Proteomic Identification Research Articles

Fluorescence Imaging-Based Discovery of Membrane Domain-Associated Proteins in Mycobacterium smegmatis.

Mycobacteria spatially organize their plasma membrane, and many enzymes involved in envelope biosynthesis associate with a membrane compartment termed the intracellular membrane domain (IMD). The IMD is concentrated in the polar regions of growing cells and becomes less polarized under nongrowing conditions. Because mycobacteria elongate from the poles, the observed polar localization of the IMD during growth likely supports the localized biosynthesis of envelope components. While we have identified more than 300 IMD-associated proteins by proteomic analyses, only a few of these have been verified by independent experimental methods. Furthermore, some IMD-associated proteins may have escaped proteomic identification and remain to be identified. Here, we visually screened an arrayed library of 523 Mycobacterium smegmatis strains, each producing a Dendra2-FLAG-tagged recombinant protein. We identified 29 fusion proteins that showed polar fluorescence patterns characteristic of IMD proteins. Twenty of these had previously been suggested to localize to the IMD based on proteomic data. Of the nine remaining IMD candidate proteins, three were confirmed by biochemical methods to be associated with the IMD. Taken together, this new colocalization strategy is effective in verifying the IMD association of proteins found by proteomic analyses while facilitating the discovery of additional IMD-associated proteins. IMPORTANCE The intracellular membrane domain (IMD) is a membrane subcompartment found in Mycobacterium smegmatis cells. Proteomic analysis of purified IMD identified more than 300 proteins, including enzymes involved in cell envelope biosynthesis. However, proteomics on its own is unlikely to detect every IMD-associated protein because of technical and biological limitations. Here, we describe fluorescent protein colocalization as an alternative, independent approach. Using a combination of fluorescence microscopy, proteomics, and subcellular fractionation, we identified three new proteins associated with the IMD. Such a robust method to rigorously define IMD proteins will benefit future investigations to decipher the synthesis, maintenance, and functions of this membrane domain and help delineate a more general mechanism of subcellular protein localization in mycobacteria.

Comparing the phenotypic, genotypic, and proteomic identification of Trichosporon species: A globally emerging yeast of medical importance.

Trichosporon spp. are widely distributed in the nature, comprising species that inhabit different ecological niches and can be found in the water, soil, and body surface of animals and humans. Such microorganisms have been classically associated with superficial infections; however, in the last decades, they have also been related to disseminated infections in immunocompromised patients, behaving as opportunistic agents, which demands rapid and accurate species identification for efficient therapy. Concordance level between the traditional phenotypic method and the molecular technique (gold standard) in the identification of all 59 Trichosporon samples was 59.3%. Identification concordance between MALDI-TOF spectrometry and the molecular technique was 71.2%. No isolate of environmental origin was identifiable by MALDI-TOF mass spectrometry (MS), and 100% of such environmental isolates were discordant for IGS region sequencing and phenotypic characterization. Both comparisons evidenced greatest concordance in the identification of T. asahii. The species T. debeurmannianum, T. dermatis, T. venhuisii and T. insectorum were not properly identified by both MALDI-TOF MS and the phenotypic technique. MALDI-TOF MS, in particular, seems to be appropriate to investigate yeasts of the genus Trichosporon; however, database updates are still necessary, especially for species that are not common in the clinical routine. With the aim of helping understand the aspects involved in early and accurate diagnosis of infections caused by this opportunistic agent, the present study compared the phenotypic, molecular (IGS region) and mass-spectrometry (MALDI-TOF) identification of 59 yeasts of the genus Trichosporon which had clinical and environmental origin and were kept in a mycology collection.

