Tandem mass tag (TMT) is a multiplexing technology widely-used in proteomic research. It enables relative quantification of proteins from multiple biological samples in a single MS run with high efficiency and high throughput. However, experiments often require more biological replicates or conditions than can be accommodated by a single run, and involve multiple TMT mixtures and multiple runs. Such larger-scale experiments combine sources of biological and technical variation in patterns that are complex, unique to TMT-based workflows, and challenging for the downstream statistical analysis. These patterns cannot be adequately characterized by statistical methods designed for other technologies, such as label-free proteomics or transcriptomics. This manuscript proposes a general statistical approach for relative protein quantification in MS- based experiments with TMT labeling. It is applicable to experiments with multiple conditions, multiple biological replicate runs and multiple technical replicate runs, and unbalanced designs. It is based on a flexible family of linear mixed-effects models that handle complex patterns of technical artifacts and missing values. The approach is implemented in MSstatsTMT, a freely available open-source R/Bioconductor package compatible with data processing tools such as Proteome Discoverer, MaxQuant, OpenMS, and SpectroMine. Evaluation on a controlled mixture, simulated datasets, and three biological investigations with diverse designs demonstrated that MSstatsTMT balanced the sensitivity and the specificity of detecting differentially abundant proteins, in large-scale experiments with multiple biological mixtures.

Natural compounds that can stimulate salivary secretion are of interest in developing treatments for xerostomia, the perception of a dry mouth, that affects between 10 and 30% of the adult and elderly population. Chemesthetic transient receptor potential (TRP) channels are expressed in the surface of the oral mucosa. The TRPV1 agonists capsaicin and piperine have been shown to increase salivary flow when introduced into the oral cavity but the sialogogic properties of other TRP channel agonists have not been investigated. In this study we have determined the influence of different TRP channel agonists on the flow and protein composition of saliva. Mouth rinsing with the TRPV1 agonist nonivamide or menthol, a TRPM8 agonist, increased whole mouth saliva (WMS) flow and total protein secretion compared with unstimulated saliva, the vehicle control mouth rinse or cinnamaldehyde, a TRPA1 agonist. Nonivamide also increased the flow of labial minor gland saliva but parotid saliva flow rate was not increased. The influence of TRP channel agonists on the composition and function of the salivary proteome was investigated using a multi-batch quantitative MS method novel to salivary proteomics. Inter-personal and inter-mouth rinse variation was observed in the secreted proteomes and, using a novel bioinformatics method, inter-day variation was identified with some of the mouth rinses. Significant changes in specific salivary proteins were identified after all mouth rinses. In the case of nonivamide, these changes were attributed to functional shifts in the WMS secreted, primarily the over representation of salivary and nonsalivary cystatins which was confirmed by immunoassay. This study provides new evidence of the impact of TRP channel agonists on the salivary proteome and the stimulation of salivary secretion by a TRPM8 channel agonist, which suggests that TRP channel agonists are potential candidates for developing treatments for sufferers of xerostomia.

The neuronal basis of complex social behavior is still poorly understood. In honeybees, reproductive investment decisions are made at the colony-level. Queens develop from female-destined larvae that receive alloparental care from nurse bees in the form of ad-libitum royal jelly (RJ) secretions. Typically, the number of raised new queens is limited but genetic breeding of "royal jelly bees" (RJBs) for enhanced RJ production over decades has led to a dramatic increase of reproductive investment in queens. Here, we compare RJBs to unselected Italian bees (ITBs) to investigate how their cognitive processing of larval signals in the mushroom bodies (MBs) and antennal lobes (ALs) may contribute to their behavioral differences. A cross-fostering experiment confirms that the RJB syndrome is mainly due to a shift in nurse bee alloparental care behavior. Using olfactory conditioning of the proboscis extension reflex, we show that the RJB nurses spontaneously respond more often to larval odors compared with ITB nurses but their subsequent learning occurs at similar rates. These phenotypic findings are corroborated by our demonstration that the proteome of the brain, particularly of the ALs differs between RJBs and ITBs. Notably, in the ALs of RJB newly emerged bees and nurses compared with ITBs, processes of energy and nutrient metabolism, signal transduction are up-regulated, priming the ALs for receiving and processing the brood signals from the antennae. Moreover, highly abundant major royal jelly proteins and hexamerins in RJBs compared with ITBs during early life when the nervous system still develops suggest crucial new neurobiological roles for these well-characterized proteins. Altogether, our findings reveal that RJBs have evolved a strong olfactory response to larvae, enabled by numerous neurophysiological adaptations that increase the nurse bees' alloparental care behavior.

