Analysis Of Proteomic Data Research Articles

Application of Machine Learning in Spatial Proteomics.

Spatial proteomics is an interdisciplinary field that investigates the localization and dynamics of proteins, and it has gained extensive attention in recent years, especially the subcellular proteomics. Numerous evidence indicate that the subcellular localization of proteins is associated with various cellular processes and disease progression. Mass spectrometry (MS)-based and imaging-based experimental approaches have been developed to acquire large-scale spatial proteomic data. To allow the reliable analysis of increasingly complex spatial proteomics data, machine learning (ML) methods have been widely used in both MS-based and imaging-based spatial proteomic data analysis pipelines. Here, we comprehensively survey the applications of ML in spatial proteomics from following aspects: (1) data resources for spatial proteome are comprehensively introduced; (2) the roles of different ML algorithms in data analysis pipelines are elaborated; (3) successful applications of spatial proteomics and several analytical tools integrating ML methods are presented; (4) challenges existing in modern ML-based spatial proteomics studies are discussed. This review provides guidelines for researchers seeking to apply ML methods to analyze spatial proteomic data and can facilitate insightful understanding of cell biology as well as the future research in medical and drug discovery communities.

Integration of single-cell RNA-Seq and CyTOF data characterises heterogeneity of rare cell subpopulations

Background: The simultaneous measurement of cellular proteins and transcriptomes of single cell data has become an exciting new possibility with the advent of highly multiplexed multi-omics methodologies. However, mass cytometry (CyTOF) is a well-established, affordable technique for the analysis of proteomic data, which is well suited for the discovery and characterisation of very rare subpopulations of cells with a wealth of publicly available datasets. Methods: We present and evaluate the multimodal integration of single cell RNA-Seq and CyTOF datasets coming from both matched and unmatched samples, using two publicly available datasets. Results: We demonstrate that the integration of well annotated CyTOF data with single cell RNA sequencing can aid in the identification and annotation of cell populations with high accuracy. Furthermore, we show that the integration can provide imputed measurements of protein markers which are comparable to the current gold standard of antibody derived tags (ADT) from CITE-Seq for both matched and unmatched datasets. Using this methodology, we identify and transcriptionally characterise a rare subpopulation of CD11c positive B cells in high resolution using publicly available data and we unravel its heterogeneity in a single cell setting without the need to sort the cells in advance, in a manner which had not been previously possible. Conclusions: This approach provides the framework for using available proteomic and transcriptomic datasets in a unified and unbiased fashion to assist ongoing and future studies of cellular characterisation and biomarker identification.

Comparative Evaluation of Proteome Discoverer and FragPipe for the TMT-Based Proteome Quantification.

Isobaric labeling-based proteomics is widely applied in deep proteome quantification. Among the platforms for isobaric labeled proteomic data analysis, the commercial software Proteome Discoverer (PD) is widely used, incorporating the search engine CHIMERYS, while FragPipe (FP) is relatively new, free for noncommercial purposes, and integrates the engine MSFragger. Here, we compared PD and FP over three public proteomic data sets labeled using 6plex, 10plex, and 16plex tandem mass tags. Our results showed the protein abundances generated by the two software are highly correlated. PD quantified more proteins (10.02%, 15.44%, 8.19%) than FP with comparable NA ratios (0.00% vs. 0.00%, 0.85% vs. 0.38%, and 11.74% vs. 10.52%) in the three data sets. Using the 16plex data set, PD and FP outputs showed high consistency in quantifying technical replicates, batch effects, and functional enrichment in differentially expressed proteins. However, FP saved 93.93%, 96.65%, and 96.41% of processing time compared to PD for analyzing the three data sets, respectively. In conclusion, while PD is a well-maintained commercial software integrating various additional functions and can quantify more proteins, FP is freely available and achieves similar output with a shorter computational time. Our results will guide users in choosing the most suitable quantification software for their needs.