LB713 Proteomic identification of new diagnostic biomarkers of early-stage Cutaneous Mycosis Fungoides

Primary cutaneous T-cell lymphoma (CTCL) is responsible for two-thirds of cutaneous lymphoma cases. Mycosis fungoides (MF), the most common subtype of CTCL comprises approximately 60% of CTCLs. Due to the similar clinical features of MF and inflammatory diseases such as eczema and psoriasis2,3, early-stage MF can easily be misdiagnosed as chronic inflammatory dermatoses, posing a diagnostic challenge to the dermatologist. Early-stage MF is characterized by a favorable prognosis with long-term survival similar to or slightly lower than that of age-matched healthy people, with a 5-year survival between 88% and 100%. In contrast, advanced-stage MF shows aggressive progression, and the median survival time of patients with lymph node and visceral involvement is only 13 months. Therefore, it is important to achieve early diagnosis to improve prognosis, yet there are few specific biomarkers for the early diagnosis and prognosis of MF. In this study, we described the pathological features of MF during the early and advanced stages through proteomics technology, providing clues for the pathogenesis of MF as well as biomarkers for malignant tumors in the early stage. More importantly, diagnostic biomarkers of early-stage MF were identified by comparing the proteomic characteristics of early-stage MF and inflammatory diseases, with the goal of preventing delayed therapy due to misdiagnosis.

Proteomic identification of the contents of small extracellular vesicles from in vivo Plasmodium yoelii infection

Small extracellular vesicles, including exosomes, are formed by the endocytic pathway and contain genetic and protein material which reflect the contents of their cells of origin. These contents have a role in vesicle-mediated information transfer, as well as physiological and pathological functions. Thus, these vesicles are of great interest as therapeutic targets, or as vehicles for immunomodulatory control. In Plasmodium spp. infections, vesicles derived from the parasite or parasite-infected cells have been shown to induce the expression of pro-inflammatory elements, which have been correlated with manifestations of clinical disease. Herein, we characterised the protein cargo of naturally occurring sEVs in the plasma of P. yoelii-infected mice. After in vivo infections, extracellular vesicles in the size range of exosomes were collected by sequential centrifugation/ultracentrifugation followed by isopycnic gradient separation. Analysis of the vesicles was performed by transmission electron microscopy, dynamic light scattering, SDS–PAGE and flow cytometry. LC-MS analysis followed by bioinformatics analysis predicted parasite protein cargo associated with exosomes. Within these small extracellular vesicles, we identified proteins of interest as vaccine candidates, uncharacterized proteins which may be targets of T cell immunoreactivity, and proteins involved in metabolic processes, regulation, homeostasis and immunity. Importantly, the small extracellular vesicles studied in our work were obtained from in vivo infection rather than from the supernatant of in vitro cultures. These findings add to the growing interest in parasite small extracellular vesicles, further our understanding of the interactions between host and parasite, and identify novel proteins which may represent potential targets for vaccination against malaria.

Proteomic Analysis of the Meniscus Cartilage in Osteoarthritis

The distribution of differential extracellular matrix (ECM) in the lateral and medial menisci can contribute to knee instability, and changes in the meniscus tissue can lead to joint disease. Thus, deep proteomic identification of the lateral and medial meniscus cartilage is expected to provide important information for treatment and diagnosis of various knee joint diseases. We investigated the proteomic profiles of 12 lateral/medial meniscus pairs obtained from excess tissue of osteoarthritis patients who underwent knee arthroscopy surgery using mass spectrometry-based techniques and measured 75 ECM protein levels in the lesions using a multiple reaction monitoring (MRM) assay we developed. A total of 906 meniscus proteins with a 1% false discovery rate (FDR) was identified through a tandem mass tag (TMT) analysis showing that the lateral and medial menisci had similar protein expression profiles. A total of 131 ECM-related proteins was included in meniscus tissues such as collagen, fibronectin, and laminin. Our data showed that 14 ECM protein levels were differentially expressed in lateral and medial lesions (p < 0.05). We present the proteomic characterization of meniscal tissue with mass spectrometry-based comparative proteomic analysis and developed an MRM-based assay of ECM proteins correlated with tissue regeneration. The mass spectrometry dataset has been deposited to the MassIVE repository with the dataset identifier MSV000087753.

