ACS Publications最新文献

Chronic liver diseases exhibit diverse backgrounds, and it is believed that numerous factors contribute to progression to cancer. To achieve effective prevention of nonviral hepatocellular carcinoma, it is imperative to identify fundamental molecular abnormalities at the patient level. Utilizing cancer-adjacent liver tissues obtained from hepatocellular carcinoma patients (chronic liver disease), we conducted RNA-Seq and metabolome analyses. In the chronic liver disease cohort, upregulation of inflammation-associated signals was observed, concomitant with accumulation of acylcarnitine and fatty acid and depletion of NADP+, gamma-tocopherol, and dehydroisoandrosterone-3-sulfate-1 (DHEAS). To minimize heterogeneity, we performed multiomics clustering, successfully categorizing the chronic liver disease cases into two distinct subtypes. Subtype 1 demonstrated elevated inflammatory levels, whereas Subtype 2 included a disproportionately high proportion of elderly cases. Furthermore, RNA-Seq analysis revealed upregulation of inflammatory signals in Subtype 1, while both subtypes exhibited downregulation of fatty acid metabolism. Metabolome analysis indicated a tendency of increased acylcarnitine levels in Subtype 1 and augmented fatty acid accumulation in Subtype 2. Validation of differentially expressed genes using the Gene Expression Omnibus (GEO) data set revealed the potential for amelioration through supplementation with antioxidants such as epigallocatechin gallate (EGCG).

Large-scale mass-spectrometry-based proteomics experiments are complex and prone to analytical variability, requiring rigorous quality checks across each step in the workflow: sample preparation, chromatography, mass spectrometry, and the bioinformatics stages. This includes quality control (QC) measures that address biological and technical variation. Most QC approaches involve detecting sample outliers and monitoring parameters related to sample preparation and mass spectrometer performance. Evaluating these parameters regularly is essential for reliable downstream analysis and proteomics research. Here, we introduce "QCeltis", a Python package designed to facilitate automated QC analysis across the proteomics workflow, aiding in the identification of technical biases and consistency verification. QCeltis is a versatile tool for detecting QC issues in large-scale data-independent acquisition proteomics experiments by not only identifying sample preparation and acquisition issues but also aiding in differentiating between QC issues vs batch effects. QCeltis is available for command-line use in Windows and Linux environments. We present three case studies showcasing QCeltis's capabilities across different data sets, including depleted plasma, whole blood vs plasma, and dried blood spot samples, emphasizing its potential impact on large-scale proteomics projects. This package can be used to enhance data reliability and enable nuanced downstream analysis and interpretation for proteomics studies.

It is well established that acute and chronic stress contributes to the onset and progression of depression, but the underlying mechanisms have not been elucidated. Here an integrated N-glycoproteomic and proteomic analysis was performed to investigate heterogeneities of glycoprotein and site-specific glycosylation between the hippocampi of control, acute stress-affected (AS), and chronic mild stress-affected (CMS) mice. 1063 unique intact N-glycopeptides, 116 N-glycan compositions, and 512 glycosylation sites were identified. CMS and AS had significant effects on glycosylation. CMS reduced multiantenna glycosylation (N8H8 and N6H5F1S1) more strongly, while AS reduced multiantenna glycosylation (N5H3F1) more strongly. CMS inhibited high-mannose synthesis with high polymerization (N2H9 and N2H8), while AS inhibited high-mannose synthesis with low polymerization (N2H6, H2H5). Furthermore, 26 and 39 glycosylation-related genes (GRGs) were identified in the AS and CMS groups, separately. Functional enrichment analysis for GRGs in the AS and CMS groups exhibited that the up-regulated functions were leading edge membrane and cell adhesion molecule binding; meanwhile, the down-regulated functions were cAMP signaling pathways. Finally, tSNE analysis based on ScRNA-seq revealed that core GRGs were highly expressed in astrocytes. All of these findings improve our understanding of glycosylation in stress-related depression, providing valuable data resources for depression pathogenesis exploration and novel therapeutic target discovery.

