Determination of double bond positions in unsaturated fatty acids by pre-column derivatization with dimethyl and dipyridyl disulfide followed by LC-SWATH-MS analysis.

Comprehensive in-depth structural characterization of free mono-unsaturated and polyunsaturated fatty acids often requires the determination of carbon-carbon double bond positions due to their impact on physiological properties and relevance in biological samples or during impurity profiling of pharmaceuticals. In this research, we report on the evaluation of disulfides as suitable derivatization reagents for the determination of carbon-carbon double bond positions of unsaturated free fatty acids by UHPLC-ESI-QTOF-MS/MS analysis and SWATH (sequential windowed acquisition of all theoretical mass spectra) acquisition. Iodine-catalyzed derivatization of C = C double bonds with dimethyl disulfide (DMDS) enabled detection of characteristic carboxy-terminal MS2 fragments for various fatty acids in ESI negative mode. The determination of double bond positions of fatty acids with up to three double bonds, the transfer of the method to plasma samples, and its limitations have been shown. To achieve charge-switching for positive ion mode MS-detection, derivatization with 2,2'-dipyridyldisulfide (DPDS) was investigated. It enabled detection of both corresponding characteristic omega-end- and carboxy-end-fragments for fatty acids with up to two double bonds in positive ion mode. It provides a straightforward strategy for designing MRM transitions for targeted LC-MS/MS assays. Both derivatization techniques represent a simple and inexpensive way for the determination of double bond positions in fatty acids with low number of double bonds. No adaptation of MS hardware is required and the specific isotopic pattern of resulting sulfur-containing products provides additional structural confirmation. This reaction scheme opens up the avenue of structural tuning of disulfide reagents beyond DMDS and DPDS using reagents like cystine and analogs to achieve enhanced performance and sensitivity.

Exploring Ion Mobility Mass Spectrometry Data File Conversions to Leverage Existing Tools and Enable New Workflows.

Ion mobility (IM) is often combined with LC-MS experiments to provide an additional dimension of separation for complex sample analysis. While highly complex samples are better characterized by the full dimensionality of LC-IM-MS experiments to uncover new information, downstream data analysis workflows are often not equipped to properly mine the additional IM dimension. For many samples the data acquisition benefits of including IM separations are all that is necessary to uncover sample information and the full dimensionality of the data is not required for data analysis. Postacquisition reduction and adaptation of the dimensions of LC-IM-MS and IM-MS experiments into an LC-MS format opens the possibility to use a plethora of existing software tools. In this work, we developed data file conversion tools to reduce the complexity of IM data analysis. Three data file transformations are introduced in the PNNL PreProcessor software: (1) mapping the IM axis to the LC axis for IM-MS data, (2) converting the drift time vs m/z space to CCS/z vs m/z space, and (3) transforming All Ions IM/MS mobility aligned fragmentation data to a standard LC-MS DDA data file format. These new data file conversions are demonstrated with corresponding lipidomics and proteomics workflows that leverage existing LC-MS data analysis software to highlight the benefits of the data transformations.

Data Independent Acquisition Mass Spectrometry Enhanced Personalized Glycosylation Profiling of Haptoglobin in Hepatocellular Carcinoma.