Identification of differential proteomics in Epstein-Barr virus-associated gastric cancer and related functional analysis

BackgroundEpstein-Barr virus-associated gastric cancer (EBVaGC) is the most common EBV-related malignancy. A comprehensive research for the protein expression patterns in EBVaGC established by high-throughput assay remains lacking. In the present study, the protein profile in EBVaGC tissue was explored and related functional analysis was performed.MethodsEpstein-Barr virus-encoded RNA (EBER) in situ hybridization (ISH) was applied to EBV detection in GC cases. Data-independent acquisition (DIA) mass spectrometry (MS) was performed for proteomics assay of EBVaGC. Functional analysis of identified proteins was conducted with bioinformatics methods. Immunohistochemistry (IHC) staining was employed to detect protein expression in tissue.ResultsThe proteomics study for EBVaGC was conducted with 7 pairs of GC cases. A total of 137 differentially expressed proteins in EBV-positive GC group were identified compared with EBV-negative GC group. A PPI network was constructed for all of them, and several proteins with relatively high interaction degrees could be the hub genes in EBVaGC. Gene enrichment analysis showed they might be involved in the biological pathways related to energy and biochemical metabolism. Combined with GEO datasets, a highly associated protein (GBP5) with EBVaGC was screened out and validated with IHC staining. Further analyses demonstrated that GBP5 protein might be associated with clinicopathological parameters and EBV infection in GC.ConclusionsThe newly identified proteins with significant differences and potential central roles could be applied as diagnostic markers of EBVaGC. Our study would provide research clues for EBVaGC pathogenesis as well as novel targets for the molecular-targeted therapy of EBVaGC.

Abstract 274: Comparison of proteomics identification pipelines for lymphocyte characterization

Abstract Proteomics analyses can be adapted to samples as small as a single cell, however such sample reduction severely limits the number proteins identified and quantified. Multiple software pipelines exist for processing the raw data to quantitative protein tables. For very small sample amounts, the sensitivity of software algorithms can have a significant impact on the final results. We collected whole blood samples for two subjects by venous blood draws. Using antibody guided flow cytometry cell sorting, we separated B and T cell lymphocytes. For two subjects and two different blood collections, five replicates of 145 B cells or T cells were prepared for mass spectrometry for proteomics using the AutoPOTS workflow. Using the raw files from these forty runs, we perform protein quantification with a selection of common tools such as MaxQuant, ProteomeDiscoverer, and FragPipe and compare the number of identified and quantified proteins. In small sample proteomics, the primary objective is obtaining the most quantified proteins. Although each tool has advantages in terms of usability, speed, and sensitivity, our primary concern is sensitivity. At such a low sample input, we observe that the number of quantified proteins increases by 30% using the most sensitive algorithm. For identifying and quantifying proteins from small samples, the sensitivity of the software is the most important factor to consider. In addition to the identification sensitivity of each algorithm, we are exploring optimal parameters for increased protein quantification. Differences between samples appear regardless of the processing tool, showing that using a less sensitive tool will still characterize the trends but with a significant cost to the number of proteins. Using the most sensitive algorithm for small sample proteomics will greatly improve the number of proteins identified and quantified. Citation Format: Michaela A. McCown, Carolyn Allen, Daniel D. Machado, Samuel H. Payne. Comparison of proteomics identification pipelines for lymphocyte characterization [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 274.

Deciphering the in vivo Dynamic Proteomics of Mesenchymal Stem Cells in Critical Limb Ischemia.

