Experimental design and data-analysis in label-free quantitative LC/MS proteomics: A tutorial with MSqRob

A Mass Spectrometry Strategy for Protein Quantification Based on the Differential Alkylation of Cysteines Using Iodoacetamide and Acrylamide

Mass spectrometry has become the most prominent yet evolving technology in quantitative proteomics. Today, a number of label-free and label-based approaches are available for the relative and absolute quantification of proteins and peptides. However, the label-based methods rely solely on the employment of stable isotopes, which are expensive and often limited in availability. Here we propose a label-based quantification strategy, where the mass difference is identified by the differential alkylation of cysteines using iodoacetamide and acrylamide. The alkylation reactions were performed under identical experimental conditions; therefore, the method can be easily integrated into standard proteomic workflows. Using high-resolution mass spectrometry, the feasibility of this approach was assessed with a set of tryptic peptides of human serum albumin. Several critical questions, such as the efficiency of labeling and the effect of the differential alkylation on the peptide retention and fragmentation, were addressed. The concentration of the quality control samples calculated against the calibration curves were within the ±20% acceptance range. It was also demonstrated that heavy labeled peptides exhibit a similar extraction recovery and matrix effect to light ones. Consequently, the approach presented here may be a viable and cost-effective alternative of stable isotope labeling strategies for the quantification of cysteine-containing proteins.

Multi-omics characterization of partial chemical reprogramming reveals evidence of cell rejuvenation

Partial reprogramming by cyclic short-term expression of Yamanaka factors holds promise for shifting cells to younger states and consequently delaying the onset of many diseases of aging. However, the delivery of transgenes and potential risk of teratoma formation present challenges for in vivo applications. Recent advances include the use of cocktails of compounds to reprogram somatic cells, but the characteristics and mechanisms of partial cellular reprogramming by chemicals remain unclear. Here, we report a multi-omics characterization of partial chemical reprogramming in fibroblasts from young and aged mice. We measured the effects of partial chemical reprogramming on the epigenome, transcriptome, proteome, phosphoproteome, and metabolome. At the transcriptome, proteome, and phosphoproteome levels, we saw widescale changes induced by this treatment, with the most notable signature being an upregulation of mitochondrial oxidative phosphorylation. Furthermore, at the metabolome level, we observed a reduction in the accumulation of aging-related metabolites. Using both transcriptomic and epigenetic clock-based analyses, we show that partial chemical reprogramming reduces the biological age of mouse fibroblasts. We demonstrate that these changes have functional impacts, as evidenced by changes in cellular respiration and mitochondrial membrane potential. Taken together, these results illuminate the potential for chemical reprogramming reagents to rejuvenate aged biological systems and warrant further investigation into adapting these approaches for in vivo age reversal.

LFQRatio: A Normalization Method to Decipher Quantitative Proteome Changes in Microbial Coculture Systems

The value of synthetic microbial communities in biotechnology is gaining traction due to their ability to undertake more complex metabolic tasks than monocultures. However, a thorough understanding of strain interactions, productivity, and stability is often required to optimize growth and scale up cultivation. Quantitative proteomics can provide valuable insights into how microbial strains adapt to changing conditions in biomanufacturing. However, current workflows and methodologies are not suitable for simple artificial coculture systems where strain ratios are dynamic. Here, we established a workflow for coculture proteomics using an exemplar system containing two members, Azotobacter vinelandii and Synechococcus elongatus. Factors affecting the quantitative accuracy of coculture proteomics were investigated, including peptide physicochemical characteristics such as molecular weight, isoelectric point, hydrophobicity, and dynamic range as well as factors relating to protein identification such as varying proteome size and shared peptides between species. Different quantification methods based on spectral counts and intensity were evaluated at the protein and cell level. We propose a new normalization method, named "LFQRatio", to reflect the relative contributions of two distinct cell types emerging from cell ratio changes during cocultivation. LFQRatio can be applied to real coculture proteomics experiments, providing accurate insights into quantitative proteome changes in each strain.

DIA-based quantitative proteomic analysis of porcine endometrium in the peri-implantation phase

The 12th day of gestation is a critical period for embryo loss and the beginning of imminent implantation in sows. Data independent acquisition (DIA) technology is one of the high-throughput, high-resolution and reproducible proteomics technologies for large-scale digital qualitative and quantitative research. The aim of this study was to identify and characterize the protein abundance landscape of Yorkshire pig endometrium on the 12th day of pregnancy (P12) and estrous cycle (C12) using DIA proteomics. A total of 1251 differentially abundant proteins (DAPs) were identified, of which 882 were up-regulated and 369 were down-regulated at P12. Functional enrichment analysis showed that the identified proteins were related to metabolism, biosynthesis and signaling pathways. Three proteins were selected for Western blot (WB) validation and the results were consistent with the DIA data. Further combined with transcriptome data, fibrinogen like 2 (FGL2) and S100 calcium binding protein A8 (S100A8) were verified to be highly abundant in the P12 endometrial epithelium. In summary, there were significantly different abundance of proteome profiles in C12 and P12 endometrium, suggesting that DAPs are associated with changes in endometrial receptivity, which laid the foundation for further research on related regulatory mechanisms. SIGNIFICANCE: The 12th day of gestation is an important point in the peri-implantation period of pigs, when the endometrium presents a receptive state under the stimulation of estrogen. DIA proteomics technology is an emerging protein identification technology in recent years, which can obtain protein information through comprehensive and unbiased scanning. In this study, DIA technology was used to characterize endometrial proteins in pigs during the peri-implantation period. The results showed that higher protein abundance was detected using the DIA technique, and some of these DAPs may be involved in regulating embryo implantation. This study will help to better reveal the related proteins involved in embryo implantation, and lay a foundation for further research on the mechanism of endometrial regulation of embryo implantation. SIGNIFICANCE OF THE STUDY: The 12th day of gestation is an important point in the peri-implantation period of pigs, when the endometrium presents a receptive state under the stimulation of estrogen. DIA proteomics technology is an emerging protein identification technology in recent years, which can obtain protein information through comprehensive and unbiased scanning. In this study, DIA technology was used to characterize endometrial proteins in pigs during the peri-implantation period. The results showed that higher protein abundance was detected using the DIA technique, and some of these DAPs may be involved in regulating embryo implantation. This study will help to better reveal the related proteins involved in embryo implantation, and lay a foundation for further research on the mechanism of endometrial regulation of embryo implantation.

WOMBAT-P: Benchmarking Label-Free Proteomics Data Analysis Workflows

The inherent diversity of approaches in proteomics research has led to a wide range of software solutions for data analysis. These software solutions encompass multiple tools, each employing different algorithms for various tasks such as peptide-spectrum matching, protein inference, quantification, statistical analysis, and visualization. To enable an unbiased comparison of commonly used bottom-up label-free proteomics workflows, we introduce WOMBAT-P, a versatile platform designed for automated benchmarking and comparison. WOMBAT-P simplifies the processing of public data by utilizing the sample and data relationship format for proteomics (SDRF-Proteomics) as input. This feature streamlines the analysis of annotated local or public ProteomeXchange data sets, promoting efficient comparisons among diverse outputs. Through an evaluation using experimental ground truth data and a realistic biological data set, we uncover significant disparities and a limited overlap in the quantified proteins. WOMBAT-P not only enables rapid execution and seamless comparison of workflows but also provides valuable insights into the capabilities of different software solutions. These benchmarking metrics are a valuable resource for researchers in selecting the most suitable workflow for their specific data sets. The modular architecture of WOMBAT-P promotes extensibility and customization. The software is available at https://github.com/wombat-p/WOMBAT-Pipelines.

