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Strategy for Simultaneous Multiomic Survey of N-Glycomic and Extracellular Matrix Proteome by Mass Spectrometry Imaging.

Recent advances in spatially resolved molecular profiling have positioned matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) as a powerful platform for multiomic tissue analyses. However, conventional workflows that sequentially target distinct molecular classes are time- and resource-intensive, requiring repeated sequential sample preparation, imaging, and data integration. Here, we evaluate streamlined strategies for simultaneous or combined acquisition of N-glycan and collagen-derived peptide information using PNGase F and collagenase. In-solution studies demonstrate that simultaneous enzymatic digestion yields comparable peptide identifications and glycan profiles relative to traditional sequential workflows, with minimal impact on enzymatic specificity. On the basis of these findings, we developed and optimized MALDI-MSI protocols enabling either simultaneous enzyme application or sequential enzyme treatment with unified matrix deposition and single-pass imaging. While direct coapplication reduced image uniformity, a hybrid approach that used sequential enzyme deposition with combined imaging preserved spatial fidelity and spectral quality while significantly reducing processing and computational demands. Application to human tissues, including vertebral bone and ocular samples, highlights the utility of this workflow for fragile specimens and exploratory multiomic surveys. Collectively, these results establish a framework for integrated glycomic and proteomic imaging targeting the extracellular microenvironment, expanding multiomic MALDI-MSI analyses.

Spectrometry, Mass, Matrix-Assisted Laser Desorpti

Spatial Metabolomics Reveals the Role of Penicillic Acid in Cheese Rind Microbiome Disruption by a Spoilage Fungus.

Microbial interactions in cheese rinds influence community structure, food safety, and product quality. But the chemical mechanisms that mediate microbial interactions in cheeses and other fermented foods are generally not known. Here, we investigate how the spoilage mold Aspergillus westerdijkiae chemically inhibits beneficial cheese-rind bacteria using a combination of omics technologies. In cheese rind community and co-culture experiments, A. westerdijkiae strongly inhibited most cheese rind community members. In co-culture with Staphylococcus equorum, A. westerdijkiae strongly affected bacterial gene expression, including upregulation of a putative bceAB gene cluster that is associated with resistance to antimicrobial compounds in other bacteria. Mass spectrometry imaging (MSI) revealed spatially localized production of secondary metabolites, including penicillic acid and ochratoxin B at the fungal-bacterial interface. Integration of LC-MS/MS and genome annotations confirmed the presence of additional bioactive metabolites, such as notoamides and circumdatins. Fungal metabolic responses varied by bacterial partner, suggesting species-specific chemical strategies. Notably, penicillic acid levels increased 2.5-fold during interaction with Brachybacterium, and experiments with purified penicillic acid showed inhibition of a range of cheese rind bacteria. These findings show that A. westerdijkiae deploys a context-dependent arsenal of mycotoxins and other metabolites, disrupting microbial community assembly in cheese rinds.

Aspergillus westerdijkiae

Longevity of cardiac and skeletal muscle proteins is dependent on tissue and subcellular compartmentation patterns.

Myocytes are exceptionally long-lived cells that must maintain proteome integrity over decades while adjusting for changes in functional output and metabolic demand. We used in vivo stable isotope labeling combined with mass spectrometry proteomics and correlated multi-isotope imaging mass spectrometry to quantify and visualize protein turnover across cardiac, fast-twitch, and slow-twitch skeletal muscles, creating a resource of hundreds of individual protein turnover rates from each tissue. We found that cardiac muscle has the highest rate of protein turnover, followed by slow-twitch skeletal muscle and then fast-twitch skeletal muscle, and that these different rates of protein turnover are driven by different levels of muscle use, rather than myosin isoform composition. We also identified protein age heterogeneity at the myofiber and sarcomere levels. These findings uncover fundamental principles of muscle protein maintenance and have broad implications for understanding cellular aging, muscle disease, and the design of therapeutic strategies targeting muscle protein turnover.