ObjectiveRegenerative therapy using mesenchymal stem cells (MSC) is a promising therapeutic method for critical limb ischemia (CLI). To understand how the cells are involved in the regenerative process of limb ischemia locally, we proposed a metabolic protein labeling method to label cell proteomes in situ and then decipher the proteome dynamics of MSCs in ischemic hind limb.Methods and ResultsIn this study, we overexpressed mutant methionyl-tRNA synthetase (MetRS), which could utilize azidonorleucine (ANL) instead of methionine (Met) during protein synthesis in MSCs. Fluorescent non-canonical amino-acid tagging (FUNCAT) was performed to detect the utilization of ANL in mutant MSCs. Mice with hindlimb ischemia (HLI) or Sham surgery were treated with MetRSmut MSCs or PBS, followed by i.p. administration of ANL at days 0, 2 6, and 13 after surgery. FUNCAT was also performed in hindlimb tissue sections to demonstrate the incorporation of ANL in transplanted cells in situ. At days 1, 3, 7, and 14 after the surgery, laser doppler imaging were performed to detect the blood reperfusion of ischemic limbs. Ischemic tissues were also collected at these four time points for histological analysis including HE staining and vessel staining, and processed for click reaction based protein enrichment followed by mass spectrometry and bioinformatics analysis. The MetRSmut MSCs showed strong green signal in cell culture and in HLI muscles as well, indicating efficient incorporation of ANL in nascent protein synthesis. By 14 days post-treatment, MSCs significantly increased blood reperfusion and vessel density, while reducing inflammation in HLI model compared to PBS. Proteins enriched by click reaction were distinctive in the HLI group vs. the Sham group. 34, 31, 49, and 26 proteins were significantly up-regulated whereas 28, 32, 62, and 27 proteins were significantly down-regulated in HLI vs. Sham at days 1, 3, 7, and 14, respectively. The differentially expressed proteins were more pronounced in the pathways of apoptosis and energy metabolism.ConclusionIn conclusion, mutant MetRS allows efficient and specific identification of dynamic cell proteomics in situ, which reflect the functions and adaptive changes of MSCs that may be leveraged to understand and improve stem cell therapy in critical limb ischemia.

Bottom-up and top-down proteomic approaches for the identification, characterization, and quantification of the low molecular weight proteome with focus on short open reading frame-encoded peptides.

The recent discovery of alternative open reading frames creates a need for suitable analytical approaches to verify their translation and to characterize the corresponding gene products at the molecular level. As the analysis of small proteins within a background proteome by means of classical bottom-up proteomics is challenging, method development for the analysis of small open reading frame encoded peptides (SEPs) have become a focal point for research. Here, we highlight bottom-up and top-down proteomics approaches established for the analysis of SEPs in both pro- and eukaryotes. Major steps of analysis, including sample preparation and (small) proteome isolation, separation and mass spectrometry, data interpretation and quality control, quantification, the analysis of post-translational modifications, and exploration of functional aspects of the SEPs by means of proteomics technologies are described. These methods do not exclusively cover the analytics of SEPs but simultaneously include the low molecular weight proteome, and moreover, can also be used for the proteome-wide analysis of proteolytic processing events.

Correlation between Bone Disease and Genotype in Patients with Type 1 Gaucher Disease. Data from the Spanish Gaucher Disease Registry

