Development of a high-throughput kinase activity platform using nanoLC-MS/MS with DIA approach for studying the anti-cancer mechanism of Taxol in ovarian cancer.

BACKGROUND: Protein phosphorylation by protein kinases plays a pivotal role in increasing protein diversity, thereby influencing various cellular functions. However, due to the relatively low abundance of phosphopeptides in a mixture of peptides and the ion-suppression effect of non-phosphorylated peptides, the detection of phosphopeptides is not straightforward. RESULTS: Herein, a quantitative high-throughput platform was developed for assessing multikinase activity using nano-LC-MS/MS with a data-independent acquisition (DIA) approach. This platform was evaluated by studying the kinase activity in Taxol-treated SKOV3 cells. A library containing 38 peptide substrates was designed and analyzed to determine the activities of major kinases involved in cancer development. Twenty-three synthetic peptide substrates showed significant phosphorylation changes in triplicate biological experiments, as further verified by western blotting. Our findings reveal that Taxol suppressed SKOV3 cell survival by activating AMPK and suppressing the PI3K-Akt-dependent pathway, ultimately leading to mTOR inhibition. Furthermore, in combination with ERK, Akt, SGK, CK1, and ErbB2 inhibitors, Taxol enhanced the inhibitory effect on ovarian cancer. SIGNIFICANCE: This platform can be an attractive approach for large-scale kinase activity studies to comprehensively uncover the mechanisms of drug-disease treatment and to investigate a more effective therapy strategy.

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.

Quantitative urinary proteome analysis reveals potential biomarkers for disease activity of Behcet's disease uveitis.

PURPOSE: Behçet's disease-associated uveitis (BDU) is a severe, recurrent inflammatory condition affecting the eye and is part of a systemic vasculitis with unknown etiology, making biomarker discovery essential for disease management. In this study, we intend to investigate potential urinary biomarkers to monitor the disease activity of BDU. METHODS: Firstly, label-free data-dependent acquisition (DDA) and tandem mass tag (TMT)-labeled quantitative proteomics methods were used to profile the proteomes of urine from active and quiescent BDU patients, respectively. For further exploration, the remaining fifty urine samples were analyzed by a data-independent acquisition (DIA) quantitative proteomics method. RESULTS: Twenty-nine and 21 differential proteins were identified in the same urine from BDU patients by label-free DDA and TMT-labeled analyses, respectively. Seventy-nine differentially expressed proteins (DEPs) were significantly changed in other active BDU urine samples compared to those in quiescent BDU urine samples by IDA analysis. Gene Ontology (GO) and protein-protein interaction (PPI) analyses revealed that the DEPs were associated with multiple functions, including the immune and neutrophil activation responses. Finally, seven proteins were identified as candidate biomarkers for BDU monitoring and recurrence prediction, namely, CD38, KCRB, DPP4, FUCA2, MTPN, S100A8 and S100A9. CONCLUSIONS: Our results showed that urine can be a good source of biomarkers for BDU. These dysregulated proteins provide potential urinary biomarkers for BDU activity monitoring and provide valuable clues for the analysis of the pathogenic mechanisms of BDU.

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.

Longitudinal analysis of the lung proteome reveals persistent repair months after mild to moderate COVID-19.

In order to assess homeostatic mechanisms in the lung after COVID-19, changes in the protein signature of bronchoalveolar lavage from 45 patients with mild to moderate disease at three phases (acute, recovery, and convalescent) are evaluated over a year. During the acute phase, inflamed and uninflamed phenotypes are characterized by the expression of tissue repair and host defense response molecules. With recovery, inflammatory and fibrogenic mediators decline and clinical symptoms abate. However, at 9 months, quantified radiographic abnormalities resolve in the majority of patients, and yet compared to healthy persons, all showed ongoing activation of cellular repair processes and depression of the renin-kallikrein-kinin, coagulation, and complement systems. This dissociation of prolonged reparative processes from symptom and radiographic resolution suggests that occult ongoing disruption of the lung proteome is underrecognized and may be relevant to recovery from other serious viral pneumonias.

A Rapid One-Pot Workflow for Sensitive Microscale Phosphoproteomics.