Coral reefs are vital to marine biodiversity and human livelihoods, but they face significant threats from climate change. Increased ocean temperatures drive massive "bleaching" events, during which corals lose their symbiotic algae and the important metabolic resources those algae provide. Proteomics is a crucial tool for understanding coral function and tolerance to thermal stress, as proteins drive physiological processes and accurately represent cell functional phenotypes. We examined the physiological condition of coral (Montipora capitata) gametes from parents that either experienced thermal bleaching or were nonbleached controls by comparing data dependent (DDA) and data independent (DIA) acquisition methods and peptide quantification (spectral counting and area-under-the-curve, AUC) strategies. For DDA, AUC captured a broader dynamic range than spectral counting. DIA yielded better coverage of low abundance proteins than DDA and a higher number of proteins, making it the more suitable method for detecting subtle, yet biologically significant, shifts in protein abundance in gamete bundles. Gametes from bleached corals showed a broadscale decrease in metabolic proteins involved in carbohydrate metabolism, citric acid cycle, and protein translation. This metabolic plasticity could reveal how organisms and their offspring acclimatize and adapt to future environmental stress, ultimately shaping the resilience and dynamics of coral populations.

Trimeric phthalides in nature are rarely reported. This research successfully isolated three novel trimeric phthalides, triangesinenolides A-C (1-3), from Angelica sinensis. These compounds, derived from Z-ligustilide via diverse linkage patterns, display intricate polycyclic skeletons. Their structures were confirmed by spectroscopic and crystallographic analyses. Unlike previously identified trimeric phthalides, triangesinenolide C (3) is the first trimeric phthalide synthesized via [2 + 2]/[4 + 2] cycloaddition. In vitro assays evaluated the anti-renal fibrosis activity of trimeric phthalides for the first time.

Alteration of glycosylation in cancer cells leads to the expression of tumor-associated glycans, which can be used as biomarkers for diagnosis and prognostic prediction of diseases. In this study, we used nano-LC-QToF to identify serum N-glycan biomarkers for the detection of brain tumors. We observed an increase in sialylated N-glycans and a decrease in fucosylated N-glycans in the serum of patients with glioblastoma (GBM) and meningioma (MG) compared to healthy individuals. In GBM, a combination of increased serum sialylated N-glycan (6_4_0_2 compound) and decreased fucosylated N-glycan (4_4_1_0 compound) was identified as the most appropriate panel, with an area under the curve (AUC) of 0.8660, 78.95% sensitivity, 84.21% specificity, and 82.89% accuracy. For MG, a combination of decreased 6_6_2_0 and 5_5_2_0 compounds and increased 4_4_1_1 compound achieved an AUC of 0.9260, 82.35% sensitivity, 78.57% specificity, and 80.26% accuracy for diagnosis of MG. Additionally, an increase in 5_5_1_0 and 4_3_0_0 compounds combined with a decrease in 7_7_4_3 was associated with high-grade MG (WHO grades II-III). In conclusion, we identified serum N-glycan profiles associated with brain tumors, highlighting their potential as biomarkers for the diagnosis and prognosis of these diseases.

The lipids that form extracellular vesicles (EVs) play critical structural and regulatory roles, and cutting-edge bioinformatics strategies have shown the ability to decipher lipid metabolism and related molecular mechanisms. We previously demonstrated that alcohol abuse induces an inflammatory immune response through Toll-like receptor 4 (TLR4), leading to structural and cognitive dysfunction. This study evaluated how TLR4 and sex as variables (male/female) impact the lipidome of plasma-resident EVs after chronic alcohol exposure. Using a mouse model of chronic ethanol exposure in wild-type and TLR4-deficient mice, enrichment networks generated by LINEX² highlighted significant ethanol-induced changes in the EV lipid substrate-product of enzyme reactions associated with glycerophospholipid metabolism. We also demonstrated ethanol-induced differences in Lipid Ontology enrichment analysis in EVs, focusing on terms related to lipid bilayer properties. A lipid abundance analysis revealed higher amounts of significant lipid subclasses in all experimental comparisons associated with inflammatory responses and EV biogenesis/secretion. These findings suggest that interrogating EV lipid abundance with a sensitive lipidomic-based strategy can provide deep insight into the molecular mechanisms underlying biological processes associated with sex, alcohol consumption, and TLR4 immune responses and open new avenues for biomarker identification and therapeutic development.