Multifactorial feature extraction and site prognosis model for protein methylation data.

Integrated studies (multi-omics studies) comprising genetic, proteomic and epigenetic data analyses have become an emerging topic in biomedical research. Protein methylation is a posttranslational modification that plays an essential role in various cellular activities. The prediction of methylation sites (arginine and lysine) is vital to understand the molecular processes of protein methylation. However, current experimental techniques used for methylation site predictions are tedious and expensive. Hence, computational techniques for predicting methylation sites in proteins are necessary. For predicting methylation sites, various computational methods have been proposed in recent years. Most existing methods require structural and evolutionary information for retrieving features, acquiring this information is not always convenient. Thus, we proposed a novel method, called multi-factorial feature extraction and site prognosis model (MufeSPM), for the prediction of protein methylation sites based on information theory features (Renyi, Shannon, Havrda-Charvat and Arimoto entropy), amino acid composition and physicochemical properties acquired from protein methylation data. A random forest algorithm was used to predict methylation sites in protein sequences. This paper also studied the impact of different features and classifiers on arginine and lysine methylation data sets. For the R methylation data set, MufeSPM yielded 82.45%($\pm $ 3.47) accuracy, and for the K methylation data set, it provided an average accuracy of 71.94%($\pm $ 2.12). Additionally, the area under the receiver operating characteristic curve for different classifiers in predicting methylation site was provided. The experimental results signify that MufeSPM performs better than the state-of-the-art predictors.

A Hybrid Spectral Library and Protein Sequence Database Search Strategy for Bottom-Up and Top-Down Proteomic Data Analysis.

Tandem mass spectrometry (MS/MS) is widely employed for the analysis of complex proteomic samples. While protein sequence database searching and spectral library searching are both well-established peptide identification methods, each has shortcomings. Protein sequence databases lack fragment peak intensity information, which can result in poor discrimination between correct and incorrect spectrum assignments. Spectral libraries usually contain fewer peptides than protein sequence databases, which limits the number of peptides that can be identified. Notably, few post-translationally modified peptides are represented in spectral libraries. This is because few search engines can both identify a broad spectrum of PTMs and create corresponding spectral libraries. Also, programs that generate spectral libraries using deep learning approaches are not yet able to accurately predict spectra for the vast majority of PTMs. Here, we address these limitations through use of a hybrid search strategy that combines protein sequence database and spectral library searches to improve identification success rates and sensitivity. This software uses Global PTM Discovery (G-PTM-D) to produce spectral libraries for a wide variety of different PTMs. These features, along with a new spectrum annotation and visualization tool, have been integrated into the freely available and open-source search engine MetaMorpheus.

RNA-binding protein ELAVL4/HuD ameliorates Alzheimer's disease-related molecular changes in human iPSC-derived neurons.

The RNA binding protein ELAVL4/HuD regulates the translation and splicing of multiple Alzheimer's disease (AD) candidate genes. We generated ELAVL4 knockout (KO) human induced pluripotent stem cell-derived neurons to study the effect that ELAVL4 has on AD-related cellular phenotypes. ELAVL4 KO significantly increased the levels of specific APP isoforms and intracellular phosphorylated tau, molecular changes that are related to the pathological hallmarks of AD. Overexpression of ELAVL4 in wild-type neurons and rescue experiments in ELAVL4 KO cells showed opposite effects and also led to a reduction of the extracellular amyloid-beta (Aβ)42/40 ratio. All these observations were made in familial AD (fAD) and fAD-corrected neurons. To gain insight into the molecular cascades involved in neuronal ELAVL4 signaling, we conducted pathway and upstream regulator analyses of transcriptomic and proteomic data from the generated neurons. These analyses revealed that ELAVL4 affects multiple biological pathways linked to AD, including those involved in synaptic function, as well as gene expression downstream of APP and tau signaling. The analyses also suggest that ELAVL4 expression is regulated by insulin receptor-FOXO1 signaling in neurons. Taken together, ELAVL4 expression ameliorates AD-related molecular changes in neurons and affects multiple synaptic pathways, making it a promising target for novel drug development.