A key point in achieving accurate intact glycopeptide identification is the definition of the glycan composition file that is used to match experimental with theoretical masses by a glycoproteomics search engine. At present, these files are mainly built from searching the literature and/or querying data sources focused on posttranslational modifications. Most glycoproteomics search engines include a default composition file that is readily used when processing MS data. We introduce here a glycan composition visualizing and comparative tool associated with the GlyConnect database and called GlyConnect Compozitor. It offers a web interface through which the database can be queried to bring out contextual information relative to a set of glycan compositions. The tool takes advantage of compositions being related to one another through shared monosaccharide counts and outputs interactive graphs summarizing information searched in the database. These results provide a guide for selecting or deselecting compositions in a file in order to reflect the context of a study as closely as possible. They also confirm the consistency of a set of compositions based on the content of the GlyConnect database. As part of the tool collection of the Glycomics@ExPASy initiative, Compozitor is hosted at https://glyconnect.expasy.org/compozitor/ where it can be run as a web application. It is also directly accessible from the GlyConnect database.

The Discoidin, CUB, and LCCL domain-containing protein (DCBLD) family consists of two type-I transmembrane scaffolding receptors, DCBLD1 and DCBLD2, which play important roles in development and cancer. The nonreceptor tyrosine kinases FYN and ABL are known to drive phosphorylation of tyrosine residues in YXXP motifs within the intracellular domains of DCBLD family members, which leads to the recruitment of the Src homology 2 (SH2) domain of the adaptors CT10 regulator of kinase (CRK) and CRK-like (CRKL). We previously characterized the FYN- and ABL-driven phosphorylation of DCBLD family YXXP motifs. However, we have identified additional FYN- and ABL-dependent phosphorylation sites on DCBLD1 and DCBLD2. This suggests that beyond CRK and CRKL, additional DCBLD interactors may be regulated by FYN and ABL activity. Here, we report a quantitative proteomics approach in which we map the FYN- and ABL-regulated interactomes of DCBLD family members. We found FYN and ABL regulated the binding of several signaling molecules to DCBLD1 and DCBLD2, including members of the 14-3-3 family of adaptors. Biochemical investigation of the DCBLD2/14-3-3 interaction revealed ABL-induced binding of 14-3-3 family members directly to DCBLD2.

Glutathionylation is an important posttranslational modification that protects proteins from further oxidative damage as well as influencing protein structure and activity. In the present study, we demonstrate that the cysteine-42 residue in protein arginine N-methyltransferase 5 (PRMT5) is glutathionylated in aged mice or in cells that have been exposed to oxidative stress. Deglutathionylation of this protein is catalyzed by glutaredoxin-1 (Grx1). Using mutagenesis and subsequent biochemical analyses, we show that glutathionylation decreased the binding affinity of PRMT5 with methylosome protein-50 (MEP50) and reduced the methyltransferase activity of PRMT5. Furthermore, overexpression of PRMT5-C42A mutant caused a significant increase in histone methylation in HEK293T and A549 cells and promoted cell growth, whereas overexpression of the PRMT5-C42D mutant, a mimic of glutathionylated PRMT5, inhibited cell proliferation. Taken together, our results demonstrate a new mechanism of regulation of PRMT5 methyltransferases activity and suggest that PRMT5 glutathionylation is partly responsible for reactive oxygen species-mediated cell growth inhibition.