The exponential increase in proteomics data presents critical challenges for conventional processing workflows. These pipelines often consist of fragmented software packages, glued together using complex in-house scripts or error-prone manual workflows running on local hardware, which are costly to maintain and scale. The MSAID Platform offers a fully automated, managed proteomics data pipeline, consolidating formerly disjointed functions into unified, API-driven services that cover the entire process from raw data to biological insights. Backed by the cloud-native search algorithm CHIMERYS, as well as scalable cloud compute instances and data lakes, the platform facilitates efficient processing of large data sets, automation of processing via the command line, systematic result storage, analysis, and visualization. The data lake supports elastically growing storage and unified query capabilities, facilitating large-scale analyses and efficient reuse of previously processed data, such as aggregating longitudinally acquired studies. Users interact with the platform via a web interface, CLI client, or API, providing flexible, automated access. Readily available tools for accessing result data include browser-based interrogation and one-click visualizations for statistical analysis. The platform streamlines research processes, making advanced and automated proteomic workflows accessible to a broader range of scientists. The MSAID Platform is globally available via https://platform.msaid.io.

Brochosomes are proteinaceous nanostructures produced by leafhopper insects with superhydrophobic and antireflective properties. Unfortunately, the production and study of brochosome-based materials has been limited by poor understanding of their major constituent subunit proteins, known as brochosomins, as well as their sensitivity to redox conditions due to essential disulfide bonds. Here, we used cell-free gene expression (CFE) to achieve recombinant production and analysis of brochosomin proteins. Through the optimization of redox environment, reaction temperature, and disulfide bond isomerase concentration, we achieved soluble brochosomin yields of up to 341 ± 30 μg/mL. Analysis using dynamic light scattering and transmission electron microscopy revealed distinct aggregation patterns among cell-free mixtures with different expressed brochosomins. We anticipate that the CFE methods developed here will accelerate the ability to change the geometries and properties of natural and modified brochosomes, as well as facilitate the expression and structural analysis of other poorly understood protein complexes.

The ethylene-forming enzyme (EFE) from the fungus Penicillium digitatum strain Pd1 was heterologously produced in Escherichia coli and its properties were compared to the extensively characterized bacterial enzyme from Pseudomonas savastanoi strain PK2. Both enzymes catalyze four reactions: the conversion of 2-oxoglutarate (2OG) to ethylene and CO₂, oxidative decarboxylation of 2OG coupled to l-arginine (l-Arg) hydroxylation, uncoupled oxidative decarboxylation of 2OG, and the production of 3-hydroxypropionate (3-HP) from 2OG. The strain Pd1 enzyme exhibited a greater ratio of ethylene production over l-Arg hydroxylation than the PK2 strain EFE. The uncoupled decarboxylation of 2OG and 3-HP production are minor reactions in both cases, but they occur to a greater extent using the fungal enzyme. Additional distinctions of the fungal versus bacterial enzyme are noted in the absorbance maxima and l-Arg dependence of their anaerobic electronic spectra. The structures of the Pd1 EFE apoprotein and the EFE·Mn(II)·2OG complex resembled the corresponding structures of the PK2 enzyme, but notable structural differences were observed in the computationally predicted Pd1 EFE·Fe(II)·2OG·l-Arg complex versus the PK2 EFE·Mn(II)·2OG·l-Arg crystal structure. These studies extend our biochemical understanding and represent the first structural and conformational characterization of a eukaryotic EFE.