Improving Peptide-Level Mass Spectrometry Analysis via Double Competition.

The analysis of shotgun proteomics data often involves generating lists of inferred peptide-spectrum matches (PSMs) and/or of peptides. The canonical approach for generating these discovery lists is by controlling the false discovery rate (FDR), most commonly through target-decoy competition (TDC). At the PSM level, TDC is implemented by competing each spectrum's best-scoring target (real) peptide match with its best match against a decoy database. This PSM-level procedure can be adapted to the peptide level by selecting the top-scoring PSM per peptide prior to FDR estimation. Here, we first highlight and empirically augment a little known previous work by He et al., which showed that TDC-based PSM-level FDR estimates can be liberally biased. We thus propose that researchers instead focus on peptide-level analysis. We then investigate three ways to carry out peptide-level TDC and show that the most common method ("PSM-only") offers the lowest statistical power in practice. An alternative approach that carries out a double competition, first at the PSM and then at the peptide level ("PSM-and-peptide"), is the most powerful method, yielding an average increase of 17% more discovered peptides at 1% FDR threshold relative to the PSM-only method.

SQuAPP—simple quantitative analysis of proteins and PTMs

The comprehensive analysis of the proteome and its modulation by post-translational modification (PTM) is increasingly used in biological and biomedical studies. As a result, proteomics data analysis is ever more carried out by scientists with limited expertise in this type of data. While excellent software solutions for comprehensive and rigorous analysis of quantitative proteomic data exist, most are complex and not well suited for non-proteomics scientists. Integrative analysis of multi-level proteomics data on protein and diverse PTMs, like phosphorylation or proteolytic processing, remains particularly challenging and inaccessible to most biologists. To fill this void, we developed SQuAPP, an R-Shiny web-based analysis pipeline for the quantitative analysis of proteomic data. SQuAPP uses a streamlined workflow model to guide expert and novice users through quality control, data pre-processing, statistical analysis and visualization steps. Processing the protein, peptide and PTM datasets in parallel and their quantitative integration enable rapid identification of protein-level-independent modulation of protein modifications and intuitive interpretation of dynamic dependencies between different protein modifications. SQuAPP is available at http://squapp.langelab.org/. The source code and local setup instructions can be accessed from https://github.com/LangeLab/SQuAPP.

The effect of Nrf2 deletion on the proteomic signature in a human colorectal cancer cell line

BackgroundColorectal cancer is one of the most common cancer and the third leading cause of death worldwide. Increased generation of reactive oxygen species (ROS) is observed in many types of cancer cells. Several studies have reported that an increase in ROS production could affect the expression of proteins involved in ROS-scavenging, detoxification and drug resistance. Nuclear factor erythroid 2 related factor 2 (Nrf2) is a known transcription factor for cellular response to oxidative stress. Several researches exhibited that Nrf2 could exert multiple functions and expected to be a promising therapeutic target in many cancers. Here, Nrf2 was knocked down in colorectal cancer cell line HT29 and changes that occurred in signaling pathways and survival mechanisms were evaluated.MethodsThe influence of chemotherapy drugs (doxorubicin and cisplatin), metastasis and cell viability were investigated. To explore the association between specific pathways and viability in HT29-Nrf2−, proteomic analysis, realtime PCR and western blotting were performed.ResultsIn the absence of Nrf2 (Nrf2−), ROS scavenging and detoxification potential were dramatically faded and the HT29-Nrf2− cells became more susceptible to drugs. However, a severe decrease in viability was not observed. Bioinformatic analysis of proteomic data revealed that in Nrf2− cells, proteins involved in detoxification processes, respiratory electron transport chain and mitochondrial-related compartment were down regulated. Furthermore, proteins related to MAPKs, JNK and FOXO pathways were up regulated that possibly helped to overcome the detrimental effect of excessive ROS production.ConclusionsOur results revealed MAPKs, JNK and FOXO pathways connections in reducing the deleterious effect of Nrf2 deficiency, which can be considered in cancer therapy.

