Relative and Absolute Quantitation in Mass Spectrometry-Based Proteomics

Hepatic Dyrk1b impairs systemic glucose homeostasis by modulating Wbp2 expression in a kinase activity-dependent manner

Patients with gain-of-function mutations of Dyrk1b have higher fasting blood glucose (FBG) levels. However, the role of Dyrk1b in glucose metabolism is not fully elucidated. Herein, we found that hepatic Dyrk1b overexpression in mice impaired systemic glucose tolerance and hepatic insulin signaling. Dyrk1b overexpression in vitro attenuated insulin signaling in a kinase activity-dependent manner, and its kinase activity was required for its effect on systemic glucose homeostasis and hepatic insulin signaling in vivo. Dyrk1b ablation improved systemic glucose tolerance and hepatic insulin signaling in mice. Quantitative proteomic analyses showed that Dyrk1b downregulated WW domain-binding protein 2 (Wbp2) protein abundance. Mechanistically, Dyrk1b enhanced Wbp2 ubiquitylation and proteasomal degradation. Restoration of hepatic Wbp2 partially rescued the impaired glucose homeostasis in Dyrk1b overexpression mice. In addition, Dyrk1b inhibition with AZ191 moderately improved systemic glucose homeostasis. Our study uncovers that hepatic Dyrk1b impairs systemic glucose homeostasis via its modulation of Wbp2 expression in a kinase activity-dependent manner.

Combining Data Independent Acquisition with Spike-in SILAC (DIA-SiS) Improves Proteome Coverage and Quantification

Data Independent Acquisition (DIA) is increasingly preferred over Data Dependent Acquisition (DDA) due to its higher throughput and fewer missing values. Whereas DDA often utilizes stable isotope labeling to improve quantification, DIA mostly relies on label-free approaches. Efforts to integrate DIA with isotope labeling include chemical methods like mTRAQ and dimethyl labeling, which, while effective, complicate sample preparation. Stable isotope labeling by amino acids in cell culture (SILAC) achieves high labeling efficiency through the metabolic incorporation of heavy labels into proteins in vivo. However, the need for metabolic incorporation limits the direct use in clinical scenarios and certain high-throughput experiments. Spike-in SILAC methods utilize an externally generated heavy sample as an internal reference, enabling SILAC-based quantification even for samples that cannot be directly labeled. Here, we combine DIA with spike-in SILAC (DIA-SiS), leveraging the robust quantification of SILAC without the complexities associated with chemical labeling. We developed DIA-SiS and rigorously assessed its performance with mixed-species benchmark samples on bulk and single cell-like amount level. We demonstrate that DIA-SiS substantially improves proteome coverage and quantification compared to label-free approaches and reduces incorrectly quantified proteins. Additionally, DIA-SiS proves effective in analyzing proteins in low-input formalin-fixed paraffin-embedded (FFPE) tissue sections. DIA-SiS combines the precision of stable isotope-based quantification with the simplicity of label-free sample preparation, facilitating simple, accurate and comprehensive proteome profiling.

Quantification of snake venom proteomes by mass spectrometry-considerations and perspectives

The advent of soft ionization mass spectrometry-based proteomics in the 1990s led to the development of a new dimension in biology that conceptually allows for the integral analysis of whole proteomes. This transition from a reductionist to a global-integrative approach is conditioned to the capability of proteomic platforms to generate and analyze complete qualitative and quantitative proteomics data. Paradoxically, the underlying analytical technique, molecular mass spectrometry, is inherently nonquantitative. The turn of the century witnessed the development of analytical strategies to endow proteomics with the ability to quantify proteomes of model organisms in the sense of "an organism for which comprehensive molecular (genomic and/or transcriptomic) resources are available." This essay presents an overview of the strategies and the lights and shadows of the most popular quantification methods highlighting the common misuse of label-free approaches developed for model species' when applied to quantify the individual components of proteomes of nonmodel species (In this essay we use the term "non-model" organisms for species lacking comprehensive molecular (genomic and/or transcriptomic) resources, a circumstance that, as we detail in this review-essay, conditions the quantification of their proteomes.). We also point out the opportunity of combining elemental and molecular mass spectrometry systems into a hybrid instrumental configuration for the parallel identification and absolute quantification of venom proteomes. The successful application of this novel mass spectrometry configuration in snake venomics represents a proof-of-concept for a broader and more routine application of hybrid elemental/molecular mass spectrometry setups in other areas of the proteomics field, such as phosphoproteomics, metallomics, and in general in any biological process where a heteroatom (i.e., any atom other than C, H, O, N) forms integral part of its mechanism.

A Tutorial Review of Labeling Methods in Mass Spectrometry-Based Quantitative Proteomics

Recent advancements in mass spectrometry (MS) have revolutionized quantitative proteomics, with multiplex isotope labeling emerging as a key strategy for enhancing accuracy, precision, and throughput. This tutorial review offers a comprehensive overview of multiplex isotope labeling techniques, including precursor-based, mass defect-based, reporter ion-based, and hybrid labeling methods. It details their fundamental principles, advantages, and inherent limitations along with strategies to mitigate the limitation of ratio-distortion. This review will also cover the applications and latest progress in these labeling techniques across various domains, including cancer biomarker discovery, neuroproteomics, post-translational modification analysis, cross-linking MS, and single-cell proteomics. This Review aims to provide guidance for researchers on selecting appropriate methods for their specific goals while also highlighting the potential future directions in this rapidly evolving field.

Statistical Testing for Protein Equivalence Identifies Core Functional Modules Conserved across 360 Cancer Cell Lines and Presents a General Approach to Investigating Biological Systems

Quantitative proteomics has enhanced our capability to study protein dynamics and their involvement in disease using various techniques, including statistical testing, to discern the significant differences between conditions. While most focus is on what is different between conditions, exploring similarities can provide valuable insights. However, exploring similarities directly from the analyte level, such as proteins, genes, or metabolites, is not a standard practice and is not widely adopted. In this study, we propose a statistical framework called QuEStVar (Quantitative Exploration of Stability and Variability through statistical hypothesis testing), enabling the exploration of quantitative stability and variability of features with a combined statistical framework. QuEStVar utilizes differential and equivalence testing to expand statistical classifications of analytes when comparing conditions. We applied our method to an extensive data set of cancer cell lines and revealed a quantitatively stable core proteome across diverse tissues and cancer subtypes. The functional analysis of this set of proteins highlighted the molecular mechanism of cancer cells to maintain constant conditions of the tumorigenic environment via biological processes, including transcription, translation, and nucleocytoplasmic transport.

Towards chemoenzymatic labeling strategies for profiling protein glycosylation

Protein glycosylation is one of the most common and important post-translational modifications of proteins involved in regulating glycoprotein functions. The chemoenzymatic glycan labeling strategy allows rapid, efficient, and selective interrogation of glycoproteins. Glycoproteomics identifies protein glycosylation events at a large scale, providing information such as peptide sequences, glycan structures, and glycosylated sites. This review discusses the recent development of chemoenzymatic labeling strategies for glycoprotein analysis, mainly including glycoprotein and glycosite profiling. Furthermore, we highlight the chemoenzymatic enrichment approaches in mass spectrometry analysis for three classes of glycan modifications, including N-glycosylation, O-GlcNAcylation, and mucin-type O-glycosylation. Finally, we highlight the emerging trends in new tools and cutting-edge technologies available for glycoproteomic research.