Multi-omics characterization of partial chemical reprogramming reveals evidence of cell rejuvenation

Partial reprogramming by cyclic short-term expression of Yamanaka factors holds promise for shifting cells to younger states and consequently delaying the onset of many diseases of aging. However, the delivery of transgenes and potential risk of teratoma formation present challenges for in vivo applications. Recent advances include the use of cocktails of compounds to reprogram somatic cells, but the characteristics and mechanisms of partial cellular reprogramming by chemicals remain unclear. Here, we report a multi-omics characterization of partial chemical reprogramming in fibroblasts from young and aged mice. We measured the effects of partial chemical reprogramming on the epigenome, transcriptome, proteome, phosphoproteome, and metabolome. At the transcriptome, proteome, and phosphoproteome levels, we saw widescale changes induced by this treatment, with the most notable signature being an upregulation of mitochondrial oxidative phosphorylation. Furthermore, at the metabolome level, we observed a reduction in the accumulation of aging-related metabolites. Using both transcriptomic and epigenetic clock-based analyses, we show that partial chemical reprogramming reduces the biological age of mouse fibroblasts. We demonstrate that these changes have functional impacts, as evidenced by changes in cellular respiration and mitochondrial membrane potential. Taken together, these results illuminate the potential for chemical reprogramming reagents to rejuvenate aged biological systems and warrant further investigation into adapting these approaches for in vivo age reversal.

Modeling collagen fibril self-assembly from extracellular medium in embryonic tendon

Collagen is a key structural component of multicellular organisms and is arranged in a highly organized manner. In structural tissues such as tendons, collagen forms bundles of parallel fibers between cells, which appear within a 24-h window between embryonic day 13.5 (E13.5) and E14.5 during mouse embryonic development. Current models assume that the organized structure of collagen requires direct cellular control, whereby cells actively lay down collagen fibrils from cell surfaces. However, such models appear incompatible with the time and length scales of fibril formation. We propose a phase-transition model to account for the rapid development of ordered fibrils in embryonic tendon, reducing reliance on active cellular processes. We develop phase-field crystal simulations of collagen fibrillogenesis in domains derived from electron micrographs of inter-cellular spaces in embryonic tendon and compare results qualitatively and quantitatively to observed patterns of fibril formation. To test the prediction of this phase-transition model that free protomeric collagen should exist in the inter-cellular spaces before the formation of observable fibrils, we use laser-capture microdissection, coupled with mass spectrometry, which demonstrates steadily increasing free collagen in inter-cellular spaces up to E13.5, followed by a rapid reduction of free collagen that coincides with the appearance of less-soluble collagen fibrils. The model and measurements together provide evidence for extracellular self-assembly of collagen fibrils in embryonic mouse tendon, supporting an additional mechanism for rapid collagen fibril formation during embryonic development.

Inhibiting de novo ceramide synthesis restores mitochondrial and protein homeostasis in muscle aging

Disruption of mitochondrial function and protein homeostasis plays a central role in aging. However, how these processes interact and what governs their failure in aging remain poorly understood. Here, we showed that ceramide biosynthesis controls the decline in mitochondrial and protein homeostasis during muscle aging. Analysis of transcriptome datasets derived from muscle biopsies obtained from both aged individuals and patients with a diverse range of muscle disorders revealed that changes in ceramide biosynthesis, as well as disturbances in mitochondrial and protein homeostasis pathways, are prevalent features in these conditions. By performing targeted lipidomics analyses, we found that ceramides accumulated in skeletal muscle with increasing age across Caenorhabditis elegans, mice, and humans. Inhibition of serine palmitoyltransferase (SPT), the rate-limiting enzyme of the ceramide de novo synthesis, by gene silencing or by treatment with myriocin restored proteostasis and mitochondrial function in human myoblasts, in C. elegans, and in the skeletal muscles of mice during aging. Restoration of these age-related processes improved health and life span in the nematode and muscle health and fitness in mice. Collectively, our data implicate pharmacological and genetic suppression of ceramide biosynthesis as potential therapeutic approaches to delay muscle aging and to manage related proteinopathies via mitochondrial and proteostasis remodeling.

High ploidy large cytoplasmic megakaryocytes are hematopoietic stem cells regulators and essential for platelet production

Megakaryocytes (MK) generate platelets. Recently, we and others, have reported MK also regulate hematopoietic stem cells (HSC). Here we show high ploidy large cytoplasmic megakaryocytes (LCM) are critical negative regulators of HSC and critical for platelet formation. Using a mouse knockout model (Pf4-Srsf3^Δ/Δ) with normal MK numbers, but essentially devoid of LCM, we demonstrate a pronounced increase in BM HSC concurrent with endogenous mobilization and extramedullary hematopoiesis. Severe thrombocytopenia is observed in animals with diminished LCM, although there is no change in MK ploidy distribution, uncoupling endoreduplication and platelet production. When HSC isolated from a microenvironment essentially devoid of LCM reconstitute hematopoiesis in lethally irradiated mice, the absence of LCM increases HSC in BM, blood and spleen, and the recapitulation of thrombocytopenia. In contrast, following a competitive transplant using minimal numbers of WT HSC together with HSC from a microenvironment with diminished LCM, sufficient WT HSC-generated LCM regulates a normal HSC pool and prevents thrombocytopenia. Importantly, LCM are conserved in humans.

Morphologically intact airways in lung fibrosis have an abnormal proteome

Honeycombing is a histological pattern consistent with Usual Interstitial Pneumonia (UIP). Honeycombing refers to cystic airways located at sites of dense fibrosis with marked mucus accumulation. Utilizing laser capture microdissection coupled mass spectrometry (LCM-MS), we interrogated the fibrotic honeycomb airway cells and fibrotic uninvolved airway cells (distant from honeycomb airways and morphologically intact) in specimens from 10 patients with UIP. Non-fibrotic airway cell specimens from 6 patients served as controls. Furthermore, we performed LCM-MS on the mucus plugs found in 6 patients with UIP and 6 patients with mucinous adenocarcinoma. The mass spectrometry data were subject to both qualitative and quantitative analysis and validated by immunohistochemistry. Surprisingly, fibrotic uninvolved airway cells share a similar protein profile to honeycomb airway cells, showing deregulation of the slit and roundabout receptor (Slit and Robo) pathway as the strongest category. We find that (BPI) fold-containing family B member 1 (BPIFB1) is the most significantly increased secretome-associated protein in UIP, whereas Mucin-5AC (MUC5AC) is the most significantly increased in mucinous adenocarcinoma. We conclude that fibrotic uninvolved airway cells share pathological features with fibrotic honeycomb airway cells. In addition, fibrotic honeycomb airway cells are enriched in mucin biogenesis proteins with a marked derangement in proteins essential for ciliogenesis. This unbiased spatial proteomic approach generates novel and testable hypotheses to decipher fibrosis progression.

