Data-independent Acquisition Mass Spectrometry Research Articles

Elevated Cerebrospinal Fluid Ubiquitin Carboxyl-Terminal Hydrolase Isozyme L1 in Asymptomatic C9orf72 Hexanucleotide Repeat Expansion Carriers.

To identify biochemical changes in individuals at higher risk of developing amyotrophic lateral sclerosis (ALS) or frontotemporal dementia (FTD) via C9orf72 hexanucleotide repeat expansion (HRE) heterozygosity. Cross-sectional observational study of 48 asymptomatic C9orf72 HRE carriers, 39 asymptomatic non-carrier controls, 19 people with sporadic ALS, 10 with C9orf72 ALS, 14 with sporadic FTD, and 10 with C9orf72 FTD. Relative abundance of 30 pre-defined cerebrospinal fluid biomarkers of ALS and FTD were compared in asymptomatic C9orf72 HRE carriers and age-matched non-carrier controls. Differential abundance of these proteins was quantified using data independent acquisition mass spectrometry or electro chemiluminescent assay for neurofilament light chain. Unbiased analysis of the entire cerebrospinal fluid proteome was then carried out. Ubiquitin carboxyl-hydrolase isozyme L1 levels were higher in asymptomatic C9orf72 HRE carriers compared with age-matched non-carriers (log2fold change 0.20, FDR-adjusted p-value = 0.034), whereas neurofilament light chain levels did not significantly differ. Ubiquitin carboxyl-hydrolase isozyme L1 levels remained elevated after matching of groups by neurofilament levels (p = 0.011), and after adjusting for age, sex, and neurofilament levels. A significant difference was also observed when restricting analysis to younger participants (<37) matched by neurofilament level (p = 0.007). Elevated cerebrospinal fluid ubiquitin carboxyl-hydrolase isozyme L1 levels in C9orf72 HRE carriers can occur in the absence of increased neurofilament levels, potentially reflecting either compensatory or pathogenic mechanisms preceding rapid neuronal loss. This brings forward the window on changes associated with the C9orf72 HRE carrier state, with potential to inform understanding of penetrance and approaches to prevention. ANN NEUROL 2024.

Proteomic profiling of oral squamous cell carcinoma tissues reveals altered immune-related proteins: implications for personalized therapy

ABSTRACT Objectives Oral squamous cell carcinoma poses a substantial global health challenge marked by rising mortality rate. Recently, immunotherapy has shown promising results in cancer management by enhancing immune response. Thus, identifying additional immune-related markers is critical for advancing immunotherapy treatments. Methods Data-independent acquisition (DIA) mass spectrometry approach was used to explore differentially expressed immune-related proteins in oral cancer tissues compared to adjacent non-cancerous tissues. Functional significance was identified through Gene Ontology, pathway, and network analysis. Gene expression of identified proteins was validated using transcriptomic data. Results DIA analysis identified 29,459 precursors corresponding to 3429 proteins. Among these, 1060 proteins were differentially expressed, with 166 being immune-related. Differentially regulated proteins were involved in innate immune response, mitochondrial ATP synthesis, and neutrophil degranulation. Pathway analysis of immune-related proteins showed perturbation in anti-tumor immunity-related pathways such as interferon signaling, TCR signaling, PD-1 signaling, and antigen processing and presentation. Significance of these pathways was further reinforced by the strong interactions identified in the protein–protein interaction network analysis. Additionally, gene expression analysis showed similar mRNA expression patterns for key proteins involved in altered pathways, including ISG15, IFIT1/3, HLA-A/C and OAS2/3. Conclusions Further validation of these proteins could establish them as potential targets for personalized therapy.

Abstract PR013: Highly accurate detection of early-stage colorectal cancer using tumor and immune extracellular vesicles biomarkers