Digital Spatial Profiling for Characterization of the Microenvironment in Adult-Type Diffusely Infiltrating Glioma.

Diffusely infiltrating gliomas are associated with high morbidity and mortality due to the infiltrative nature of tumor spread. They are morphologically complex tumors, with a high degree of proteomic variability across both the tumor itself and its heterogenous microenvironment. The malignant potential of these tumors is enhanced by the dysregulation of proteins involved in several key pathways, including processes that maintain cellular stability and preserve the structural integrity of the microenvironment. Although there have been numerous bulk and single-cell glioma analyses, there is a relative paucity of spatial stratification of these proteomic data. Understanding differences in spatial distribution of tumorigenic factors and immune cell populations between the intrinsic tumor, invasive edge, and microenvironment offers valuable insight into the mechanisms underlying tumor proliferation and propagation. Digital spatial profiling (DSP) represents a powerful technology that can form the foundation for these important multilayer analyses. DSP is a method that efficiently quantifies protein expression within user-specified spatial regions in a tissue specimen. DSP is ideal for studying the differential expression of multiple proteins within and across regions of distinction, enabling multiple levels of quantitative and qualitative analysis. The DSP protocol is systematic and user-friendly, allowing for customized spatial analysis of proteomic data. In this experiment, tissue microarrays are constructed from archived glioblastoma core biopsies. Next, a panel of antibodies is selected, targeting proteins of interest within the sample. The antibodies, which are preconjugated to UV-photocleavable DNA oligonucleotides, are then incubated with the tissue sample overnight. Under fluorescence microscopy visualization of the antibodies, regions of interest (ROIs) within which to quantify protein expression are defined with the samples. UV light is then directed at each ROI, cleaving the DNA oligonucleotides. The oligonucleotides are microaspirated and counted within each ROI, quantifying the corresponding protein on a spatial basis.

Integrative Expression, Survival Analysis and Cellular miR-2909 Molecular Interplay in MRN Complex Check Point Sensor Genes (MRN-CSG) Involved in Breast Cancer

BackgroundBreast cancer, an emerging global challenge, is evidenced by recent studies of miRNAs involvement in DNA repair gene variants (MRE11, RAD50, and NBN as checkpoint sensor genes (CSG) – MRN-CSG). The identification of various mutations in MRN-CSG and their interactions with miRNAs is still not understood. The emerging studies of miR-2909 involvement in other cancers led us to explore its role as molecular mechanistic marker in breast cancer. Materials and MethodsThe genomic and proteomic data of MRN-CSG of breast cancer patients (8426 samples) was evaluated to identify the mutation types linked with the patient's survival rate. Additionally, molecular, 3D-structural and functional analysis was performed to identify miR-2909 as regulator of MRN-CSG. ResultsThe genomic and proteomic data analysis shows genetic alterations with majority of missense mutations [RAD50 (0.7%), MRE11 (1.5%), and NBN (11%)], though with highest MRE11 mRNA expression in invasive ductal breast carcinoma as compared to other breast cancer types. The Kaplan–Meier survival curves suggest higher survival rate for unaltered groups as compared to the altered group. Network analysis and disease association of miR-2909 and MRN-CSG shows strong interactions with other partners. The molecular hybridization between miR-2909-RAD50 and miR-2909-MRE11 complexes showed thermodynamically stable structures. Further, argonaute protein, involved in RNA silencing, docking studies with miR-MRE11-mRNA and miR-RAD50-mRNA hybridized complexes showed strong binding affinity. ConclusionThe results suggest that miR-2909 forms strong thermodynamically stable molecular hybridized complexes with MRE11 and RAD50 mRNAs which further strongly interacts with argonaute protein to show potential molecular mechanistic role in breast cancer.

