene mining from a related abundance dataset.

The histopathological subtype of lung adenocarcinoma (LUAD) is closely associated with prognosis. Micropapillary or solid predominant LUAD tends to relapse after surgery at an early stage, whereas lepidic pattern shows a favorable outcome. However, the molecular mechanism underlying this phenomenon remains unknown. Here, we recruited 31 lepidic predominant LUADs (LR: low-risk subtype group) and 28 micropapillary or solid predominant LUADs (HR: high-risk subtype group). Tissues of these cases were obtained and label-free quantitative proteomic and bioinformatic analyses were performed. Additionally, prognostic impact of targeted proteins was validated using The Cancer Genome Atlas databases (n=492) and tissue microarrays composed of early-stage LUADs (n=228). A total of 192 differentially expressed proteins were identified between tumor tissues of LR and HR and three clusters were identified via hierarchical clustering excluding eight proteins. Cluster 1 (65 proteins) showed a sequential decrease in expression from normal tissues to tumor tissues of LR and then to HR and was predominantly enriched in pathways such as tyrosine metabolism and ECM-receptor interaction, and increased matched mRNA expression of 18 proteins from this cluster predicted favorable prognosis. Cluster 2 (70 proteins) demonstrated a sequential increase in expression from normal tissues to tumor tissues of LR and then to HR and was mainly enriched in pathways such as extracellular organization, DNA replication and cell cycle, and high matched mRNA expression of 25 proteins indicated poor prognosis. Cluster 3 (49 proteins) showed high expression only in LR, with high matched mRNA expression of 20 proteins in this cluster indicating favorable prognosis. Furthermore, high expression of ERO1A and FEN1 at protein level predicted poor prognosis in early-stage LUAD, supporting the mRNA results. In conclusion, we discovered key differentially expressed proteins and pathways between low-risk and high-risk subtypes of early-stage LUAD. Some of these proteins could serve as potential biomarkers in prognostic evaluation.

Extracellular vesicle (EV) proteins from acute myeloid leukemia (AML) cell lines were analyzed using mass spectrometry. The analyses identified 2450 proteins, including 461 differentially expressed proteins (290 upregulated and 171 downregulated). CD53 and CD47 were upregulated and were selected as candidate biomarkers. The association between survival of patients with AML and the expression levels of CD53 and CD47 at diagnosis was analyzed using mRNA expression data from The Cancer Genome Atlas database. Patients with higher expression levels showed significantly inferior survival than those with lower expression levels. Enzyme-linked immunosorbent assay results of the expression levels of CD53 and CD47 from EVs in the bone marrow of patients with AML at diagnosis and at the time of complete remission with induction chemotherapy revealed that patients with downregulated CD53 and CD47 expression appeared to relapse less frequently. Network model analysis of EV proteins revealed several upregulated kinases, including LYN, CSNK2A1, SYK, CSK, and PTK2B. The potential cytotoxicity of several clinically applicable drugs that inhibit these kinases was tested in AML cell lines. The drugs lowered the viability of AML cells. The collective data suggest that AML-derived EVs could reflect essential leukemia biology.

Open searching has proven to be an effective strategy for identifying both known and unknown modifications in shotgun proteomics experiments. Rather than being limited to a small set of user-specified modifications, open searches identify peptides with any mass shift that may correspond to a single modification or a combination of several modifications. Here we present PTM-Shepherd, a bioinformatics tool that automates characterization of PTM profiles detected in open searches based on attributes such as amino acid localization, fragmentation spectra similarity, retention time shifts, and relative modification rates. PTM-Shepherd can also perform multi-experiment comparisons for studying changes in modification profiles, e.g. in data generated in different laboratories or under different conditions. We demonstrate how PTM-Shepherd improves the analysis of data from formalin-fixed paraffin-embedded samples, detects extreme underalkylation of cysteine in some datasets, discovers an artefactual modification introduced during peptide synthesis, and uncovers site-specific biases in sample preparation artifacts in a multi-center proteomics profiling study.