Animals

Acoustic ejection mass spectrometry: the potential for personalized medicine.

INTRODUCTION: The emergence of personalized medicine (PM) has shifted the focus of healthcare from the traditional 'one-size-fits-all' approach to strategies tailored to individual patients, accounting for genetic, environmental, and lifestyle factors. Acoustic ejection mass spectrometry (AEMS) is a novel technology that offers a robust and scalable platform for high-throughput MS readout. AEMS achieves analytical speeds of one sample per second while maintaining high data quality, broad compound coverage, and minimal sample preparation, making it an invaluable tool for PM. AREAS COVERED: This article explores the potential of AEMS in critical PM applications, including therapeutic drug monitoring (TDM), proteomics, metabolomics, and mass spectrometry imaging. AEMS simplifies conventional workflows by minimizing sample preparation, enhancing automation compatibility, and enabling direct analysis of complex biological matrices. EXPERT OPINION: Integrating AEMS with orthogonal separation techniques such as differential mobility spectrometry (DMS) further addresses challenges in isomer discrimination, expanding the platform's analytical capabilities. Additionally, the development of high-throughput data processing tools could further enable AEMS to accelerate the development of personalized medicine.

Humans

Chemical Imaging of Retinal Pigment Epithelium in Frozen Sections of Zebrafish Larvae Using ToF-SIMS.

Variants of the SLC24A5 gene, which encodes a putative potassium-dependent sodium-calcium exchanger (NCKX5) that most likely resides in the melanosome or its precursor, affect pigmentation in both humans and zebrafish (Danio rerio). This finding suggests that genetic variations influencing human skin pigmentation alter melanosome biogenesis via ionic changes. Gaining an understanding of how changes in the ionic environment of organelles impact melanosome morphogenesis and pigmentation will require a spatially resolved way to characterize the chemical environment of melanosomes in pigmented tissue such as retinal pigment epithelium (RPE). The imaging mass spectrometry technique most suited for this type of cell and tissue analysis is time-of-flight secondary ion mass spectrometry (ToF-SIMS) because it is able to detect many biochemical species with high sensitivity and with submicron spatial resolution. Here, we describe chemical imaging of the RPE in frozen-hydrated sections of larval zebrafish using cryo-ToF-SIMS. To facilitate the data interpretation, positive and negative polarity ToF-SIMS image data were transformed into a single hyperspectral data set and analyzed using principal component analysis. The combination of a novel protocol and the use of multivariate data analysis allowed us to discover new marker ions that are attributable to leucodopachrome, a metabolite specific to the biosynthesis of eumelanin. The described methodology may be adapted for the investigation of other classes of molecules in frozen tissues from zebrafish and other organisms.

Animals

Biomarker identification through spatial proteomics for the characterization of indeterminate thyroid nodules.

PURPOSE: The identification of novel molecular biomarkers may assist in the characterization of indeterminate thyroid nodules, which pose significant diagnostic challenges. Here, we aimed to explore the potential of proteomic analyses to support biomarker discovery in challenging thyroid lesions. METHODS: Linear Discriminant Analysis (LDA) was applied to Matrix-Assisted Laser Desorption Ionization Mass Spectrometry Imaging (MALDI-MSI) data from 44 thyroid neoplasms to select the most impactful molecular features for the classification of different tumor histologies, as well as for the distinction between NRAS-mutant (mNRAS) and NRAS-wild-type (wtNRAS) tumors. Relevant peaks were subsequently identified through nanoscale liquid chromatography electrospray ionization tandem mass spectrometry (nLC-ESI-MS/MS). RESULTS: The LDA selected nine relevant molecular markers distinguishing noninvasive follicular thyroid neoplasms with papillary-like nuclear features (NIFTPs) from other tumor histologies (balanced accuracy = 73%), as well as 19 relevant markers able to identify mNRAS cases (balanced accuracy = 84%). Nine differentially expressed proteins were putatively identified: among them, ATP-dependent RNA helicase DDX42 showed a similar distribution between NIFTPs and papillary thyroid carcinomas (PTCs) / follicular variant PTCs (FVPTCs), while the distribution of the Histone H4 signal was similar between NIFTPs and follicular adenomas (FAs). In addition, Protein disulfide-isomerase A1 and Complement C4-B were overexpressed in wtNRAS compared to mNRAS cases, regardless of histology. CONCLUSION: The LDA-selected features enable to distinguish NIFTPs from morphologically similar lesions and to discriminate between mNRAS and wtNRAS cases. The identified markers might complement genetic analyses and provide insights into the distinct pathogenic drivers behind the development of mNRAS compared to wtNRAS lesions.

