Large-Scale, Ion-Current-Based Proteomic Investigation of the Rat Striatal Proteome in a Model of Short- and Long-Term Cocaine Withdrawal

The Multifaceted Role of Cofilin in Neurodegeneration and Stroke: Insights into Pathogenesis and Targeting as a Therapy

This comprehensive review explores the complex role of cofilin, an actin-binding protein, across various neurodegenerative diseases (Alzheimer's, Parkinson's, schizophrenia, amyotrophic lateral sclerosis (ALS), Huntington's) and stroke. Cofilin is an essential protein in cytoskeletal dynamics, and any dysregulation could lead to potentially serious complications. Cofilin's involvement is underscored by its impact on pathological hallmarks like Aβ plaques and α-synuclein aggregates, triggering synaptic dysfunction, dendritic spine loss, and impaired neuronal plasticity, leading to cognitive decline. In Parkinson's disease, cofilin collaborates with α-synuclein, exacerbating neurotoxicity and impairing mitochondrial and axonal function. ALS and frontotemporal dementia showcase cofilin's association with genetic factors like C9ORF72, affecting actin dynamics and contributing to neurotoxicity. Huntington's disease brings cofilin into focus by impairing microglial migration and influencing synaptic plasticity through AMPA receptor regulation. Alzheimer's, Parkinson's, and schizophrenia exhibit 14-3-3 proteins in cofilin dysregulation as a shared pathological mechanism. In the case of stroke, cofilin takes center stage, mediating neurotoxicity and neuronal cell death. Notably, there is a potential overlap in the pathologies and involvement of cofilin in various diseases. In this context, referencing cofilin dysfunction could provide valuable insights into the common pathologies associated with the aforementioned conditions. Moreover, this review explores promising therapeutic interventions, including cofilin inhibitors and gene therapy, demonstrating efficacy in preclinical models. Challenges in inhibitor development, brain delivery, tissue/cell specificity, and long-term safety are acknowledged, emphasizing the need for precision drug therapy. The call to action involves collaborative research, biomarker identification, and advancing translational efforts. Cofilin emerges as a pivotal player, offering potential as a therapeutic target. However, unraveling its complexities requires concerted multidisciplinary efforts for nuanced and effective interventions across the intricate landscape of neurodegenerative diseases and stroke, presenting a hopeful avenue for improved patient care.

High-quality and robust protein quantification in large clinical/pharmaceutical cohorts with IonStar proteomics investigation

Robust, reliable quantification of large sample cohorts is often essential for meaningful clinical or pharmaceutical proteomics investigations, but it is technically challenging. When analyzing very large numbers of samples, isotope labeling approaches may suffer from substantial batch effects, and even with label-free methods, it becomes evident that low-abundance proteins are not reliably measured owing to unsufficient reproducibility for quantification. The MS1-based quantitative proteomics pipeline IonStar was designed to address these challenges. IonStar is a label-free approach that takes advantage of the high sensitivity/selectivity attainable by ultrahigh-resolution (UHR)-MS1 acquisition (e.g., 120-240k full width at half maximum at m/z = 200) which is now widely available on ultrahigh-field Orbitrap instruments. By selectively and accurately procuring quantitative features of peptides within precisely defined, very narrow m/z windows corresponding to the UHR-MS1 resolution, the method minimizes co-eluted interferences and substantially enhances signal-to-noise ratio of low-abundance species by decreasing noise level. This feature results in high sensitivity, selectivity, accuracy and precision for quantification of low-abundance proteins, as well as fewer missing data and fewer false positives. This protocol also emphasizes the importance of well-controlled, robust experimental procedures to achieve high-quality quantification across a large cohort. It includes a surfactant cocktail-aided sample preparation procedure that achieves high/reproducible protein/peptide recoveries among many samples, and a trapping nano-liquid chromatography-mass spectrometry strategy for sensitive and reproducible acquisition of UHR-MS1 peptide signal robustly across a large cohort. Data processing and quality evaluation are illustrated using an example dataset ( http://proteomecentral.proteomexchange.org ), and example results from pharmaceutical project and one clinical project (patients with acute respiratory distress syndrome) are shown. The complete IonStar pipeline takes ~1-2 weeks for a sample cohort containing ~50-100 samples.