Bone involvement is the most common cause of functional limitation, disability and poor quality of life in patients with lysosomal storage disease (LSD). In type 1 Gaucher disease (GD1) intraosseous lesions have a vascular involvement with obstruction and / or local hemorrhage, once established it, secondary irreversible sequels with structure changes are developed. The intimate mechanisms that induce these feared complications are only partially known and the risk factors are not well established. There has been speculation about the relationship between the type of genetic defect and the severity of the disease. The most frequent mutations found in the Spanish population are point direction changes in the GBA gene (c.1226A> G (N370S) and c.1448T> C (L444P). However, other types of mutations (nonsense, splicing, frameshift stop codon, deletions, insertions, recombination) that induce a lower residual enzyme activity and, consequently, a higher storage rate and therefore more intensity of symptoms are found.Objective: To analyze the relationship between different missense mutation and the presence and developing of vascular bone lesions in GD 1 patients.Patients and Methods: We have analyzed the data obtained in our LSD Unit over the last 20 years in the evaluation of bone disease by magnetic resonance imaging with semi-quantitative estimation of the involvement by S-MRI and BMB scores and determination of bone mineral density by ultrasound in 131 GD patients (Andrade-Campos et al 2016). Here we present a sub-study in 85 patients heterozygous for c.1226A> G comparing the data obtained related to their genetic characteristics. According to the genotype were classified the patients in three groups, A: c.1226A> G / c.1448T> C (34), B: c.1226A> G in heterozygosity with other missense mutation (34) and C: c.1226A> G mutation with other types (17). In addition to search for plasma biomarkers associated to bone disease and verify them we have applied a targeted proteomics strategy. We have transformed monocytes to osteoclasts from these groups of patients and by proteomics profiles we have compared with osteoclasts of healthy controls by isobaric tag for relative and absolute quantification (iTRAQ). Labelling with iTRAQ in combination with sample pre-fractionation and nano liquid chromatography-tandem MS was used for identification and quantification of the basal proteome. More than 700 proteins were identified with this strategy. To verify the quantification of the proteins identified we have selected the MRM (multiple reaction monitoring) strategy for differential quantification according their highly sensitive, reproducible and accurate.Results: The mean age at diagnosis for each of the groups was similar. A: 30.85 (6-68)> 30 years: 19 (55.88%), C: 28.52 (4-56)> 30 years: 8 (47%) And significantly lower in B: 18.32 (3-60;> 30years: 10 (29.41%) (p = 0.01). The M/F ratio was balanced in group A (F: 52.9%) while there was predominance of females in group C (64.72%) and males in group B (64.70%). The proportion of splenectomized patients in groups A and B was similar (32.3%), whereas in group C was significantly higher (52.9%) (p = 0.01). Patients in groups A and B presented similar scores S-MRI and BMB 11,2 / 7,32 while group C showed a significant higher score 14.3 / 8.23 (p: 0.01) and higher incidence of osteopenia and osteoporosis. Only 17% of patients in group C and 20% in group B showed uncomplicated bone marrow infiltration compared to 50% in group A. The mean concentration of the biomarkers (chitotriosidase activity and CCL18/PARC) was similar between the three groups. The results of iTRAQ study has identified some target proteins with potential use as biomarkers that will be present in the meeting if the work is selected to be presented.In conclusion, patients with genotype that include an allele with complex mutations, present greater risk of suffering vascular bone complications (osteonecrosis, infarctions, bone demineralization). It is important to consider these genetic characteristics to establish recommendations about the importance of diagnosis and early treatment as well as to avoid splenectomy and to follow up by annual MRI and DEXA. The standard biomarkers of the disease do not establish differences between risk groups. The protein expression analyzed has show differential expression of some proteins that need to be addressed by target proteomic strategy. DisclosuresNo relevant conflicts of interest to declare.

Proximity Proteomics Has Potential for Extracellular Vesicle Identification.

Extracellular vesicles (EVs) are biomarkers and mediators of intercellular communication. In biological samples, EVs are secreted by various types of cells. The proteomic identification of proteins expressed in EVs has potential to contribute to research and clinical applications, particularly for cancer. In this study, the proximity-labeling method-based proteomic approach was used for EV identification, labeling membrane components proximal to a given molecule on the EV membrane surface. Due to the small labeling range, proteins on the surface of the same EVs are likely to be labeled by selecting a given EV surface antigen. The protein group of cancer cell-secreted EV (cEV), which abundantly expresses a close homologue of L1 (CHL1), was examined using a model mouse for lung cancer (LC). cEV-expressed proteins were identified by proteomic analysis of enzyme-mediated activation of radical sources by comparing serum EVs from wild-type and LC mice. SLC4A1 was found to be co-expressed in CHL1-expressing EVs, highlighting EVs expressing both CHL1 and SLC4A1 as candidates for cEVs. Serum EVs expressing both CHL1 and caspase 14 were significantly elevated in LC patients compared with healthy individuals. Thus, the combination of proximity labeling and proteomic analysis allows for effective EV identification.

DIMA: Data-Driven Selection of an Imputation Algorithm.