Compared to advancements in single-cell proteomics, phosphoproteomics sensitivity has lagged behind due to low abundance, complex sample preparation, and substantial sample input requirements. We present a simple and rapid one-pot phosphoproteomics workflow (SOP-Phos) integrated with data-independent acquisition mass spectrometry (DIA-MS) for microscale phosphoproteomic analysis. SOP-Phos adapts sodium deoxycholate based one-step lysis, reduction/alkylation, direct trypsinization, and phosphopeptide enrichment by TiO2 beads in a single-tube format. By reducing surface adsorptive losses via utilizing n-dodecyl ß-d-maltoside precoated tubes and shortening the digestion time, SOP-Phos is completed within 3-4 h with a 1.4-fold higher identification coverage. SOP-Phos coupled with DIA demonstrated >90% specificity, enhanced sensitivity, lower missing values (<1%), and improved reproducibility (8%-10% CV). With a sample size-comparable spectral library, SOP-Phos-DIA identified 33,787 ± 670 to 22,070 ± 861 phosphopeptides from 5 to 0.5 µg cell lysate and 30,433 ± 284 to 6,548 ± 21 phosphopeptides from 50,000 to 2,500 cells. Such sensitivity enabled mapping key lung cancer signaling sites, such as EGFR autophosphorylation sites Y1197/Y1172 and drug targets. The feasibility of SOP-Phos-DIA was demonstrated on EGFR-TKI sensitive and resistant cells, revealing the interplay of multipathway Hippo-EGFR-ERBB signaling cascades underlying the mechanistic insight into EGFR-TKI resistance. Overall, SOP-Phos-DIA is an efficient and robust protocol that can be easily adapted in the community for microscale phosphoproteomic analysis.

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.

PeakQC: A Software Tool for Omics-Agnostic Automated Quality Control of Mass Spectrometry Data.

Mass spectrometry is broadly employed to study complex molecular mechanisms in various biological and environmental fields, enabling 'omics' research such as proteomics, metabolomics, and lipidomics. As study cohorts grow larger and more complex with dozens to hundreds of samples, the need for robust quality control (QC) measures through automated software tools becomes paramount to ensure the integrity, high quality, and validity of scientific conclusions from downstream analyses and minimize the waste of resources. Since existing QC tools are mostly dedicated to proteomics, automated solutions supporting metabolomics are needed. To address this need, we developed the software PeakQC, a tool for automated QC of MS data that is independent of omics molecular types (i.e., omics-agnostic). It allows automated extraction and inspection of peak metrics of precursor ions (e.g., errors in mass, retention time, arrival time) and supports various instrumentations and acquisition types, from infusion experiments or using liquid chromatography and/or ion mobility spectrometry front-end separations and with/without fragmentation spectra from data-dependent or independent acquisition analyses. Diagnostic plots for fragmentation spectra are also generated. Here, we describe and illustrate PeakQC's functionalities using different representative data sets, demonstrating its utility as a valuable tool for enhancing the quality and reliability of omics mass spectrometry analyses.

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.

Reusing Peptide Spectral Reference Libraries to Discover Putative Plasma Biomarkers of Response to Cancer Chemotherapy.

Biofluids such as blood plasma are rich reservoirs of potential biomarkers for disease diagnosis, prognosis, and prediction of treatment response. However, mass spectrometry analysis of circulating plasma proteins remains challenging. The introduction of data-independent acquisition mass spectrometry (DIA-MS) is an important step toward addressing detection of less abundant plasma proteins. Numerous plasma peptide MS/MS spectral library datasets produced from extensive plasma fractionation are accessible from public archives, and these can be repurposed as spectral reference libraries to increase the depth of proteomic analysis when DIA-MS is used. Here we describe the workflow that relies on reusing the existing spectral reference libraries by populating them with locally obtained peptide MS/MS data acquired by DIA-MS. This approach was demonstrated effectively to identify putative plasma biomarkers of response to neoadjuvant chemotherapy in the setting of pancreatic ductal adenocarcinoma (PDAC) (O'Rourke et al., J Proteomics 231:103998, 2021).

Quantitative Proteomics Analysis of Serum and Urine With DIA Mass Spectrometry Reveals Biomarkers for Pediatric Obstructive Sleep Apnea.