Aberrant glycosylation has gained significant interest for biomarker discovery. However, low detectability, complex glycan structures, and heterogeneity present challenges in glycoprotein assay development. Using haptoglobin (Hp) as a model, we developed an integrated platform combining functionalized magnetic nanoparticles and zwitterionic hydrophilic interaction liquid chromatography (ZIC-HILIC) for highly specific glycopeptide enrichment, followed by a data-independent acquisition (DIA) strategy to establish a deep cancer-specific Hp-glycosylation profile in hepatitis B virus (HBV, n = 5) and hepatocellular carcinoma (HCC, n = 5) patients. The DIA strategy established one of the deepest Hp-glycosylation landscapes (1029 glycopeptides, 130 glycans) across serum samples, including 54 glycopeptides exclusively detected in HCC patients. Additionally, single-shot DIA searches against a DIA-based spectral library outperformed the DDA approach by 2-3-fold glycopeptide coverage across patients. Among the four N-glycan sites on Hp (N-184, N-207, N-211, N-241), the total glycan type distribution revealed significantly enhanced detection of combined fucosylated-sialylated glycans, which were the most dominant glycoforms identified in HCC patients. Quantitation analysis revealed 48 glycopeptides significantly enriched in HCC (p < 0.05), including a hybrid monosialylated triantennary glycopeptide on the N-184 site with nearly none-to-all elevation to differentiate HCC from the HBV group (HCC/HBV ratio: 2462 ± 766, p < 0.05). In summary, DIA-MS presents an unbiased and comprehensive alternative for targeted glycoproteomics to guide discovery and validation of glyco-biomarkers.

Improving Proteomic Identification Using Narrow Isolation Windows with Zeno SWATH Data-Independent Acquisition.

Data-independent acquisition (DIA) techniques such as sequential window acquisition of all theoretical mass spectra (SWATH) acquisition have emerged as the preferred strategies for proteomic analyses. Our study optimized the SWATH-DIA method using a narrow isolation window placement approach, improving its proteomic performance. We optimized the acquisition parameter combinations of narrow isolation windows with different widths (1.9 and 2.9 Da) on a ZenoTOF 7600 (Sciex); the acquired data were analyzed using DIA-NN (version 1.8.1). Narrow SWATH (nSWATH) identified 5916 and 7719 protein groups on the digested peptides, corresponding to 400 ng of protein from mouse liver and HEK293T cells, respectively, improving identification by 7.52 and 4.99%, respectively, compared to conventional SWATH. The median coefficient of variation of the quantified values was less than 6%. We further analyzed 200 ng of benchmark samples comprising peptides from known ratios ofEscherichia coli, yeast, and human peptides using nSWATH. Consequently, it achieved accuracy and precision comparable to those of conventional SWATH, identifying an average of 95,456 precursors and 9342 protein groups across three benchmark samples, representing 12.6 and 9.63% improved identification compared to conventional SWATH. The nSWATH method improved identification at various loading amounts of benchmark samples, identifying 40.7% more protein groups at 25 ng. These results demonstrate the improved performance of nSWATH, contributing to the acquisition of deeper proteomic data from complex biological samples.

Comparative proteomics analysis of root and nodule mitochondria of soybean.

Legumes perform symbiotic nitrogen fixation through rhizobial bacteroids housed in specialised root nodules. The biochemical process is energy-intensive and consumes a huge carbon source to generate sufficient reducing power. To maintain the symbiosis, malate is supplied by legume nodules to bacteroids as their major carbon and energy source in return for ammonium ions and nitrogenous compounds. To sustain the carbon supply to bacteroids, nodule cells undergo drastic reorganisation of carbon metabolism. Here, a comprehensive quantitative comparison of the mitochondrial proteomes between root nodules and uninoculated roots was performed using data-independent acquisition proteomics, revealing the modulations in nodule mitochondrial proteins and pathways in response to carbon reallocation. Corroborated our findings with that from the literature, we believe nodules preferably allocate cytosolic phosphoenolpyruvates towards malate synthesis in lieu of pyruvate synthesis, and nodule mitochondria prefer malate over pyruvate as the primary source of NADH for ATP production. Moreover, the differential regulation of respiratory chain-associated proteins suggests that nodule mitochondria could enhance the efficiencies of complexes I and IV for ATP synthesis. This study highlighted a quantitative proteomic view of the mitochondrial adaptation in soybean nodules.

Improved drug target deconvolution with PISA-DIA using an extended, overlapping temperature gradient.