Gonadal soma-derived factor (gsdf) has been demonstrated to be essential for testicular differentiation in medaka (Oryzias latipes). To understand the protein dynamics of Gsdf in spermatogenesis regulation, we used a His-tag "pull-down" assay coupled with shotgun LC-MS/MS to identify a group of potential interacting partners for Gsdf, which included cytoplasmic dynein light chain 2, eukaryotic polypeptide elongation factor 1 alpha (eEF1α), and actin filaments in mature medaka testis. As for the interaction with TGFβ-dynein being critical for spermatogonial division in Drosophila melanogaster, the physical interactions of Gsdf-dynein and Gsdf-eEF1α were identified through a yeast 2-hybrid (Y2H) screening of an adult testis cDNA library using Gsdf as bait, which were verified by a paired Y2H assay. Co-immunoprecipitation of Gsdf and eEF1α was defined in adult testes as supporting the requirement of a Gsdf and eEF1α interaction in testis development. Proteomics analysis (data are available via ProteomeXchange with identifier PXD022153) and ultrastrutural observations showed that Gsdf deficiency activated eEF1α-mediated protein synthesis and ribosomal biogenesis, which in turn led to the differentiation of undifferentiated germ cells. Thus, our results provide a framework and new insight into the coordination of a Gsdf (TGFβ and eEF1α complex in the basic processes of germ cell proliferation, transcriptional and translational control of sexual RNA which may be fundamentally conserved across phyla during sexual differentiation.

Staphylococcus aureus is a major cause of infections worldwide and infection results in a variety of diseases. As of no surprise, protein phosphorylation is an important game player in signaling cascades and has been shown to be involved in S. aureus virulence. Albeit long neglected, eukaryotic-type serine/threonine kinases in S. aureus have been implicated in this complex signaling cascades. Due to the sub-stoichiometric nature of protein phosphorylation and a lack of suitable analysis tools, the knowledge of these cascades is however, to date, still limited.

Here, were apply an optimized protocol for efficient phosphopeptide enrichment via Fe3+-IMAC followed by LC-MS/MS to get a better understanding of the impact of protein phosphorylation on the complex signaling networks involved in pathogenicity. By profiling a serine/threonine kinase and phosphatase mutant from a methicillin-resistant S. aureus mutant library, we generated the most comprehensive phosphoproteome dataset of S. aureus to date, aiding a better understanding of signaling in bacteria. With the identification of 3800 class I p-sites we were able to increase the number of identifications by more than 21 times compared to recent literature. In addition, we were able to identify 74 downstream targets of the only reported eukaryotic-type Ser/Thr kinase of the S. aureus strain USA300, Stk1. This work allowed an extensive analysis of the bacterial phosphoproteome and indicates that Ser/Thr kinase signaling is far more abundant than previously anticipated in S. aureus.

The goal of clinical proteomics is to identify, quantify, and characterize proteins in body fluids or tissue to assist diagnosis, prognosis, and treatment of patients. In this way, it is similar to more mature omics technologies, such as genomics, that are increasingly applied in biomedicine. We argue that, similar to those fields, proteomics also faces ethical issues related to the kinds of information that is inherently obtained through sample measurement, although their acquisition was not the primary purpose. Specifically, we demonstrate the potential to identify individuals both by their characteristic, individual-specific protein levels and by variant peptides reporting on coding single nucleotide polymorphisms. Furthermore, it is in the nature of blood plasma proteomics profiling that it broadly reports on the health status of an individual – beyond the disease under investigation. Finally, we show that private and potentially sensitive information, such as ethnicity and pregnancy status, can increasingly be derived from proteomics data. Although this is potentially valuable not only to the individual, but also for biomedical research, it raises ethical questions similar to the incidental findings obtained through other omics technologies. We here introduce the necessity of - and argue for the desirability for - ethical and human rights-related issues to be discussed within the proteomics community. Those thoughts are more fully developed in our accompanying manuscript. Appreciation and discussion of ethical aspects of proteomic research will allow for deeper, better-informed, more diverse, and, most importantly, wiser guidelines for clinical proteomics.