Abstract Introduction: Colorectal cancer (CRC) is one of the most common cancers worldwide, and early detection is critical for successful treatment and improved survival rates. However, precancerous and early-stage CRC present significant diagnostic challenges due to the small size of lesions and the very low expression of tumor-specific biomarkers in the bloodstream. Current genomics-based diagnostic methods struggle to detect these lesions, making it imperative to develop more sensitive and specific approaches. Circulating extracellular vesicles (EVs) are emerging as a promising solution for early-stage CRC detection, since EVs are produced by tumor cells as well as the tumor microenviroment and host immune cells. Thus, EVs may offer the means to detect and monitor small early-stage tumors through both direct detection of tumor- associated biomarkers as well through tumor specific host response and tumor microenvironment biomarkers. Methods: To identify novel early-stage CRC specific biomarkers, we performed proteomics analysis on EVs purified from patient plasma using size exclusion chromatography and a proprietary buffer system that enhances EV and corona protein recovery. TrueDiscovery™ Data-independent acquisition (DIA) mass spectrometry (MS) analysis was conducted on 24 pre- cancer/stage 0 and 25 stage 1 CRC patients and 75 normal patient plasma samples. An in-house developed machine learning pipeline was used to identify differentially expressed proteins and model candidate multiplexes to identify those with extremely high diagnostic accuracy (&amp;gt; 0.99). Results: An average of ∼2,500 proteins were identified per sample and included in bioinformatics analysis. Comparative analysis between control patient EVs and either pre- cancerous/Stage 0, or Stage 1 colon cancer EVs using an in-house Machine Learning pipeline identified 336 and 493 differentially expressed proteins for each group (FDR adjusted p-value &amp;lt; 0.001). Of these, the best 17 pre/stage 0 proteins and 53 stage 1 proteins were trained and tested using Support Vector Machine to assess all potential 3-plexes in order to identify those with mean diagnostic accuracy &amp;gt; 99%. This yielded 5 3-plexes in precancer/stage 0 and 34 3-plexes in stage 1 with near perfect diagnostic accuracy. These plexes are currently being assessed in single analyte and multiplex immunoassays with the goal of translating candidate 3-plexes to the clinical. Conclusions: Key biomarkers from CRC patient EVs indicate the potential to detect and diagnose early-stage and low tumor burden cancers using our methods. Interestingly, the most accurate 3-plexes consist of proteins from immune, inflammatory and metabolic processes, suggesting that for the detection of early stage cancer it may be critical to include both tumor and host specific biomarkers. Citation Format: Poorva Mudgal, Cheryl Bandoski, Zachary Opheim, Martin Mehnert, Valesca Anschau, Issa Isaac, Alan Ezrin, Todd Hembrough. Highly accurate detection of early-stage colorectal cancer using tumor and immune extracellular vesicles biomarkers [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr PR013.

Assessment of Data-Independent Acquisition Mass Spectrometry (DIA-MS) for the Identification of Single Amino Acid Variants

Proteogenomics integrates genomic and proteomic data to elucidate cellular processes by identifying variant peptides, including single amino acid variants (SAAVs). In this study, we assessed the capability of data-independent acquisition mass spectrometry (DIA-MS) to identify SAAV peptides in HeLa cells using various search engine pipelines. We developed a customised sequence database (DB) incorporating SAAV sequences from the HeLa genome and conducted searches using DIA-NN, Spectronaut, and Fragpipe-MSFragger. Our evaluation focused on identifying true positive SAAV peptides and false positives through entrapment DBs. This study revealed that DIA-MS provides reproducible and comprehensive coverage of the proteome, identifying a substantial proportion of SAAV peptides. Notably, the DIA-MS searches maintained consistent identification of SAAV peptides despite varying sizes of the entrapment DB. A comparative analysis showed that Fragpipe-MSFragger (FP-DIA) demonstrated the most conservative and effective performance, exhibiting the lowest false discovery match ratio (FDMR). Additionally, integrating DIA and data-dependent acquisition (DDA) MS data search outputs enhanced SAAV peptide identification, with a lower false discovery rate (FDR) observed in DDA searches. The validation using stable isotope dilution and parallel reaction monitoring (SID-PRM) confirmed the SAAV peptides identified by DIA-MS and DDA-MS searches, highlighting the reliability of our approach. Our findings underscore the effectiveness of DIA-MS in proteogenomic workflows for identifying SAAV peptides, offering insights into optimising search engine pipelines and DB construction for accurate proteomics analysis. These methodologies advance the understanding of proteome variability, contributing to cancer research and the identification of novel proteoform therapeutic targets.

Differential expression of ferroptosis-related proteins in urinary exosomes: potential indicators for monitoring acute gout attack

BackgroundGout is the most prevalent form of inflammatory arthritis, characterized by significant pain during acute episodes. Current diagnostic and monitoring techniques are invasive and fail to predict the onset of acute attacks. Recent studies have implicated ferroptosis-related proteins in the pathogenesis of inflammation and gout; however, their clinical relevance in gout patients remains largely unexplored. This study aimed to evaluate the expression of these proteins in urinary exosomes from gout patients and to investigate their potential as noninvasive biomarkers.MethodsUtilizing data-independent acquisition (DIA) mass spectrometry and advanced bioinformatics techniques, we assessed the expression of ferroptosis-related proteins in the urinary exosomes of three groups: acute gout patients (AD group), intermittent gout patients (ID group), and normal controls (NC group). We constructed receiver operating characteristic (ROC) curves to determine the clinical utility of these proteins in monitoring acute gout attacks.ResultsOur analysis of urinary exosome proteomics identified 13 ferroptosis-related proteins. Notably, in comparison to the ID group, the proteins ACSL4, VDAC2, GPX4, and GSS were significantly upregulated in the AD group. ROC curve analysis revealed that the presence of ACSL4, VDAC2, and GPX4 in urinary exosomes possesses substantial predictive value for acute gout attacks.ConclusionIn patients with gout, numerous protein alterations occur within urinary exosomes. Specifically, changes in ferroptosis-related proteins such as ACSL4, VDAC2, GPX4, and GSS may serve as promising biomarkers for the monitoring of acute gout attacks.