Proteomic Analysis of Human Breast Cancer MCF-7 Cells to Identify Cellular Targets of the Anticancer Pigment OR3 from Streptomyces coelicolor JUACT03.

Search for ideal compounds with known pathways of anticancer mechanism is still a priority research focus for cancer, as it continues to be a major health challenge across the globe. Hence, in the present study, anticancer potential of a yellow pigment fraction, OR3, isolated from Streptomyces coelicolor JUACT03 was assessed on the breast cancer cell line MCF-7. TLC-fractionated OR3 pigment was subjected to HPLC and GC-MS analysis for characterization and identification of the bioactive component. MCF-7 cells were treated with IC50 concentration of OR3 and the molecular alterations were analyzed using mass spectrometry-based quantitative proteomic analysis. Bioinformatics tools such as STRING analysis and Ingenuity Pathway Analysis were performed to analyze proteomics data and to identify dysregulated signaling pathways. As per our obtained data, OR3 treatment decreased cell proliferation and induced apoptotic cell death due to significant dysregulation of protein expressions in MCF-7 cells. Altered expression included the ribosomal, mRNA processing and vesicle-mediated transport proteins as a result of OR3 treatment. Downregulation of MAPK proteins, NFkB, and estradiol signaling was identified in OR3-treated MCF-7 cells. Mainly eIF2, mTOR, and eIF4 signaling pathways were altered in OR3-treated cells. GC-MS data indicated the presence of novel compounds in OR3 fraction. It can be concluded that OR3 exhibits potent anticancer activity on the breast cancer cells mainly through altering the expression and affecting the signaling proteins which are involved in different cell proliferation/apoptotic pathways thereby causing inhibition of cancer cell proliferation, survival and metastasis.

Proteomic evaluation of human osteoblast responses to titanium implants over time.

Titanium is widely used in bone prostheses due to its excellent biocompatibility and osseointegration capacity. To understand the effect of sandblasted acid-etched (SAE) Ti implants on the biological responses of human osteoblast (HOb), their proteomic profiles were analyzed using nLC-MS/MS. The cells were cultured with the implant materials, and 2544 distinct proteins were detected in samples taken after 1, 3, and 7 days. Comparative analyses of proteomic data were performed using Perseus software. The expression of proteins related to EIF2, mTOR, insulin-secretion and IGF pathways showed marked differences in cells grown with SAE-Ti in comparison with cells cultured without Ti. Moreover, the proteomic profiles obtained with SAE-Ti were compared over time. The affected proteins were related to adhesion, immunity, oxidative stress, coagulation, angiogenesis, osteogenesis, and extracellular matrix formation functions. The proliferation, mineralization and osteogenic gene expression in HObs cultured with SAE-Ti were characterized in vitro. The results showed that the osteoblasts exposed to this material increase their mineralization rate and expression of COLI, RUNX2, SP7, CTNNB1, CAD13, IGF2, MAPK2, and mTOR. Overall, the observed proteomic profiles can explain the SAE-Ti osteogenic properties, widening our knowledge of key signaling pathways taking part in the early stages of the osseointegration process in this type of implantations.

The Brain Protein Atlas: A conglomerate of proteomics datasets of human neural tissue.