Thermal proteome profiling (TPP) is a powerful tool for drug target deconvolution. Recently, data-independent acquisition mass spectrometry (DIA-MS) approaches have demonstrated significant improvements to depth and missingness in proteome data, but traditional TPP (a.k.a. CEllular Thermal Shift Assay "CETSA") workflows typically employ multiplexing reagents reliant on data-dependent acquisition (DDA). Herein, we introduce a new experimental design for the Proteome Integral Solubility Alteration via label-free DIA approach (PISA-DIA). We highlight the proteome coverage and sensitivity achieved by using multiple overlapping thermal gradients alongside DIA-MS, which maximizes efficiencies in PISA sample concatenation and safeguards against missing protein targets that exist at high melting temperatures. We demonstrate our extended PISA-DIA design has superior proteome coverage as compared to using tandem-mass tags (TMT) necessitating DDA-MS analysis. Importantly, we demonstrate our PISA-DIA approach has the quantitative and statistical rigor using A-1331852, a specific inhibitor of BCL-xL. Due to the high melt temperature of this protein target, we utilized our extended multiple gradient PISA-DIA workflow to identify BCL-xL. We assert our novel overlapping gradient PISA-DIA-MS approach is ideal for unbiased drug target deconvolution, spanning a large temperature range whilst minimizing target dropout between gradients, increasing the likelihood of resolving the protein targets of novel compounds.

Benefit of In Silico Predicted Spectral Libraries in Data-Independent Acquisition Data Analysis Workflows.

Data-independent acquisition (DIA) has become a well-established method for MS-based proteomics. However, the list of options to analyze this type of data is quite extensive, and the use of spectral libraries has become an important factor in DIA data analysis. More specifically the use of in silico predicted libraries is gaining more interest. By working with a differential spike-in of human standard proteins (UPS2) in a constant yeast tryptic digest background, we evaluated the sensitivity, precision, and accuracy of the use of in silico predicted libraries in data DIA data analysis workflows compared to more established workflows. Three commonly used DIA software tools, DIA-NN, EncyclopeDIA, and Spectronaut, were each tested in spectral library mode and spectral library-free mode. In spectral library mode, we used independent spectral library prediction tools PROSIT and MS2PIP together with DeepLC, next to classical data-dependent acquisition (DDA)-based spectral libraries. In total, we benchmarked 12 computational workflows for DIA. Our comparison showed that DIA-NN reached the highest sensitivity while maintaining a good compromise on the reproducibility and accuracy levels in either library-free mode or using in silico predicted libraries pointing to a general benefit in using in silico predicted libraries.

Transformer-based de novo peptide sequencing for data-independent acquisition mass spectrometry.

Tandem mass spectrometry (MS/MS) stands as the predominant high-throughput technique for comprehensively analyzing protein content within biological samples. This methodology is a cornerstone driving the advancement of proteomics. In recent years, substantial strides have been made in Data-Independent Acquisition (DIA) strategies, facilitating impartial and non-targeted fragmentation of precursor ions. The DIA-generated MS/MS spectra present a formidable obstacle due to their inherent high multiplexing nature. Each spectrum encapsulates fragmented product ions originating from multiple precursor peptides. This intricacy poses a particularly acute challenge in de novo peptide/protein sequencing, where current methods are ill-equipped to address the multiplexing conundrum. In this paper, we introduce Transformer-DIA, a deep-learning model based on transformer architecture. It deciphers peptide sequences from DIA mass spectrometry data. Our results show significant improvements over existing STOA methods, including DeepNovo-DIA and PepNet. Transformer-DIA enhances precision by 15.14% to 34.8%, recall by 11.62% to 31.94% at the amino acid level, and boosts precision by 59% to 81.36% at the peptide level. Integrating DIA data and our Transformer-DIA model holds considerable promise to uncover novel peptides and more comprehensive profiling of biological samples. Transformer-DIA is freely available under the GNU GPL license at https://github.com/Biocomputing-Research-Group/Transformer-DIA.

Simultaneous targeted and discovery-driven clinical proteotyping using hybrid-PRM/DIA.