Characterisation of plasma proteins and circulating microRNAs in out-of-hospital cardiac arrest patients - approaches for predicting outcome

Abstract Rationale: Early prognostication after out-of-hospital cardiac arrest (OHCA) is an unmet clinical need. There is limited data on serial release kinetics of cardiac, brain and systemic biomarkers such as Neuron Specific Enolase (NSE) and S-100B. miRNAs are sensitive biomarker candidates in acute myocardial infarction (MI). Objective The objective was to characterise circulating biomolecules in the early release kinetics after OHCA, compared with a STEMI control group and assessed with clinical outcome. Methods and Results Serial blood samples of patients with OHCA with STEMI (n=20) and STEMI control (n=20) were collected at baseline and at 6, 12, 24 and 48h after admission. Untargeted plasma proteomics were performed using data-independent acquisition–mass spectrometry (DIA–MS). Candidate proteins and miRNAs were quantified. Proteomics returned n=156 proteins with significantly different kinetics between OHCA and STEMI. Six clusters, depicting distinct biological pathways, with characteristic differences between OHCA and STEMI were identified. The most distinct and significant differences between OHCA and STEMI patients were seen for acute phase reactants LBP, AACT, CRP and the lipid metabolite A2GL as well as endothelial and platelet miR-126 and cardiomyocyte-apoptosis/ischemia-reperfusion related miR-320a. Cardiogenic shock was best characterised by differential kinetics of coagulation and complement related proteins FA9, HRG, FHR1 and inflammation related miR-146. The primary endpoint was poor neurological recovery at 30-days. Individuals with good outcome showed distinct differences in adaptive immune response, complement and coagulation cascades and hemostasis. Circulating plasma levels of hypoxia-associated miR-101, miR-210 and S-100B protein were significantly elevated at baseline in OHCA patients. These molecules further showed higher baseline values in patients with poor outcome. For current prognosis marker NSE, this difference only develops after 48h. Cardiac/muscle miRNAs followed an early release-pattern similar to high-sensitivity cardiac troponin-T (hs-cTnT). Machine learning models were used to calculate the AUC for the prediction of poor neurological outcome at baseline. This was highest for hypoxia-induced miR-210 (AUC: 0.854), which performed significantly better than the protein biomarkers NSE (AUC: 0.646) and S-100B (AUC:0.656). Conclusions Untargeted mass spectrometry reveals distinct biological pathways and candidate biomolecules affected in patients suffering from OHCA compared with STEMI controls, in patients developing cardiogenic shock and in those with adverse versus good neurological outcome. Platelet-, inflammation and hypoxia associated miRNAs are associated with OHCA, cardiogenic shock and prognostication of adverse neurological outcome. Pronounced differences in early release kinetics of RNA and protein biomarkers may improve identification of OHCA patients at risk of poor outcome.

Unbiased complexome profiling and global proteomics analysis reveals mitochondrial impairment and potential changes at the intercalated disk in presymptomatic R14Δ/+ mice hearts.

Phospholamban (PLN) is a sarco-endoplasmic reticulum (SER) membrane protein that regulates cardiac contraction/relaxation by reversibly inhibiting the SERCA2a Ca2+-reuptake pump. The R14Δ-PLN mutation causes severe cardiomyopathy that is resistant to conventional treatment. Protein complexes and higher-order supercomplexes such as intercalated disk components and Ca+2-cycling domains underlie many critical cardiac functions, a subset of which may be disrupted by R14Δ-PLN. Complexome profiling (CP) is a proteomics workflow for systematic analysis of high molecular weight (MW) protein complexes and supercomplexes. We hypothesize that R14Δ-PLN may alter a subset of these assemblies, and apply CP workflows to explore these changes in presymptomatic R14Δ/+ mice hearts. Ventricular tissues from presymptomatic 28wk-old WT and R14Δ/+ mice were homogenized under non-denaturing conditions, fractionated by size-exclusion chromatography (SEC) with a linear MW-range exceeding 5 MDa, and subjected to quantitative data-independent acquisition mass spectrometry (DIA-MS) analysis. Unfortunately, current workflows for the systematic analysis of CP data proved ill-suited for use in cardiac samples. Most rely upon curated protein complex databases to provide ground-truth for analysis; however, these are derived primarily from cancerous or immortalized cell lines and, consequently, cell-type specific complexes (including cardiac-specific machinery potentially affected in R14Δ-PLN hearts) are poorly covered. We thus developed PERCOM: a novel CP data-analysis strategy that does not rely upon these databases and can, furthermore, be implemented on widely available spreadsheet software. Applying PERCOM to our CP dataset resulted in the identification of 296 proteins with disrupted elution profiles. Hits were significantly enriched for mitochondrial and intercalated disk (ICD) supercomplex components. Changes to mitochondrial supercomplexes were associated with reduced expression of mitochondrial proteins and maximal oxygen consumption rate. The observed alterations to mitochondrial and ICD supercomplexes were replicated in a second cohort of "juvenile" 9wk-old mice. These early-stage changes to key cardiac machinery may contribute to R14Δ-PLN pathogenesis.