To identify novel autoantibodies of Takayasu arteritis (TAK) using HuProt array-based approach. A two-phase approach was adopted. In Phase I, serum samples collected from 40 TAK patients, 15 autoimmune disease patients, and 20 healthy subjects were screened to identify TAK-specific autoantibodies using human protein (HuProt) arrays. In Phase II, the identified candidate autoantibodies were validated with TAK-focused arrays using an additional cohort comprised of 109 TAK patients, 110 autoimmune disease patients, and 96 healthy subjects. Subsequently, the TAK-specific autoantibodies validated in Phase II were further confirmed using Western blot analysis. We identified and validated eight autoantibodies as potential TAK-specific diagnostic biomarkers, including anti-SPATA7, -QDPR, -SLC25A2, -PRH2, -DIXDC1, -IL17RB, -ZFAND4, and -NOLC1 antibodies, with AUC of 0.803, 0.801, 0.780, 0.696, 0.695, 0.678, 0.635 and 0.613, respectively. SPATA7 could distinguish TAK from healthy and disease controls with 73.4% sensitivity at 85.4% specificity, while QDPR showed 71.6% sensitivity at 86.4% specificity. SLC25A22 showed the highest sensitivity of 80.7%, but at lower specificity of 67.0%. In addition, PRH2, IL17RB and NOLC1 showed good specificities of 88.3%, 85.9% and 86.9%, respectively, but at lower sensitivities (<50%). Finally, DIXDC1 and ZFAND4 showed moderate performance as compared with the other autoantibodies. Using a decision tree model, we could reach a specificity of 94.2% with AUC of 0.843, a significantly improved performance as compared to that by each individual biomarker. The performance of three autoantibodies, namely anti-SPATA7, -QDPR and -PRH2, were successfully confirmed with Western blot analysis. Using this two-phase strategy, we identified and validated eight novel autoantibodies as TAK–specific biomarker candidates, three of which could be readily adopted in a clinical setting.

Sporozoites are a motile form of malaria-causing Plasmodium falciparum parasites that migrate from the site of transmission in the dermis through the bloodstream to invade hepatocytes. Sporozoites interact with many cells within the host, but the molecular identity of these interactions and their role in the pathology of malaria is poorly understood. Parasite proteins that are secreted and embedded within membranes are known to be important for these interactions, but our understanding of how they interact with each other to form functional complexes is largely unknown. Here, we compile a library of recombinant proteins representing the repertoire of cell surface and secreted proteins from the P. falciparum sporozoite and use an assay designed to detect extracellular interactions to systematically identify complexes. We identify three protein complexes including an interaction between two components of the p24 complex that is involved in the trafficking of glycosylphosphatidylinositol (GPI)-anchored proteins through the secretory pathway. Plasmodium parasites lacking either gene are strongly inhibited in the establishment of liver stage infections. These findings reveal an important role for the p24 complex in malaria pathogenesis and show that the library of recombinant proteins represents a valuable resource to investigate P. falciparum sporozoite biology.

The F-box protein MORE AXILLARY GROWTH 2 (MAX2) is a central component in the signaling cascade of strigolactones (SLs) as well as of the smoke derived karrikins (KARs) and the so far unknown endogenous KAI2 ligand (KL). The two groups of molecules are involved in overlapping and unique developmental processes, and signal-specific outcomes are attributed to perception by the paralogous α/β-hydrolases DWARF14 (D14) for SL and KARRIKIN INSENSITIVE 2/ HYPOSENSITIVE TO LIGHT (KAI2/HTL) for KAR/KL. Additionally, depending on which receptor is activated, specific members of the SUPPRESSOR OF MAX2 1 (SMAX1) – LIKE (SMXL) family control KAR/KL and SL responses. As proteins that function in the same signal transduction pathway often occur in large protein complexes, we aimed at discovering new players of the MAX2, D14 and KAI2 protein network by tandem affinity purification using Arabidopsis cell cultures. When using MAX2 as a bait, various proteins were co-purified among which general components of the Skp1-Cullin-F-box complex and members of the CONSTITUTIVE PHOTOMORPHOGENIC 9 signalosome. Here, we report the identification of a novel interactor of MAX2, a type 5 serine/threonine protein phosphatase, designated PHYTOCHROME-ASSOCIATED PROTEIN PHOSPHATASE 5 (PAPP5). Quantitative affinity purification pointed at PAPP5 as being more present in KAI2 rather than D14 protein complexes. In agreement, mutant analysis suggests that PAPP5 modulates KAR/KL-dependent seed germination in suboptimal conditions and seedling development. Additionally, a phosphopeptide enrichment experiment revealed that PAPP5 might dephosphorylate MAX2 in vivo independently of the synthetic strigolactone analog, rac-GR24. Together, by analyzing the protein complexes to which MAX2, D14 and KAI2 belong, we revealed a new MAX2 interactor, PAPP5, that might act through dephosphorylation of MAX2 to control mainly KAR/KL- related phenotypes and, hence, provide another link with the light pathway.