BACKGROUND: Clinical samples are irreplaceable, and their transformation into searchable and reusable digital biobanks is critical for conducting statistically empowered retrospective and integrative research studies. Currently, mainly data-independent acquisition strategies are employed to digitize clinical sample cohorts comprehensively. However, the sensitivity of DIA is limited, which is why selected marker candidates are often additionally measured targeted by parallel reaction monitoring. METHODS: Here, we applied the recently co-developed hybrid-PRM/DIA technology as a new intelligent data acquisition strategy that allows for the comprehensive digitization of rare clinical samples at the proteotype level. Hybrid-PRM/DIA enables enhanced measurement sensitivity for a specific set of analytes of current clinical interest by the intelligent triggering of multiplexed parallel reaction monitoring (MSxPRM) in combination with the discovery-driven digitization of the clinical biospecimen using DIA. Heavy-labeled reference peptides were utilized as triggers for MSxPRM and monitoring of endogenous peptides. RESULTS: We first evaluated hybrid-PRM/DIA in a clinical context on a pool of 185 selected proteotypic peptides for tumor-associated antigens derived from 64 annotated human protein groups. We demonstrated improved reproducibility and sensitivity for the detection of endogenous peptides, even at lower concentrations near the detection limit. Up to 179 MSxPRM scans were shown not to affect the overall DIA performance. Next, we applied hybrid-PRM/DIA for the integrated digitization of biobanked melanoma samples using a set of 30 AQUA peptides against 28 biomarker candidates with relevance in molecular tumor board evaluations of melanoma patients. Within the DIA-detected approximately 6500 protein groups, the selected marker candidates such as UFO, CDK4, NF1, and PMEL could be monitored consistently and quantitatively using MSxPRM scans, providing additional confidence for supporting future clinical decision-making. CONCLUSIONS: Combining PRM and DIA measurements provides a new strategy for the sensitive and reproducible detection of protein markers from patients currently being discussed in molecular tumor boards in combination with the opportunity to discover new biomarker candidates.

[Deciphering cellular processes responding to lethality of 17ß-estradiol by quantitative phosphoproteomics].