Humans

Spatial Proteomics of the Normal Breast Collagen Stroma: Links to Density and Body Mass Index.

Collagen breast stroma can become a breast cancer risk factor, yet proteomic regulation of normal breast stroma remains poorly defined. This study evaluates the spatial regulation of the collagen proteome from normal breast tissue. Normal breast tissue sections from the Susan G. Komen tissue bank were used (n = 40), with data including genetic ancestry (n = 20 African ancestry; n = 20 European ancestry), body-mass-index (BMI), age, and mammogram density by the Breast Imaging Reporting and Data System (BI-RADS). 10-plex cell marker staining showed CD44 and COL1A1 markers modulated with BMI. Collagen fiber widths by second harmonic generation microscopy contrasted in BMI categories by genetic ancestry. Targeted extracellular matrix proteomics mass spectrometry imaging showed the collagen alpha-1(I) chain proteome was spatially heterogeneous across the normal breast microenvironment with site-specific post-translational modification of proline hydroxylation. Signatures computationally extracted from stroma-rich regions reported that 47 collagen peptides distinguished BI-RADS categories (area under the receiver operating curve >0.7; p-value >0.05). Multivariate modeling of collagen peptides, fiber metrics, and clinical features supported a strong positive association with BMI as a determinant of collagen alterations in the normal breast. This study provides a foundation for larger studies investigating the clinical value of spatial collagen proteome alterations in human breast.

Humans

DREAMS illuminates spatial DNA and RNA modification landscapes.

DNA and RNA modifications regulate gene expression and RNA processing, but their spatial organization in complex tissues remains elusive. Here we developed DNA RNA Elements Areal Mass Spectrometry (DREAMS), a mass spectrometry imaging platform that spatially maps diverse nucleic acid modifications simultaneously. Applying DREAMS to TET-deficient mouse brains (Tet1Δ/Δ and triple Tet1/2/3Δ/Δ), we uncover TET1's unexpected role in modulating N1-methyladenosine (m1A), a pivotal RNA modification. While DREAMS reveals broad modification landscapes altered across TET knockouts, we identify TET1-mediated changes in m1A that correlate with transcriptome alterations. Our work establishes DREAMS as a transformative tool for spatial epigenomics/epitranscriptomics and suggests that TET enzymes could influence multiple DNA and RNA modifications with potential gatekeeping roles in nucleic acid regulation.

Animals

Proteoform profiling of endogenous single cells from rat hippocampus at scale.

We perform intact proteoform profiling of 10,809 endogenous single cells from the rat hippocampus using single-cell proteoform imaging mass spectrometry (scPiMS). scPiMS directly extracts whole proteins and demonstrates high throughput for MS-based single-cell proteomics compared with existing approaches. We develop an informatics workflow dedicated to this datatype and use it to assign neurons, astrocytes or microglia cell types according to their proteoform signatures.

Animals

A voyage of reprogrammable metabolic bioengineering reshapes plant defense: from editing tools to synthetic systems.