The genetic background shapes the susceptibility to mitochondrial dysfunction and NASH progression

Non-alcoholic steatohepatitis (NASH) is a global health concern without treatment. The challenge in finding effective therapies is due to the lack of good mouse models and the complexity of the disease, characterized by gene-environment interactions. We tested the susceptibility of seven mouse strains to develop NASH. The severity of the clinical phenotypes observed varied widely across strains. PWK/PhJ mice were the most prone to develop hepatic inflammation and the only strain to progress to NASH with extensive fibrosis, while CAST/EiJ mice were completely resistant. Levels of mitochondrial transcripts and proteins as well as mitochondrial function were robustly reduced specifically in the liver of PWK/PhJ mice, suggesting a central role of mitochondrial dysfunction in NASH progression. Importantly, the NASH gene expression profile of PWK/PhJ mice had the highest overlap with the human NASH signature. Our study exposes the limitations of using a single mouse genetic background in metabolic studies and describes a novel NASH mouse model with features of the human NASH.

Proteomics of the dentate gyrus reveals semantic dementia specific molecular pathology

Semantic dementia (SD) is a clinical subtype of frontotemporal dementia consistent with the neuropathological diagnosis frontotemporal lobar degeneration (FTLD) TDP type C, with characteristic round TDP-43 protein inclusions in the dentate gyrus. Despite this striking clinicopathological concordance, the pathogenic mechanisms are largely unexplained forestalling the development of targeted therapeutics. To address this, we carried out laser capture microdissection of the dentate gyrus of 15 SD patients and 17 non-demented controls, and assessed relative protein abundance changes by label-free quantitative mass spectrometry. To identify SD specific proteins, we compared our results to eight other FTLD and Alzheimer's disease (AD) proteomic datasets of cortical brain tissue, parallel with functional enrichment analyses and protein-protein interactions (PPI). Of the total 5,354 quantified proteins, 151 showed differential abundance in SD patients (adjusted P-value < 0.01). Seventy-nine proteins were considered potentially SD specific as these were not detected, or demonstrated insignificant or opposite change in FTLD/AD. Functional enrichment indicated an overrepresentation of pathways related to the immune response, metabolic processes, and cell-junction assembly. PPI analysis highlighted a cluster of interacting proteins associated with adherens junction and cadherin binding, the cadherin-catenin complex. Multiple proteins in this complex showed significant upregulation in SD, including β-catenin (CTNNB1), γ-catenin (JUP), and N-cadherin (CDH2), which were not observed in other neurodegenerative proteomic studies, and hence may resemble SD specific involvement. A trend of upregulation of all three proteins was observed by immunoblotting of whole hippocampus tissue, albeit only significant for N-cadherin. In summary, we discovered a specific increase of cell adhesion proteins in SD constituting the cadherin-catenin complex at the synaptic membrane, essential for synaptic signaling. Although further investigation and validation are warranted, we anticipate that these findings will help unravel the disease processes underlying SD.

Synaptic activity-dependent changes in the hippocampal palmitoylome

Dynamic protein S-palmitoylation is critical for neuronal function, development, and synaptic plasticity. Synaptic activity-dependent changes in palmitoylation have been reported for a small number of proteins. Here, we characterized the palmitoylome in the hippocampi of male mice before and after context-dependent fear conditioning. Of the 121 differentially palmitoylated proteins identified, just over half were synaptic proteins, whereas others were associated with metabolic functions, cytoskeletal organization, and signal transduction. The synapse-associated proteins generally exhibited increased palmitoylation after fear conditioning. In contrast, most of the proteins that exhibited decreased palmitoylation were associated with metabolic processes. Similar results were seen in cultured rat hippocampal neurons in response to chemically induced long-term potentiation. Furthermore, we found that the palmitoylation of one of the synaptic proteins, plasticity-related gene-1 (PRG-1), also known as lipid phosphate phosphatase-related protein type 4 (LPPR4), was important for synaptic activity-induced insertion of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPARs) into the postsynaptic membrane. The findings identify proteins whose dynamic palmitoylation may regulate their role in synaptic plasticity, learning, and memory.

The UIP/IPF fibroblastic focus is a collagen biosynthesis factory embedded in a distinct extracellular matrix

Usual interstitial pneumonia (UIP) is a histological pattern characteristic of idiopathic pulmonary fibrosis (IPF). The UIP pattern is patchy with histologically normal lung adjacent to dense fibrotic tissue. At this interface, fibroblastic foci (FF) are present and are sites where myofibroblasts and extracellular matrix (ECM) accumulate. Utilizing laser capture microdissection-coupled mass spectrometry, we interrogated the FF, adjacent mature scar, and adjacent alveoli in 6 fibrotic (UIP/IPF) specimens plus 6 nonfibrotic alveolar specimens as controls. The data were subjected to qualitative and quantitative analysis and histologically validated. We found that the fibrotic alveoli protein signature is defined by immune deregulation as the strongest category. The fibrotic mature scar classified as end-stage fibrosis whereas the FF contained an overabundance of a distinctive ECM compared with the nonfibrotic control. Furthermore, FF were positive for both TGFB1 and TGFB3, whereas the aberrant basaloid cell lining of FF was predominantly positive for TGFB2. In conclusion, spatial proteomics demonstrated distinct protein compositions in the histologically defined regions of UIP/IPF tissue. These data revealed that FF are the main site of collagen biosynthesis and that the adjacent alveoli are abnormal. This essential information will inform future mechanistic studies on fibrosis progression.

Deep Learning-Based Label-Free Surface-Enhanced Raman Scattering Screening and Recognition of Small-Molecule Binding Sites in Proteins

Identification of small-molecule binding sites in proteins is of great significance in analysis of protein function and drug design. Modified sites can be recognized via proteolytic cleavage followed by liquid chromatography-mass spectrometry (LC-MS); however, this has always been impeded by the complexity of peptide mixtures and the elaborate synthetic design for tags. Here, we demonstrate a novel technique for identifying protein binding sites using a deep learning-based label-free surface-enhanced Raman scattering (SERS) screening (DLSS) strategy. In DLSS, the deep learning model that was trained with large SERS signals could detect signal features of small molecules with high accuracy (>99%). Without any secondary tag, the small molecules are directly complexed with proteins. After proteolysis and LC, SERS signals of all LC fractions are collected and input into the model, whereby the fractions containing the small-molecule-modified peptides can be recognized by the model and sent to MS/MS to identify the binding site(s). By using an automated DLSS system, we successfully identified the modification sites of fomepizole in alcohol dehydrogenase, which is coordinated with zinc along with three peptides. We also showed that the DLSS strategy works for identification of amino-acid residues that covalently bond with ibrutinib in Bruton tyrosine kinase. These results suggest that the DLSS strategy, which provides high molecular recognition capability to LC-MS analysis, has potential in drug discovery, proteomics, and metabolomics.

Empirical Bayes functional models for hydrogen deuterium exchange mass spectrometry

Hydrogen deuterium exchange mass spectrometry (HDX-MS) is a technique to explore differential protein structure by examining the rate of deuterium incorporation for specific peptides. This rate will be altered upon structural perturbation and detecting significant changes to this rate requires a statistical test. To determine rates of incorporation, HDX-MS measurements are frequently made over a time course. However, current statistical testing procedures ignore the correlations in the temporal dimension of the data. Using tools from functional data analysis, we develop a testing procedure that explicitly incorporates a model of hydrogen deuterium exchange. To further improve statistical power, we develop an empirical Bayes version of our method, allowing us to borrow information across peptides and stabilise variance estimates for low sample sizes. Our approach has increased power, reduces false positives and improves interpretation over linear model-based approaches. Due to the improved flexibility of our method, we can apply it to a multi-antibody epitope-mapping experiment where current approaches are inapplicable due insufficient flexibility. Hence, our approach allows HDX-MS to be applied in more experimental scenarios and reduces the burden on experimentalists to produce excessive replicates. Our approach is implemented in the R-package “hdxstats”: https://github.com/ococrook/hdxstats.