OBJECTIVES: Identification of suitable biomarkers that facilitate the screening and evaluation of pediatric obstructive sleep apnea (OSA) and its severity was explored. METHODS: Data-independent acquisition quantitative proteomic analysis was employed to identify serum and urine proteins with differential expression patterns between children with OSA and controls. Differentially expressed proteins that gradually increased or decreased with the severity of OSA were retained as potential biomarkers and underwent ELISA validation. RESULTS: We found that with increasing severity of OSA, there was a gradual upregulation of 34 proteins in the serum and 124 proteins in the urine, along with a respective downregulation of 10 serum proteins and 64 urinary proteins in the initial cohort of 40 children. These proteins primarily participate in immune activation, the complement pathway, oxygen transport, and reactive oxygen metabolism. Notably, cathepsin Z exhibited a positive correlation with the obstructive apnea hypopnea index, whereas sex hormone-binding globulin (SHBG) was negatively correlated. These proteins were then validated by ELISA in an independent cohort (n=21). Circulating cathepsin Z and SHBG levels displayed acceptable diagnostic performance of OSA with AUC values of 0.863 and 0.738, respectively. CONCLUSIONS: We identified two promising circulating proteins as novel biomarkers for clinical diagnosis and assessment of pediatric OSA severity. Furthermore, the comprehensive proteomic profile in pediatric OSA should aid in exploring the underlying pathophysiological mechanisms associated with this prevalent condition.

Cerebrospinal Fluid Proteome Map Reveals Molecular Signatures of Reversible Cerebral Vasoconstriction Syndrome.

Reversible cerebral vasoconstriction syndrome (RCVS) is a complex neurovascular disorder characterized by repetitive thunderclap headaches and reversible cerebral vasoconstriction. The pathophysiological mechanism of this mysterious syndrome remains underexplored and there is no clinically available molecular biomarker. To provide insight into the pathogenesis of RCVS, this study reported the first landscape of dysregulated proteome of cerebrospinal fluid (CSF) in patients with RCVS (n = 21) compared to the age- and sex-matched controls (n  = 20) using data-independent acquisition mass spectrometry. Protein-protein interaction and functional enrichment analysis were employed to construct functional protein networks using the RCVS proteome. An RCVS-CSF proteome library resource of 1054 proteins was established, which illuminated large groups of upregulated proteins enriched in the brain and blood-brain barrier (BBB). Personalized RCVS-CSF proteomic profiles from 17 RCVS patients and 20 controls reveal proteomic changes involving the complement system, adhesion molecules, and extracellular matrix, which may contribute to the disruption of BBB and dysregulation of neurovascular units. Moreover, an additional validation cohort validated a panel of biomarker candidates and a two-protein signature predicted by machine learning model to discriminate RCVS patients from controls with an area under the curve of 0.997. This study reveals the first RCVS proteome and a potential pathogenetic mechanism of BBB and neurovascular unit dysfunction. It also nominates potential biomarker candidates that are mechanistically plausible for RCVS, which may offer potential diagnostic and therapeutic opportunities beyond the clinical manifestations.

Protein Profiles and Novel Molecular Biomarkers of Schizophrenia Based on 4D-DIA Proteomics.

Schizophrenia is a severe psychological disorder. The current diagnosis mainly relies on clinical symptoms and lacks laboratory evidence, which makes it very difficult to make an accurate diagnosis especially at an early stage. Plasma protein profiles of schizophrenia patients were obtained and compared with healthy controls using 4D-DIA proteomics technology. Furthermore, 79 DEPs were identified between schizophrenia and healthy controls. GO functional analysis indicated that DEPs were predominantly associated with responses to toxic substances and platelet aggregation, suggesting the presence of metabolic and immune dysregulation in patients with schizophrenia. KEGG pathway enrichment analysis revealed that DEPs were primarily enriched in the chemokine signaling pathway and cytokine receptor interactions. A diagnostic model was ultimately established, comprising three proteins, namely, PFN1, GAPDH and ACTBL2. This model demonstrated an AUC value of 0.972, indicating its effectiveness in accurately identifying schizophrenia. PFN1, GAPDH and ACTBL2 exhibit potential as biomarkers for the early detection of schizophrenia. The findings of our studies provide novel insights into the laboratory-based diagnosis of schizophrenia.

Exploring Infantile Epileptic Spasm Syndrome: A Proteomic Analysis of Plasma Using the Data-Independent Acquisition Approach.