The human brain represents one of the most complex biological structures with significant spatiotemporal molecular plasticity occurring through early development, learning, aging, and disease. While much progress has been made in mapping its transcriptional architecture, more downstream phenotypic readouts are relatively scarce due to limitations with tissue heterogeneity and accessibility, as well as an inability to amplify protein species prior to global -OMICS analysis. To address some of these barriers, our group has recently focused on using mass-spectrometry workflows compatible with small amounts of formalin-fixed paraffin-embedded tissue samples. This has enabled exploration into spatiotemporal proteomic signatures of the brain and disease across otherwise inaccessible neurodevelopmental timepoints and anatomical niches. Given the similar theme and approaches, we introduce an integrated online portal, "The Brain Protein Atlas (BPA)" (www.brainproteinatlas.org), representing a public resource that allows users to access and explore these amalgamated datasets. Specifically, this portal contains a growing set of peer-reviewed mass-spectrometry-based proteomic datasets, including spatiotemporal profiles of human cerebral development, diffuse gliomas, clinically aggressive meningiomas, and a detailed anatomic atlas of glioblastoma. One barrier to entry in mass spectrometry-based proteomics data analysis is the steep learning curve required to extract biologically relevant data. BPA, therefore, includes several built-in analytical tools to generate relevant plots (e.g., volcano plots, heatmaps, boxplots, and scatter plots) and evaluate the spatiotemporal patterns of proteins of interest. Future iterations aim to expand available datasets, including those generated by the community at large, and analytical tools for exploration. Ultimately, BPA aims to improve knowledge dissemination of proteomic information across the neuroscience community in hopes of accelerating the biological understanding of the brain and various maladies.

Prediction of peptide mass spectral libraries with machine learning.

The recent development of machine learning methods to identify peptides in complex mass spectrometric data constitutes a major breakthrough in proteomics. Longstanding methods for peptide identification, such as search engines and experimental spectral libraries, are being superseded by deep learning models that allow the fragmentation spectra of peptides to be predicted from their amino acid sequence. These new approaches, including recurrent neural networks and convolutional neural networks, use predicted in silico spectral libraries rather than experimental libraries to achieve higher sensitivity and/or specificity in the analysis of proteomics data. Machine learning is galvanizing applications that involve large search spaces, such as immunopeptidomics and proteogenomics. Current challenges in the field include the prediction of spectra for peptides with post-translational modifications and for cross-linked pairs of peptides. Permeation of machine-learning-based spectral prediction into search engines and spectrum-centric data-independent acquisition workflows for diverse peptide classes and measurement conditions will continue to push sensitivity and dynamic range in proteomics applications in the coming years.

Systems biology approach delineates critical pathways associated with disease progression in rheumatoid arthritis

Rheumatoid Arthritis (RA) is a chronic systemic autoimmune disease leading to inflammation, cartilage cell death, synoviocyte proliferation, and increased and impaired differentiation of osteoclasts and osteoblasts leading to joint erosions and deformities. Transcriptomics, proteomics, and metabolomics datasets were analyzed to identify the critical pathways that drive the RA pathophysiology. Single nucleotide polymorphisms (SNPs) associated with RA were analyzed for the functional implications, clinical outcomes, and blood parameters later validated by literature. SNPs associated with RA were grouped into pathways that drive the immune response and cytokine production. Further gene set enrichment analysis (GSEA) was performed on gene expression omnibus (GEO) data sets of peripheral blood mononuclear cells (PBMCs), synovial macrophages, and synovial biopsies from RA patients showed enrichment of Th1, Th2, Th17 differentiation, viral and bacterial infections, metabolic signalling and immunological pathways with potential implications for RA. The proteomics data analysis presented pathways with genes involved in immunological signaling and metabolic pathways, including vitamin B12 and folate metabolism. Metabolomics datasets analysis showed significant pathways like amino-acyl tRNA biosynthesis, metabolism of amino acids (arginine, alanine aspartate, glutamate, glutamine, phenylalanine, and tryptophan), and nucleotide metabolism. Furthermore, our commonality analysis of multi-omics datasets identified common pathways with potential implications for joint remodeling in RA. Disease-modifying anti-rheumatic drugs (DMARDs) and biologics treatments were found to modulate many of the pathways that were deregulated in RA. Overall, our analysis identified molecular signatures associated with the observed symptoms, joint erosions, potential biomarkers, and therapeutic targets in RA. Communicated by Ramaswamy H. Sarma

Abstract P1007: Cardiac Fibroblast BAG3 Controls TGFBR2 Signaling And Contributes To Engineered Cardiac Tissue Function.