A statistical analysis approach for HDX-MS time series data incorporates correlations in time, reducing false positives and improving statistical power and data interpretation.

Integrated multi-omics reveal polycomb repressive complex 2 restricts human trophoblast induction

Human naive pluripotent stem cells have unrestricted lineage potential. Underpinning this property, naive cells are thought to lack chromatin-based lineage barriers. However, this assumption has not been tested. Here we define the chromatin-associated proteome, histone post-translational modifications and transcriptome of human naive and primed pluripotent stem cells. Our integrated analysis reveals differences in the relative abundance and activities of distinct chromatin modules. We identify a strong enrichment of polycomb repressive complex 2 (PRC2)-associated H3K27me3 in the chromatin of naive pluripotent stem cells and H3K27me3 enrichment at promoters of lineage-determining genes, including trophoblast regulators. PRC2 activity acts as a chromatin barrier restricting the differentiation of naive cells towards the trophoblast lineage, whereas inhibition of PRC2 promotes trophoblast-fate induction and cavity formation in human blastoids. Together, our results establish that human naive pluripotent stem cells are not epigenetically unrestricted, but instead possess chromatin mechanisms that oppose the induction of alternative cell fates.

Two side-by-side papers report that H3K27me3 deposited by polycomb repressive complex 2 represents an epigenetic barrier that restricts naive human pluripotent cell differentiation into alternative lineages including trophoblasts.

Impact of Maternal Feed Restriction at Different Stages of Gestation on the Proteomic Profile of the Newborn Skeletal Muscle

We aimed to investigate the effects of the maternal plane of nutrition during gestation on the proteome profile of the skeletal muscle of the newborn. Pregnant goats were assigned to the following experimental treatments: restriction maintenance (RM) where pregnant dams were fed at 50% of their maintenance requirements from 8−84 days of gestation, and then feed of 100% of the maintenance requirements was supplied from 85—parturition (n = 6); maintenance restriction (MR) where pregnant dams were fed at 100% of their maintenance requirements from 8−84 days of gestation, and then experienced feed restriction of 50% of the maintenance requirements from 85—parturition (n = 8). At birth, newborns were euthanized and samples of the Longissimus dorsi muscle were collected and used to perform HPLC-MS/MS analysis. The network analyses were performed to identify the biological processes and KEGG pathways of the proteins identified as differentially abundant protein and were deemed significant when the adjusted p-value (FDR) < 0.05. Our results suggest that treatment RM affects the energy metabolism of newborns’ skeletal muscle by changing the energy-investment phase of glycolysis, in addition to utilizing glycogen as a carbon source. Moreover, the RM plane of nutrition may contribute to fatty acid oxidation and increases in the cytosolic α-KG and mitochondrial NADH levels in the skeletal muscle of the newborn. On the other hand, treatment MR likely affects the energy-generation phase of glycolysis, contributing to the accumulation of mitochondrial α-KG and the biosynthesis of glutamine.

Quantifying phosphorylation dynamics in primary neuronal cultures using LC-MS/MS

Cellular processes require tight and coordinated control of protein abundance, localization, and activity. One of the core mechanisms to achieve specific regulation of proteins is protein phosphorylation. Here we present a workflow to monitor protein abundance and phosphorylation in primary cultured neurons using liquid chromatography-coupled mass spectrometry. Our protocol provides a detailed guide on all steps for detection and label-free-quantification of phosphorylated and unmodified proteins of primary cortical neurons, including primary cell culture, phosphoproteomic sample preparation and data-processing, and evaluation. For complete details on the use and execution of this protocol, please refer to Desch et al. (2021).

Bone protein analysis via label-free quantitative proteomics in patients with periprosthetic joint infection

Periprosthetic joint infection (PJI) is a catastrophic complication of arthroplasty. The treatment of PJI often requires multiple operations and long-term use of antibiotics, making PJI a substantial health and economic burden for patients. Therefore, there is an urgent need to elucidate the pathological mechanism of PJI to explore new therapeutic methods. This study aimed to explore proteomics changes in bone tissue around the prosthesis during PJI development, to explain the pathological mechanism and to provide new treatment ideas. Ten patients who underwent revision surgery at our institution were included: 5 patients with Staphylococcus aureus PJI and 5 patients with aseptic failure. The proteomics changes in bone tissues after PJI were investigated by label-free quantitative proteomics, and the pathways affected by the differential proteins were analyzed by GO annotation, GO enrichment analysis, KEGG enrichment analysis and protein-protein interaction network analysis. We identified 435 differentially expressed proteins (DEPs), with 213 upregulated and 222 downregulated proteins. Analysis revealed activation of immune-related pathways, such as complement and coagulation cascades, phagocytosis, and neutrophil activation, and inhibition of energy metabolism pathways represented by the TCA cycle. We also observed an altered balance between osteoblasts and osteoclasts during S. aureus PJI. We hope that these processes will reveal new treatment ideas. SIGNIFICANCE: PJI is a catastrophic complication of arthroplasty. When infection occurs, bacteria may invade periprosthetic bone tissue to escape immunity and cause damage. So far, only few studies focused on the changes of proteomics associated to PJI. This is the first study to describe the proteomics changes of periprosthetic bone tissue of patients with PJI. We found that the pathological process of S. aureus PJI mainly involves activation of the immune system, decreased energy metabolism, and an altered balance of osteoblasts and osteoclasts.

Relevant proteins for the monitoring of engraftment phases after allogeneic hematopoietic stem cell transplantation

INTRODUCTION

Hematopoietic Stem Cell Transplant (HSCT) has been successfully used as standard therapy for hematological disorders. After conditioning therapy, patients undergoing allogeneic HSCT, present three different phases of engraftment: early pre-engraftment, early post-engraftment, and late engraftment. Severe complications are associated with morbidity, mortality, and malignancies in these phases, which include effects on the oral cavity.

OBJECTIVES

The changes in the salivary composition after HSCT may contribute to identifying relevant proteins that could map differences among the phases of diseases, driven for personalized diagnostics and therapy.

METHODS

Unstimulated whole saliva was collected from patients submitted to HSCT. The samples were submitted to trypsin digestion for a Mass spectrometry analysis. MaxQuant processed the Data analysis, and the relevant expressed proteins were subjected to pathway and network analyses.

RESULTS

Differences were observed in the most identified proteins, specifically in proteins involved with the regulation of body fluid levels and the mucosal immune response. The heatmap showed a list of proteins exclusively expressed during the different phases of HSCT: HBB, KNG1, HSPA, FGB, APOA1, PFN1, PRTN3, TMSB4X, YWHAZ, CAP1, ACTN1, CLU and ALDOA. Bioinformatics analysis implicated pathways involved in protein processing in the endoplasmic reticulum, complement and coagulation cascades, apoptosis signaling, and cholesterol metabolism.

CONCLUSION

The compositional changes in saliva reflected the three phases of HSCT and demonstrated the usefulness of proteomics and computational approaches as a revolutionary field in diagnostic methods.