Alpha-1-acid glycoprotein (AGP) is an acute phase glycoprotein in blood, which is primarily synthetized in the liver and whose biological role is not completely understood. It consists of 45% carbohydrates that are present in the form of five N-linked complex glycans. AGP N-glycosylation was shown to be changed in many different diseases and some changes appear to be disease-specific, thus it has a great diagnostic and prognostic potential. However, AGP glycosylation was mainly analyzed in small cohorts and without detailed site-specific glycan information. Here, we developed a cost-effective method for a high-throughput and site-specific N-glycosylation LC-MS analysis of AGP which can be applied on large cohorts, aid in search for novel disease biomarkers and enable better understanding of AGP’s role and function in health and disease. The method does not require isolation of AGP with antibodies and affinity chromatography, but AGP is enriched by acid precipitation from 5 μl of bloodplasma in a 96 well format. After trypsinization, AGP glycopeptides are purified using a hydrophilic interaction chromatography based solid-phase extraction and analyzed by RP-LC-ESI-MS. We used our method to show for the first time that AGP N-glycan profile is stable in healthy individuals (14 individuals in 3 time points), which is a requirement for evaluation of its diagnostic potential. Furthermore, we tested our method on a population including individuals with registered hyperglycemia in critical illness (59 cases and 49 controls), which represents a significantly increased risk of developing type 2 diabetes. Individuals at higher risk of diabetes presented increased N-glycan branching on AGP’s second glycosylation site and lower sialylation of N-glycans on AGP’s third and AGP1’s fourth glycosylation site. Although this should be confirmed on a larger prospective cohort, it indicates that site-specific AGP N-glycan profile could help distinguish individuals who are at risk of type 2 diabetes.

Giardia lamblia (G. lamblia) disease is a zoonosis with a-infection rate affecting the general population of the world. Despite the constant possibility of damage due to their own metabolism, G. lamblia have survived and evolved to adapt to various environments. However, research on energy-metabolism conversion in G. lamblia is limited. This study aimed to reveal the dynamic metabolism-conversion mechanism in G. lamblia under sugar starvation by detecting global lysine acetylation and 2-hydroxyisobutyrylation sites combined with quantitative proteome analyses. A total of 2999 acetylation sites on 956 proteins and 8877 2-hydroxyisobutyryl sites on 1546 proteins were quantified under sugar starvation. Integrated Kac and Khib data revealed that modified proteins were associated with arginine biosynthesis, glycolysis/gluconeogenesis, and alanine, aspartate, and glutamate metabolism. These findings suggested that lysine acetylation and 2-hydroxyisobutyrylation were ubiquitous and provided deep insight into the metabolism-conversion mechanism in G. lamblia under sugar starvation. Overall, these results can help understand the biology of G. lamblia infections and reveal the evolution rule from prokaryote to eukaryote.

Recent advances in MS-based proteomics have vastly increased the quality and scope of biological information that can be derived from human samples. These advances have rendered current workflows increasingly applicable in biomedical and clinical contexts. As proteomics is poised to take an important role in the clinic, associated ethical responsibilities increase in tandem with the impact on the health, privacy, and well-being of individuals. Here we conducted and report a systematic literature review of ethical issues in clinical proteomics. We add our perspectives from a background of bioethics, the results of our accompanying paper extracting individual-sensitive results from patient samples, and the literature addressing similar issues in genomics. The spectrum of potential issues ranges from patient re-identification to incidental findings of clinical significance. The latter can be divided into actionable and unactionable findings. Some of these have the potential to be employed in discriminatory or privacy-infringing ways. However, incidental findings may also have great positive potential. A plasma proteome profile, for instance, could inform on the general health or disease status of an individual regardless of the narrow diagnostic question that prompted it. We suggest that early discussion of ethical issues in clinical proteomics is important to ensure that eventual regulations reflect the considered judgment of the community as well as to anticipate opportunities and problems that may arise as the technology matures further.