Meta-Analysis and DIA-MS-Based Proteomic Investigation of COPD Patients and Asymptomatic Smokers in the Indian Population.

Chronic obstructive pulmonary disease (COPD) is India's second largest cause of death and is largely caused by smoking. Asymptomatic smokers develop COPD due to genetic, environmental, and molecular variables, making early screening crucial. Data-independent acquisition mass spectrometry (DIA-MS) based-proteomics offers an unbiased method to analyze proteomic profiles. This study is the first to use DIA-based proteomics to analyze individual serum samples from three distinct male cohorts: healthy individuals (n = 10), asymptomatic smokers (n = 10), and COPD patients (n = 10). This comprehensive approach identified 667 proteins with a 1% false discovery rate. Differentially expressed proteins included 40 in the normal versus asymptomatic comparison, 88 in the COPD versus normal comparison, and 40 in the COPD versus asymptomatic comparison. Among them, protein-associated genes such as PRDX6, ELANE, PRKCSH, PRTN3, and MNDA could help differentiate COPD from asymptomatic smokers, while ELANE, H3-3A, IGHE, SLC4A1, and SERPINA11 could differentiate COPD from healthy subjects. Pathway enrichment and protein-protein interaction analyses revealed significant alterations in hemostasis, immune system functions, fibrin clot formation, and post-translational protein modifications. Key proteins were validated using a parallel reaction monitoring assay. DIA data are available via ProteomeXchange with identifier PXD055242. Our findings reveal key protein classifiers in COPD patients, asymptomatic smokers, and healthy individuals, helping clinicians understand disease pathobiology and improve disease management and quality of life.

A Proteomics Pipeline for Generating Clinical Grade Biomarker Candidates from Data‐Independent Acquisition Mass Spectrometry (DIA‐MS) Discovery

Clinical biomarker development has been stymied by inaccurate protein quantification from mass spectrometry (MS) discovery data and a prolonged validation process. To mitigate these issues, we created the Targeted Extraction Assessment of Quantification (TEAQ) software package that uses data‐independent acquisition analysis from a discovery cohort to select precursors, peptides, and proteins that adhere to analytical criteria required for established targeted assays. TEAQ was applied to DIA‐MS data from plasma samples acquired on a new high resolution accurate mass (HRAM) mass spectrometry platform where precursors were evaluated for linearity, specificity, repeatability, reproducibility, and intra‐protein correlation based on 8‐ or 11‐point loading curves at three throughputs. This data can be used as a general resource for developing other targeted assays. TEAQ analysis of data from a case and control cohort for inflammatory bowel disease (n=492) identified 1110 signature peptides for 326 quantifiable proteins from the 1179 identified proteins. Applying TEAQ analysis to discovery data will streamline targeted assay development and the transition to validation and clinical studies.

A Proteomics Pipeline for Generating Clinical Grade Biomarker Candidates from Data-Independent Acquisition Mass Spectrometry (DIA-MS) Discovery.

Clinical biomarker development has been stymied by inaccurate protein quantification from mass spectrometry (MS) discovery data and a prolonged validation process. To mitigate these issues, we created the Targeted Extraction Assessment of Quantification (TEAQ) software package that uses data-independent acquisition analysis from a discovery cohort to select precursors, peptides, and proteins that adhere to analytical criteria required for established targeted assays. TEAQ was applied to DIA-MS data from plasma samples acquired on a new high resolution accurate mass (HRAM) mass spectrometry platform where precursors were evaluated for linearity, specificity, repeatability, reproducibility, and intra-protein correlation based on 8- or 11-point loading curves at three throughputs. This data can be used as a general resource for developing other targeted assays. TEAQ analysis of data from a case and control cohort for inflammatory bowel disease (n=492) identified 1110 signature peptides for 326 quantifiable proteins from the 1179 identified proteins. Applying TEAQ analysis to discovery data will streamline targeted assay development and the transition to validation and clinical studies.