Metabolic bioengineering has emerged as a transformative approach for reshaping plant defense by targeting intrinsic biosynthetic pathways to enhance immunity in modern agriculture. Moving beyond proof-of-concept metabolomics to broad-spectrum programmable pathway engineering addresses gaps in plant rational design and optimizes resilience in response to diverse environmental cues. This review aims to comprehensively highlight the transition of innovative approaches to phenolics, alkaloids, flavonoids, terpenoids, and benzoxazinoids, inferring adaptive reprogramming that mediates the growth-defense balance and functions as molecular sentinels in plants. Furthermore, decoding the volatile metabolome reveals a dynamic signaling interface that influences defense responses and stress-induced plant-microbe interactions, with the shikimate, jasmonate, and salicylate pathways functioning as central hubs for microbial deterrence and priming immune memory. Recent developments in multi-scalar genome-editing strategies, including CRISPR-driven combinatorial edits, enzyme orthogonalization, fluxomics, and spatially resolved multi-omics, reconfigure central and specialized metabolic fluxes toward improved defense function and regulation. Additionally, emerging tools, such as WUSCHEL2 and BABY BOOM transcriptional modules, and artificial engineering strategies integrating deep learning model-driven predictions facilitate rapid development of synthetic genetic circuits and support a predictive engineering of plants. Moreover, Mass spectrometry imaging (MSI) in spatial metabolomics enables to obtain structures and locations of unidentified endogenous metabolites within cells and tissues. Overall, this review emphasizes a diverse array of primary and secondary metabolites, spanning molecular concepts to recent advances in plant immune mechanisms. It also illustrates new frontiers in programmable metabolic engineering that accelerate the understanding of plant-microbe-metabolite cross-talks, offering strategies to improve plant resistance and advance sustainable agricultural solutions.

metabolic bioengineering

Decoding the spatiotemporal patterns of food spoilage microbial communities: Integrating multi-omics and artificial intelligence to enable precision preservation.

In the global food supply chain, food wastage caused by spoilage has resulted in significant economic losses, food shortages, and environmental pressure. This process is fundamentally driven by the spatiotemporal dynamics of microbial communities. However, traditional research methods struggle to elucidate the complex mechanisms of spatial heterogeneity, interspecies interactions, and functional succession. This limits the development of effective preservation strategies. This review systematically reviews the cutting-edge progress of integrating multi-omics technologies and artificial intelligence (AI) to study food spoilage microbial communities, breaking through this bottleneck. We propose an intelligent theoretical framework that could potentially analyze microbial metabolic activities and predict dynamic shelf life if implemented. The conceptual framework integrates multidimensional data, including spatial metabolomics, temporal metatranscriptomics, single-cell transcriptomics, and longitudinal metagenomics. It can also be combined with AI models, such as graph neural networks. The article elaborates on the principles and applications of spatio-temporal monitoring technologies, such as nano secondary ion mass spectrometry, hyperspectral imaging, and the Internet of Things sensing. Through illustrative cases of typical perishable foods, it also explores how such a multi-omics - AI system might be applied to spoilage warning and precise intervention. Additionally, the article addresses the current challenges in data coverage, model generalization, and federated learning implementation. Then the research further explores emerging areas such as engineered probiotics, edge AI, and microfluidic sensing. These areas are targeted at transforming food preservation from an empirical control approach to a data-driven, precise regulatory framework. This transformation provides theoretical support and technical approaches for developing a smart, sustainable food preservation system.

Multiomics

Revealing Hidden Variables in DESI-Based Spatial Metabolomics: Solvent Composition and Tissue Type as Critical Drivers.

In the development of a desorption electrospray ionization (DESI) workflow for spatial metabolomics, we investigated the impact of two commonly used solvent systems, 90% acetonitrile (ACN) and 90% methanol (MeOH), on the spatial metabolomic profiling of various murine tissues. The performance of both solvents was evaluated across several metabolite classes (central carbon metabolites, amino acids, and fatty acids). Although the ACN-based solvent system led to higher signal intensities for small polar metabolites involved in glycolysis, the tricarboxylic acid (TCA) cycle, and amino acid metabolism, the MeOH-based solvent system provided superior signal intensities for fatty acids. These findings demonstrate that the solvent composition differentially influences metabolite extraction and ionization processes in DESI and should be carefully matched to the biological questions and metabolite classes of interest. As a proof-of-principle, the ACN solvent system was applied to a pilot study based on a rat model of renal ischemic injury, revealing region-specific metabolic changes between normoxic and ischemic conditions. Together, these results demonstrate the importance of solvent selection in DESI-based spatial metabolomics and showcase the ability of this approach to uncover spatially resolved metabolic adaptations associated with tissue injury.