Relapse-like behavior and nAChR sensitization following intermittent access nicotine self-administration

Many tobacco smokers consume nicotine intermittently, but the underlying mechanisms and neurobiological changes associated with intermittent nicotine intake are unclear. Understanding intermittent nicotine intake is a high priority, as it could promote therapeutic strategies to attenuate tobacco consumption. We examined nicotine intake behavior and neurobiological changes in male rats that were trained to self-administer nicotine during brief (5 min) trials interspersed with longer (15 min) drug-free periods. Rats readily adapted to intermittent access (IntA) SA following acquisition on a continuous access (ContA) schedule. Probabilistic analysis of IntA nicotine SA suggested reduced nicotine loading behavior compared to ContA, and nicotine pharmacokinetic modeling revealed that rats taking nicotine intermittently may have increased intake to maintain blood levels of nicotine that are comparable to ContA SA. After IntA nicotine SA, rats exhibited an increase in unreinforced responses for nicotine-associated cues (incubation of craving) and specific alterations in the striatal proteome after 7 days without nicotine. IntA nicotine SA also induced nAChR functional upregulation in the interpeduncular nucleus (IPN), and it enhanced nicotine binding in the brain as determined via [¹¹C]nicotine positron emission tomography. Reducing the saliency of the cue conditions during the 5 min access periods attenuated nicotine intake, but incubation of craving was preserved. Together, these results indicate that IntA conditions promote nicotine SA and nicotine seeking after a nicotine-free period.

Alterations in the Proteome and Phosphoproteome Profiles of Rat Hippocampus after Six Months of Morphine Withdrawal: Comparison with the Forebrain Cortex

The knowledge about proteome changes proceeding during protracted opioid withdrawal is lacking. Therefore, the aim of this work was to analyze the spectrum of altered proteins in the rat hippocampus in comparison with the forebrain cortex after 6-month morphine withdrawal. We utilized 2D electrophoretic workflow (Pro-Q® Diamond staining and Colloidal Coomassie Blue staining) which was preceded by label-free quantification (MaxLFQ). The phosphoproteomic analysis revealed six significantly altered hippocampal (Calm1, Ywhaz, Tuba1b, Stip1, Pgk1, and Aldoa) and three cortical proteins (Tubb2a, Tuba1a, and Actb). The impact of 6-month morphine withdrawal on the changes in the proteomic profiles was higher in the hippocampus—14 proteins, only three proteins were detected in the forebrain cortex. Gene Ontology (GO) enrichment analysis of differentially expressed hippocampal proteins revealed the most enriched terms related to metabolic changes, cytoskeleton organization and response to oxidative stress. There is increasing evidence that energy metabolism plays an important role in opioid addiction. However, the way how morphine treatment and withdrawal alter energy metabolism is not fully understood. Our results indicate that the rat hippocampus is more susceptible to changes in proteome and phosphoproteome profiles induced by 6-month morphine withdrawal than is the forebrain cortex.

Impact of three-month morphine withdrawal on rat brain cortex, hippocampus, striatum and cerebellum: proteomic and phosphoproteomic studies