This study aimed to identify characteristic proteins in infantile epileptic spasm syndrome (IESS) patients' plasma, offering insights into potential early diagnostic biomarkers and its underlying causes. Plasma samples were gathered from 60 patients with IESS and 40 healthy controls. Data-independent acquisition proteomic analysis was utilized to identify differentially expressed proteins (DEPs). These DEPs underwent functional annotation through Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Gene set enrichment analysis (GSEA) was employed for both GO (GSEA-GO) and KEGG (GSEA-KEGG) analyses to examine the gene expression profiles. Receiver operating characteristic (ROC) curves assessed biomarkers' discriminatory capacity. A total of 124 DEPs were identified in IESS patients' plasma, mainly linked to pathways, encompassing chemokines, cytokines, and oxidative detoxification. GSEA-GO and GSEA-KEGG analyses indicated significant enrichment of genes associated with cell migration, focal adhesion, and phagosome pathways. ROC curve analysis demonstrated that the combination of PRSS1 and ACTB, PRSS3, ACTB, and PRSS1 alone exhibited AUC values exceeding 0.7. This study elucidated the significant contribution of cytokines, chemokines, oxidative detoxification, and phagosomes to the IESS pathogenesis. The combination of PRSS1 and ACTB holds promise as biomarkers for the early diagnosis of IESS.

Application of molecular networking to improve the compound annotation in liquid chromatography-mass spectrometry-based metabolomics analysis: A case study of Bupleuri radix.

INTRODUCTION: Compound annotation is always a challenging step in metabolomics studies. The molecular networking strategy has been developed recently to organize the relationship between compounds as a network based on their tandem mass (MS2) spectra similarity, which can be used to improve compound annotation in metabolomics analysis. OBJECTIVE: This study used Bupleuri Radix from different geographic areas to evaluate the performance of molecular networking strategy for compound annotation in liquid chromatography-mass spectrometry (LC-MS)-based metabolomics. METHODOLOGY: The Bupleuri Radix extract was analyzed by LC-quadrupole time-of-flight MS under MSe acquisition mode. After raw data preprocessing, the resulting dataset was used for statistical analysis, including principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). The chemical makers related to the sample growth place were selected using variable importance in projection (VIP) > 2, fold change (FC) > 2, and p < 0.05. The molecular networking analysis was applied to conduct the compound annotation. RESULTS: The score plots of PCA showed that the samples were classified into two clusters depending on their growth place. Then, the PLS-DA model was constructed to explore the chemical changes of the samples further. Sixteen compounds were selected as chemical makers and tentatively annotated by the feature-based molecular networking (FBMN) analysis. CONCLUSION: The results showed that the molecular networking method fully exploits the MS information and is a promising tool for facilitating compound annotation in metabolomics studies. However, the software used for feature extraction influenced the results of library searching and molecular network construction, which need to be taken into account in future studies.

An automated workflow based on data independent acquisition for practical and high-throughput personalized assay development and minimal residual disease monitoring in multiple myeloma patients.

OBJECTIVES: Minimal residual disease (MRD) status in multiple myeloma (MM) is an important prognostic biomarker. Personalized blood-based targeted mass spectrometry detecting M-proteins (MS-MRD) was shown to provide a sensitive and minimally invasive alternative to MRD-assessment in bone marrow. However, MS-MRD still comprises of manual steps that hamper upscaling of MS-MRD testing. Here, we introduce a proof-of-concept for a novel workflow using data independent acquisition-parallel accumulation and serial fragmentation (dia-PASEF) and automated data processing. METHODS: Using automated data processing of dia-PASEF measurements, we developed a workflow that identified unique targets from MM patient sera and personalized protein sequence databases. We generated patient-specific libraries linked to dia-PASEF methods and subsequently quantitated and reported M-protein concentrations in MM patient follow-up samples. Assay performance of parallel reaction monitoring (prm)-PASEF and dia-PASEF workflows were compared and we tested mixing patient intake sera for multiplexed target selection. RESULTS: No significant differences were observed in lowest detectable concentration, linearity, and slope coefficient when comparing prm-PASEF and dia-PASEF measurements of serial dilutions of patient sera. To improve assay development times, we tested multiplexing patient intake sera for target selection which resulted in the selection of identical clonotypic peptides for both simplex and multiplex dia-PASEF. Furthermore, assay development times improved up to 25× when measuring multiplexed samples for peptide selection compared to simplex. CONCLUSIONS: Dia-PASEF technology combined with automated data processing and multiplexed target selection facilitated the development of a faster MS-MRD workflow which benefits upscaling and is an important step towards the clinical implementation of MS-MRD.