Animals

Moving Beyond Morphology to Multiplexed Molecular Imaging as the Next Frontier in Diagnostic Pathology.

Diagnostic pathology has long relied on the morphologic interpretation of hematoxylin and eosin-stained tissues to guide diagnosis and assess prognostic features. Although pathologists intuitively recognize spatial patterns and architectural organization, these assessments remain largely qualitative and difficult to quantify systematically. Immunohistochemistry and immunofluorescence have introduced molecular specificity but are limited in multiplexing capacity, whereas bulk genomic and transcriptomic assays provide high molecular depth but lose spatial context by averaging signals across heterogeneous cell populations. Recent advances in spatial proteomics-including mass spectrometry-based imaging and cyclic immunofluorescence-now enable multiplexed, single-cell protein analysis within intact tissue architecture. These technologies have revealed complex immune and stromal microenvironments, spatially organized biomarkers predictive of therapeutic response, and molecular gradients underlying disease progression. By integrating histologic and molecular information, spatial proteomics bridges traditional microscopy with high-dimensional omics, allowing quantitative, spatially resolved insights into tissue organization and disease mechanisms. This review summarizes recent developments in multiplexed spatial proteomics from both scientific and pathologic perspectives, highlighting how these technologies extend beyond morphology to quantify histologic patterns, refine biomarker discovery, and facilitate clinical translation. The review also examines translational challenges and barriers to clinical implementation, including costs, standardization requirements, and workflow integration.

Humans

Proximity Labeling of Cell Surface Proteins via Cell Surface Remodeling.

Within the complex interplay of proteins, lipids and carbohydrates at the cell surface is the surfaceome, a dense layer of proteins and their posttranslationally modified counterparts that serves as a hub for cell signaling and signal transduction. The surfaceome plays crucial roles in mediating interactions between cells and the extracellular environment, which combined with their availability at the cell surface make it an attractive therapeutic target. Despite its importance, the development of technologies to selectively target cell surface proteins for empirical identification is challenged by their structural complexity. Here, we describe a proximity labeling-based technique to covalently label proteins at the cell surface with a biotin handle, enabling downstream streptavidin-based enrichment and manipulation in a variety of modalities, including fluorescence imaging, western blotting, and mass spectrometry-based proteomics.

Membrane Proteins

Identification of Biomarkers for Right Ventricular Dysfunction in Idiopathic Dilated Cardiomyopathy Via Urinary Proteomics and Machine Learning.