Evolution of techniques and tools for replication fork proteome and protein interaction studies

Understanding the complex network of protein-protein interactions (PPI) that govern cellular functions is essential for unraveling the molecular basis of biological processes and diseases. Mass spectrometry (MS) has emerged as a powerful tool for studying protein dynamics, enabling comprehensive analysis of protein function, structure, post-translational modifications, interactions, and localization. This article provides an overview of MS techniques and their applications in proteomics studies, with a focus on the replication fork proteome. The replication fork is a multi-protein assembly involved in DNA replication, and its proper functioning is crucial for maintaining genomic integrity. By combining quantitative MS labeling techniques with various data acquisition methods, researchers have made significant strides in elucidating the complex processes and molecular mechanisms at the replication fork. Overall, MS has revolutionized our understanding of protein dynamics, offering valuable insights into cellular processes and potential targets for therapeutic interventions.

Cordyceps sinensis relieves non-small cell lung cancer by inhibiting the MAPK pathway

OBJECTIVE

To determine the pharmacodynamic mechanism underlying Cordyceps sinensis relief in a murine model of non-small cell lung cancer (NSCLC).

METHODS

We created a murine model of NSCLC and studied the potential molecular mechanism by which C. sinensis relieved NSCLC using a combination of transcriptomics, proteomics, and experimental validation.

RESULTS

C. sinensis markedly suppressed the fluorescence values in mice with NSCLC, improved the pathologic morphology of lung tissue, ameliorated inflammatory cytokines (tumor necrosis factor-alpha, interleukin-6, interleukin-10, and the oxidative stress indicators superoxide dismutase, malondialdehyde, and glutathione peroxidase). Transcriptomics results showed that the therapeutic effect of C. sinensis was primarily involved in the differentiation and activation of T cells. Based on the proteomic results, C. sinensis likely exerted a protective effect by recruiting immune cells and suppressing tumor cell proliferation via the MAPK pathway. Finally, the experimental validation results indicated that C. sinensis significantly decreased the VEGF and Ki67 expression, downregulated RhoA, Raf-1, and c-fos expression, which are related to cell migration and invasion, increased the serum concentration of hematopoietic factors (EPO and GM-CSF), and improved the percentage of immune cells (natural killer cells, dendritic cells, and CD4+ and CD8+ lymphocytes), which enhanced immune function.

CONCLUSIONS

Based on our preclinical study, C. sinensis was shown to exert a protective effect on NSCLC, primarily by inhibiting the MAPK pathway.

Targeting G9a translational mechanism of SARS-CoV-2 pathogenesis for multifaceted therapeutics of COVID-19 and its sequalae

By largely unknown mechanism(s), SARS-CoV-2 hijacks the host translation apparatus to promote COVID-19 pathogenesis. We report that the histone methyltransferase G9a noncanonically regulates viral hijacking of the translation machinery to bring about COVID-19 symptoms of hyperinflammation, lymphopenia, and blood coagulation. Chemoproteomic analysis of COVID-19 patient peripheral mononuclear blood cells (PBMC) identified enhanced interactions between SARS-CoV-2-upregulated G9a and distinct translation regulators, particularly the N 6 -methyladenosine (m 6 A) RNA methylase METTL3. These interactions with translation regulators implicated G9a in translational regulation of COVID-19. Inhibition of G9a activity suppressed SARS-CoV-2 replication in human alveolar epithelial cells. Accordingly, multi-omics analysis of the same alveolar cells identified SARS-CoV-2-induced changes at the transcriptional, m 6 A-epitranscriptional, translational, and post-translational (phosphorylation or secretion) levels that were reversed by inhibitor treatment. As suggested by the aforesaid chemoproteomic analysis, these multi-omics-correlated changes revealed a G9a-regulated translational mechanism of COVID-19 pathogenesis in which G9a directs translation of viral and host proteins associated with SARS-CoV-2 replication and with dysregulation of host response. Comparison of proteomic analyses of G9a inhibitor-treated, SARS-CoV-2 infected cells, or ex vivo culture of patient PBMCs, with COVID-19 patient data revealed that G9a inhibition reversed the patient proteomic landscape that correlated with COVID-19 pathology/symptoms. These data also indicated that the G9a-regulated, inhibitor-reversed, translational mechanism outperformed G9a-transcriptional suppression to ultimately determine COVID-19 pathogenesis and to define the inhibitor action, from which biomarkers of serve symptom vulnerability were mechanistically derived. This cell line-to-patient conservation of G9a-translated, COVID-19 proteome suggests that G9a inhibitors can be used to treat patients with COVID-19, particularly patients with long-lasting COVID-19 sequelae.

Pine wilt disease: what do we know from proteomics?

Pine wilt disease (PWD) is a devastating forest disease caused by the pinewood nematode (PWN), Bursaphelenchus xylophilus, a migratory endoparasite that infects several coniferous species. During the last 20 years, advances have been made for understanding the molecular bases of PWN-host trees interactions. Major advances emerged from transcriptomic and genomic studies, which revealed some unique features related to PWN pathogenicity and constituted fundamental data that allowed the development of postgenomic studies. Here we review the proteomic approaches that were applied to study PWD and integrated the current knowledge on the molecular basis of the PWN pathogenicity. Proteomics has been useful for understanding cellular activities and protein functions involved in PWN-host trees interactions, shedding light into the mechanisms associated with PWN pathogenicity and being promising tools to better clarify host trees PWN resistance/susceptibility.

Label-free quantification of host cell protein impurity in recombinant hemoglobin materials

Quantitative analysis relies on pure-substance primary calibrators with known mass fractions of impurity. Here, label-free quantification (LFQ) is being evaluated as a readily available, reliable method for determining the mass fraction of host cell proteins (HCPs) in bioengineered proteins which are intended for use as protein calibration standards. In this study a purified hemoglobin-A2 (HbA2) protein, obtained through its overexpression in E. coli, was used. Two different materials were produced: natural and U15N-labeled HbA2. For the quantification of impurities, precursor ion (MS1-) intensities were integrated over all E. coli proteins identified and divided by the intensities obtained for HbA2. This ratio was calibrated against the corresponding results for an E. coli cell lysate, which had been spiked at known mass ratios to pure HbA2. To demonstrate the universal applicability of LFQ, further proteomes (yeast and human K562) were then alternatively used for calibration and found to produce comparable results. Valid results were also obtained when the complexity of the calibrator was reduced to a mix of just nine proteins, and a minimum of five proteins was estimated to be sufficient to keep the sampling error below 15%. For the studied materials, HbA2 mass fractions (or purities) of 923 and 928 mg(HbA2)/g(total protein) were found with expanded uncertainties (U) of 2.8 and 1.3%, resp. Value assignment by LFQ thus contributes up to about 3% of the overall uncertainty of HbA2 quantification when these materials are used as calibrators. Further purification of the natural HbA2 yielded a mass fraction of 999.1 mg/g, with a negligible uncertainty (U = 0.02%), though at a significant loss of material. If an overall uncertainty of 5% is acceptable for protein quantification, working with the original materials would therefore definitely be viable, circumventing the need of further purification.