17ß-Estradiol (E2), an important endocrine hormone in the mammalian body, participates in the regulation of the physiological functions of the reproductive system, mammary glands, bone, and cardiovascular system, among others. Paradoxically, despite the physiological actions of endogenous E2 (0.2-1.0 nmol/L), numerous clinical and experimental studies have demonstrated that high-dose E2 treatment can cause tumor regression and exert pro-apoptotic actions in multiple cell types; however, the underlying mechanism remains undescribed. In particular, little information of the cellular processes responding to the lethality of E2 is available. In the present study, we attempted to characterize the cellular processes responding to high-dose (µmol/L) E2 treatment using quantitative phosphoproteomics to obtain a better understanding of the regulatory mechanism of E2-induced cell death. First, the cell phenotype induced by high-dose E2 was determined by performing Cell Counting Kit-8 assay (CCK8), cell cytotoxicity analysis by trypan blue staining, and microscopic imaging on HeLa cells treated with 1-10 µmol/L E2 or dimethyl sulfoxide (DMSO) for 1-3 d. E2 inhibited cell proliferation and induced cell death in a dose- and time-dependent manner. Compared with the DMSO-treated HeLa cells, the cells treated with 5 µmol/L E2 for 2 d demonstrated >74% growth inhibition and approximately 50% cell death. Thus, these cells were used for quantitative phosphoproteomic analysis. Next, a solid-phase extraction (SPE)-based immobilized titanium ion affinity chromatography (Ti4+-IMAC) phosphopeptide-enrichment method coupled with data-independent acquisition (DIA)-based quantitative proteomics was employed for the in-depth screening of high-dose E2-regulated phosphorylation sites to investigate the intracellular processes responding to high-dose E2 treatment. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) identified over 10000 phosphorylation sites regulated by E2 and DMSO in HeLa cells. In comparison with the DMSO-treated cells, the cells treated with 5 µmol/L E2 showed 537 upregulated phosphorylation sites and 387 downregulated phosphorylation sites, with a threshold of p<0.01 and |log2(fold change)|≥1. A total of 924 phosphorylation sites on 599 proteins were significantly regulated by high-dose E2, and these sites were subjected to enrichment analysis. In addition, 453 differently regulated phosphorylation sites on 325 proteins were identified only in the E2- or DMSO-treated cell samples. These phosphorylation sites may be phosphorylated or dephosphorylated in response to high-dose E2 stimulation and were subjected to parallel enrichment analyses. Taken together, 1218 phosphorylation sites on 741 proteins were significantly regulated by high-dose E2 treatment. The functional phosphoproteins in these two groups were then analyzed using Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA) to determine the biological processes in which they participate and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database. Consistent with the cell-phenotype data, cell cycle-related proteins were highly enriched in the two groups of E2-regulated phosphoproteins (p<0.05), indicating that high-dose E2 treatment can regulate cell proliferation. In addition, E2-regulated phosphoproteins were highly enriched in the cellular processes of ribosome biogenesis, nucleocytoplasmic transport, and messenger ribonucleic acid (mRNA) processing/splicing (p<0.05), indicating that the activation of these processes may contribute to high-dose E2-induced cell death. These results further confirm that high-dose E2 treatment inhibits protein translation and induces cell death. Furthermore, the significant upregulation of multiple phosphorylation sites associated with epidermal growth factor receptor (EGFR) and mitogen-activated protein kinases (MAPKs) MAPK1, MAPK4, and MAPK14 by high-dose E2 indicates that the EGFR and MAPK signaling pathways are likely involved in the regulation of E2-induced cell death. These phosphorylation sites likely play vital roles in E2-induced cell death in HeLa cells. Overall, our phosphoproteomic data could be a valuable resource for uncovering the regulatory mechanisms of E2 in the micromolar range.

Quantitative proteomic analysis to identify potential biomarkers linked to quality traits of beef tripe from different sources.

In this work, the 4D data-independent acquisition (DIA) quantitative strategy was used for differential proteomic analysis of four beef tripe samples from different sources to explore the associations between differentially expressed proteins (DEPs) and meat quality traits. A total of 68 shared DEPs were identified in all comparison groups, which were mainly involved in phosphorylation signaling pathway, peroxisome proliferator-activated receptor (PPAR) signaling pathway, and glucuronic acid pathway. In the correlation analysis between DEPs and quality traits of beef tripe, it was found that 21 proteins were significantly associated with the quality traits in beef tripe, which could be considered as the potential biomarkers of beef tripe quality. This study has successfully uncovered the protein composition of beef tripe for the very first time, which helps to understand the key proteins and biological processes associated with the quality traits of beef tripe from different sources and improve the quality control of beef tripe.

Profiling Protein-Protein Interactions in the Human Brain by Refined Cofractionation Mass Spectrometry.

Proteins usually execute their biological functions through interactions with other proteins and by forming macromolecular complexes, but global profiling of protein complexes directly from human tissue samples has been limited. In this study, we utilized cofractionation mass spectrometry (CF-MS) to map protein complexes within the postmortem human brain with experimental replicates. First, we used concatenated anion and cation Ion Exchange Chromatography (IEX) to separate native protein complexes in 192 fractions and then proceeded with Data-Independent Acquisition (DIA) mass spectrometry to analyze the proteins in each fraction, quantifying a total of 4,804 proteins with 3,260 overlapping in both replicates. We improved the DIA's quantitative accuracy by implementing a constant amount of bovine serum albumin (BSA) in each fraction as an internal standard. Next, advanced computational pipelines, which integrate both a database-based complex analysis and an unbiased protein-protein interaction (PPI) search, were applied to identify protein complexes and construct protein-protein interaction networks in the human brain. Our study led to the identification of 486 protein complexes and 10054 binary protein-protein interactions, which represents the first global profiling of human brain PPIs using CF-MS. Overall, this study offers a resource and tool for a wide range of human brain research, including the identification of disease-specific protein complexes in the future.