BACKGROUND: Right ventricular dysfunction (RVD) is a common complication of idiopathic dilated cardiomyopathy linked to poor outcomes. However, reliable noninvasive biomarkers for RVD remain lacking. This study aimed to identify urinary proteomic markers using mass spectrometry and machine learning. METHODS: In this prospective cohort, patients with idiopathic dilated cardiomyopathy were classified by cardiac magnetic resonance imaging into groups with RVD (RV ejection fraction <45%) and without RVD groups. Baseline urine samples were profiled by data-independent acquisition mass spectrometry. Differentially expressed proteins were identified and selected by least absolute shrinkage and selection operator regression to build a diagnostic model, developed in a training set, and validated in a test set. The primary end point was a composite of cardiovascular death, heart failure rehospitalization, left ventricular assist device implantation, or heart transplantation. RESULTS: The study enrolled 147 patients with idiopathic dilated cardiomyopathy (64 with RVD, 83 without), with a median follow-up of 19.3&#x2009;months. Of 3579 quantified urinary proteins, 46 were differentially expressed between groups. A 3-protein panel (RARRES1 [retinoic acid receptor responder protein 1], MVB12B [multivesicular body subunit 12B], GSK3A [glycogen synthase kinase 3 alpha]) was identified and showed excellent diagnostic accuracy (training area under the curve 0.946; validation area under the curve0.935), outperforming both NT-proBNP (N-terminal pro-brain natriuretic peptide) and tricuspid annular plane systolic excursion. The risk score derived from this panel effectively stratified patients, with the high-risk group exhibiting significantly worse outcomes than the low-risk group (hazard ratio, 3.24 [95% CI, 1.56-6.71], P=0.002). CONCLUSIONS: The urinary proteomic panel developed in this study demonstrates diagnostic and prognostic potential for identifying RVD in idiopathic dilated cardiomyopathy, providing a promising noninvasive tool for precise detection and clinical risk stratification.

Humans

Plasma von Willebrand Factor and ADAMTS13 Interact With APOE-&#x3b5;4 in Predicting Longitudinal Brain Atrophy and Cognitive Decline Over a 9-Year Follow-Up.

BACKGROUND: Von Willebrand factor (VWF) and ADAMTS13 (a disintegrin and metalloproteinase with thrombospondin type 1 motif, 13) are linked to dementia risk, and limited evidence suggests apolipoprotein E (APOE)-&#x3b5;4 alters VWF release. This study assessed whether baseline VWF and ADAMTS13 levels predict neurodegeneration and cognitive decline and evaluated effect modification by APOE-&#x3b5;4 carriership. METHODS: Vanderbilt Memory and Aging Project cohort participants (n=332, 73&#xb1;7&#x2009;years, 59% male) completed serial blood draw, neuropsychological assessment, and brain magnetic resonance imaging over 6.4&#x2009;years (range 1.4-9.7&#x2009;years). Baseline plasma VWF and ADAMTS13 levels were quantified using mass spectrometry and Olink. Fully adjusted linear mixed-effects models related protein&#xd7;time and protein&#xd7;APOE-&#x3b5;4&#xd7;time interaction terms to longitudinal brain magnetic resonance imaging and neuropsychological outcomes. RESULTS: Lower baseline ADAMTS13 predicted faster declines in language (&#x3b2;=0.11, P=0.01), information processing speed (&#x3b2;=0.27, P=0.001), executive function (&#x3b2;=0.01, P=0.03), episodic memory (&#x3b2;=0.01, P=0.03), and visuospatial ability (&#x3b2;=0.11, P=0.001) and faster increases in global (&#x3b2;=-0.29, P=0.01) and frontal (&#x3b2;=-0.17, P=0.01) white matter hyperintensity volumes. Associations between ADAMTS13 and faster rates of cognitive decline and white matter injury were driven by APOE-&#x3b5;4 carriers. Models relating VWF to longitudinal outcomes were null. APOE-&#x3b5;4 interacted with VWF on longitudinal gray matter volumetric outcomes, such that faster rates of global gray matter atrophy were observed with higher baseline VWF levels among APOE-&#x3b5;4 noncarriers only (&#x3b2;=-1530.5, P<0.001). CONCLUSIONS: ADAMTS13 shows promise as a potential plasma biomarker for brain aging outcomes, but additional research is warranted to understand the performance of VWF in the presence versus absence of an APOE-&#x3b5;4 allele.

Humans

Rapidly decellularized adipose tissue induces soft tissue vascularization in potential anatomical spaces.