Co-fractionation MS (CF-MS) is a technique with potential to characterize endogenous and unmanipulated protein complexes on an unprecedented scale. However this potential has been offset by a lack of guidelines for best-practice CF-MS data collection and analysis. To obtain such guidelines, this study thoroughly evaluates novel and published Saccharomyces cerevisiae CF-MS data sets using very high proteome coverage libraries of yeast gold standard complexes. A new method for identifying gold standard complexes in CF-MS data, Reference Complex Profiling, and the Extending 'Guilt-by-Association' by Degree (EGAD) R package are used for these evaluations, which are verified with concurrent analyses of published human data. By evaluating data collection designs, which involve fractionation of cell lysates, it is found that near-maximum recall of complexes can be achieved with fewer samples than published studies. Distributing sample collection across orthogonal fractionation methods, rather than a single high resolution data set, leads to particularly efficient recall. By evaluating 17 different similarity scoring metrics, which are central to CF-MS data analysis, it is found that two metrics rarely used in past CF-MS studies – Spearman and Kendall correlations – and the recently introduced Co-apex metric frequently maximize recall, whereas a popular metric—Euclidean distance—delivers poor recall. The common practice of integrating external genomic data into CF-MS data analysis is also evaluated, revealing that this practice may improve the precision and recall of known complexes but is generally unsuitable for predicting novel complexes in model organisms. If studying nonmodel organisms using orthologous genomic data, it is found that particular subsets of fractionation profiles (e.g. the lowest abundance quartile) should be excluded to minimize false discovery. These assessments are summarized in a series of universally applicable guidelines for precise, sensitive and efficient CF-MS studies of known complexes, and effective predictions of novel complexes for orthogonal experimental validation.

Renal Cell Carcinoma (RCC) is one of the most commonly diagnosed cancers worldwide with research efforts dramatically improving understanding of the biology of the disease. To investigate the role of the immune system in treatment-naïve clear cell Renal Cell Carcinoma (ccRCC), we interrogated the immune infiltrate in patient-matched ccRCC tumor samples, benign normal adjacent tissue (NAT) and peripheral blood mononuclear cells (PBMCs isolated from whole blood, focusing our attention on the myeloid cell infiltrate. Using flow cytometric, MS, and ExCYT analysis, we discovered unique myeloid populations in PBMCs across patient samples. Furthermore, normal adjacent tissues and ccRCC tissues contained numerous myeloid populations with a unique signature for both tissues. Enrichment of the immune cell (CD45⁺) fraction and subsequent gene expression analysis revealed a number of myeloid-related genes that were differentially expressed. These data provide evidence, for the first time, of an immunosuppressive and pro-tumorigenic role of myeloid cells in early, clinically localized ccRCC. The identification of a number of immune proteins for therapeutic targeting provides a rationale for investigation into the potential efficacy of earlier intervention with single-agent or combination immunotherapy for ccRCC.

We performed an in-depth characterization and comparison of the pediatric and adult urinary glycomes using a nanoLC-MS/MS based glycomics method, which included normal healthy pediatric (1–10 years, n = 21) and adult (21–50 years, n = 22) individuals. A total of 116 N-glycan compositions were identified, and 46 of them could be reproducibly quantified. We performed quantitative comparisons of the 46 glycan compositions between different age and sex groups. The results showed significant quantitative changes between the pediatric and adult cohorts. The pediatric urinary N-glycome was found to contain a higher level of high-mannose (HM), asialylated/afucosylated glycans (excluding HM), neutral fucosylated and agalactosylated glycans, and a lower level of trisialylated glycans compared with the adult. We further analyzed gender-associated glycan changes in the pediatric and adult group, respectively. In the pediatric group, there was almost no difference of glycan levels between males and females. In adult, the majority of glycans were more abundant in males than females, except the high-mannose and tetrasialylated glycans. These findings highlight the importance to consider age-matching and adult sex-matching for urinary glycan studies. The identified normal pediatric and adult urinary glycomes can serve as a baseline reference for comparisons to other disease states affected by glycosylation.

Coronavirus disease 2019 (COVID-19) is a highly contagious infection and threating the human lives in the world. The elevation of cytokines in blood is crucial to induce cytokine storm and immunosuppression in the transition of severity in COVID-19 patients. However, the comprehensive changes of serum proteins in COVID-19 patients throughout the SARS-CoV-2 infection is unknown. In this work, we developed a high-density antibody microarray and performed an in-depth proteomics analysis of serum samples collected from early COVID-19 (n = 15) and influenza (n = 13) patients. We identified a large set of differentially expressed proteins (n = 132) that participate in a landscape of inflammation and immune signaling related to the SARS-CoV-2 infection. Furthermore, the significant correlations of neutrophil and lymphocyte with the CCL2 and CXCL10 mediated cytokine signaling pathways was identified. These information are valuable for the understanding of COVID-19 pathogenesis, identification of biomarkers and development of the optimal anti-inflammation therapy.