Probing Protein Complexes Composition, Stoichiometry, and Interactions by Peptide-Based Mass Spectrometry

The characterization of a protein complex by mass spectrometry can be conducted at different levels. Initial steps regard the qualitative composition of the complex and subunit identification. After that, quantitative information such as stoichiometric ratios and copy numbers for each subunit in a complex or super-complex is acquired. Peptide-based LC-MS/MS offers a wide number of methods and protocols for the characterization of protein complexes. This chapter concentrates on the applications of peptide-based LC-MS/MS for the qualitative, quantitative, and structural characterization of protein complexes focusing on subunit identification, determination of stoichiometric ratio and number of subunits per complex as well as on cross-linking mass spectrometry and hydrogen/deuterium exchange as methods for the structural investigation of the biological assemblies.

Developmental stage-specific proteome analysis of the legume pod borer Maruca vitrata provides insights on relevant proteins

The spotted pod borer, Maruca vitrata (Lepidoptera: Crambidae) is a destructive insect pest that inflicts significant productivity losses on important leguminous crops. Unravelling insect proteomes is vital to comprehend their fundamental molecular mechanisms. This research delved into the proteome profiles of four distinct stages -three larval and pupa of M. vitrata, utilizing LC-MS/MS label-free quantification-based methods. Employing comprehensive proteome analysis with fractionated datasets, we mapped 75 % of 3459 Drosophila protein orthologues out of which 2695 were identified across all developmental stages while, 137 and 94 were exclusive to larval and pupal stages respectively. Cluster analysis of 2248 protein orthologues derived from MaxQuant quantitative dataset depicted six clusters based on expression pattern similarity across stages. Consequently, gene ontology and protein-protein interaction network analyses using STRING database identified cluster 1 (58 proteins) and cluster 6 (25 proteins) associated with insect immune system and lipid metabolism. Furthermore, qRT-PCR-based expression analyses of ten selected proteins-coding genes authenticated the proteome data. Subsequently, functional validation of these chosen genes through gene silencing reduced their transcript abundance accompanied by a marked increase in mortality among dsRNA-injected larvae. Overall, this is a pioneering study to effectively develop a proteome atlas of M. vitrata as a potential resource for crop protection programs.

Proteomics analysis of Toxoplasma gondii merozoites reveals regulatory proteins involved in sexual reproduction

Sexual reproduction plays a crucial role in the transmission and life cycle of toxoplasmosis. The merozoites are the only developmental stage capable of differentiation into male and female gametes, thereby initiating sexual reproduction to form oocysts that are excreted into the environment. Hence, our study aimed to perform proteomic analyses of T. gondii Pru strain merozoites, a pre-sexual developmental stage in cat IECs, and tachyzoites, an asexual developmental stage, using the tandem mass tag (TMT) method in order to identify the differentially expressed proteins (DEPs) of merozoites. Proteins functions were subjected to cluster analysis, and DEPs were validated through the qPCR method. The results showed that a total of 106 proteins were identified, out of which 85 proteins had quantitative data. Among these, 15 proteins were differentially expressed within merozoites, with four exhibiting up-regulation and being closely associated with the material and energy metabolism as well as the cell division of T. gondii. Two novel DEPs, namely S8GHL5 and A0A125YP41, were identified, and their homologous family members have been demonstrated to play regulatory roles in oocyte maturation and spermatogenesis in other species. Therefore, they may potentially exhibit regulatory functions during the differentiation of micro- and macro-gametophytes at the initiation stage of sexual reproduction in T. gondii. In conclusion, our results showed that the metabolic and divisional activities in the merozoites surpass those in the tachyzoites, thereby providing structural, material, and energetic support for gametophytes development. The discovery of two novel DEPs associated with sexual reproduction represents a significant advancement in understanding Toxoplasma sexual reproduction initiation and oocyst formation.

Non-canonical function of histone methyltransferase G9a in the translational regulation of chronic inflammation

We report a novel translation-regulatory function of G9a, a histone methyltransferase and well-understood transcriptional repressor, in promoting hyperinflammation and lymphopenia; two hallmarks of endotoxin tolerance (ET)-associated chronic inflammatory complications. Using multiple approaches, we demonstrate that G9a interacts with multiple translation regulators during ET, particularly the N6-methyladenosine (m6A) RNA methyltransferase METTL3, to co-upregulate expression of certain m6A-modified mRNAs that encode immune-checkpoint and anti-inflammatory proteins. Mechanistically, G9a promotes m6A methyltransferase activity of METTL3 at translational/post-translational level by regulating its expression, its methylation, and its cytosolic localization during ET. Additionally, from a broader view extended from the G9a-METTL3-m6A translation regulatory axis, our translatome proteomics approach identified numerous "G9a-translated" proteins that unite the networks associated with inflammation dysregulation, T cell dysfunction, and systemic cytokine response. In sum, we identified a previously unrecognized function of G9a in protein-specific translation that can be leveraged to treat ET-related chronic inflammatory diseases.

Novel brain-penetrant inhibitor of G9a methylase blocks Alzheimer's disease proteopathology for precision medication

Current amyloid beta-targeting approaches for Alzheimer's disease (AD) therapeutics only slow cognitive decline for small numbers of patients. This limited efficacy exists because AD is a multifactorial disease whose pathological mechanism(s) and diagnostic biomarkers are largely unknown. Here we report a new mechanism of AD pathogenesis in which the histone methyltransferase G9a noncanonically regulates translation of a hippocampal proteome that defines the proteopathic nature of AD. Accordingly, we developed a novel brain-penetrant inhibitor of G9a, MS1262, across the blood-brain barrier to block this G9a-regulated, proteopathologic mechanism. Intermittent MS1262 treatment of multiple AD mouse models consistently restored both cognitive and noncognitive functions to healthy levels. Comparison of proteomic/phosphoproteomic analyses of MS1262-treated AD mice with human AD patient data identified multiple pathological brain pathways that elaborate amyloid beta and neurofibrillary tangles as well as blood coagulation, from which biomarkers of early stage of AD including SMOC1 were found to be affected by MS1262 treatment. Notably, these results indicated that MS1262 treatment may reduce or avoid the risk of blood clot burst for brain bleeding or a stroke. This mouse-to-human conservation of G9a-translated AD proteopathology suggests that the global, multifaceted effects of MS1262 in mice could extend to relieve all symptoms of AD patients with minimum side effect. In addition, our mechanistically derived biomarkers can be used for stage-specific AD diagnosis and companion diagnosis of individualized drug effects.

Inclusion of Porous Graphitic Carbon Chromatography Yields Greater Protein Identification and Compartment and Process Coverage and Enables More Reflective Protein-Level Label-Free Quantitation

The ubiquity of mass spectrometry-based bottom-up proteomic analyses as a component of biological investigation mandates the validation of methodologies that increase acquisition efficiency, improve sample coverage, and enhance profiling depth. Chromatographic separation is often ignored as an area of potential improvement, with most analyses relying on traditional reversed-phase liquid chromatography (RPLC); this consistent reliance on a single chromatographic paradigm fundamentally limits our view of the observable proteome. Herein, we build upon early reports and validate porous graphitic carbon chromatography (PGC) as a facile means to substantially enhance proteomic coverage without changes to sample preparation, instrument configuration, or acquisition methods. Analysis of offline fractionated cell line digests using both separations revealed an increase in peptide and protein identifications by 43% and 24%, respectively. Increased identifications provided more comprehensive coverage of cellular components and biological processes independent of protein abundance, highlighting the substantial quantity of proteomic information that may go undetected in standard analyses. We further utilize these data to reveal that label-free quantitative analyses using RPLC separations alone may not be reflective of actual protein constituency. Together, these data highlight the value and comprehension offered through PGC-MS proteomic analyses. RAW proteomic data have been uploaded to the MassIVE repository with the primary accession code MSV000091495.