Comprehensive Micro-SPE-Based Bottom-Up Proteomic Workflow for Sensitive Analysis of Limited Samples.

Clinical and biological samples are often scarce and precious (e.g., rare cell isolates, microneedle tissue biopsies, small-volume liquid biopsies, and even single cells or organelles). Typical large-scale proteomic methods, where significantly higher protein amounts are analyzed, are not directly transferable to the analysis of limited samples due to their incompatibility with pg-, ng-, and low-µg-level protein sample amounts. Here, we report the on-microsolid-phase extraction tip (OmSET)-based sample preparation workflow for sensitive analysis of limited biological samples to address this challenge. The developed platform was successfully tested for the analysis of 100-10,000 typical mammalian cells and is scalable to allow for lower and larger protein amounts and more samples to be analyzed (i.e., higher throughput of analysis).

Comparative proteomics analysis of samples from hepatic cystic echinococcosis patients using data-independent acquisition approach.

Cystic echinococcosis is a zoonotic disease resulting from infection caused by the larval stage of Echinococcus granulosus. This study aimed to assess the specific proteins that are potential candidates for the development of a vaccine against E. granulosus. The data-independent acquisition approach was employed to identify differentially expressed proteins (DEPs) in E. granulosus samples. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was employed to identify several noteworthy proteins. Results: The DEPs in E. granulosus samples were identified (245 pericystic wall vs. parasite-free yellowish granuloma (PYG, 1725 PY vs. PYG, 2274 PN vs. PYG). Further examination of these distinct proteins revealed their predominant enrichment in metabolic pathways, amyotrophic lateral sclerosis, and neurodegeneration-associated pathways. Notably, among these DEPs, SH3BGRL, MST1, TAGLN2, FABP5, UBE2V2, and RARRES2 exhibited significantly higher expression levels in the PYG group compared with the PY group (P < 0.05). The findings may contribute to the understanding of the pathological mechanisms underlying echinococcosis, providing valuable insights into the development of more effective diagnostic tools, treatment modalities, and preventive strategies. SIGNIFICANCE: CE is a major public health hazard in the western regions of China, Central Asia, South America, the Mediterranean countries, and eastern Africa. Echinococcus granulosus is responsible for zoonotic disease through infection Our analysis focuses on the proteins in various samples by data-dependent acquisition (DIA) for proteomic analysis. The importance of this research is to develop new strategies and targets to protect against E. granulosus infections in humans.

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.

Proteo-transcriptomic profiles reveal key regulatory pathways and functions of LDHA in the ovulation of domestic chickens (Gallus gallus).

BACKGROUND: In poultry, the smooth transition of follicles from the preovulatory-to-postovulatory phase impacts egg production in hens and can benefit the poultry industry. However, the regulatory mechanism underlying follicular ovulation in avians is a complex biological process that remains unclear. RESULTS: Critical biochemical events involved in ovulation in domestic chickens (Gallus gallus) were evaluated by transcriptomics, proteomics, and in vitro assays. Comparative transcriptome analyses of the largest preovulatory follicle (F1) and postovulatory follicle (POF1) in continuous laying (CL) and intermittent laying (IL) chickens indicated the greatest difference between CL_F1 and IL_F1, with 950 differentially expressed genes (DEGs), and the smallest difference between CL_POF1 and IL_POF1, with 14 DEGs. Additionally, data-independent acquisition proteomics revealed 252 differentially abundant proteins between CL_F1 and IL_F1. Perivitelline membrane synthesis, steroid biosynthesis, lysosomes, and oxidative phosphorylation were identified as pivotal pathways contributing to ovulation regulation. In particular, the regulation of zona pellucida sperm-binding protein 3, plasminogen activator, cathepsin A, and lactate dehydrogenase A (LDHA) was shown to be essential for ovulation. Furthermore, the inhibition of LDHA decreased cell viability and promoted apoptosis of ovarian follicles in vitro. CONCLUSIONS: This study reveals several important biochemical events involved in the process of ovulation, as well as crucial role of LDHA. These findings improve our understanding of ovulation and its regulatory mechanisms in avian species.