MS-based proteome profiling has become increasingly comprehensive and quantitative, yet a persistent shortcoming has been the relatively large samples required to achieve an in-depth measurement. Such bulk samples, typically comprising thousands of cells or more, provide a population average and obscure important cellular heterogeneity. Single-cell proteomics capabilities have the potential to transform biomedical research and enable understanding of biological systems with a new level of granularity. Recent advances in sample processing, separations and MS instrumentation now make it possible to quantify >1000 proteins from individual mammalian cells, a level of coverage that required an input of thousands of cells just a few years ago. This review discusses important factors and parameters that should be optimized across the workflow for single-cell and other low-input measurements. It also highlights recent developments that have advanced the field and opportunities for further development.

Cross-linking MS (XL-MS) has been recognized as an effective source of information about protein structures and interactions. In contrast to regular peptide identification, XL-MS has to deal with a quadratic search space, where peptides from every protein could potentially be cross-linked to any other protein. To cope with this search space, most tools apply different heuristics for search space reduction. We introduce a new open-source XL-MS database search algorithm, OpenPepXL, which offers increased sensitivity compared with other tools. OpenPepXL searches the full search space of an XL-MS experiment without using heuristics to reduce it. Because of efficient data structures and built-in parallelization OpenPepXL achieves excellent runtimes and can also be deployed on large compute clusters and cloud services while maintaining a slim memory footprint. We compared OpenPepXL to several other commonly used tools for identification of noncleavable labeled and label-free cross-linkers on a diverse set of XL-MS experiments. In our first comparison, we used a data set from a fraction of a cell lysate with a protein database of 128 targets and 128 decoys. At 5% FDR, OpenPepXL finds from 7% to over 50% more unique residue pairs (URPs) than other tools. On data sets with available high-resolution structures for cross-link validation OpenPepXL reports from 7% to over 40% more structurally validated URPs than other tools. Additionally, we used a synthetic peptide data set that allows objective validation of cross-links without relying on structural information and found that OpenPepXL reports at least 12% more validated URPs than other tools. It has been built as part of the OpenMS suite of tools and supports Windows, macOS, and Linux operating systems. OpenPepXL also supports the MzIdentML 1.2 format for XL-MS identification results. It is freely available under a three-clause BSD license at https://openms.org/openpepxl.

Trypsin is the protease of choice in bottom-up proteomics. However, its application can be limited by the amino acid composition of target proteins and the pH of the digestion solution. In this study we characterize ProAlanase, a protease from the fungus Aspergillus niger that cleaves primarily on the C-terminal side of proline and alanine residues. ProAlanase achieves high proteolytic activity and specificity when digestion is carried out at acidic pH (1.5) for relatively short (2 h) time periods. To elucidate the potential of ProAlanase in proteomics applications, we conducted a series of investigations comprising comparative multi-enzymatic profiling of a human cell line proteome, histone PTM analysis, ancient bone protein identification, phosphosite mapping and de novo sequencing of a proline-rich protein and disulfide bond mapping in mAb. The results demonstrate that ProAlanase is highly suitable for proteomics analysis of the arginine- and lysine-rich histones, enabling high sequence coverage of multiple histone family members. It also facilitates an efficient digestion of bone collagen thanks to the cleavage at the C terminus of hydroxyproline which is highly prevalent in collagen. This allows to identify complementary proteins in ProAlanase- and trypsin-digested ancient bone samples, as well as to increase sequence coverage of noncollagenous proteins. Moreover, digestion with ProAlanase improves protein sequence coverage and phosphosite localization for the proline-rich protein Notch3 intracellular domain (N3ICD). Furthermore, we achieve a nearly complete coverage of N3ICD protein by de novo sequencing using the combination of ProAlanase and tryptic peptides. Finally, we demonstrate that ProAlanase is efficient in disulfide bond mapping, showing high coverage of disulfide-containing regions in a nonreduced mAb.