Novel brain-penetrant inhibitor of G9a methylase blocks Alzheimer's disease proteopathology for precision medication

Current amyloid beta-targeting approaches for Alzheimer's disease (AD) therapeutics only slow cognitive decline for small numbers of patients. This limited efficacy exists because AD is a multifactorial disease whose pathological mechanism(s) and diagnostic biomarkers are largely unknown. Here we report a new mechanism of AD pathogenesis in which the histone methyltransferase G9a noncanonically regulates translation of a hippocampal proteome that defines the proteopathic nature of AD. Accordingly, we developed a novel brain-penetrant inhibitor of G9a, MS1262, across the blood-brain barrier to block this G9a-regulated, proteopathologic mechanism. Intermittent MS1262 treatment of multiple AD mouse models consistently restored both cognitive and noncognitive functions to healthy levels. Comparison of proteomic/phosphoproteomic analyses of MS1262-treated AD mice with human AD patient data identified multiple pathological brain pathways that elaborate amyloid beta and neurofibrillary tangles as well as blood coagulation, from which biomarkers of early stage of AD including SMOC1 were found to be affected by MS1262 treatment. Notably, these results indicated that MS1262 treatment may reduce or avoid the risk of blood clot burst for brain bleeding or a stroke. This mouse-to-human conservation of G9a-translated AD proteopathology suggests that the global, multifaceted effects of MS1262 in mice could extend to relieve all symptoms of AD patients with minimum side effect. In addition, our mechanistically derived biomarkers can be used for stage-specific AD diagnosis and companion diagnosis of individualized drug effects.

One-Sentence Summary

A brain-penetrant inhibitor of G9a methylase blocks G9a translational mechanism to reverse Alzheimer's disease related proteome for effective therapy.

Recent methodological advances towards single-cell proteomics

Studying the central dogma at the single-cell level has gained increasing attention to reveal hidden cell lineages and functions that cannot be studied using traditional bulk analyses. Nonetheless, most single-cell studies exploiting genomic and transcriptomic levels fail to address information on proteins that are central to many important biological processes. Single-cell proteomics enables understanding of the functional status of individual cells and is particularly crucial when the specimen is composed of heterogeneous entities of cells. With the growing importance of this field, significant methodological advancements have emerged recently. These include miniaturized and automated sample preparation, multi-omics analyses, and combined analyses of multiple techniques such as mass spectrometry and microscopy. Moreover, artificial intelligence and single-molecule detection technologies have advanced throughput and improved sensitivity limitations, respectively, over conventional methods. In this review, we summarize cutting-edge methodologies for single-cell proteomics and relevant emerging technologies that have been reported in the last 5 years, and provide an outlook on this research field.

Pan-Cancer Proteomics Analysis to Identify Tumor-Enriched and Highly Expressed Cell Surface Antigens as Potential Targets for Cancer Therapeutics

The National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC) provides unique opportunities for cancer target discovery using protein expression. Proteomics data from CPTAC tumor types have been primarily generated using a multiplex tandem mass tag (TMT) approach, which is designed to provide protein quantification relative to reference samples. However, relative protein expression data are suboptimal for prioritization of targets within a tissue type, which requires additional reprocessing of the original proteomics data to derive absolute quantitation estimation. We evaluated the feasibility of using differential protein analysis coupled with intensity-based absolute quantification (iBAQ) to identify tumor-enriched and highly expressed cell surface antigens, employing tandem mass tag (TMT) proteomics data from CPTAC. Absolute quantification derived from TMT proteomics data was highly correlated with that of label-free proteomics data from the CPTAC colon adenocarcinoma cohort, which contains proteomics data measured by both approaches. We validated the TMT-iBAQ approach by comparing the iBAQ value to the receptor density value of HER2 and TROP2 measured by flow cytometry in about 30 selected breast and lung cancer cell lines from the Cancer Cell Line Encyclopedia. Collections of these tumor-enriched and highly expressed cell surface antigens could serve as a valuable resource for the development of cancer therapeutics, including antibody-drug conjugates and immunotherapeutic agents.

Mouse Model of Fragile X Syndrome Analyzed by Quantitative Proteomics: A Comparison of Methods

Multiple methods for quantitative proteomics are available for proteome profiling. It is unclear which methods are most useful in situations involving deep proteome profiling and the detection of subtle distortions in the proteome. Here, we compared the performance of seven different strategies in the analysis of a mouse model of Fragile X Syndrome, involving the knockout of the fmr1 gene that is the leading cause of autism spectrum disorder. Focusing on the cerebellum, we show that data-independent acquisition (DIA) and the tandem mass tag (TMT)-based real-time search method (RTS) generated the most informative profiles, generating 334 and 329 significantly altered proteins, respectively, although the latter still suffered from ratio compression. Label-free methods such as BoxCar and a conventional data-dependent acquisition were too noisy to generate a reliable profile, while TMT methods that do not invoke RTS showed a suppressed dynamic range. The TMT method using the TMTpro reagents together with complementary ion quantification (ProC) overcomes ratio compression, but current limitations in ion detection reduce sensitivity. Overall, both DIA and RTS uncovered known regulators of the syndrome and detected alterations in calcium signaling pathways that are consistent with calcium deregulation recently observed in imaging studies. Data are available via ProteomeXchange with the identifier PXD039885.

Multi-omics quantitative data of tomato fruit unveils regulation modes of least variable metabolites

BACKGROUND

The composition of ripe fruits depends on various metabolites which content evolves greatly throughout fruit development and may be influenced by the environment. The corresponding metabolism regulations have been widely described in tomato during fruit growth and ripening. However, the regulation of other metabolites that do not show large changes in content have scarcely been studied.

RESULTS

We analysed the metabolites of tomato fruits collected on different trusses during fruit development, using complementary analytical strategies. We identified the 22 least variable metabolites, based on their coefficients of variation. We first verified that they had a limited functional link with the least variable proteins and transcripts. We then posited that metabolite contents could be stabilized through complex regulations and combined their data with the quantitative proteome or transcriptome data, using sparse partial-least-square analyses. This showed shared regulations between several metabolites, which interestingly remained linked to early fruit development. We also examined regulations in specific metabolites using correlations with individual proteins and transcripts, which revealed that a stable metabolite does not always correlate with proteins and transcripts of its known related pathways.

CONCLUSIONS

The regulation of the least variable metabolites was then interpreted regarding their roles as hubs in metabolic pathways or as signalling molecules.

