Comparative Study on the Sperm Proteomes of Horses and Donkeys

The reproductive performance of horse sperm and donkey sperm has been reported to differ. Sperm proteins play a crucial role in sperm viability and fertility. Although differences between species are known, no prior study has investigated disparities in the sperm proteome between horses and donkeys. Therefore, this study characterized and compared the sperm proteomes of horses and donkeys using 4D-DIA mass spectrometry technology. We identified 3436 proteins in horse sperm and 3404 proteins in donkey sperm. Of these, 3363 proteins were expressed in both horse and donkey sperm, with 73 proteins being specifically expressed in horse sperm, and 41 in donkey sperm. According to data analysis, donkeys exhibited a greater percentage of motility and progressive movement in straight-line sperm than horses, as well as lower percentages of static and slow sperm than horses. Joint analysis of the results from the horse and donkey sperm proteomes and their CEROS II-read parameters demonstrated a possible association between sperm proteins and their sperm viability patterns. These findings suggest that there are discrepancies in the expression levels and protein compositions of horse and donkey sperm and that certain specific proteins may be responsible for the differences in performance between these two species.

ProPept-MT: A Multi-Task Learning Model for Peptide Feature Prediction

In the realm of quantitative proteomics, data-independent acquisition (DIA) has emerged as a promising approach, offering enhanced reproducibility and quantitative accuracy compared to traditional data-dependent acquisition (DDA) methods. However, the analysis of DIA data is currently hindered by its reliance on project-specific spectral libraries derived from DDA analyses, which not only limits proteome coverage but also proves to be a time-intensive process. To overcome these challenges, we propose ProPept-MT, a novel deep learning-based multi-task prediction model designed to accurately forecast key features such as retention time (RT), ion intensity, and ion mobility (IM). Leveraging advanced techniques such as multi-head attention and BiLSTM for feature extraction, coupled with Nash-MTL for gradient coordination, ProPept-MT demonstrates superior prediction performance. Integrating ion mobility alongside RT, mass-to-charge ratio (m/z), and ion intensity forms 4D proteomics. Then, we outline a comprehensive workflow tailored for 4D DIA proteomics research, integrating the use of 4D in silico libraries predicted by ProPept-MT. Evaluation on a benchmark dataset showcases ProPept-MT's exceptional predictive capabilities, with impressive results including a 99.9% Pearson correlation coefficient (PCC) for RT prediction, a median dot product (DP) of 96.0% for fragment ion intensity prediction, and a 99.3% PCC for IM prediction on the test set. Notably, ProPept-MT manifests efficacy in predicting both unmodified and phosphorylated peptides, underscoring its potential as a valuable tool for constructing high-quality 4D DIA in silico libraries.

Proteomic profiling of prostate cancer reveals molecular signatures under antiandrogen treatment

BACKGROUND

Tumorigenesis and progression of prostate cancer (PCa) are indispensably dependent on androgen receptor (AR). Antiandrogen treatment is the principal preference for patients with advanced PCa. However, the molecular characteristics of PCa with antiandrogen intervention have not yet been fully uncovered.

METHODS

We first performed proteome analysis with 32 PCa tumor samples and 10 adjacent tissues using data-independent acquisition (DIA)- parallel accumulation serial fragmentation (PASEF) proteomics. Then label-free quantification (LFQ) mass spectrometry was employed to analyze protein profiles in LNCaP and PC3 cells.

RESULTS

M-type creatine kinase CKM and cartilage oligomeric matrix protein COMP were demonstrated to have the potential to be diagnostic biomarkers for PCa at both mRNA and protein levels. Several E3 ubiquitin ligases and deubiquitinating enzymes (DUBs) were significantly altered in PCa and PCa cells under enzalutamide treatment, and these proteins might reprogram proteostasis at protein levels in PCa. Finally, we discovered 127 significantly varied proteins in PCa samples with antiandrogen therapy and further uncovered 4 proteins in LNCaP cells upon enzalutamide treatment.

CONCLUSIONS

Our research reveals new potential diagnostic biomarkers for prostate cancer and might help resensitize resistance to antiandrogen therapy.

Data-Independent Acquisition: A Milestone and Prospect in Clinical Mass Spectrometry-Based Proteomics

Data-independent acquisition (DIA) has revolutionized the field of mass spectrometry (MS)-based proteomics over the past few years. DIA stands out for its ability to systematically sample all peptides in a given m/z range, allowing an unbiased acquisition of proteomics data. This greatly mitigates the issue of missing values and significantly enhances quantitative accuracy, precision, and reproducibility compared to many traditional methods. This review focuses on the critical role of DIA analysis software tools, primarily focusing on their capabilities and the challenges they address in proteomic research. Advances in MS technology, such as trapped ion mobility spectrometry, or high field asymmetric waveform ion mobility spectrometry require sophisticated analysis software capable of handling the increased data complexity and exploiting the full potential of DIA. We identify and critically evaluate leading software tools in the DIA landscape, discussing their unique features, and the reliability of their quantitative and qualitative outputs. We present the biological and clinical relevance of DIA-MS and discuss crucial publications that paved the way for in-depth proteomic characterization in patient-derived specimens. Furthermore, we provide a perspective on emerging trends in clinical applications and present upcoming challenges including standardization and certification of MS-based acquisition strategies in molecular diagnostics. While we emphasize the need for continuous development of software tools to keep pace with evolving technologies, we advise researchers against uncritically accepting the results from DIA software tools. Each tool may have its own biases, and some may not be as sensitive or reliable as others. Our overarching recommendation for both researchers and clinicians is to employ multiple DIA analysis tools, utilizing orthogonal analysis approaches to enhance the robustness and reliability of their findings.