Decellularized tissues provide biological cues owing to the wealth of structural and regulatory factors that promote angiogenesis, adipogenesis, and myogenesis and facilitate neurite outgrowth. Here, we demonstrated the advantages of decellularized adipose tissue (adipoECM) over defined collagen-based biomaterials for host tissue integration. Three batches of human adipose tissue were decellularized using a rapid decellularization protocol and analyzed using mass spectrometry. To assess the biological activity of the decellularized materials, adipoECM and a reference standard of care biomaterial (Integra&#xae;DRT, also containing collagen I and glycosaminoglycans) were implanted subcutaneously, but far from the wound bed (in anatomical potential spaces) of immunocompetent BALB/c mice. The mice were euthanized in the acute (1 day) and chronic (day 60) inflammatory reaction phases, followed by biomaterial excision and Masson&#x2019;s trichrome immunohistofluorescence imaging of the paraffin-embedded specimens. Each batch of processed tissue passed a quality control check, showing a low level of donor genomic DNA, lack of nuclei, lipids, endotoxins, and bacterial contamination. Mass spectrometry revealed that all batches of decellularized tissue mainly contained collagen I and, to a lesser degree, collagen III, collagen IV, collagen V, laminin, fibrillin, fibronectin, tenascin, and elastin. No acute inflammatory reaction was observed in either material one day post-transplantation. At 60&#x2009;days post-implantation, different cell types were detected in adipoECM specimens, whereas Integra&#xae;DRT remained acellular. Additional immunohistochemical staining of adipoECM revealed CD31-positive cells in the blood vessels. Mesenchymal (CD90 positive) and myeloid (CD14 positive) cells were also detected. Primary cell types involved in soft tissue healing and remodeling were found in the adipoECM-treated group. The ingrowth of blood vessels and mesenchymal cells confirmed the effective integration of adipoECM with host tissues. Our results demonstrate that decellularized adipose tissue implanted away from the wound bed possesses contextual biological activities that promote efficient integration with host tissues.

Adipose Tissue

Phenotyping of post-fertilization sperm mitophagy determinants discovered in a mammalian gamete-based cell-free system.

The targeted, substrate-specific degradation of paternal mitochondria inside the zygote, known as post-fertilization sperm mitophagy, is a crucial and evolutionarily conserved early embryonic event. It ensures the exclusive maternal inheritance of the mitochondrial genome. Post-fertilization sperm mitophagy was initially thought to only be achieved via the ubiquitin-proteasome system. Until pro-autophagic receptor proteins such as SQSTM1, GABARAP, as well as the proteasome-interacting ubiquitinated protein dislocase VCP, were identified as contributors to the degradation of the sperm mitochondria early after mammalian fertilization. This synergy of proteasomal and autophagic pathways ensures a timely degradation of sperm mitochondria shortly after fertilization. The discovery of these autophagic receptors lead researchers to believe there might be other autophagic receptors and determinants necessary for proper post-fertilization sperm mitophagy. Based on the established inventory of proteins from mass spectrometry trials of boar spermatozoa exposed to porcine oocyte extracts in an intra-specific porcine cell-free system (CFS), five candidate mitophagy determinants were further investigated in this study, namely LACTB, PRDX3, PSMA8, TOMM34, and FUNDC1. These proteins of interest were studied and validated by using in vitro fertilization (IVF) protocols, cell imaging of spermatids, spermatozoa, oocytes and zygotes, protein interactome analysis, and the porcine CFS. The proteins PSMA8 and TOMM34 behaved in accordance with our proteomic study predictions. The PSMA8 labeling increased after exposure to CFS; in agreement with the classification PSMA8 was given from the mass spectrometry findings. TOMM34 underwent a visible decrease in labeling after exposure to CFS, which also agreed with its proteomic classification; this labeling persisted in IVF zygotes. Except for LACTB, the examined proteins showed mutual interactions as well as interactions with previously identified sperm mitophagy factors in the STRING interactome analysis. Results from this study validate the novel porcine CFS as a valuable tool for the exploration of early fertilization events at a molecular level. Future phenotyping and functional studies using porcine CFS will advance the understanding of mitochondrial inheritance and zygotic development and potentially shed light on the origins of certain mitochondrial diseases arising from the failure of post-fertilization sperm mitophagy.

Animals