DiLeu Isobaric Labeling Coupled with Limited Proteolysis Mass Spectrometry for High-Throughput Profiling of Protein Structural Changes in Alzheimer's Disease

High-throughput quantitative analysis of protein conformational changes has a profound impact on our understanding of the pathological mechanisms of Alzheimer's disease (AD). To establish an effective workflow enabling quantitative analysis of changes in protein conformation within multiple samples simultaneously, here we report the combination of N,N-dimethyl leucine (DiLeu) isobaric tag labeling with limited proteolysis mass spectrometry (DiLeu-LiP-MS) for high-throughput structural protein quantitation in serum samples collected from AD patients and control donors. Twenty-three proteins were discovered to undergo structural changes, mapping to 35 unique conformotypic peptides with significant changes between the AD group and the control group. Seven out of 23 proteins, including CO3, CO9, C4BPA, APOA1, APOA4, C1R, and APOA, exhibited a potential correlation with AD. Moreover, we found that complement proteins (e.g., CO3, CO9, and C4BPA) related to AD exhibited elevated levels in the AD group compared to those in the control group. These results provide evidence that the established DiLeu-LiP-MS method can be used for high-throughput structural protein quantitation, which also showed great potential in achieving large-scale and in-depth quantitative analysis of protein conformational changes in other biological systems.

Deep Bottom-up Proteomics Enabled by the Integration of Liquid-Phase Ion Trap

In bottom-up proteomics, the complexity of the proteome requires advanced peptide separation and/or fractionation methods to acquire an in-depth understanding of protein profiles. Proposed earlier as a solution-phase ion manipulation device, liquid phase ion traps (LPITs) were used in front of mass spectrometers to accumulate target ions for improved detection sensitivity. In this work, an LPIT-reversed phase liquid chromatography-tandem mass spectrometry (LPIT-RPLC-MS/MS) platform was established for deep bottom-up proteomics. LPIT was used here as a robust and effective method for peptide fractionation, which also shows good reproducibility and sensitivity on both qualitative and quantitative levels. LPIT separates peptides based on their effective charges and hydrodynamic radii, which is orthogonal to that of RPLC. With excellent orthogonality, the integration of LPIT with RPLC-MS/MS could effectively increase the number of peptides and proteins being detected. When HeLa cells were analyzed, peptide and protein coverages were increased by ∼89.2% and 50.3%, respectively. With high efficiency and low cost, this LPIT-based peptide fraction method could potentially be used in routine deep bottom-up proteomics.

LFQ-Based Peptide and Protein Intensity Differential Expression Analysis

Testing for significant differences in quantities at the protein level is a common goal of many LFQ-based mass spectrometry proteomics experiments. Starting from a table of protein and/or peptide quantities from a given proteomics quantification software, many tools and R packages exist to perform the final tasks of imputation, summarization, normalization, and statistical testing. To evaluate the effects of packages and settings in their substeps on the final list of significant proteins, we studied several packages on three public data sets with known expected protein fold changes. We found that the results between packages and even across different parameters of the same package can vary significantly. In addition to usability aspects and feature/compatibility lists of different packages, this paper highlights sensitivity and specificity trade-offs that come with specific packages and settings.

Certification of protein biomarker standards using element MS and generic standards: Application to human cytokines

The availability of protein standards and methods for their characterization, quantification, and purity assessment are currently a bottleneck in absolute quantitative proteomics. In this work, we introduce an absolute quantitative analytical strategy based on ICP-MS sulfur detection that uses sulfate as generic standard to quantify and certify the mass purity of protein standards. The methodology combines capillary chromatographic separation with parallel detection with ICP-MS and ESI-MS to determine proteoforms concentration and identity, respectively. The workability of the methodology was demonstrated using recombinant human cytokine standards IP-10 and Flt3L (2 batches), which are relevant biomarkers for carcinoma or inflammatory diseases. Every key factor (transport efficiency, column recovery, signal stability and internal standard suitability) was taken into account and certified BSA standard was used as quality control for validation purposes. Protein quantification values and resulting mass purity certification of IP-10 and one batch of Flt3L were very high (100 and 86%, respectively). Lower mass purity obtained for another batch of Flt3L (<70%) concurred with the finding of significant proteoforms resulted from oxidation processes as observed by parallel ESI-MS.

Activation of hypothalamic-enhanced adult-born neurons restores cognitive and affective function in Alzheimer's disease

Patients with Alzheimer's disease (AD) exhibit progressive memory loss, depression, and anxiety, accompanied by impaired adult hippocampal neurogenesis (AHN). Whether AHN can be enhanced in impaired AD brain to restore cognitive and affective function remains elusive. Here, we report that patterned optogenetic stimulation of the hypothalamic supramammillary nucleus (SuM) enhances AHN in two distinct AD mouse models, 5×FAD and 3×Tg-AD. Strikingly, the chemogenetic activation of SuM-enhanced adult-born neurons (ABNs) rescues memory and emotion deficits in these AD mice. By contrast, SuM stimulation alone or activation of ABNs without SuM modification fails to restore behavioral deficits. Furthermore, quantitative phosphoproteomics analyses reveal activation of the canonical pathways related to synaptic plasticity and microglia phagocytosis of plaques following acute chemogenetic activation of SuM-enhanced (vs. control) ABNs. Our study establishes the activity-dependent contribution of SuM-enhanced ABNs in modulating AD-related deficits and informs signaling mechanisms mediated by the activation of SuM-enhanced ABNs.

Modeling Clinical Phenotype Variability: Consideration of Genomic Variations, Computational Methods, and Quantitative Proteomics

Advances in biomedical and computer technologies have presented the modeling community the opportunity for mechanistically modeling and simulating the variability in a disease phenotype or in a drug response. The capability to quantify response variability can inform a drug development program. Quantitative systems pharmacology scientists have published various computational approaches for creating virtual patient populations (VPops) to model and simulate drug response variability. Genomic variations can impact disease characteristics and drug exposure and response. Quantitative proteomics technologies are increasingly used to facilitate drug discovery and development and inform patient care. Incorporating variations in genomics and quantitative proteomics may potentially inform creation of VPops to model and simulate virtual patient trials, and may help account for, in a predictive manner, phenotypic variations observed clinically.

Clusters of co-abundant proteins in the brain cortex associated with fronto-temporal lobar degeneration

BACKGROUND

Frontotemporal lobar degeneration (FTLD) is characterized pathologically by neuronal and glial inclusions of hyperphosphorylated tau or by neuronal cytoplasmic inclusions of TDP43. This study aimed at deciphering the molecular mechanisms leading to these distinct pathological subtypes.

METHODS

To this end, we performed an unbiased mass spectrometry-based proteomic and systems-level analysis of the middle frontal gyrus cortices of FTLD-tau (n = 6), FTLD-TDP (n = 15), and control patients (n = 5). We validated these results in an independent patient cohort (total n = 24).

RESULTS

The middle frontal gyrus cortex proteome was most significantly altered in FTLD-tau compared to controls (294 differentially expressed proteins at FDR = 0.05). The proteomic modifications in FTLD-TDP were more heterogeneous (49 differentially expressed proteins at FDR = 0.1). Weighted co-expression network analysis revealed 17 modules of co-regulated proteins, 13 of which were dysregulated in FTLD-tau. These modules included proteins associated with oxidative phosphorylation, scavenger mechanisms, chromatin regulation, and clathrin-mediated transport in both the frontal and temporal cortex of FTLD-tau. The most strongly dysregulated subnetworks identified cyclin-dependent kinase 5 (CDK5) and polypyrimidine tract-binding protein 1 (PTBP1) as key players in the disease process. Dysregulation of 9 of these modules was confirmed in independent validation data sets of FLTD-tau and control temporal and frontal cortex (total n = 24). Dysregulated modules were primarily associated with changes in astrocyte and endothelial cell protein abundance levels, indicating pathological changes in FTD are not limited to neurons.