Opioid addiction is characterized by compulsive drug seeking and taking behavior, which is thought to result from persistent neuroadaptations. However, there is a lack of information about the changes at both the cellular and molecular levels occurring after cessation of drug administration. The aim of our study was to determine alterations of both phosphoproteome and proteome in selected brain regions of the rats (brain cortex, hippocampus, striatum, and cerebellum) 3 months after cessation of 10-day morphine treatment. Phosphoproteome profiling was performed by Pro-Q® Diamond staining. The gel-based proteomic approach accompanied by label-free quantification (MaxLFQ) was used for characterization of proteome changes. The phosphoproteomic analysis revealed the largest change in the hippocampus (14); only few altered proteins were detected in the forebrain cortex (5), striatum (4), and cerebellum (3). The change of total protein composition, determined by 2D electrophoresis followed by LFQ analysis, identified 22 proteins with significantly altered expression levels in the forebrain cortex, 19 proteins in the hippocampus, 12 in the striatum and 10 in the cerebellum. The majority of altered proteins were functionally related to energy metabolism and cytoskeleton reorganization. As the most important change we regard down-regulation of 14-3-3 proteins in rat cortex and hippocampus. Our findings indicate that i) different parts of the brain respond in a distinct manner to the protracted morphine withdrawal, ii) characterize changes of protein composition in these brain parts, and iii) enlarge the scope of evidence for adaptability and distinct neuroplasticity proceeding in the brain of drug-addicted organism.

MS1 ion current-based quantitative proteomics: A promising solution for reliable analysis of large biological cohorts

The rapidly-advancing field of pharmaceutical and clinical research calls for systematic, molecular-level characterization of complex biological systems. To this end, quantitative proteomics represents a powerful tool but an optimal solution for reliable large-cohort proteomics analysis, as frequently involved in pharmaceutical/clinical investigations, is urgently needed. Large-cohort analysis remains challenging owing to the deteriorating quantitative quality and snowballing missing data and false-positive discovery of altered proteins when sample size increases. MS1 ion current-based methods, which have become an important class of label-free quantification techniques during the past decade, show considerable potential to achieve reproducible protein measurements in large cohorts with high quantitative accuracy/precision. Nonetheless, in order to fully unleash this potential, several critical prerequisites should be met. Here we provide an overview of the rationale of MS1-based strategies and then important considerations for experimental and data processing techniques, with the emphasis on (i) efficient and reproducible sample preparation and LC separation; (ii) sensitive, selective and high-resolution MS detection; iii)accurate chromatographic alignment; (iv) sensitive and selective generation of quantitative features; and (v) optimal post-feature-generation data quality control. Prominent technical developments in these aspects are discussed. Finally, we reviewed applications of MS1-based strategy in disease mechanism studies, biomarker discovery, and pharmaceutical investigations.

Surfactant Cocktail-Aided Extraction/Precipitation/On-Pellet Digestion Strategy Enables Efficient and Reproducible Sample Preparation for Large-Scale Quantitative Proteomics

For quantitative proteomics, efficient, robust, and reproducible sample preparation with high throughput is critical yet challenging, especially when large cohorts are involved, as is often required by clinical/pharmaceutical studies. We describe a rapid and straightforward surfactant cocktail-aided extraction/precipitation/on-pellet digestion (SEPOD) strategy to address this need. Prior to organic solvent precipitation and on-pellet digestion, SEPOD treats samples with a surfactant cocktail (SC) containing multiple nonionic/anionic surfactants, which achieves (i) exhaustive/reproducible protein extraction, including membrane-bound proteins; (ii) effective removal of detrimental nonprotein matrix components (e.g., >94% of phospholipids); (iii) rapid/efficient proteolytic digestion owing to dual (surfactants + precipitation) denaturation. The optimal SC composition and concentrations were determined by Orthogonal-Array-Design investigation of their collective/individuals effects on protein extraction/denaturation. Key parameters for cleanup and digestion were experimentally identified as well. The optimized SEPOD procedures allowed a rapid 6 h digestion providing a clean digest with high peptide yields and excellent quantitative reproducibility (especially low-abundance proteins). Compared with filter-assisted sample preparation (FASP) and in-solution digestion, SEPOD showed superior performance by recovering substantially more peptide/proteins (including integral membrane proteins), yielding significantly higher peptide intensities and improving quantification for peptides with extreme physicochemical properties. SEPOD was further applied in a large-cohort temporal investigation of 44 IAV-infected mouse lungs, providing efficient and reproducible peptide yields (77.9 ± 4.6%) across all samples. With the IonStar pipeline, >6 400 unique protein groups were quantified (≥2 peptide/protein, peptide-FDR < 0.05%), ∼99% without missing data in any sample with <7% technical median-intragroup CV. Altered proteome patterns revealed interesting novel insights into pathophysiological changes by IAV infection. In summary, SEPOD offers a feasible solution for rapid, efficient, and reproducible preparation of biological samples, facilitating high-quality proteomic quantification of large sample cohorts.