Responsiveness of anti-VEGF treatment for polypoidal choroidal vasculopathy based on aqueous humour proteomics: A preliminary study.

Patients with polypoidal choroidal vasculopathy (PCV) exhibit variability in response to anti-VEGF therapy. This study aimed to analyse the aqueous humour proteomic profiles of PCV patients and provide preliminary insights for the identification of biomarkers associated with anti-VEGF drug responsiveness. PCV patients who were treatment-naïve or untreated for more than 3 months were prospectively recruited from two hospitals in Beijing and Tianjin. Based on the relative changes in central macular thickness (ΔCMT/baseline-CMT) before and after anti-VEGF treatment, the PCV patients were divided into a good response (GR) group (≤-25%) and a poor response (PR) group (>-25%). Aqueous humour proteomics was performed by the Data-independent Acquisition-Mass Spectrometry (DIA-MS) method, and differentially expressed proteins (DEPs) analysis between the different PCV groups and the control group was conducted. Key DEPs were selected for preliminary validation in the aqueous humour using the Luminex method retrospectively. A total of 31 PCV patients (31 eyes) were included, 13 in the GR group and 18 in the PR group. A total of 414 DEPs were identified, including 36 significantly upregulated proteins, such as G protein regulatory factor 10 (RGS10), podocin (PODN) and epidermal growth factor (EGF), and 32 downregulated proteins, including RAB11FIP4 (Rab11 family-interacting protein 4), α-synuclein (SNCA), haemoglobin subunit δ (HBD) and interleukin 6 (IL6). Compared to the cataract control group (10 eyes), 134 proteins were significantly upregulated, and 72 were downregulated. KEGG pathway enrichment analysis revealed that the GR and PR groups differ in terms of cell communication, and cell signal transduction. Protein-protein interaction analysis revealed interactions between EGF and various DEPs. Validation of aqueous humour proteins using the Luminex method revealed that changes in the levels of EGF were associated with the anti-VEGF treatment response in PCV patients. PCV patients with good or poor anti-VEGF responses exhibit distinct aqueous humour proteomic profiles. Aqueous EGF may serve as a biomarker for the 'precise treatment' of PCV.

Mapping Nanoscale-To-Single-Cell Phosphoproteomic Landscape by Chip-DIA.

Protein phosphorylation plays a crucial role in regulating disease phenotypes and serves as a key target for drug development. Mapping nanoscale-to-single-cell samples can unravel the heterogeneity of cellular signaling events. However, it remains a formidable analytical challenge due to the low detectability, abundance, and stoichiometry of phosphorylation sites. Here, we present a Chip-DIA strategy,integratinga microfluidic-based phosphoproteomic chip (iPhosChip) with data-independent acquisition mass spectrometry (DIA-MS) for ultrasensitive nanoscale-to-single-cell phosphoproteomic profiling. The iPhosChip operates as an all-in-one station that accommodates both quantifiable cell capture/imaging and the entire phosphoproteomic workflow in a highly streamlined and multiplexed manner. Coupled with a sample size-comparable library-based DIA-MS strategy, Chip-DIA achieved ultra-high sensitivity, detecting 1076±158 to 15869±1898 phosphopeptides from 10±0 to 1013±4 cells, and revealed the first single-cell phosphoproteomic landscape comprising druggable sites and basal phosphorylation-mediated networks in lung cancer. Notably, the sensitivity and coverage enabled the illumination of heterogeneous cytoskeleton remodeling and cytokeratin signatures in patient-derived cells resistant to third-generation EGFR therapy, stratifying mixed-lineage adenocarcinoma-squamous cell carcinoma subtypes, and identifying alternative targeted therapy for late-stage patients. With flexibility in module design and functionalization, Chip-DIA can be adapted to other PTM-omics to explore dysregulated PTM landscapes, thereby guiding therapeutic strategies toward precision oncology.