MSPoisDM: A Novel Peptide Identification Algorithm Optimized for Tandem Mass Spectra

Tandem mass spectrometry (MS/MS) plays an extremely important role in proteomics research. Thousands of spectra can be generated in modern experiments, how to interpret the LC-MS/MS is a challenging problem in tandem mass spectra analysis. Our peptide identification algorithm, MSPoisDM, is integrated the intensity information which produced by target-decoy statistics, although intensity information often undervalued. Furthermore, in order to combine the intensity information for better, we propose a novel concept scoring model which based on Poisson distribution. Compared with commonly used commercial software Mascot and Sequest at 1% FDR, the results show MSPoisDM is robust and versatile for various datasets which obtained from different instruments. We expect our algorithm MSPoisDM will be broadly applied in the proteomics studies.

protti: an R package for comprehensive data analysis of peptide- and protein-centric bottom-up proteomics data

Summary

We present a flexible, user-friendly R package called protti for comprehensive quality control, analysis and interpretation of quantitative bottom-up proteomics data. protti supports the analysis of protein-centric data such as those associated with protein expression analyses, as well as peptide-centric data such as those resulting from limited proteolysis-coupled mass spectrometry analysis. Due to its flexible design, it supports analysis of label-free, data-dependent, data-independent and targeted proteomics datasets. protti can be run on the output of any search engine and software package commonly used for bottom-up proteomics experiments such as Spectronaut, Skyline, MaxQuant or Proteome Discoverer, adequately exported to table format.

Availability and implementation

protti is implemented as an open-source R package. Release versions are available via CRAN (https://CRAN.R-project.org/package=protti) and work on all major operating systems. The development version is maintained on GitHub (https://github.com/jpquast/protti). Full documentation including examples is provided in the form of vignettes on our package website (jpquast.github.io/protti/).

Candidatus Kaistella beijingensis sp. nov., Isolated from a Municipal Wastewater Treatment Plant, Is Involved in Sludge Foaming

Biological foaming (or biofoaming) is a frequently occurring problem in wastewater treatment plants (WWTPs) and is attributed to the overwhelming growth of filamentous bulking and foaming bacteria (BFB). Biological foaming has been intensively investigated, with BFB like Microthrix and Skermania having been identified from WWTPs and implicated in foaming. Nevertheless, studies are still needed to improve our understanding of the microbial diversity of WWTP biofoams and how microbial activities contribute to foaming. In this study, sludge foaming at the Qinghe WWTP of China was monitored, and sludge foams were investigated using culture-dependent and culture-independent microbiological methods. The foam microbiomes exhibited high abundances of Skermania, Mycobacterium, Flavobacteriales, and Kaistella. A previously unknown bacterium, Candidatus Kaistella beijingensis, was cultivated from foams, its genome was sequenced, and it was phenotypically characterized. Ca. K. beijingensis exhibits hydrophobic cell surfaces, produces extracellular polymeric substances (EPS), and metabolizes lipids. Ca. K. beijingensis abundances were proportional to EPS levels in foams. Several proteins encoded by the Ca. K. beijingensis genome were identified from EPS that was extracted from sludge foams. Ca. K. beijingensis populations accounted for 4 to 6% of the total bacterial populations in sludge foam samples within the Qinghe WWTP, although their abundances were higher in spring than in other seasons. Cooccurrence analysis indicated that Ca. K. beijingensis was not a core node among the WWTP community network, but its abundances were negatively correlated with those of the well-studied BFB Skermania piniformis among cross-season Qinghe WWTP communities. IMPORTANCE Biological foaming, also known as scumming, is a sludge separation problem that has become the subject of major concern for long-term stable activated sludge operation in decades. Biological foaming was considered induced by foaming bacteria. However, the occurrence and deterioration of foaming in many WWTPs are still not completely understood. Cultivation and characterization of the enriched bacteria in foaming are critical to understand their genetic, physiological, phylogenetic, and ecological traits, as well as to improve the understanding of their relationships with foaming and performance of WWTPs.

Phosphorylated protein modification analysis on normal liver and Exo-celiac liver of Glyptosternum maculatum

BACKGROUND

This study aimed to reveal the biological function and molecular mechanism of phosphorylated proteins in the normal liver (NG) and Exo-celiac liver (WG) of Glyptosternum maculatum and potential plateau-adaption mechanisms of G. maculatum.

METHODS

A multivariate analysis was performed on proteomic quantitative data (label-free group) and phosphorylated proteome data (phosphorylation group) to reveal protein characteristics. The differentially expressed proteins (DEPs) between NG and WG in the two groups were analysed. Enrichment analysis of these DEPs was performed prior to the protein-protein interaction (PPI) analysis. Finally, an integrated interaction network was constructed to reveal the biological mechanism of the DEP-mediated signal transduction process.

RESULT

The NG and WG samples in the phosphorylation group were well distinguished compared to the label-free group. A total of 49 and 313 DEPs were identified in the label-free and phosphorylation groups, respectively. These DEPs, including LIM and calponin homology domains-containing protein 1 (LIMCH1) and DEAD(Asp-Glu-Ala-Asp)-Box Helicase 51 (DDX51), were mainly assembled in functions such as cell adhesion. Two PPI networks were constructed using DEPs in the two groups. Finally, an integrated interaction network was constructed using co-DEP Ferredoxin 1 (FDX1) and associated pathways, including RNA transport.

CONCLUSION

LIMCH1 and DDX51 might play important roles in the organogenesis of normal liver and Exo-celiac liver in G. maculatum via the cell adhesion function. Moreover, FXD1 might be associated with the plateau-adaption mechanisms of G. maculatum via participation in the RNA transport pathway.

Neuronal ribosomes exhibit dynamic and context-dependent exchange of ribosomal proteins

Owing to their morphological complexity and dense network connections, neurons modify their proteomes locally, using mRNAs and ribosomes present in the neuropil (tissue enriched for dendrites and axons). Although ribosome biogenesis largely takes place in the nucleus and perinuclear region, neuronal ribosomal protein (RP) mRNAs have been frequently detected remotely, in dendrites and axons. Here, using imaging and ribosome profiling, we directly detected the RP mRNAs and their translation in the neuropil. Combining brief metabolic labeling with mass spectrometry, we found that a group of RPs rapidly associated with translating ribosomes in the cytoplasm and that this incorporation was independent of canonical ribosome biogenesis. Moreover, the incorporation probability of some RPs was regulated by location (neurites vs. cell bodies) and changes in the cellular environment (following oxidative stress). Our results suggest new mechanisms for the local activation, repair and/or specialization of the translational machinery within neuronal processes, potentially allowing neuronal synapses a rapid means to regulate local protein synthesis.

Application of Proteomics in Cancer: Recent Trends and Approaches for Biomarkers Discovery

Proteomics has become an important field in molecular sciences, as it provides valuable information on the identity, expression levels, and modification of proteins. For example, cancer proteomics unraveled key information in mechanistic studies on tumor growth and metastasis, which has contributed to the identification of clinically applicable biomarkers as well as therapeutic targets. Several cancer proteome databases have been established and are being shared worldwide. Importantly, the integration of proteomics studies with other omics is providing extensive data related to molecular mechanisms and target modulators. These data may be analyzed and processed through bioinformatic pipelines to obtain useful information. The purpose of this review is to provide an overview of cancer proteomics and recent advances in proteomic techniques. In particular, we aim to offer insights into current proteomics studies of brain cancer, in which proteomic applications are in a relatively early stage. This review covers applications of proteomics from the discovery of biomarkers to the characterization of molecular mechanisms through advances in technology. Moreover, it addresses global trends in proteomics approaches for translational research. As a core method in translational research, the continued development of this field is expected to provide valuable information at a scale beyond that previously seen.