Over the past decade, modern methods of MS (MS) have emerged that allow reliable, fast and cost-effective identification of pathogenic microorganisms. Although MALDI-TOF MS has already revolutionized the way microorganisms are identified, recent years have witnessed also substantial progress in the development of liquid chromatography (LC)-MS based proteomics for microbiological applications. For example, LC-tandem MS (LC-MS²) has been proposed for microbial characterization by means of multiple discriminative peptides that enable identification at the species, or sometimes at the strain level. However, such investigations can be laborious and time-consuming, especially if the experimental LC-MS² data are tested against sequence databases covering a broad panel of different microbiological taxa. In this proof of concept study, we present an alternative bottom-up proteomics method for microbial identification. The proposed approach involves efficient extraction of proteins from cultivated microbial cells, digestion by trypsin and LC–MS measurements. Peptide masses are then extracted from MS¹ data and systematically tested against an in silico library of all possible peptide mass data compiled in-house. The library has been computed from the UniProt Knowledgebase covering Swiss-Prot and TrEMBL databases and comprises more than 12,000 strain-specific in silico profiles, each containing tens of thousands of peptide mass entries. Identification analysis involves computation of score values derived from correlation coefficients between experimental and strain-specific in silico peptide mass profiles and compilation of score ranking lists. The taxonomic positions of the microbial samples are then determined by using the best-matching database entries. The suggested method is computationally efficient – less than 2 mins per sample - and has been successfully tested by a test set of 39 LC-MS¹ peak lists obtained from 19 different microbial pathogens. The proposed method is rapid, simple and automatable and we foresee wide application potential for future microbiological applications.

Pathway analyses are key methods to analyze 'omics experiments. Nevertheless, integrating data from different 'omics technologies and different species still requires considerable bioinformatics knowledge.

Here we present the novel ReactomeGSA resource for comparative pathway analyses of multi-omics datasets. ReactomeGSA can be used through Reactome's existing web interface and the novel ReactomeGSA R Bioconductor package with explicit support for scRNA-seq data. Data from different species is automatically mapped to a common pathway space. Public data from ExpressionAtlas and Single Cell ExpressionAtlas can be directly integrated in the analysis. ReactomeGSA greatly reduces the technical barrier for multi-omics, cross-species, comparative pathway analyses.

We used ReactomeGSA to characterize the role of B cells in anti-tumor immunity. We compared B cell rich and poor human cancer samples from five of the Cancer Genome Atlas (TCGA) transcriptomics and two of the Clinical Proteomic Tumor Analysis Consortium (CPTAC) proteomics studies. B cell-rich lung adenocarcinoma samples lacked the otherwise present activation through NFkappaB. This may be linked to the presence of a specific subset of tumor associated IgG+ plasma cells that lack NFkappaB activation in scRNA-seq data from human melanoma. This showcases how ReactomeGSA can derive novel biomedical insights by integrating large multi-omics datasets.

Extracellular vesicles (EVs) secreted by the epididymal epithelium transfer to spermatozoa key proteins that are essential in promoting motility and subsequent fertilization success. Using the domestic cat model, the objectives were to (1) characterize and compare protein content of EVs between segments of the epididymis, and (2) compare EV protein compositions between normo- and teratospermic individuals (producing >60% of abnormal spermatozoa). Epididymal EVs from adult cats were isolated and assessed via liquid chromatography tandem MS. Both male types shared 3008 proteins in total, with 98 and 20 EV proteins unique to normospermic and teratospermic males, respectively. Expression levels of several proteins changed between epididymal segments in both male types. Several proteins in both groups were related to sperm motility (e.g. hexokinase 1, adenylate kinase isoenzyme) and zona pellucida or oolemma binding (e.g. disintegrin and metalloproteinase domain proteins, zona binding proteins 1 and 2). Interestingly, seven cauda-derived EV proteins trended downward in teratospermic compared with normospermic males, which may relate to poor sperm quality. Collective results revealed, for the first time, EV proteins related to sequential sperm maturation with differences observed between normospermic and teratospermic individuals.

Endometrial carcinoma (EC) is the most common gynecologic malignancy in the United States, with limited effective targeted therapies. Endometrial tumors exhibit frequent alterations in protein kinases, yet only a small fraction of the kinome has been therapeutically explored. To identify kinase therapeutic avenues for EC, we profiled the kinome of endometrial tumors and normal endometrial tissues using Multiplexed Inhibitor Beads and Mass Spectrometry (MIB-MS). Our proteomics analysis identified a network of kinases overexpressed in tumors, including Serine/Arginine-Rich Splicing Factor Kinase 1 (SRPK1). Immunohistochemical (IHC) analysis of endometrial tumors confirmed MIB-MS findings and showed SRPK1 protein levels were highly expressed in endometrioid and uterine serous cancer (USC) histological subtypes. Moreover, querying large-scale genomics studies of EC tumors revealed high expression of SRPK1 correlated with poor survival. Loss-of-function studies targeting SRPK1 in an established USC cell line demonstrated SRPK1 was integral for RNA splicing, as well as cell cycle progression and survival under nutrient deficient conditions. Profiling of USC cells identified a compensatory response to SRPK1 inhibition that involved EGFR and the up-regulation of IGF1R and downstream AKT signaling. Co-targeting SRPK1 and EGFR or IGF1R synergistically enhanced growth inhibition in serous and endometrioid cell lines, representing a promising combination therapy for EC.