IonStar enables high-precision, low-missing-data proteomics quantification in large biological cohorts

Reproducible quantification of large biological cohorts is critical for clinical/pharmaceutical proteomics yet remains challenging because most prevalent methods suffer from drastically declined commonly quantified proteins and substantially deteriorated quantitative quality as cohort size expands. MS2-based data-independent acquisition approaches represent tremendous advancements in reproducible protein measurement, but often with limited depth. We developed IonStar, an MS1-based quantitative approach enabling in-depth, high-quality quantification of large cohorts by combining efficient/reproducible experimental procedures with unique data-processing components, such as efficient 3D chromatographic alignment, sensitive and selective direct ion current extraction, and stringent postfeature generation quality control. Compared with several popular label-free methods, IonStar exhibited far lower missing data (0.1%), superior quantitative accuracy/precision [∼5% intragroup coefficient of variation (CV)], the widest protein abundance range, and the highest sensitivity/specificity for identifying protein changes (<5% false altered-protein discovery) in a benchmark sample set (n = 20). We demonstrated the usage of IonStar by a large-scale investigation of traumatic injuries and pharmacological treatments in rat brains (n = 100), quantifying >7,000 unique protein groups (>99.8% without missing data across the 100 samples) with a low false discovery rate (FDR), two or more unique peptides per protein, and high quantitative precision. IonStar represents a reliable and robust solution for precise and reproducible protein measurement in large cohorts.

Temporal Effects of Combined Birinapant and Paclitaxel on Pancreatic Cancer Cells Investigated via Large-Scale, Ion-Current-Based Quantitative Proteomics (IonStar)

Despite decades of effort, pancreatic adenocarcinoma (PDAC) remains an intractable clinical challenge. An insufficient understanding of mechanisms underlying tumor cell responses to chemotherapy contributes significantly to the lack of effective treatment regimens. Here, paclitaxel, a first-line chemotherapeutic agent, was observed to interact synergistically with birinapant, a second mitochondrial-derived activator of caspases mimetic. Therefore, we investigated molecular-level drug interaction mechanisms using comprehensive, reproducible, and well-controlled ion-current-based MS1 quantification (IonStar). By analyzing 40 biological samples in a single batch, we compared temporal proteomic responses of PDAC cells treated with birinapant and paclitaxel, alone and combined. Using stringent criteria (e.g. strict false-discovery-rate (FDR) control, two peptides/protein), we quantified 4069 unique proteins confidently (99.8% without any missing data), and 541 proteins were significantly altered in the three treatment groups, with an FDR of <1%. Interestingly, most of these proteins were altered only by combined birinapant/paclitaxel, and these predominantly represented three biological processes: mitochondrial function, cell growth and apoptosis, and cell cycle arrest. Proteins responsible for activation of oxidative phosphorylation, fatty acid β-oxidation, and inactivation of aerobic glycolysis were altered largely by combined birinapant/paclitaxel compared with single drugs, suggesting the Warburg effect, which is critical for survival and proliferation of cancer cells, was alleviated by the combination treatment. Metabolic profiling was performed to confirm substantially greater suppression of the Warburg effect by the combined agents compared with either drug alone. Immunoassays confirmed proteomic data revealing changes in apoptosis/survival signaling pathways, such as inhibition of PI3K/AKT, JAK/STAT, and MAPK/ERK signal transduction, as well as induction of G2/M arrest, and showed the drug combination induced much more apoptosis than did single agents. Overall, this in-depth, large-scale proteomics study provided novel insights into molecular mechanisms underlying synergy of combined birinapant/paclitaxel and describes a proteomics/informatics pipeline that can be applied broadly to the development of cancer drug combination regimens.