Biochar Mitigates N2O Emission of Microbial Denitrification through Modulating Carbon Metabolism and Allocation of Reducing Power

To elucidate the direct effects of biochar on denitrification metabolism at the cellular level, the global response of model denitrifying soil bacterium (Paracoccus denitrificans) to biochar addition was investigated by physiological, proteomic, and metabolomics analyses. The enhancement effect on denitrification was positively correlated with its pyrolysis temperatures (300-500 °C) and dosages (0.1-1%), regardless of precursors [corn straw (CS) or wheat straw). Moreover, the stimulating effect of CS biochar made at 500 °C (CS-500) was mainly attributed to the bulk particles rather than the released soluble compounds. Without direct contact with cells, bulk CS-500 particles might directly modulate the carbon metabolism by the adsorption of extracellular metabolites. Since carbon flux to storage was shifted to oxidative catabolism and growth assimilation, more share of the produced reducing power was used for nitrogen reduction. Meanwhile, except for nitrate reductase, both protein expressions and enzyme activities of nitrite reductase, nitric oxide reductase, and nitrous oxide reductase were up-regulated. Accordingly, the accumulation of N₂O was reduced by 98% due to the optimized electron distribution among denitrifying enzymes. Eventually, the growth rate of Pc. denitrificans enhanced because of the improved energy utilization efficiency. These results updated the regulation mechanism of biochar on denitrification metabolism and N₂O mitigation.

IL-13 deficiency exacerbates lung damage and impairs epithelial-derived type 2 molecules during nematode infection

IL-13 is implicated in effective repair after acute lung injury and the pathogenesis of chronic diseases such as allergic asthma. Both these processes involve matrix remodelling, but understanding the specific contribution of IL-13 has been challenging because IL-13 shares receptors and signalling pathways with IL-4. Here, we used Nippostrongylus brasiliensis infection as a model of acute lung damage comparing responses between WT and IL-13-deficient mice, in which IL-4 signalling is intact. We found that IL-13 played a critical role in limiting tissue injury and haemorrhaging in the lung, and through proteomic and transcriptomic profiling, identified IL-13-dependent changes in matrix and associated regulators. We further showed a requirement for IL-13 in the induction of epithelial-derived type 2 effector molecules such as RELM-α and surfactant protein D. Pathway analyses predicted that IL-13 induced cellular stress responses and regulated lung epithelial cell differentiation by suppression of Foxa2 pathways. Thus, in the context of acute lung damage, IL-13 has tissue-protective functions and regulates epithelial cell responses during type 2 immunity.

Evaluating Spatiotemporal Dynamics of Phosphorylation of RNA Polymerase II Carboxy-Terminal Domain by Ultraviolet Photodissociation Mass Spectrometry

The critical role of site-specific phosphorylation in eukaryotic transcription has motivated efforts to decipher the complex phosphorylation patterns exhibited by the carboxyl-terminal domain (CTD) of RNA polymerase II. Phosphorylation remains a challenging post-translational modification to characterize by mass spectrometry owing to the labile phosphate ester linkage and low stoichiometric prevalence, two features that complicate analysis by high-throughput MS/MS methods. Identifying phosphorylation sites represents one significant hurdle in decrypting the CTD phosphorylation, a problem exaggerated by a large number of potential phosphorylation sites. An even greater obstacle is decoding the dynamic phosphorylation pattern along the length of the periodic CTD sequence. Ultraviolet photodissociation (UVPD) is a high-energy ion activation method that provides ample backbone cleavages of peptides while preserving labile post-translational modifications that facilitate their confident localization. Herein, we report a quantitative parallel reaction monitoring (PRM) method developed to monitor spatiotemporal changes in site-specific Ser5 phosphorylation of the CTD by cyclin-dependent kinase 7 (CDK7) using UVPD for sequence identification, phosphosite localization, and differentiation of phosphopeptide isomers. We capitalize on the series of phospho-retaining fragment ions produced by UVPD to create unique transition lists that are pivotal for distinguishing the array of phosphopeptides generated from the CTD.

The proteome of granulovacuolar degeneration and neurofibrillary tangles in Alzheimer's disease

Granulovacuolar degeneration (GVD) is a common feature in Alzheimer's disease (AD). The occurrence of GVD is closely associated with that of neurofibrillary tangles (NFTs) and GVD is even considered to be a pre-NFT stage in the disease process of AD. Currently, the composition of GVD bodies, the mechanisms associated with GVD and how GVD exactly relates to NFTs is not well understood. By combining immunohistochemistry (IHC) and laser microdissection (LMD) we isolated neurons with GVD and those bearing tangles separately from human post-mortem AD hippocampus (n = 12) using their typical markers casein kinase (CK)1δ and phosphorylated tau (AT8). Control neurons were isolated from cognitively healthy cases (n = 12). 3000 neurons per sample were used for proteome analysis by label free LC-MS/MS. In total 2596 proteins were quantified across samples and a significant change in abundance of 115 proteins in GVD and 197 in tangle bearing neurons was observed compared to control neurons. With IHC the presence of PPIA, TOMM34, HSP70, CHMP1A, TPPP and VXN was confirmed in GVD containing neurons. We found multiple proteins localizing specifically to the GVD bodies, with VXN and TOMM34 being the most prominent new protein markers for GVD bodies. In general, protein groups related to protein folding, proteasomal function, the endolysosomal pathway, microtubule and cytoskeletal related function, RNA processing and glycolysis were found to be changed in GVD neurons. In addition to these protein groups, tangle bearing neurons show a decrease in ribosomal proteins, as well as in various proteins related to protein folding. This study, for the first time, provides a comprehensive human based quantitative assessment of protein abundances in GVD and tangle bearing neurons. In line with previous functional data showing that tau pathology induces GVD, our data support the model that GVD is part of a pre-NFT stage representing a phase in which proteostasis and cellular homeostasis is disrupted. Elucidating the molecular mechanisms and cellular processes affected in GVD and its relation to the presence of tau pathology is highly relevant for the identification of new drug targets for therapy.

Benchmarking Quantitative Performance in Label-Free Proteomics

Previous benchmarking studies have demonstrated the importance of instrument acquisition methodology and statistical analysis on quantitative performance in label-free proteomics. However, the effects of these parameters in combination with replicate number and false discovery rate (FDR) corrections are not known. Using a benchmarking standard, we systematically evaluated the combined impact of acquisition methodology, replicate number, statistical approach, and FDR corrections. These analyses reveal a complex interaction between these parameters that greatly impacts the quantitative fidelity of protein- and peptide-level quantification. At a high replicate number (n = 8), both data-dependent acquisition (DDA) and data-independent acquisition (DIA) methodologies yield accurate protein quantification across statistical approaches. However, at a low replicate number (n = 4), only DIA in combination with linear models for microarrays (LIMMA) and reproducibility-optimized test statistic (ROTS) produced a high level of quantitative fidelity. Quantitative accuracy at low replicates is also greatly impacted by FDR corrections, with Benjamini-Hochberg and Storey corrections yielding variable true positive rates for DDA workflows. For peptide quantification, replicate number and acquisition methodology are even more critical. A higher number of replicates in combination with DIA and LIMMA produce high quantitative fidelity, while DDA performs poorly regardless of replicate number or statistical approach. These results underscore the importance of pairing instrument acquisition methodology with the appropriate replicate number and statistical approach for optimal quantification performance.