Sepsis-induced acute kidney injury (S-AKI) is the most common complication in hospitalized and critically ill patients, highlighted by a rapid decline of kidney function occurring a few hours or days after sepsis onset. Systemic inflammation elicited by microbial infections is believed to lead to kidney damage under immunocompromised conditions. However, although AKI has been recognized as a disease with long-term sequelae, partly because of the associated higher risk of chronic kidney disease (CKD), the understanding of kidney pathophysiology at the molecular level and the global view of dynamic regulations in situ after S-AKI, including the transition to CKD, remains limited. Existing studies of S-AKI mainly focus on deriving sepsis biomarkers from body fluids. In the present study, we constructed a mid-severity septic murine model using cecal ligation and puncture (CLP), and examined the temporal changes to the kidney proteome and phosphoproteome at day 2 and day 7 after CLP surgery, corresponding to S-AKI and the transition to CKD, respectively, by employing an ultrafast and economical filter-based sample processing method combined with the label-free quantitation approach. Collectively, we identified 2,119 proteins and 2950 phosphosites through multi-proteomics analyses. Among them, we identified an array of highly promising candidate marker proteins indicative of disease onset and progression accompanied by immunoblot validations, and further denoted the pathways that are specifically responsive to S-AKI and its transition to CKD, which include regulation of cell metabolism regulation, oxidative stress, and energy consumption in the diseased kidneys. Our data can serve as an enriched resource for the identification of mechanisms and biomarkers for sepsis-induced kidney diseases.

At neuronal synapses, activation of group I metabotropic glutamate receptors (mGluR1/5) triggers a form of long-term depression (mGluR-LTD) that relies on new protein synthesis and the internalization of AMPA-type glutamate receptors. Dysregulation of these processes has been implicated in the development of mental disorders such as autism spectrum disorders and therefore merit a better understanding on a molecular level. Here, to study mGluR-induced signaling pathways, we integrated quantitative phosphoproteomics with the analyses of newly synthesized proteins via bio-orthogonal amino acids (azidohomoalanine) in a pulsed labeling strategy in cultured hippocampal neurons stimulated with DHPG, a specific agonist for group I mGluRs. We identified several kinases with important roles in DHPG-induced mGluR activation, which we confirmed using small molecule kinase inhibitors. Furthermore, changes in the AMPA receptor endocytosis pathway in both protein synthesis and protein phosphorylation were identified, whereby Intersectin-1 was validated as a novel player in this pathway. This study revealed several new insights into the molecular pathways downstream of group I mGluR activation in hippocampal neurons, and provides a rich resource for further analyses.

Huanglongbing (HLB) is the most devastating and widespread citrus disease. All commercial citrus varieties are susceptible to the HLB-associated bacterium, Candidatus Liberibacter asiaticus (CLas), which resides in the phloem. The phloem is part of the plant vascular system and is involved in sugar transport. To investigate the plant response to CLas, we enriched for proteins surrounding the phloem in an HLB susceptible sweet orange variety, Washington navel (Citrus sinensis (L) Osbeck). Quantitative proteomics revealed global changes in the citrus proteome after CLas inoculation. Plant metabolism and translation were suppressed, whereas defense-related proteins such as peroxidases, proteases and protease inhibitors were induced in the vasculature. Transcript accumulation and enzymatic activity of plant peroxidases in CLas infected sweet orange varieties under greenhouse and field conditions were assessed. Although peroxidase transcript accumulation was induced in CLas infected sweet orange varieties, peroxidase enzymatic activity varied. Specific serine proteases were up-regulated in Washington navel in the presence of CLas based on quantitative proteomics. Subsequent activity-based protein profiling revealed increased activity of two serine proteases, and reduced activity of one protease in two C. sinensis sweet orange varieties under greenhouse and field conditions. The observations in the current study highlight global reprogramming of the citrus vascular proteome and differential regulation of enzyme classes in response to CLas infection. These results open an avenue for further investigation of diverse responses to HLB across different environmental conditions and citrus genotypes.