Imputation is a prominent strategy when dealing with missing values (MVs) in proteomics data analysis pipelines. However, it is difficult to assess the performance of different imputation methods and varies strongly depending on data characteristics. To overcome this issue, we present the concept of a data-driven selection of an imputation algorithm (DIMA). The performance and broad applicability of DIMA are demonstrated on 142 quantitative proteomics data sets from the PRoteomics IDEntifications (PRIDE) database and on simulated data consisting of 5-50% MVs with different proportions of missing not at random and missing completely at random values. DIMA reliably suggests a high-performing imputation algorithm, which is always among the three best algorithms and results in a root mean square error difference (ΔRMSE) ≤ 10% in 80% of the cases. DIMA implementation is available in MATLAB at github.com/kreutz-lab/OmicsData and in R at github.com/kreutz-lab/DIMAR.

Mining the Royal Jelly Proteins: Combinatorial Hexapeptide Ligand Library Significantly Improves the MS-Based Proteomic Identification in Complex Biological Samples.

Royal jelly (RJ) is a complex, creamy secretion produced by the glands of worker bees. Due to its health-promoting properties, it is used by humans as a dietary supplement. However, RJ compounds are not fully characterized yet. Hence, in this research, we aimed to broaden the knowledge of the proteomic composition of fresh RJ. Water extracts of the samples were pre-treated using combinatorial hexapeptide ligand libraries (ProteoMinerTM kit), trypsin-digested, and analyzed by a nanoLC-MALDI-TOF/TOF MS system. To check the ProteoMinerTM performance in the MS-based protein identification, we also examined RJ extracts that were not prepared with the ProteoMinerTM kit. We identified a total of 86 proteins taxonomically classified to Apis spp. (bees). Among them, 74 proteins were detected in RJ extracts pre-treated with ProteoMinerTM kit, and only 50 proteins were found in extracts non-enriched with this technique. Ten of the identified features were hypothetical proteins whose existence has been predicted, but any experimental evidence proves their in vivo expression. Additionally, we detected four uncharacterized proteins of unknown functions. The results of this research indicate that the ProteoMinerTM strategy improves proteomic identification in complex biological samples. Broadening the knowledge of RJ composition may contribute to the development of standards and regulations, enhancing the quality of RJ, and consequently, the safety of its supplementation.

Pushing the Proteomics Envelope: More sensitive technology spurs proteomic and metabolomic biomarker identification, clinical use

Pushing the Proteomics Envelope: More sensitive technology spurs proteomic and metabolomic biomarker identification, clinical use

Proteomic Exploration of Endocytosis of Framework Nucleic Acids.

Efficient cell internalization of framework nucleic acid nanostructures free of transfection agents provides new opportunities for developing biocompatible and intelligent nanoprobes and drug delivery carriers. Here, a proteomic identification method to screen target proteins that interact with tetrahedral DNA nanostructures (TDNs) during the process of endocytosis by combining drug affinity responsive target stability (DARTS) with liquid chromatography/tandem mass spectrometry (LC-MS/MS) techniques, is reported. It is found that that caveolin-1 (CAV1) and macropinocytosis-related protein sorting nexin5 (SNX5) are associated with the endocytosis of TNDs, which is further validated by microscale thermophoresis (MST) analysis. CAV1- and SNX5- knockout experiments reveal that both caveolae-mediated endocytosis and macropinocytosis mediate the cellular uptake of TDNs, which complement previous findings with fluorescence tracing methods. This method provides a generic strategy to analyze cellular internalization process of DNA nanostructures for biomedical applications.