Toward a Sample Metadata Standard in Public Proteomics Repositories

Metadata is essential in proteomics data repositories and is crucial to interpret and reanalyze the deposited data sets. For every proteomics data set, we should capture at least three levels of metadata: (i) data set description, (ii) the sample to data files related information, and (iii) standard data file formats (e.g., mzIdentML, mzML, or mzTab). While the data set description and standard data file formats are supported by all ProteomeXchange partners, the information regarding the sample to data files is mostly missing. Recently, members of the European Bioinformatics Community for Mass Spectrometry (EuBIC) have created an open-source project called Sample to Data file format for Proteomics (https://github.com/bigbio/proteomics-metadata-standard/) to enable the standardization of sample metadata of public proteomics data sets. Here, the project is presented to the proteomics community, and we call for contributors, including researchers, journals, and consortiums to provide feedback about the format. We believe this work will improve reproducibility and facilitate the development of new tools dedicated to proteomics data analysis.

Environmentally Relevant Concentration of Bisphenol S Shows Slight Effects on SIHUMIx

Bisphenol S (BPS) is an industrial chemical used in the process of polymerization of polycarbonate plastics and epoxy resins and thus can be found in various plastic products and thermal papers. The microbiota disrupting effect of BPS on the community structure of the microbiome has already been reported, but little is known on how BPS affects bacterial activity and function. To analyze these effects, we cultivated the simplified human intestinal microbiota (SIHUMIx) in bioreactors at a concentration of 45 µM BPS. By determining biomass, growth of SIHUMIx was followed but no differences during BPS exposure were observed. To validate if the membrane composition was affected, fatty acid methyl esters (FAMEs) profiles were compared. Changes in the individual membrane fatty acid composition could not been described; however, the saturation level of the membranes slightly increased during BPS exposure. By applying targeted metabolomics to quantify short-chain fatty acids (SCFA), it was shown that the activity of SIHUMIx was unaffected. Metaproteomics revealed temporal effect on the community structure and function, showing that BPS has minor effects on the structure or functionality of SIHUMIx.

PolySTest: Robust Statistical Testing of Proteomics Data with Missing Values Improves Detection of Biologically Relevant Features

Statistical testing remains one of the main challenges for high-confidence detection of differentially regulated proteins or peptides in large-scale quantitative proteomics experiments by mass spectrometry. Statistical tests need to be sufficiently robust to deal with experiment intrinsic data structures and variations and often also reduced feature coverage across different biological samples due to ubiquitous missing values. A robust statistical test provides accurate confidence scores of large-scale proteomics results, regardless of instrument platform, experimental protocol and software tools. However, the multitude of different combinations of experimental strategies, mass spectrometry techniques and informatics methods complicate the decision of choosing appropriate statistical approaches. We address this challenge by introducing PolySTest, a user-friendly web service for statistical testing, data browsing and data visualization. We introduce a new method, Miss test, that simultaneously tests for missingness and feature abundance, thereby complementing common statistical tests by rescuing otherwise discarded data features. We demonstrate that PolySTest with integrated Miss test achieves higher confidence and higher sensitivity for artificial and experimental proteomics data sets with known ground truth. Application of PolySTest to mass spectrometry based large-scale proteomics data obtained from differentiating muscle cells resulted in the rescue of 10-20% additional proteins in the identified molecular networks relevant to muscle differentiation. We conclude that PolySTest is a valuable addition to existing tools and instrument enhancements that improve coverage and depth of large-scale proteomics experiments. A fully functional demo version of PolySTest and Miss test is available via http://computproteomics.bmb.sdu.dk/Apps/PolySTest.

Potential mechanisms of action of celastrol against rheumatoid arthritis: Transcriptomic and proteomic analysis

The clinical efficacy for treating of celastrol rheumatoid arthritis (RA) has been well-documented, but its mechanism of action remains unclear. Here we explored through what proteins and processes celastrol may act in activated fibroblast-like synoviocytes (FLS) from RA patients. Differential expression of genes and proteins after celastrol treatment of FLS was examined using RNA sequencing, label-free relatively quantitative proteomics and molecular docking. In this paper, expression of 26,565 genes and 3,372 proteins was analyzed. Celastrol was associated with significant changes in genes that respond to oxidative stress and oxygen levels, as well as genes that stabilize or synthesize components of the extracellular matrix. These results identify several potential mechanisms through which celastrol may inhibit inflammation in RA.

Robust Summarization and Inference in Proteome-wide Label-free Quantification

Label-Free Quantitative mass spectrometry based workflows for differential expression (DE) analysis of proteins impose important challenges on the data analysis because of peptide-specific effects and context dependent missingness of peptide intensities. Peptide-based workflows, like MSqRob, test for DE directly from peptide intensities and outperform summarization methods which first aggregate MS1 peptide intensities to protein intensities before DE analysis. However, these methods are computationally expensive, often hard to understand for the non-specialized end-user, and do not provide protein summaries, which are important for visualization or downstream processing. In this work, we therefore evaluate state-of-the-art summarization strategies using a benchmark spike-in dataset and discuss why and when these fail compared with the state-of-the-art peptide based model, MSqRob. Based on this evaluation, we propose a novel summarization strategy, MSqRobSum, which estimates MSqRob's model parameters in a two-stage procedure circumventing the drawbacks of peptide-based workflows. MSqRobSum maintains MSqRob's superior performance, while providing useful protein expression summaries for plotting and downstream analysis. Summarizing peptide to protein intensities considerably reduces the computational complexity, the memory footprint and the model complexity, and makes it easier to disseminate DE inferred on protein summaries. Moreover, MSqRobSum provides a highly modular analysis framework, which provides researchers with full flexibility to develop data analysis workflows tailored toward their specific applications.

Harmonization of quality metrics and power calculation in multi-omic studies

Multi-omic studies combine measurements at different molecular levels to build comprehensive models of cellular systems. The success of a multi-omic data analysis strategy depends largely on the adoption of adequate experimental designs, and on the quality of the measurements provided by the different omic platforms. However, the field lacks a comparative description of performance parameters across omic technologies and a formulation for experimental design in multi-omic data scenarios. Here, we propose a set of harmonized Figures of Merit (FoM) as quality descriptors applicable to different omic data types. Employing this information, we formulate the MultiPower method to estimate and assess the optimal sample size in a multi-omics experiment. MultiPower supports different experimental settings, data types and sample sizes, and includes graphical for experimental design decision-making. MultiPower is complemented with MultiML, an algorithm to estimate sample size for machine learning classification problems based on multi-omic data.

BayesENproteomics: Bayesian Elastic Nets for Quantification of Peptidoforms in Complex Samples

Multivariate regression modelling provides a statistically powerful means of quantifying the effects of a given treatment while compensating for sources of variation and noise, such as variability between human donors and the behavior of different peptides during mass spectrometry. However, methods to quantify endogenous post-translational modifications (PTMs) are typically reliant on summary statistical methods that fail to consider sources of variability such as changes in the levels of the parent protein. Here, we compare three multivariate regression methods, including a novel Bayesian elastic net algorithm (BayesENproteomics) that enables assessment of relative protein abundances while also quantifying identified PTMs for each protein. We tested the ability of these methods to accurately quantify expression of proteins in a mixed-species benchmark experiment and to quantify synthetic PTMs induced by stable isotope labelling. Finally, we extended our regression pipeline to calculate fold changes at the pathway level, providing a complement to commonly used enrichment analysis. Our results show that BayesENproteomics can quantify changes to protein levels across a broad dynamic range while also accurately quantifying PTM and pathway-level fold changes.