Introduction: Mutations in BAG3 result in dilated cardiomyopathy with cardiac fibrosis. Currently, the role of BAG3 in non-myocyte cell types is not known. We tested the hypothesis that BAG3 controls cardiac fibroblast (CF) function and that the loss of fibroblast-specific BAG3 is deleterious, using cells and engineered cardiac tissues (ECT) derived from pluripotent stem cells (iPSC). Methods: Isogenic iPSCs containing homozygous knockout (KO) of BAG3 were differentiated into CFs. Mass spectrometry measured the effect of BAG3 loss on CF proteome. EdU staining and flow cytometry were used to assess CF proliferation. Transforming growth factor beta (TGF) signaling pathway activity was measured using a luciferase reporter and Western blotting. Affinity purification mass spectrometry (AP-MS) identified high-confidence interactors of BAG3 in CFs. To assess the role of fibroblast-specific BAG3 in cardiac function, conditional KO ECTs were generated using wild-type cardiomyocytes mixed with either wild-type CFs or KO CFs. ECTs were functionally assessed using video microscopy and immunofluorescence. The effects of CF BAG3 loss on tissue gene expression are under study by single nuclear RNA sequencing. Results: Analysis of proteomics data revealed enrichment of TGF and proliferation pathways in KO cells. KO-CFs had increased proliferation measured by EdU staining and increased SMAD luciferase reporter activity. Western blotting showed that KO CFs have higher expression of TGF beta receptor 2 (TGFBR2) and activation of canonical TGF signaling. AP-MS identified TGFBR2 as an interacting partner of BAG3, suggesting BAG3 mediated TGFBR2 turnover. In ECT, the loss of CF BAG3 caused a reduction in cardiac tissue contractile force (50% reduction, n = 10-12 tissues per group). Immunofluorescence revealed significantly increased tissue fibrosis in CF-KO cardiac tissues quantified by vimentin staining. Conclusions: BAG3 loss results in increased TGFBR2 signaling and proliferation in cardiac fibroblasts. Conditional KO ECTs demonstrate that CF BAG3 affects tissue function. These data suggest that fibrosis related to BAG3 pathology may be a result of a primary deficiency in CFs rather than a secondary response to cardiomyocyte death.

Integrated Analysis of Cortex Single-Cell Transcriptome and Serum Proteome Reveals the Novel Biomarkers in Alzheimer's Disease.

Blood-based proteomic analysis is a routine practice for detecting the biomarkers of human disease. The results obtained from blood alone cannot fully reflect the alterations of nerve cells, including neurons and glia cells, in Alzheimer’s disease (AD) brains. Therefore, the present study aimed to investigate novel potential AD biomarker candidates, through an integrated multi-omics approach in AD. We propose a comprehensive strategy to identify high-confidence candidate biomarkers by integrating multi-omics data from AD, including single-nuclei RNA sequencing (snRNA-seq) datasets of the prefrontal and entorhinal cortices, as wells as serum proteomic datasets. We first quantified a total of 124,658 nuclei, 8 cell types, and 3701 differentially expressed genes (DEGs) from snRNA-seq dataset of 30 human cortices, as well as 1291 differentially expressed proteins (DEPs) from serum proteomic dataset of 11 individuals. Then, ten DEGs/DEPs (NEBL, CHSY3, STMN2, MARCKS, VIM, FGD4, EPB41L2, PLEKHG1, PTPRZ1, and PPP1R14A) were identified by integration analysis of snRNA-seq and proteomics data. Finally, four novel candidate biomarkers (NEBL, EPB41L2, FGD4, and MARCKS) for AD further stood out, according to bioinformatics analysis, and they were verified by enzyme-linked immunosorbent assay (ELISA) verification. These candidate biomarkers are related to the regulation process of the actin cytoskeleton, which is involved in the regulation of synaptic loss in the AD brain tissue. Collectively, this study identified novel cell type-related biomarkers for AD by integrating multi-omics datasets from brains and serum. Our findings provided new targets for the clinical treatment and prognosis of AD.