CONCLUSIONS

Using this innovative workflow and zooming in on the most strongly dysregulated proteins of the identified modules, we were able to identify disease-associated mechanisms in FTLD-tau with high potential as biomarkers and/or therapeutic targets.

Quantitative proteomics identified circulating biomarkers in lung adenocarcinoma diagnosis

BACKGROUND

Lung cancer (LC) is a common malignant tumor with a high incidence and poor prognosis. Early LC could be cured, but the 5-year-survival rate for patients advanced is extremely low. Early screening of tumor biomarkers through plasma could allow more LC to be detected at an early stage, leading to a earlier treatment and a better prognosis.

METHODS

This study was based on total proteomic analysis and parallel reaction monitoring validation of peripheral blood from 20 lung adenocarcinoma patients and 20 healthy individuals. Furthermore, differentially expressed proteins closely related to prognosis were analysed using Kaplan-Meier Plotter and receiver operating characteristic curve (ROC) curve analysis.

RESULTS

The candidate proteins GAPDH and RAC1 showed the highest connectivity with other differentially expressed proteins between the lung adenocarcinoma group and the healthy group using STRING. Kaplan-Meier Plotter analysis showed that lung adenocarcinoma patients with positive ATCR2, FHL1, RAB27B, and RAP1B expression had observably longer overall survival than patients with negative expression (P < 0.05). The high expression of ARPC2, PFKP, PNP, RAC1 was observably negatively correlated with prognosis (P < 0.05). 17 out of 27 proteins showed a high area under the curve (> 0.80) between the lung adenocarcinoma and healthy plasma groups. Among those proteins, UQCRC1 had an area under the curve of 0.960, and 5 proteins had an area under the curve from 0.90 to 0.95, suggesting that these hub proteins might have discriminatory potential in lung adenocarcinoma, P < 0.05.

CONCLUSIONS

These findings provide UQCRC1, GAPDH, RAC1, PFKP have potential as novel biomarkers for the early screening of lung adenocarcinoma.

Advances in mass spectrometry-based phosphoproteomics for elucidating abscisic acid signaling and plant responses to abiotic stress

Abiotic stresses have significant impacts on crop yield and quality. Even though significant efforts during the past decade have been devoted to uncovering the core signaling pathways associated with the phytohormone abscisic acid (ABA) and abiotic stress in plants, abiotic stress signaling mechanisms in most crops remain largely unclear. The core components of the ABA signaling pathway, including early events in the osmotic stress-induced phosphorylation network, have recently been elucidated in Arabidopsis with the aid of phosphoproteomics technologies. We now know that SNF1-related kinases 2 (SnRK2s) are not only inhibited by the clade A type 2C protein phosphatases (PP2Cs) through dephosphorylation, but also phosphorylated and activated by upstream mitogen-activated protein kinase kinase kinases (MAP3Ks). Through describing the course of studies to elucidate abiotic stress and ABA signaling, we will discuss how we can take advantage of the latest innovations in mass-spectrometry-based phosphoproteomics and structural proteomics to boost our investigation of plant regulation and responses to ABA and abiotic stress.

Standard-Free Absolute Quantitation of Antibody Deamidation Degradation and Host Cell Proteins by Coulometric Mass Spectrometry

Proteomic absolute quantitation strategies mainly rely on the use of synthetic stable isotope-labeled peptides or proteins as internal standards, which are highly costly and time-consuming to synthesize. To circumvent this limitation, we recently developed a coulometric mass spectrometry (CMS) approach for absolute quantitation of proteins without the use of standards, based on the electrochemical oxidation of oxidizable surrogate peptides, followed by mass spectrometry measurement of the peptide oxidation yield. Previously, CMS was only applied for single-protein quantitation. In this study, first, we demonstrated absolute quantitation of multiple proteins in a mixture (e.g., β-lactoglobulin B, α-lactalbumin, and carbonic anhydrase) by CMS in one run, without using any standards. The CMS quantitation result was validated with a traditional isotope dilution method. Second, CMS can be used for absolute quantitation of a low-level target protein in a mixture; for instance, 500 ppm of PLBL2, a problematic host cell protein (HCP), in the presence of a highly abundant monoclonal antibody (mAb) was successfully quantified by CMS with no use of standards. Third, taking one step further, this study demonstrated the unprecedented quantitative analysis of deamidated peptide products arising from the mAb heavy chain deamidation reaction. In particular, absolute quantitation of the deamidation succinimide intermediate which had not been performed before due to the lack of standard was conducted by CMS, for the first time. Overall, our data suggest that CMS has potential utilities for quantitative proteomics and biotherapeutic drug discovery.

Surrogate peptide selection and internal standardization for accurate quantification of endogenous proteins

Relative quantification techniques have dominated the field of proteomics. However, biomarker discovery, mathematical model development and studies on transporter-mediated drug disposition still need absolute quantification of proteins. The quality of data of trace-level protein quantification is solely dependent on the specific selection of surrogate peptides. Selection of surrogate peptides has a major impact on the accuracy of the method. In this article, the advanced approaches for selection of surrogate peptides, which can provide absolute quantification of the proteins are discussed. In addition, internal standardization, which accounts for variations in the quantitation process to achieve absolute protein quantification is discussed.

MStoCIRC: A powerful tool for downstream analysis of MS/MS data to predict translatable circRNAs

CircRNAs are formed by a non-canonical splicing method and appear circular in nature. CircRNAs are widely distributed in organisms and have the features of time- and tissue-specific expressions. CircRNAs have attracted increasing interest from scientists because of their non-negligible effects on the growth and development of organisms. The translation capability of circRNAs is a novel and valuable direction in the functional research of circRNAs. To explore the translation potential of circRNAs, some progress has been made in both experimental identification and computational prediction. For computational prediction, both CircCode and CircPro are ribosome profiling-based software applications for predicting translatable circRNAs, and the online databases riboCIRC and TransCirc analyze as many pieces of evidence as possible and list the predicted translatable circRNAs of high confidence. Simultaneously, mass spectrometry in proteomics is often recognized as an efficient method to support the identification of protein and peptide sequences from diverse complex templates. However, few applications fully utilize mass spectrometry to predict translatable circRNAs. Therefore, this research aims to build up a scientific analysis pipeline with two salient features: 1) it starts with the data analysis of raw tandem mass spectrometry data; and 2) it also incorporates other translation evidence such as IRES. The pipeline has been packaged into an analysis tool called mass spectrometry to translatable circRNAs (MStoCIRC). MStoCIRC is mainly implemented by Python3 language programming and could be downloaded from GitHub (https://github.com/QUMU00/mstocirc-master). The tool contains a main program and several small, independent function modules, making it more multifunctional. MStoCIRC can process data efficiently and has obtained hundreds of translatable circRNAs in humans and Arabidopsis thaliana.