Proteomic identification of biomarkers in maternal plasma that predict the outcome of rescue cerclage for cervical insufficiency

We sought to identify plasma protein biomarkers that are predictive of the outcome of rescue cerclage in patients with cervical insufficiency. This retrospective cohort study included 39 singleton pregnant women undergoing rescue cerclage for cervical insufficiency (17-25 weeks) who gave plasma samples. Three sets of pooled plasma samples from controls (cerclage success, n = 10) and cases (cerclage failure, n = 10, defined as spontaneous preterm delivery at <33 weeks) were labeled with 6-plex tandem mass tag (TMT) reagents and analyzed by liquid chromatography-tandem mass spectrometry. Differentially expressed proteins between the two groups were selected from the TMT-based quantitative analysis. Multiple reaction monitoring-mass spectrometry (MRM-MS) analysis was further used to verify the candidate proteins of interest in patients with cervical insufficiency in the final cohort (n = 39). From MRM-MS analysis of the 40 proteins showing statistically significant changes (P < 0.05) from the TMT-based quantitative analysis, plasma IGFBP-2, PSG4, and PGLYRP2 levels were found to be significantly increased, whereas plasma MET and LXN levels were significantly decreased in women with cerclage failure. Of these, IGFBP-2, PSG4, and LXN levels in plasma were independent of cervical dilatation. A multiple-biomarker panel was developed for the prediction of cerclage failure, using a stepwise regression procedure, which included the plasma IGFBP-2, PSG4, and LXN (area under the curve [AUC] = 0.916). The AUC for this multiple-biomarker panel was significantly greater than the AUC for any single biomarker included in the multi-biomarker model. Proteomic analysis identified useful and independent plasma biomarkers (IGFBP-2, PSG4, and LXN; verified by MRM) that predict poor pregnancy outcome following rescue cerclage. Their combined analysis in a multi-biomarker panel significantly improved predictability.

Prognostic and Functional Significant of Heat Shock Proteins (HSPs) in Breast Cancer Unveiled by Multi-Omics Approaches.

Simple SummaryIn this study, we investigated the expression pattern and prognostic significance of the heat shock proteins (HSPs) family members in breast cancer (BC) by using several bioinformatics tools and proteomics investigations. Our results demonstrated that, collectively, HSPs were deregulated in BC, acting as both oncogene and onco-suppressor genes. In particular, two different HSP-clusters were significantly associated with a poor or good prognosis. Interestingly, the HSPs deregulation impacted gene expression and miRNAs regulation that, in turn, affected important biological pathways involved in cell cycle, DNA replication, and receptors-mediated signaling. Finally, the proteomic identification of several HSPs members and isoforms revealed much more complexity of HSPs roles in BC and showed that their expression is quite variable among patients. In conclusion, we elaborated two panels of HSPs that could be further explored as potential biomarkers for BC progression and prognosis.Heat shock proteins (HSPs) are a well-characterized molecular chaperones protein family, classified into six major families, according to their molecular size. A wide range of tumors have been shown to express atypical levels of one or more HSPs, suggesting that they could be used as biomarkers. However, the collective role and the possible coordination of HSP members, as well as the prognostic significance and the functional implications of their deregulated expression in breast cancer (BC) are poorly investigated. Here, we used a systematic multi-omics approach to assess the HSPs expression, the prognostic value, and the underlying mechanisms of tumorigenesis in BC. By using data mining, we showed that several HSPs were deregulated in BC and significantly correlated with a poor or good prognosis. Functional network analysis of HSPs co-expressed genes and miRNAs highlighted their regulatory effects on several biological pathways involved in cancer progression. In particular, these pathways concerned cell cycle and DNA replication for the HSPs co-expressed genes, and miRNAs up-regulated in poor prognosis and Epithelial to Mesenchymal Transition (ETM), as well as receptors-mediated signaling for the HSPs co-expressed genes up-regulated in good prognosis. Furthermore, the proteomic expression of HSPs in a large sample-set of breast cancer tissues revealed much more complexity in their roles in BC and showed that their expression is quite variable among patients and confined into different cellular compartments. In conclusion, integrative analysis of multi-omics data revealed the distinct impact of several HSPs members in BC progression and indicate that collectively they could be useful as biomarkers and therapeutic targets for BC management.

Peak Identification and Quantification by Proteomic Mass Spectrogram Decomposition.