Integrated strategy for high-confident global profiling of the histidine phosphoproteome

BackgroundHistidine phosphorylation (pHis) plays a key role in signal transduction in prokaryotes and regulates tumour initiation and progression in mammals. However, the pHis substrates and their functions are rarely known due to the lack of effective analytical strategies. ResultsHerein, we provide a strategy for unbiased enrichment and assignment of the pHis peptides. First, the entire procedure was designed under alkaline conditions to maintain the stability of the N–P bond of pHis and high-pH reverse-phase chromatography was used to efficiently separate the pHis peptides. Second, exploiting the coelution benefits of diethyl labelling, the ratios of light- and heavy-labelled peptides were accurately quantified, and the sites of phosphorylated histidine were assigned. Finally, Cu-IDA bead enrichment and data-independent acquisition mass spectrometry analysis were used to improve the coverage of the histidine phosphoproteome. With this novel strategy, 768 and 1125 potential pHis peptides were identified from lysates of E. coli and HeLa cells, respectively. And these values represent the highest coverage of the histidine phosphoproteome for both cell types. SignificanceThese data strongly support the presumption that pHis modifications are widely present in bacteria. The study provides an efficient strategy and can lead to a better understanding of pHis-modified substrates and their biological functions.

Comparative proteomic analysis of regenerative mechanisms in mouse retina to identify markers for neuro-regeneration in glaucoma

The retina is part of the central nervous system (CNS). Neurons in the CNS and retinal ganglion cells lack the ability to regenerate axons spontaneously after injury. The intrinsic axonal growth regulators, their interaction and roles that enable or inhibit axon growth are still largely unknown. This study endeavored to characterize the molecular characteristics under neurodegenerative and regenerative conditions. Data-Independent Acquisition Mass Spectrometry was used to map the comprehensive proteome of the regenerative retina from 14-day-old mice (Reg-P14) and adult mice after lens injury (Reg-LI) both showing regrowing axons in vitro, untreated adult mice, and retina from adult mice subjected to two weeks of elevated intraocular pressure showing degeneration. A total of 5750 proteins were identified (false discovery rate < 1%). Proteins identified in both Reg-P14 and Reg-LI groups were correlated to thyroid hormone, Notch, Wnt, and VEGF signaling pathways. Common interactors comprising E1A binding protein P300 (EP300), CREB binding protein (CBP), calcium/calmodulin dependent protein kinase II alpha (CaMKIIα) and sirtuin 1 (SIRT1) were found in both Reg-P14 and Reg-LI retinas. Proteins identified in both regenerating and degenerative groups were correlated to thyroid hormone, Notch, mRNA surveillance and measles signaling pathways, along with PD-L1 expression and the PD-1 checkpoint pathway. Common interactors across regenerative and degenerative retinas comprising NF-kappa-B p65 subunit (RELA), RNA-binding protein with serine-rich domain 1 (RNPS1), EP300 and SIN3 transcription regulator family member A (SIN3A). The findings from our study provide the first mapping of regenerative mechanisms across postnatal, mature and degenerative mouse retinas, revealing potential biomarkers that could facilitate neuro-regeneration in glaucoma.

In-depth proteomics and Phosphoproteomics reveal biomarkers and molecular pathways of chronic intermittent hypoxia in mice

Obstructive sleep apnea (OSA) syndrome is characterized by Chronic Intermittent Hypoxia (CIH). In this study, we employed Data-independent acquisition (DIA) Mass Spectrometry to conduct comprehensive proteomic and phosphoproteomic profiling of a murine model subjected to Chronic Intermittent Hypoxia (CIH), a model we had previously established. Utilizing three CIH and three normal control genioglossus samples, we gathered valuable insights into the molecular alterations associated with CIH. Our analyses identified a total of 4576 protein groups and 13,867 phosphosites. Differential analysis of the proteomic data highlighted a significant upregulation of Ras signaling (Egf, Ngf, and Fyb1) and calcium signaling (Tnn, Thbs4, and Ppp2r2d) in CIH samples, contrasting with a notable decrease in oxidative phosphorylation (Atp5mf, Atp5me, and Atp5mg). Additionally, we observed a substantial increase in the phosphorylation of PI3K-AKT signaling (Ptk2_Y861, Mapk3_T203, and Eif4b_S230) and HIF-1 signaling (Gapdh_S208, Eno3_T229, and Camk2b_T382) in CIH samples. These findings prompted a deeper investigation into the association of the characterized proteins and phosphoproteins with Obstructive Sleep Apnea (OSA). The comprehensive profiling revealed molecular signatures that may serve as valuable insights into the pathophysiology of chronic intermittent hypoxia and its link to obstructive sleep apnea. Our observations provide a foundation for future research endeavors, offering potential avenues for advancing our understanding and treatment strategies for these conditions. SignificanceThe significance of this study lies in its comprehensive exploration of the molecular mechanisms underpinning Chronic Intermittent Hypoxia (CIH), a key feature of Obstructive Sleep Apnea (OSA). By employing Data-independent acquisition (DIA) Mass Spectrometry, this research provides an in-depth proteomic and phosphoproteomic analysis, uncovering critical signaling pathways and molecular alterations associated with CIH. The identification of significant changes in Ras and calcium signaling pathways, along with increased phosphorylation in PI3K-AKT and HIF-1 signaling, offers novel insights into the pathophysiological processes involved in CIH and OSA. These findings not only enhance our understanding of the molecular basis of OSA but also pave the way for the development of targeted therapeutic strategies, ultimately contributing to better management and treatment of OSA and related conditions.