Determination of the Time since Deposition of Blood Traces Utilizing a Liquid Chromatography-Mass Spectrometry-Based Proteomics Approach

Knowledge about when a bloodstain was deposited at a crime scene can be of critical value in forensic investigation. A donor of a genetically identified bloodstain could be linked to a suspected time frame and the crime scene itself. Determination of the time since deposition (TsD) has been extensively studied before but has yet to reach maturity. We therefore conducted a proof-of-principle study to study time- and storage-dependent changes of the proteomes of dried blood stains. A bottom-up proteomics approach was employed, and high-resolution liquid-chromatography-mass-spectrometry (HR-LC-MS) and data-independent acquisition (DIA) were used to analyze samples aged over a 2 month period and two different storage conditions. In multivariate analysis, samples showed distinct clustering according to their TsD in both principal component analysis (PCA) and in partial least square discriminant analysis (PLS DA). The storage condition alters sample aging and yields different separation-driving peptides in hierarchical clustering and in TsD marker peptide selection. Certain peptides and amino acid modifications were identified and further assessed for their applicability in assessing passed TsD. A prediction model based on data resampling (Jackknife) was applied, and prediction values for selected peptide ratios were created. Depending on storage conditions and actual sample age, mean prediction performances ranges in between 70 and 130% for the majority of peptides and time points. This places this study as a first in investigating LC-MS based bottom-up proteomics approaches for TsD determination.

Advances in enrichment methods for mass spectrometry-based proteomics analysis of post-translational modifications

Post-translational modifications (PTMs) occur during or after protein biosynthesis and increase the functional diversity of proteome. They comprise phosphorylation, acetylation, methylation, glycosylation, ubiquitination, sumoylation (among many other modifications), and influence all aspects of cell biology. Mass-spectrometry (MS)-based proteomics is the most powerful approach for PTM analysis. Despite this, it is challenging due to low abundance and labile nature of many PTMs. Hence, enrichment of modified peptides is required for MS analysis. This review provides an overview of most common PTMs and a discussion of current enrichment methods for MS-based proteomics analysis. The traditional affinity strategies, including immunoenrichment, chromatography and protein pull-down, are outlined together with their strengths and shortcomings. Moreover, a special attention is paid to chemical enrichment strategies, such as capture by chemoselective probes, metabolic and chemoenzymatic labelling, which are discussed with an emphasis on their recent progress. Finally, the challenges and future trends in the field are discussed.

Arsenic trioxide increases apoptosis of SK-N-BE (2) cells partially by inducing GPX4-mediated ferroptosis

BACKGROUND

Neuroblastoma (NB) is the most common extracranial tumor in central nervous system threatening children's health with limited therapeutic options. Arsenic trioxide (ATO) has been identified the cytotoxicity in NB cells but the potential mechanism remains unclear. In this study, we attempted to obtain some insight into the mechanisms of cell death induced by ATO in NB cells.

METHODS AND RESULTS

Proteomic analyses found that ATO can affect the signaling pathway associated with ferroptosis, including the upregulation of iron absorption (FTL, FTH1, HO-1), ferritinophagy (LC3, P62, ATG7, NCOA4) and modifier of glutathione synthesis (GCLM); downregulation of glutamine synthetase (GS) and GPX4, which was the critical inhibitor of ferroptosis. Western blot analysis revealing GPX4 expression in SK-N-BE (2) cells decreased after treatment with ATO (7.3 µM), resulting in a loss of GPX4 activity. Furthermore, Ferroptosis inhibitor ferrostatin-1 partially blocked ATO-induced cell death.

CONCLUSIONS

Our study revealed that ATO may induce ferroptosis in neuroblastoma cell SK-N-BE (2) by facilitating the downregulation of GPX4, ultimately resulting in iron-dependent oxidative death.

Mass Spectrometry Measurements of Neuropeptides: From Identification to Quantitation

Neuropeptides (NPs), a unique class of neuronal signaling molecules, participate in a variety of physiological processes and diseases. Quantitative measurements of NPs provide valuable information regarding how these molecules are differentially regulated in a multitude of neurological, metabolic, and mental disorders. Mass spectrometry (MS) has evolved to become a powerful technique for measuring trace levels of NPs in complex biological tissues and individual cells using both targeted and exploratory approaches. There are inherent challenges to measuring NPs, including their wide endogenous concentration range, transport and postmortem degradation, complex sample matrices, and statistical processing of MS data required for accurate NP quantitation. This review highlights techniques developed to address these challenges and presents an overview of quantitative MS-based measurement approaches for NPs, including the incorporation of separation methods for high-throughput analysis, MS imaging for spatial measurements, and methods for NP quantitation in single neurons.

A1 Ions: Peptide-Specific and Intensity-Enhanced Fragment Ions for Accurate and Multiplexed Proteome Quantitation

Accurate proteome quantitation is of great significance to deeply understand various cellular and physiological processes. Since a1 ions, generated from dimethyl-labeled peptides, exhibited high formation efficiency (up to 99%) and enhanced intensities (2.34-fold by average) in tandem mass spectra, herein, we proposed an a1 ion-based proteome quantitation (APQ) method, which showed high quantitation accuracy (relative errors < 7%) and precision (median coefficients of variation ≤ 11%) even in a 20-fold dynamic range. Notably, due to the mass differences of a1 ions from peptides with different N-terminal amino acids, APQ demonstrated interference-free capacity by distinguishing target peptides from the coisolated ones. By designing an isobaric dimethyl labeling strategy, we achieved simultaneous proteome-wide measurements across up to eight samples. Using APQ to quantify the time-resolved proteomic profiles during a TGF-β-induced epithelial-mesenchymal transition, we found many differentially expressed proteins associated with fatty acid degradation, indicating that fatty acid metabolism reprogramming occurred during the process. The APQ method combines high quantitation accuracy with multiplexing capacity, which is suitable for deep mining and understanding of dynamic biological processes.

Recent progress of proteomic analysis on spermatogenesis

Testis, the only organ responsible for generating sperm, is by far the organ with the largest variety of proteins and tissue-specific proteins in humans. In testis, spermatogenesis is a multi-step complex process well-accepted that protein and mRNA are decoupled in certain stages of spermatogenesis. With the fast development of mass spectrometry-based proteomics, it is possible to systemically study protein abundances and modifications in testis and sperm to help us understand the molecular mechanisms of spermatogenesis. This review provides an overview of the recent progress of proteomics analysis on spermatogenesis, including protein expression and multiple PTMs, such as phosphorylation, glycosylation, ubiquitylation, and acetylation.

Nicotinamide Mononucleotide Alleviates Cardiomyopathy Phenotypes Caused by Short-Chain Enoyl-Coa Hydratase 1 Deficiency

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ECHS1 hydrates medium- and short-chain enoyl CoAs and catalyzes the oxidation of fatty acids and branched-chain amino acids.

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The mechanism driving ECHS1 deficiency–associated cardiomyopathy was investigated using conventional biochemistry and molecular biology methods, including immunoprecipitation and polymerase chain reaction.

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Echs1 heterogeneous knockout mice displayed cardiac dysfunction as evaluated by echocardiography.

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ECHS1 deficiency causes cardiomyopathy by enhancing p300-mediated H3K9ac.

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ECHS1 deficiency–induced cardiomyopathy can be prevented using an intervention approach targeting H3K9ac.