Tumor necrosis factor α-induced protein 1 as a novel tumor suppressor through selective downregulation of CSNK2B blocks nuclear factor-κB activation in hepatocellular carcinoma

Background

Tumor necrosis factor α-induced protein 1 (TNFAIP1) is frequently downregulated in cancer cell lines and promotes cancer cell apoptosis. However, its role, clinical significance and molecular mechanisms in hepatocellular carcinoma (HCC) are unknown.

Methods

The expression of TNFAIP1 in HCC tumor tissues and cell lines was measured by Western blot and immunohistochemistry. The effects of TNFAIP1 on HCC proliferation, apoptosis, metastasis, angiogenesis and tumor formation were evaluated by Cell Counting Kit-8 (CCK8), Terminal deoxynucleotidyl transferase dUTP Nick-End Labeling (TUNEL), transwell, tube formation assay in vitro and nude mice experiments in vivo. The interaction between TNFAIP1 and CSNK2B was validated by liquid chromatography-tandem mass spectrometry (LC-MS/MS), Co-immunoprecipitation and Western blot. The mechanism of how TNFAIP1 regulated nuclear factor-kappaB (NF-κB) pathway was analyzed by dual-luciferase reporter, immunofluorescence, quantitative Real-time polymerase chain reaction (RT-qPCR) and Western blot.

Findings

The TNFAIP1 expression is significantly decreased in HCC tissues and cell lines, and negatively correlated with the increased HCC histological grade. Overexpression of TNFAIP1 inhibits HCC cell proliferation, metastasis, angiogenesis and promotes cancer cell apoptosis both in vitro and in vivo, whereas the knockdown of TNFAIP1 in HCC cell displays opposite effects. Mechanistically, TNFAIP1 interacts with CSNK2B and promotes its ubiquitin-mediated degradation with Cul3, causing attenuation of CSNK2B-dependent NF-κB trans-activation in HCC cell. Moreover, the enforced expression of CSNK2B counteracts the inhibitory effects of TNFAIP1 on HCC cell proliferation, migration, and angiogenesis in vitro and in vivo.

Interpretation

Our results support that TNFAIP1 can act as a tumor suppressor of HCC by modulating TNFAIP1/CSNK2B/NF-κB pathway, implying that TNFAIP1 may represent a potential marker and a promising therapeutic target for HCC.

Methods and Algorithms for Quantitative Proteomics by Mass Spectrometry

Protein quantitation by mass spectrometry has always been a resourceful technique in protein discovery, and more recently it has leveraged the advent of clinical proteomics. A single mass spectrometry analysis experiment provides identification and quantitation of proteins as well as information on posttranslational modifications landscape. By contrast, protein array technologies are restricted to quantitation of targeted proteins and their modifications. Currently, there are an overwhelming number of quantitative mass spectrometry methods for protein and peptide quantitation. The aim here is to provide an overview of the most common mass spectrometry methods and algorithms used in quantitative proteomics and discuss the computational aspects to obtain reliable quantitative measures of proteins, peptides and their posttranslational modifications. The development of a pipeline using commercial or freely available software is one of the main challenges in data analysis of many experimental projects. Recent developments of R statistical programming language make it attractive to fully develop pipelines for quantitative proteomics. We discuss concepts of quantitative proteomics that together with current R packages can be used to build highly customizable pipelines.

Methodologies and Applications of Proteomics for Study of Yeast Strains: An Update

Yeasts are one of the mostly used microorganisms as models in several studies. A wide range of applications in different processes can be attributed to their intrinsic characteristics. They are eukaryotes and therefore valuable expression hosts that require elaborate post-translational modifications. Their arsenal of proteins has become a valuable biochemical tool for the catalysis of several reactions of great value to the food (beverages), pharmaceutical and energy industries. Currently, the main challenge in systemic yeast biology is the understanding of the expression, function and regulation of the protein pool encoded by such microorganisms. In this review, we will provide an overview of the proteomic methodologies used in the analysis of yeasts. This research focuses on the advantages and improvements in their most recent applications with an understanding of the functionality of the proteins of these microorganisms, as well as an update of the advances of methodologies employed in mass spectrometry.

Oncoproteomics: Current status and future opportunities

Oncoproteomics is the systematic study of cancer samples using omics technologies to detect changes implicated in tumorigenesis. Recent progress in oncoproteomics is already opening new avenues for the identification of novel biomarkers for early clinical stage cancer detection, targeted molecular therapies, disease monitoring, and drug development. Such information will lead to new understandings of cancer biology and impact dramatically on the future care of cancer patients. In this review, we will summarize the advantages and limitations of the key technologies used in (onco)proteogenomics, (the Omics Pipeline), explain how they can assist us in understanding the biology behind the overarching "Hallmarks of Cancer", discuss how they can advance the development of precision/personalised medicine and the future directions in the field.

Multi-omics dataset to decipher the complexity of drug resistance in diffuse large B-cell lymphoma

The prognosis of patients with relapsed/refractory (R/R) diffuse large B-cell lymphoma (DLBCL) remains unsatisfactory and, despite major advances in genomic studies, the biological mechanisms underlying chemoresistance are still poorly understood. We conducted for the first time a large-scale differential multi-omics investigation on DLBCL patient’s samples in order to identify new biomarkers that could early identify patients at risk of R/R disease and to identify new targets that could determine chemorefractoriness. We compared a well-characterized cohort of R/R versus chemosensitive DLBCL patients by combining label-free quantitative proteomics and targeted RNA sequencing performed on the same tissues samples. The cross-section of both data levels allowed extracting a sub-list of 22 transcripts/proteins pairs whose expression levels significantly differed between the two groups of patients. In particular, we identified significant targets related to tumor metabolism (Hexokinase 3), microenvironment (IDO1, CXCL13), cancer cells proliferation, migration and invasion (S100 proteins) or BCR signaling pathway (CD79B). Overall, this study revealed several extremely promising biomarker candidates related to DLBCL chemorefractoriness and highlighted some new potential therapeutic drug targets. The complete datasets have been made publically available and should constitute a valuable resource for the future research.

Developing a CO2 bicarbonation absorber for promoting microalgal growth rates with an improved photosynthesis pathway

In order to solve the problems of the short residence time and low utilization efficiency of carbon dioxide (CO₂) gas added directly to a raceway pond, a CO₂ bicarbonation absorber (CBA) was proposed to efficiently convert CO₂ gas and sodium carbonate (Na₂CO₃) solution to sodium bicarbonate (NaHCO₃), which was dissolved easily in the culture medium and left to promote the microalgal growth rate. The CO₂ gas reacted with the Na₂CO₃ solution (initial concentration = 200 mM L⁻¹ and volume ratio in CBA = 60%) for 90 min at 0.3 MPa to give the optimized molar proportion (92%) of NaHCO₃ product in total inorganic carbon and increase the microalgal growth rate by 5.0 times. Quantitative label-free protein analysis showed that the expression levels of the photosystem II (PSII) reaction centre protein (PsbH) and PSII cytochrome (PsbV2) in the photosynthesis pathway increased by 4.8 and 3.4 times, respectively, while that of the RuBisCO enzyme (rbcL) in the carbon fixation pathway increased by 3.5 times in Arthrospira platensis cells cultivated with the NaHCO₃ product in the CBA at 0.3 MPa.

To increase the residence time of CO₂ gas added directly to the raceway pond, a CO₂ bicarbonation absorber was proposed to convert CO₂ gas and Na₂CO₃ to NaHCO₃, which was dissolved easily in the solution and left to promote the biomass growth rate.