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.

L’utilisation du féminin dans les titres de professions dans le domaine de la santé a beaucoup évolué ces dernières années. Cela reflète une prise de conscience croissante de certaines réalités des métiers de la santé et la reconnaissance de la présence de plus en plus importante des femmes dans le domaine. Vous demandez-vous quand mettre les professions au féminin […]

L’article Quand mettre les professions au féminin dans le domaine de la santé ? est apparu en premier sur Ortho Doc France.

Middle East and North Africa

Research on the Middle East and North Africa region focuses on changes to politics and society, economics, and security issues.

This is a turbulent period for the region following the Arab Spring, with conflict in Syria continuing to impact its neighbours, governance in Libya under scrutiny, and increasing pressures on the Gulf monarchies, especially around human rights.   

Key research areas include the Gulf States and the Gulf Cooperation Council (GCC), the future of the state, mapping the region’s war economies, the Yemen conflict, Iraq’s reconstruction, and the influence of Saudi Arabia and Iran.

Withdrawn by Author.

No abstract

After successful completion of the Human Genome Project (HGP), HUPO has recently officially launched a global Human Proteome Project (HPP) which is designed to map the entire human protein set. Given the presence of about 30% undisclosed proteins out of 20,300 protein gene products, a systematic global effort is necessary to achieve this goal with respect to protein abundance, distribution, subcellular localization, interaction with other biomolecules, and functions at specific time points. As a general experimental strategy, HPP groups employ the three working pillars for HPP: mass spectrometry, antibody capture, and bioinformatics tools and knowledge base. The HPP participants will take advantage of the output and cross-analyses from the ongoing HUPO initiatives and a chromosome-based protein mapping strategy, termed C-HPP with many national teams currently engaged. In addition, numerous biologically-driven projects will be stimulated and facilitated by the HPP. Timely planning with proper governance of HPP will deliver a protein parts list, reagents and tools for protein studies and analyses, and a stronger basis for personalized medicine. HUPO urges each national research funding agency and the scientific community at large to identify their preferred pathways to participate in aspects of this highly promising project in a HPP consortium of funders and investigators.

The development of the HUPO-PSI's (Proteomics Standards Initiative) standard data formats and MIAPE (Minimum Information About a Proteomics Experiment) guidelines should improve proteomics data sharing within the scientific community. Proteomics journals have encouraged the use of these standards and guidelines to improve the quality of experimental reporting and ease the evaluation and publication of manuscripts. However, there is an evident lack of bioinformatics tools specifically designed to create and edit standard file formats and reports, or embed them within proteomics workflows. In this article, we describe a new web-based software suite (The ProteoRed MIAPE web toolkit) that performs several complementary roles related to proteomic data standards. Firstly, it can verify the reports fulfill the minimum information requirements of the corresponding MIAPE modules, highlighting inconsistencies or missing information. Secondly, the toolkit can convert several XML-based data standards directly into human readable MIAPE reports stored within the ProteoRed MIAPE repository. Finally, it can also perform the reverse operation, allowing users to export from MIAPE reports into XML files for computational processing, data sharing or public database submission. The toolkit is thus the first application capable of automatically linking the PSI's MIAPE modules with the corresponding XML data exchange standards, enabling bidirectional conversions. This toolkit is freely available at http://www.proteored.org/MIAPE/.

none

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.

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.

Glycopeptides in peptide or digested protein samples pose a number of analytical and bioinformatics challenges beyond those posed by unmodified peptides or peptides with smaller posttranslational modifications. Exact structural elucidation of glycans is generally beyond the capability of a single mass spectrometry experiment, so a reasonable level of identification for tandem mass spectrometry, taken by several glycopeptide software tools, is that of peptide sequence and glycan composition, meaning the number of monosaccharides of each distinct mass, for example HexNAc(2)Hex(5) rather than man5. Even at this level, however, glycopeptide analysis poses challenges:  finding glycopeptide spectra when they are a tiny fraction of the total spectra; assigning spectra with unanticipated glycans, not in the initial glycan database; and finding, scoring, and labeling diagnostic peaks in tandem mass spectra.  Here we discuss recent improvements to Byonic, a glycoproteomics search program, that address these three issues. Byonic now supports filtering spectra by m/z peaks, so that the user can limit attention to spectra with diagnostic peaks, for example, at least two out of three of 204.087 for HexNAc, 274.092 for NeuAc (with water loss), and 366.139 for HexNAc-Hex, all within a set mass tolerance, for example, ± 0.01 Daltons. Also new is glycan "wildcard" search, which allows an unspecified mass within a user-set mass range to be applied to N- or O-linked glycans and enables assignment of spectra with unanticipated glycans. Finally the next release of Byonic supports user-specified peak annotations from user-defined posttranslational modifications. We demonstrate the utility of these new software features by finding previously unrecognized glycopeptides in publicly available data, including glycosylated neuropeptides from rat brain.