Recent advances in the liquid chromatography/mass spectrometry (LC/MS) technology have improved the sensitivity, resolution, and speed of proteome analysis, resulting in increasing demand for more sophisticated algorithms to interpret complex mass spectrograms. Here, we propose a novel statistical method, proteomic mass spectrogram decomposition (ProtMSD), for joint identification and quantification of peptides and proteins. Given the proteomic mass spectrogram and the reference mass spectra of all possible peptide ions associated with proteins as a dictionary, ProtMSD estimates the chromatograms of those peptide ions under a group sparsity constraint without using the conventional careful preprocessing (e.g., thresholding and peak picking). We show that the method was significantly improved using protein-peptide hierarchical relationships, isotopic distribution profiles, reference retention times of peptide ions, and prelearned mass spectra of noise. We examined the concept of database search, library search, and match-between-runs. Our ProtMSD showed excellent agreements of 3277 peptide ions (94.79%) and 493 proteins (98.21%) with Mascot/Skyline for an Escherichia coli proteome sample and of 4460 peptide ions (103%) and 588 proteins (101%) with match-between-runs by MaxQuant for a yeast proteome sample. This is the first attempt to use a matrix decomposition technique as a tool for LC/MS-based proteome identification and quantification.

Proteomic identification of select protein variants of the SNARE interactome associated with cognitive reserve in a large community sample.

Age-related neuropathologies progressively impair cognitive abilities by damaging synaptic function. We aimed to identify key components within the presynaptic SNARE (soluble N-ethylmaleimide-sensitive factor attachment protein receptor) machinery associated with cognitive performance and estimate their potential contribution to brain reserve in old age. We used targeted SRM proteomics to quantify amounts of 60 peptides, encoded in 30 different genes, in postmortem specimens of the prefrontal cortex from 1209 participants of two aging studies, with available antemortem cognitive evaluations and postmortem neuropathologic assessments. We found that select (but not all) proteoforms are strongly associated with cognitive function and the burden of Alzheimer's disease (AD) pathology. Specifically, greater abundance of STX1A (but not other syntaxins), SYT12, full-length SNAP25, and the GABAergic STXBP1 variant were robustly associated with better cognitive performance. By contrast, greater abundance of other presynaptic proteins (e.g., STXBP5 or tomosyn, STX7, or SYN2) showed a negative influence on cognition. Regression models adjusting for demographic and pathologic variables showed that altered levels of these protein species explained 7.7% additional between-subject variance in cognition (more than any individual age-related neuropathology in the model), suggesting that these molecules constitute key elements of brain reserve. Network analyses indicated that those peptides associated with brain reserve, and closest to the SNARE fusogenic activity, showed greater centrality measures and were better connected in the network. Validation assays confirmed the selective loss of the STX1A (but not STX1B) isoform in cognitively impaired cases. In rodent and human brains, STX1A was selectively located at glutamatergic terminals. However, in AD brains, STX1A was redistributed adjacent to neuritic pathology, and markedly expressed in astrocytes. Our study provides strong evidence, indicating that select presynaptic proteins are key in maintaining brain reserve. Compromised ability to sustain expression levels of these proteins may trigger synaptic dysfunction and concomitant cognitive impairment.

Improving the performance of proteomic analysis via VAILase cleavage and 193-nm ultraviolet photodissociation

Further improving the proteomic identification coverage and reliability is still challenging in the mass spectrometry (MS)-based proteomics. Herein, we combine VAILase and trypsin digestion with 193-nm ultraviolet photodissociation (UVPD) and higher-energy collision dissociation (HCD) to improve the performance of bottom-up proteomics. As VAILase exhibits high complementarity to trypsin, the proteome sequence coverage is improved obviously whether with HCD or 193-nm UVPD. The high diversity of fragment ion types produced by UVPD contributes to the improvements of identification reliability for both trypsin- and VAILase-digested peptides with an average XCorr score improvement of 10%.