3D-MPEA: A Graph Attention Model-Guided Computational Approach for Annotating Unknown Metabolites in Interactomics via Mass Spectrometry-Focused Multilayer Molecular Networking

The spectral matching strategy of MS2 fragment spectrograms serves as a ubiquitous method for compound characterization within the matrix. Nevertheless, challenges arise due to the deficiency of distinctions in spectra across instruments caused by coelution peak-derived fragments and incompleteness of the current spectral reference database, leading to dilemma of multidimensional omics annotation. The graph attention model embedded with long short-term memory was proposed as an optimized approach involving integrating similar MS2 spectra into molecular networks according to the isotopic ion peak cluster spacing features to collapse diverse ion species and expand the spectral reference library, which efficiently evaluated the substance capture capacity to 123.1% than classic substance perception tactics. The versatility and utility of the established annotation procedure were showcased in a study on the stimulation of pork mediated by 2,2-bis(4-hydroxyphenyl)propane and enabled the global metabolite annotation from knowns to unknowns at metabolite-lipid-protein level. On the spectra for which in silico extended spectral library search provided a group truth, 83.5-117.1% accuracy surpassed 1.2-14.3% precision after manual validation. β-Ala-His dipeptidase was first evidenced as the critical node related to the transformation of α-helical (36.57 to 35.74%) to random coil (41.53 to 42.36%) mediated by 2,2-bis(4-hydroxyphenyl)propane, ultimately triggering an augment of catalytic performance, inducing a series of oxidative stress, and further intervening in the availability of animal-derived substrates. The integration of ionic fragment feature networks and long short-term memory models allows the effective annotation of recurrent unknowns in organisms and the deciphering of unacquainted matter in multiomics.

Silk Fibroin Encapsulated Icariin Nanoparticles Mitigate Bisphenol A-Induced Spermatogenesis Dysfunction

Bisphenol A (BPA) is a prevalent endocrine disruptor found in natural environments. Exposure to BPA has been associated with male infertility. The natural phytochemical icariin (ICA) has demonstrated significant promise for the treatment of male infertility. However, its effectiveness is limited due to its low bioavailability, poor water solubility, and insufficient targeting abilities. Herein, novel nanoparticles are generated from the natural silk fibroin, which are used to load ICA. The efficient drug delivery system (ICA-SNPs) result in significantly focused drug distribution to spermatogonium, enhancing the anti-infertility properties of ICA, and can effectively mitigate spermatogenesis dysfunction induced by BPA, control serum sex hormone levels, and enhance testicular ultrastructure. Additionally, the ICA-SNPs restore spermatogenesis dysfunction primarily via the hormone biosynthesis, spermatogonium meiosis process, and glycerophospholipid metabolism.

Acquisition and Analysis of DIA-Based Proteomic Data: A Comprehensive Survey in 2023

Data-independent acquisition (DIA) mass spectrometry (MS) has emerged as a powerful technology for high-throughput, accurate, and reproducible quantitative proteomics. This review provides a comprehensive overview of recent advances in both the experimental and computational methods for DIA proteomics, from data acquisition schemes to analysis strategies and software tools. DIA acquisition schemes are categorized based on the design of precursor isolation windows, highlighting wide-window, overlapping-window, narrow-window, scanning quadrupole-based, and parallel accumulation-serial fragmentation-enhanced DIA methods. For DIA data analysis, major strategies are classified into spectrum reconstruction, sequence-based search, library-based search, de novo sequencing, and sequencing-independent approaches. A wide array of software tools implementing these strategies are reviewed, with details on their overall workflows and scoring approaches at different steps. The generation and optimization of spectral libraries, which are critical resources for DIA analysis, are also discussed. Publicly available benchmark datasets covering global proteomics and phosphoproteomics are summarized to facilitate performance evaluation of various software tools and analysis workflows. Continued advances and synergistic developments of versatile components in DIA workflows are expected to further enhance the power of DIA-based proteomics.

4D-diaXLMS: Proteome-wide Four-Dimensional Data-Independent Acquisition Workflow for Cross-Linking Mass Spectrometry

Cross-linking mass spectrometry (XL-MS) is a powerful tool for examining protein structures and interactions. Nevertheless, analysis of low-abundance cross-linked peptides is often limited in the data-dependent acquisition (DDA) mode due to its semistochastic nature. To address this issue, we introduced a workflow called 4D-diaXLMS, representing the first-ever application of four-dimensional data-independent acquisition for proteome-wide cross-linking analysis. Cross-linking studies of the HeLa cell proteome were evaluated using the classical cross-linker disuccinimidyl suberate as an example. Compared with the DDA analysis, 4D-diaXLMS exhibited marked improvement in the identification coverage of cross-linked peptides, with a total increase of 36% in single-shot analysis across all 16 SCX fractions. This advantage was further amplified when reducing the fraction number to 8 and 4, resulting in 125 and 149% improvements, respectively. Using 4D-diaXLMS, up to 83% of the cross-linked peptides were repeatedly identified in three replicates, more than twice the 38% in the DDA mode. Furthermore, 4D-diaXLMS showed good performance in the quantitative analysis of yeast cross-linked peptides even in a 15-fold excess amount of HeLa cell matrix, with a low coefficient of variation and high quantitative accuracies in all concentrations. Overall, 4D-diaXLMS was proven to have high coverage, good reproducibility, and accurate quantification for in-depth XL-MS analysis in complex samples, demonstrating its immense potential for advances in the field.