DEWNA: dynamic entropy weight network analysis and its application to the DNA-binding proteome in A549 cells with cisplatin-induced damage.

Cisplatin is one of the most commonly used chemotherapy drugs for treating solid tumors. As a genotoxic agent, cisplatin binds to DNA and forms platinum-DNA adducts that cause DNA damage and activate a series of signaling pathways mediated by various DNA-binding proteins (DBPs), ultimately leading to cell death. Therefore, DBPs play crucial roles in the cellular response to cisplatin and in determining cell fate. However, systematic studies of DBPs responding to cisplatin damage and their temporal dynamics are still lacking. To address this, we developed a novel and user-friendly stand-alone software, DEWNA, designed for dynamic entropy weight network analysis to reveal the dynamic changes of DBPs and their functions. DEWNA utilizes the entropy weight method, multiscale embedded gene co-expression network analysis and generalized reporter score-based analysis to process time-course proteome expression data, helping scientists identify protein hubs and pathway entropy profiles during disease progression. We applied DEWNA to a dataset of DBPs from A549 cells responding to cisplatin-induced damage across 8 time points, with data generated by data-independent acquisition mass spectrometry (DIA-MS). The results demonstrate that DEWNA can effectively identify protein hubs and associated pathways that are significantly altered in response to cisplatin-induced DNA damage, and offer a comprehensive view of how different pathways interact and respond dynamically over time to cisplatin treatment. Notably, we observed the dynamic activation of distinct DNA repair pathways and cell death mechanisms during the drug treatment time course, providing new insights into the molecular mechanisms underlying the cellular response to DNA damage.

Data-Independent Acquisition Mass Spectrometry as a Tool for Metaproteomics: Interlaboratory Comparison Using a Model Microbiome.

Mass spectrometry (MS)-based metaproteomics is used to identify and quantify proteins in microbiome samples, with the frequently used methodology being Data-Dependent Acquisition mass spectrometry (DDA-MS). However, DDA-MS is limited in its ability to reproducibly identify and quantify lower abundant peptides and proteins. To address DDA-MS deficiencies, proteomics researchers have started using Data-Independent Acquisition Mass Spectrometry (DIA-MS) for reproducible detection and quantification of peptides and proteins. We sought to evaluate the reproducibility and accuracy of DIA-MS metaproteomic measurements relative to DDA-MS using a mock community of known taxonomic composition. Artificial microbial communities of known composition were analyzed independently in three laboratories using DDA- and DIA-MS acquisition methods. DIA-MS yielded more protein and peptide identifications than DDA-MS in each laboratory. In addition, the protein and peptide identifications were more reproducible in all laboratories and provided an accurate quantification of proteins and taxonomic groups in the samples. We also identified some limitations of current DIA tools when applied to metaproteomic data, highlighting specific needs to improve DIA tools enabling analysis of metaproteomic datasets from complex microbiomes. Ultimately, DIA-MS represents a promising strategy for MS-based metaproteomics due to its large number of detected proteins and peptides, reproducibility, deep sequencing capabilities, and accurate quantitation.

Abstract A067: Identifying and targeting FOLFIRINOX resistance mechanisms to improve outcomes in pancreatic cancer