Plasma proteome analysis and validation of patients with community-acquired pneumonia: A cohort study.

PURPOSE: This study aimed to investigate the diagnostic potential of plasma biomarkers of community-acquired pneumonia (CAP) and their severity grading. EXPERIMENTAL DESIGN: Plasma proteomes from cohort I (n = 32) with CAP were analyzed by data-independent acquisition mass spectrometry (MS). MetaboAnalyst 5.0 was used to statistically evaluate significant differences in proteins from different samples, and demographic and clinical data were recorded for all enrolled patients. Cohort II (n = 80) was used to validate candidate biomarkers. Plasma protein levels were determined using quantitative enzyme-linked immunosorbent assay (ELISA). Correlations were assessed using Pearson's correlation coefficient. A receiver operating characteristic curve was used to verify the association between the variables, CAP diagnosis, and prognosis. RESULTS: 121 differentially expressed proteins (DEPs) were obtained between CAP and controls. These DEPs were mainly aggregated in pathways of phagosome(hsa04145) and complement and coagulation cascades (hsa04610). No significant differential proteins were detected in bacterial, viral, and mixed infection groups. The plasma levels of fetuin-A, alpha-1-antichymotrypsin (AACT), α1-acid glycoprotein (A1AG), and S100A8/S100A9 heterodimers detected by ELISA were consistent with those of MS. AACT, A1AG, S100A8/S100A9 heterodimer, and fetuin-A can potentially be used as diagnostic predictors, and fetuin-A and AACT are potential predictors of SCAP. CONCLUSIONS AND CLINICAL RELEVANCE: Plasma protein profiling can successfully identify potential biomarkers for CAP diagnosis and disease severity assessment. These biomarkers should be further studied for their clinical application.

Multispecies Benchmark Analysis for LC-MS/MS Validation and Performance Evaluation in Bottom-Up Proteomics.

We present an instrument-independent benchmark procedure and software (LFQ_bout) for the validation and comparative evaluation of the performance of LC-MS/MS and data processing workflows in bottom-up proteomics. The procedure enables a back-to-back comparison of common and emerging workflows, e.g., diaPASEF or ScanningSWATH, and evaluates the impact of arbitrary and inadequately documented settings or black-box data processing algorithms. It enhances the overall performance and quantification accuracy by recognizing and reporting common quantification errors.

Evaluation of urinary limonene metabolites as biomarkers of exposure to greenness.

Exposure to plants is known to improve physical and mental health and living in areas of high vegetation is associated with better health. The addition of quantitative measures of greenness exposure at individual-level to other objective and subjective study measures will help establish cause-and-effect relationships between greenspaces and human health. Because limonene is one of the most abundant biogenic volatile organic compounds emitted by plants, we hypothesized that urinary metabolites of inhaled limonene can serve as biomarkers of exposure to greenness. To test our hypothesis, we analyzed urine samples collected from eight human volunteers after limonene inhalation or after greenness exposure using liquid chromatography-high resolution mass spectrometry-based profiling. Eighteen isomers of nine metabolites were detected in urine after limonene inhalation, and their kinetic parameters were estimated using nonlinear mixed effect models. Urinary levels of most abundant limonene metabolites were elevated after brief exposure to a forested area, and the ratio of urinary limonene metabolites provided evidence of recent exposure. The identities and structures of these metabolites were validated using stable isotope tracing and tandem mass spectral comparison. Together, these data suggest that urinary metabolites of limonene, especially uroterpenol glucuronide and dihydroperillic acid glucuronide, could be used as individualized biomarkers of greenness exposure.