O-GlcNAcylation, the addition of a single N-acetylglucosamine residue to serine and threonine residues of cytoplasmic, nuclear, or mitochondrial proteins, is a widespread regulatory post-translational modification. It is involved in response to nutritional status and stress and its dysregulation is associated with diseases ranging from Alzheimer’s to diabetes.  While the modification was first detected over thirty-five years ago, research into the function of O-GlcNAcylation has accelerated dramatically in the last ten years due to the development of new enrichment and mass spectrometry techniques that facilitate its analysis.  This article summarizes methods for O-GlcNAc enrichment, key mass spectrometry instrumentation advancements, particularly those that allow modification site localization, and software tools that allow analysis of data from O-GlcNAc modified peptides.

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.

Glycosylation is a highly diverse set of co- and post-translational modification of proteins. For mammalian glycoproteins, glycosylation is often site-, tissue- and species-specific, and diversified by microheterogeneity. Multitudinous biochemical, cellular, physiological and organismic effects of their glycans have been revealed, either intrinsic to the carrier proteins or mediated by endogenous reader proteins with carbohydrate recognition domains. Furthermore, glycans frequently form the first line of access by or defense from foreign invaders, and new roles for nucleocytoplasmic glycosylation are blossoming. We now know enough to conclude that the same general principles apply in invertebrate animals and unicellular eukaryotes – different branches of which spawned the plants or fungi and animals. The two major driving forces for exploring the glycomes of invertebrates and protists are (i) to understand the biochemical basis of glycan-driven biology in these organisms, especially of pathogens, and (ii) to uncover the evolutionary relationships between glycans, their biosynthetic enzyme genes, and biological functions for new glycobiological insights. With an emphasis on emerging areas of protist glycobiology, here we offer an overview of glycan diversity and evolution, to promote future access to this treasure trove of glycobiological processes.

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.

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.

The complexity and dynamics of the immensely heterogeneous glycoproteome of the prostate cancer (PCa) tumour micro-environment remain incompletely mapped, a knowledge gap that impedes our molecular-level understanding of the disease. To this end, we have used sensitive glycomics and glycoproteomics to map the protein-, cell- and tumour grade-specific N- and O-glycosylation in surgically-removed PCa tissues spanning five histological grades (n = 10/grade) and tissues from patients with benign prostatic hyperplasia (n = 5). Quantitative glycomics revealed PCa grade-specific alterations of the oligomannosidic-, paucimannosidic- and branched sialylated complex-type N-glycans, and dynamic remodelling of the sialylated core 1- and core 2-type O-glycome. Deep quantitative glycoproteomics identified ~7,400 unique N-glycopeptides from 500 N-glycoproteins and ~500 unique O-glycopeptides from nearly 200 O-glycoproteins. With reference to a recent Tissue and Blood Atlas, our data indicate that paucimannosidic glycans of the PCa tissues arise mainly from immune cell-derived glycoproteins. Further, the grade-specific PCa glycosylation arises primarily from dynamics in the cellular makeup of the PCa tumour microenvironment across grades involving increased oligomannosylation of prostate-derived glycoproteins and decreased bisecting GlcNAcylation of N-glycans carried by the extracellular matrix proteins. Further, elevated expression of several oligosaccharyltransferase subunits and enhanced N-glycoprotein site occupancy were observed associated with PCa progression. Finally, correlations between the protein-specific glycosylation and PCa progression were observed including increased site-specific core 2-type O-glycosylation of collagen VI. In conclusion, integrated glycomics and glycoproteomics have enabled new insight into the complexity and dynamics of the tissue glycoproteome associated with PCa progression generating an important resource to explore the underpinning disease mechanisms.

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.

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.

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.