Abstract Pancreatic ductal adenocarcinoma (PDAC) is a devastating disease with a 5-year survival rate of only 12%. More than 80% of PDAC patients cannot undergo potentially curative surgical resection due to the presentation of advanced, metastatic disease, and are instead offered systemic chemotherapy regimens to which resistance is rapidly acquired. FOLFIRINOX chemotherapy offers an 11.1-month median survival compared to 6.8 months with Gemcitabine. Rapid onset of resistance to FOLFIRINOX, coupled with the lack of alternative targeted therapies, ultimately results in patients succumbing to the disease. My project aims to identify mechanisms by which PDAC cells acquire resistance to FOLFIRINOX treatment and investigate interventions aimed at enhancing and/or reinstating FOLFIRINOX sensitivity. Our lab has propagated distinct patient derived xenografts (PDXs) from the Avner Australian Pancreatic Cancer Matrix Atlas (APMA) and I have exposed them to 11 - 15 rounds of FOLFIRINOX (or vehicle control) in vivo to reflect the clinical management of PDAC. Additionally, intrapancreatic orthotopic tumors derived from cells from the KPC mouse model (LSL-KrasG12D/+;LSL-Trp53R172H/+;Pdx-1-Cre) have received 5 - 6 rounds of FOLFIRINOX (or vehicle control). These tumors will undergo quantitative data-independent acquisition mass spectrometry proteomic analysis and RNA sequencing transcriptomic analysis (RNAseq) to reveal resistance pathways and/or proteins operating in the chronically treated tumors and the surrounding pancreatic tumor microenvironment. Additionally, we have developed and cultured matched treatment naïve and FOLFIRINOX-treated patient-derived cell lines (PDCLs) and mouse cell lines from the PDX and KPC-derived tumors. These lines will allow us to validate future targets via gene knockdown and/or pharmacological intervention. 3D assays, such as our organotypic matrix models, will be performed to assess the metastatic potential of cancer cells and their response to FOLFIRINOX in vitro. Furthermore, subcutaneous and orthotopic (intrapancreatic) models coupled with intravital (in vivo) imaging and biosensor technology will allow us to assess targeting in live tumors and metastatic sites in real time. It is the hope that this research will lead to the identification of proteins or protein pathways which can be manipulated in order to re-sensitize tumors to FOLFIRINOX chemotherapy. Citation Format: Victoria Tyma, Ying-Fei Liew, Alice Tran, Anna Howell, Shona Ritchie, Avner Australian Pancreatic Cancer Matrix Atlas, Australian Pancreatic Genome Initiative, Thomas Cox, Marina Pajic, David Herrmann, Brooke Pereira, Paul Timpson, Katie Gordon. Identifying and targeting FOLFIRINOX resistance mechanisms to improve outcomes in pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr A067.

MetaExpertPro: A Computational Workflow for Metaproteomics Spectral Library Construction and Data-Independent Acquisition Mass Spectrometry Data Analysis

Analysis of large-scale data-independent acquisition mass spectrometry metaproteomics data remains a computational challenge. Here, we present a computational pipeline called metaExpertPro for metaproteomics data analysis. This pipeline encompasses spectral library generation using data-dependent acquisition MS, protein identification and quantification using data-independent acquisition mass spectrometry, functional and taxonomic annotation, as well as quantitative matrix generation for both microbiota and hosts. By integrating FragPipe and DIA-NN, metaExpertPro offers compatibility with both Orbitrap and timsTOF MS instruments. To evaluate the depth and accuracy of identification and quantification, we conducted extensive assessments using human fecal samples and benchmark tests. Performance tests conducted on human fecal samples indicated that metaExpertPro quantified an average of 45,000 peptides in a 60-min diaPASEF injection. Notably, metaExpertPro outperformed three existing software tools by characterizing a higher number of peptides and proteins. Importantly, metaExpertPro maintained a low factual false discovery rate of approximately 5% for protein groups across four benchmark tests. Applying a filter of five peptides per genus, metaExpertPro achieved relatively high accuracy (F-score=0.67-0.90) in genus diversity and showed a high correlation (rSpearman=0.73-0.82) between the measured and true genus relative abundance in benchmark tests. Additionally, the quantitative results at the protein, taxonomy, and function levels exhibited high reproducibility and consistency across the commonly adopted public human gut microbial protein databases IGC and UHGP. In a metaproteomic analysis of dyslipidemia patients, metaExpertPro revealed characteristic alterations in microbial functions and potential interactions between the microbiota and the host.

Comprehensive Proteomic Profiling of Converted Adipocyte-like Cells from Normal Human Dermal Fibroblasts Using Data-Independent Acquisition Mass Spectrometry.

Adipocytes play an important role in the regulation of systemic energy homeostasis and are closely related to metabolic disorders, such as type-2 diabetes and inflammatory bowel diseases. Particularly, there is an increasing need for a human adipocyte model for studying metabolic diseases and obesity. However, utilizing human primary adipocyte culture and stem-cell-based models presents several practical limitations due to their time-consuming nature, requirement for relatively intensive labor, and high cost. Here, we applied direct conversion of normal human dermal fibroblasts (NHDFs) into adipocyte-like cells using an adipogenic cocktail containing 3-isobutyl-1-methylxanthine (IBMX), dexamethasone, insulin, and rosiglitazone and confirmed prominent lipid droplet accumulation in the converted cells. For profiling the proteome changes in the converted cells, we conducted a comprehensive quantitative proteome analysis of both the intracellular and extracellular proteome fractions using data-independent acquisition mass spectrometry. We observed that several proteins, which are known to be highly expressed in adipocytes specifically, were dominantly increased in the converted cells. In this study, we suggest that NHDFs can be converted into adipocyte-like cells by an adipogenic cocktail and can serve as a useful tool for studying human adipocytes and their metabolism.