Identification of novel serum protein biomarkers in the context of 3P medicine for intravenous leiomyomatosis: a data-independent acquisition mass spectrometry-based proteomics study.

Background: Intravenous leiomyomatosis (IVL) is a rare endocrine-associated tumor with unique characteristics of intravascular invasion. This study aimed to identify reliable biomarkers to supervise the development or recurrence of IVL in the context of predictive, preventive, and personalized medicine (PPPM/3PM). Methods: A total of 60 cases were recruited to detect differentially expressed proteins (DEPs) in serum samples from IVL patients. These cases included those with recurrent IVL, non-recurrent IVL, uterine myoma, and healthy individuals without uterine myoma, with 15 cases in each category. Then, weighted gene co-expression network analysis (WGCNA), lasso-penalized Cox regression analysis (Lasso), trend clustering, and a generalized linear regression model (GLM) were utilized to screen the hub proteins involved in IVL progression. Results: First, 93 differentially expressed proteins (DEPs) were determined from 2582 recognizable proteins, with 54 proteins augmented in the IVL group, and the remaining proteins declined. These proteins were enriched in the modulation of the immune environment, mainly by activating the function of B cells. After the integrated analyses mentioned above, a model based on four proteins (A0A5C2FUE5, A0A5C2GPQ1, A0A5C2GNC7, and A0A5C2GBR3) was developed to efficiently determine the potential of IVL lesions to progress. Among these featured proteins, our results demonstrated that the risk factor A0A5C2FUE5 was associated with IVL progression (OR = 2.64). Conversely, A0A5C2GPQ1, A0A5C2GNC7, and A0A5C2GBR3 might act in a protective manner and prevent disease development (OR = 0.32, 0.60, 0.53, respectively), which was further supported by the multi-class receiver operator characteristic curve analysis. Conclusion: Four hub proteins were eventually identified based on the integrated bioinformatics analyses. This study potentiates the promising application of these novel biomarkers to predict the prognosis or progression of IVL by a 3PM approach. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-023-00338-0.

Integrated proteomic and phosphoproteomic data-independent acquisition data evaluate the personalized drug responses of primary and metastatic tumors in colorectal cancer.

Mass spectrometry (MS)-based proteomics and phosphoproteomics are powerful methods to study the biological mechanisms, diagnostic biomarkers, prognostic analysis, and drug therapy of tumors. Data-independent acquisition (DIA) mode is considered to perform better than data-dependent acquisition (DDA) mode in terms of quantitative reproducibility, specificity, accuracy, and identification of low-abundance proteins. Mini patient derived xenograft (MiniPDX) model is an effective model to assess the response to antineoplastic drugs in vivo and is helpful for the precise treatment of cancer patients. Kinases are favorable spots for tumor-targeted drugs, and their functional completion relies on signaling pathways through phosphorylating downstream substrates. Kinase-phosphorylation networks or edge interactions are considered more credible and permanent for characterizing complex diseases. Here, we provide a workflow for personalized drug response assessment in primary and metastatic colorectal cancer (CRC) tumors using DIA proteomic data, DIA phosphoproteomic data, and MiniPDX models. Three kinase inhibitors, afatinib, gefitinib, and regorafenib, are tested pharmacologically. The process mainly includes the following steps: clinical tissue collection, sample preparation, hybrid spectral libraries establishment, MS data acquisition, kinase-substrate network construction, in vivo drug test, and elastic regression modeling. Our protocol gives a more direct data basis for individual drug responses, and will improve the selection of treatment strategies for patients without the druggable mutation.

CRISPR/Cas9-mediated knockout of the ubiquitin variant UbKEKS reveals a role in regulating nucleolar structures and composition.