Short-chain enoyl-CoA hydratase 1 (ECHS1) deficiency plays a role in cardiomyopathy. Whether ECHS1 deficiency causes or is only associated with cardiomyopathy remains unclear. By using Echs1 heterogeneous knockout (Echs1^+/-) mice, we found that ECHS1 deficiency caused cardiac dysfunction, as evidenced by diffuse myocardial fibrosis and upregulated fibrosis-related genes. Mechanistically, ECHS1 interacts with the p300 nuclear localization sequence, preventing its nuclear translocation in fibroblasts. ECHS1 deficiency promotes p300 nuclear translocation, leading to increased H3K9 acetylation, a known risk factor for cardiomyopathy. Nicotinamide mononucleotide–mediated acetylation targeting suppressed ECHS1 deficiency–induced cardiomyopathy phenotypes in Echs1^+/- mice. Thus, enhancing p300-mediated H3K9ac is a potential interventional approach for preventing ECHS1 deficiency–induced cardiomyopathy.

Identifying Potential Mitochondrial Proteome Signatures Associated with the Pathogenesis of Pulmonary Arterial Hypertension in the Rat Model

Pulmonary arterial hypertension (PAH) is a severe and progressive disease that affects the heart and lungs and a global health concern that impacts individuals and society. Studies have reported that some proteins related to mitochondrial metabolic functions could play an essential role in the pathogenesis of PAH, and their specific expression and biological function are still unclear. We successfully constructed a monocrotaline- (MCT-) induced PAH rat model in the present research. Then, the label-free quantification proteomic technique was used to determine mitochondrial proteins between the PAH group (n = 6) and the normal group (n = 6). Besides, we identified 1346 mitochondrial differentially expressed proteins (DEPs) between these two groups. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to analyze the mainly mitochondrial DEPs' biological functions and the signal pathways. Based on the protein-protein interaction (PPI) network construction and functional enrichment, we screened 19 upregulated mitochondrial genes (Psmd1, Psmc4, Psmd13, Psmc2, etc.) and 123 downregulated mitochondrial genes (Uqcrfs1, Uqcrc1, Atp5c1, Atp5a1, Uqcrc2, etc.) in rats with PAH. Furthermore, in an independent cohort dataset and experiments with rat lung tissue using qPCR, validation results consistently showed that 6 upregulated mitochondrial genes (Psmd2, Psmc4, Psmc3, Psmc5, Psmd13, and Psmc2) and 3 downregulated mitochondrial genes (Lipe, Cat, and Prkce) were significantly differentially expressed in the lung tissue of PAH rats. Using the RNAInter database, we predict potential miRNA target hub mitochondrial genes at the transcriptome level. We also identified bortezomib and carfilzomib as the potential drugs for treatment in PAH. Finally, this study provides us with a new perspective on critical biomarkers and treatment strategies in PAH.

Fungal pathogens of cereal crops: Proteomic insights into fungal pathogenesis, host defense, and resistance

Fungal infections of cereal crops pose a significant risk to global food security through reduced grain production and quality, as well as contamination of animal feed and human products for consumption. To combat fungal disease, we need to understand how the pathogen adapts and survives within the hostile environment of the host and how the host's defense response can be modulated for protection from disease. Such investigations offer insight into fungal pathogenesis, host immunity, the development of resistance, and mechanisms of action for currently-used control strategies. Mass spectrometry-based proteomics provides a technologically-advanced platform to define differences among fungal pathogens and their hosts at the protein level, supporting the discovery of proteins critical for disease, and uncovering novel host responses driving susceptibly or resistance of the host. In this Review, we explore the role of mass spectrometry-based proteomics in defining the intricate relationship between a pathogen and host during fungal disease of cereal crops with a focus on recent discoveries derived from the globally-devastating diseases of Fusarium head blight, Rice blast, and Powdery mildew. We highlight advances made for each of these diseases and discuss opportunities to extrapolate findings to further our fight against fungal pathogens on a global scale.

Comparison of Different Label-Free Techniques for the Semi-Absolute Quantification of Protein Abundance

In proteomics, it is essential to quantify proteins in absolute terms if we wish to compare results among studies and integrate high-throughput biological data into genome-scale metabolic models. While labeling target peptides with stable isotopes allow protein abundance to be accurately quantified, the utility of this technique is constrained by the low number of quantifiable proteins that it yields. Recently, label-free shotgun proteomics has become the “gold standard” for carrying out global assessments of biological samples containing thousands of proteins. However, this tool must be further improved if we wish to accurately quantify absolute levels of proteins. Here, we used different label-free quantification techniques to estimate absolute protein abundance in the model yeast Saccharomyces cerevisiae. More specifically, we evaluated the performance of seven different quantification methods, based either on spectral counting (SC) or extracted-ion chromatogram (XIC), which were applied to samples from five different proteome backgrounds. We also compared the accuracy and reproducibility of two strategies for transforming relative abundance into absolute abundance: a UPS2-based strategy and the total protein approach (TPA). This study mentions technical challenges related to UPS2 use and proposes ways of addressing them, including utilizing a smaller, more highly optimized amount of UPS2. Overall, three SC-based methods (PAI, SAF, and NSAF) yielded the best results because they struck a good balance between experimental performance and protein quantification.

Absolute Quantification of TGF-β Signaling Proteins Using Quantitative Western Blot

Cell signaling governs the basic functions of cells by molecular interactions that involve of many proteins. The abundance of signaling proteins can directly influence cellular responses to external signal, contributing to cellular heterogeneity. Absolute quantification of proteins is important for modeling and understanding the complex signaling network. Here, we introduce how to measure the amount of TGF-β signaling proteins using quantitative immunoblotting. In addition, we discuss how to convert the measurements of protein abundance to the quantities of absolute molecules per cell. This method is generally applicable to the absolute quantification of other proteins.

Protein Preparation for Proteomic Analysis of the Unfolded Protein Response in Arabidopsis thaliana

Excessive accumulation of unfolded or misfolded proteins in the endoplasmic reticulum (ER) leads to a potentially cytotoxic condition known as the ER stress. Upon ER stress, cells initiate a homeostatic response called unfolded protein response (UPR) to assist proper folding the unfolded or misfolded proteins. Proteomics have been broadly used in plants with Liquid Chromatography coupled to tandem MS (LC-MS/MS) technologies. LC-MS/MS techniques have also been a great tool for studies of posttranslational modifications (PTMs). Here we describe our protocol of a fast method for large amount of seedling treatment and collection for UPR study in Arabidopsis thaliana and the preparation of total proteins for proteomic analysis.

New advances in quantitative proteomics research and current applications in asthma

INTRODUCTION

Asthma is the most common chronic respiratory disease and has been declared a global public health problem by the World Health Organization. Due to the high heterogeneity and complexity, asthma can be classified into different 'phenotypes' and it is still difficult to assess the phenotypes and stages of asthma by traditional methods. In recent years, mass spectrometry-based proteomics studies have made significant progress in sensitivity and accuracy of protein identification and quantitation, and are able to obtain differences in protein expression across samples, which provides new insights into the mechanisms and classification of asthma.

AREAS COVERED

In this article, we summarize research strategies in quantitative proteomics, including labeled, label-free and targeted quantification, and highlight the advantages and disadvantages of each. In addition, new applications of quantitative proteomics and the current status of research in asthma have also been discussed. In this study, online resources such as PubMed and Google Scholar were used for literature retrieval.

EXPERT OPINION

The application of quantitative proteomics in asthma has an important role in identifying asthma subphenotypes, revealing potential pathogenesis and therapeutic targets. But the proteomic studies on asthma are not sufficient, as most of them are in the phase of biomarker discovery.