Label-Free Mass Spectrometry-Based Quantitative Proteomics to Evaluate the Effects of the Calcium-Sensing Receptor Agonist Cinacalcet on Protein Expression in Rat Brains and Livers.

BackgroundCinacalcet is a calcium-sensing receptor agonist that is clinically approved for the treatment of secondary hyperparathyroidism in chronic kidney disease and hypercalcemia in patients with parathyroid carcinoma. This study aimed to use quantitative mass spectrometry-based label-free proteomics to evaluate the effects of cinacalcet on protein expression in rat brains and livers.Material/MethodsWe randomly assigned 18 Wistar rats to 2 groups: an untreated control group (n=6) and a group treated with cinacalcet at a dose corresponding to the maximum dose used in humans (2 mg/kg/body weight, 5 days/week) divided into 7-day (n=6) and 21-day (n=6) treatment subgroups. A mass-spectrometry-based label-free quantitative proteomics approach using peptides peak area calculation was used to evaluate the changes in protein expression in examined tissues. Bioinformatics analysis of quantitative proteomics data was done using MaxQuant and Perseus environment.ResultsNo changes in protein expression were revealed in the 7-day treatment subgroup. We detected 10 upregulated and 3 downregulated proteins in the liver and 1 upregulated protein in the brain in the 21-day treatment subgroup compared to the control group. Based on Gene Ontology classification, all identified differentially expressed proteins were indicated as molecular functions involved in the enzyme regulator activity (36%), binding (31%), and catalytic activity (19%).ConclusionsThese findings indicate that long-term cinacalcet therapy can impair phase II of enzymatic detoxication and can cause disturbances in blood hemostasis, lipid metabolism, and inflammatory mediators or contribute to the acceleration of cognitive dysfunction; therefore, appropriate patient monitoring should be considered.

Proteomics data analysis using multiple statistical approaches identified proteins and metabolic networks associated with sucrose accumulation in sugarcane

BackgroundSugarcane is the most important sugar crop, contributing > 80% of global sugar production. High sucrose content is a key target of sugarcane breeding, yet sucrose improvement in sugarcane remains extremely slow for decades. Molecular breeding has the potential to break through the genetic bottleneck of sucrose improvement. Dissecting the molecular mechanism(s) and identifying the key genetic elements controlling sucrose accumulation will accelerate sucrose improvement by molecular breeding. In our previous work, a proteomics dataset based on 12 independent samples from high- and low-sugar genotypes treated with ethephon or water was established. However, in that study, employing conventional analysis, only 25 proteins involved in sugar metabolism were identified .ResultsIn this work, the proteomics dataset used in our previous study was reanalyzed by three different statistical approaches, which include a logistic marginal regression, a penalized multiple logistic regression named Elastic net, as well as a Bayesian multiple logistic regression method named Stochastic search variable selection (SSVS) to identify more sugar metabolism-associated proteins. A total of 507 differentially abundant proteins (DAPs) were identified from this dataset, with 5 of them were validated by western blot. Among the DAPs, 49 proteins were found to participate in sugar metabolism-related processes including photosynthesis, carbon fixation as well as carbon, amino sugar, nucleotide sugar, starch and sucrose metabolism. Based on our studies, a putative network of key proteins regulating sucrose accumulation in sugarcane is proposed, with glucose-6-phosphate isomerase, 2-phospho-D-glycerate hydrolyase, malate dehydrogenase and phospho-glycerate kinase, as hub proteins.ConclusionsThe sugar metabolism-related proteins identified in this work are potential candidates for sucrose improvement by molecular breeding. Further, this work provides an alternative solution for omics data processing.