Ubiquitination is a post-translational modification responsible for one of the most complex multilayered communication and regulation systems in the cell. Over the past decades, new ubiquitin variants and ubiquitin-like proteins arose to further enrich this mechanism. Recently discovered ubiquitin variant UbKEKS can specifically target several proteins and yet, functional consequences of this new modification remain unknown. Depletion of UbKEKS induces accumulation of lamin A in the nucleoli, highlighting the need for deeper investigations about protein composition and functions regulation of this highly dynamic and membrane-less compartment. Using data-independent acquisition mass spectrometry and microscopy, we show that despite not impacting protein stability, UbKEKS is required to maintain a normal nucleolar organization. The absence of UbKEKS increases nucleoli's size and accentuate their circularity while disrupting dense fibrillar component and fibrillar centre structures. Moreover, depletion of UbKEKS leads to distinct changes in nucleolar composition. Lack of UbKEKS favours nucleolar sequestration of known apoptotic regulators such as IFI16 or p14ARF, resulting in an increase of apoptosis observed by flow cytometry and real-time monitoring. Overall, these results identify the first cellular functions of the UbKEKS variant and lay the foundation stone to establish UbKEKS as a new universal layer of regulation in the ubiquitination system.

Data-independent acquisition-based global phosphoproteomics reveal the diverse roles of casein kinase 1 in plant development.

Casein kinase 1 (CK1) is serine/threonine protein kinase highly conserved among eukaryotes, and regulates multiple developmental and signaling events through phosphorylation of target proteins. Arabidopsis early flowering 1 (EL1)-like (AELs) are plant-specific CK1s with varied functions, but identification and validation of their substrates is a major bottleneck in elucidating their physiological roles. Here, we conducted a quantitative phosphoproteomic analysis in data-independent acquisition mode to systematically identify CK1 substrates. We extracted proteins from seedlings overexpressing individual AEL genes (AEL1/2/3/4-OE) or lacking AEL function (all ael single mutants and two triple mutants) to identify the high-confidence phosphopeptides with significantly altered abundance compared to wild-type Col-0. Among these, we selected 3985 phosphopeptides with higher abundance in AEL-OE lines or lower abundance in ael mutants compared with Col-0 as AEL-upregulated phosphopeptides, and defined 1032 phosphoproteins. Eight CK1s substrate motifs were enriched among AEL-upregulated phosphopeptides and verified, which allowed us to predict additional candidate substrates and functions of CK1s. We functionally characterized a newly identified substrate C3H17, a CCCH-type zinc finger transcription factor, through biochemical and genetic analyses, revealing a role for AEL-promoted C3H17 protein stability and transactivation activity in regulating embryogenesis. As CK1s are highly conserved across eukaryotes, we searched the rice, mouse, and human protein databases using newly identified CK1 substrate motifs, yielding many more candidate substrates than currently known, largely expanding our understanding of the common and distinct functions exerted by CK1s in Arabidopsis and humans, facilitating future mechanistic studies of CK1-mediated phosphorylation in different species.

Comparison of Quantitative Mass Spectrometric Methods for Drug Target Identification by Thermal Proteome Profiling.

Thermal proteome profiling (TPP) provides a powerful approach to studying proteome-wide interactions of small therapeutic molecules and their target and off-target proteins, complementing phenotypic-based drug screens. Detecting differences in thermal stability due to target engagement requires high quantitative accuracy and consistent detection. Isobaric tandem mass tags (TMTs) are used to multiplex samples and increase quantification precision in TPP analysis by data-dependent acquisition (DDA). However, advances in data-independent acquisition (DIA) can provide higher sensitivity and protein coverage with reduced costs and sample preparation steps. Herein, we explored the performance of different DIA-based label-free quantification approaches compared to TMT-DDA for thermal shift quantitation. Acute myeloid leukemia cells were treated with losmapimod, a known inhibitor of MAPK14 (p38α). Label-free DIA approaches, and particularly the library-free mode in DIA-NN, were comparable of TMT-DDA in their ability to detect target engagement of losmapimod with MAPK14 and one of its downstream targets, MAPKAPK3. Using DIA for thermal shift quantitation is a cost-effective alternative to labeled quantitation in the TPP pipeline.