Molecular & Cellular Proteomics最新文献

{"title":"PTMFusionNet: A Deep Learning Approach for Predicting Disease Related Post-Translational Modification and Classifying Disease Subtypes.","authors":"Jie Ni, Yifan Zhou, Bin Li, Xinting Zhang, Yuanyuan Deng, Jie Sun, Donghui Yan, Shengqi Jing, Shan Lu, Zhuoying Xie, Xin Zhang, Yun Liu","doi":"10.1016/j.mcpro.2025.101009","DOIUrl":"https://doi.org/10.1016/j.mcpro.2025.101009","url":null,"abstract":"<p><p>With the advancement of technologies such as mass spectrometry, it has become possible to simultaneously perform large-scale detection of protein intensity and corresponding post-translational modification (PTM) information, thereby facilitating clinical diagnosis and treatment. However, existing PTM information is insufficient to fully integrate with protein expression data. We propose a deep learning method called PTMFusionNet, which predicts potential disease-related PTMs and integrates them with protein expression data to classify disease subtypes. PTMFusionNet includes two Graph Convolutional Network (GCN) models: the Layer Attention Graph Convolutional Network (LAGCN) and the Feature Weighting Graph Convolutional Network (FWGCN). LAGCN is used to predict PTM potentiality scores, while FWGCN integrates these scores with protein expression data for disease subtype classification. Experimental results across three datasets (KIPAN, COADREAD, and THCA) demonstrate that PTMFusionNet outperforms benchmark algorithms in accuracy, F1 score, and AUC, highlighting its robustness in identifying critical PTM biomarkers and advancing disease subtyping.</p>","PeriodicalId":18712,"journal":{"name":"Molecular & Cellular Proteomics","volume":" ","pages":"101009"},"PeriodicalIF":6.1,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144225897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"msqrob2TMT: robust linear mixed models for inferring differential abundant proteins in labelled experiments with arbitrarily complex design.","authors":"Stijn Vandenbulcke, Christophe Vanderaa, Oliver Crook, Lennart Martens, Lieven Clement","doi":"10.1016/j.mcpro.2025.101002","DOIUrl":"https://doi.org/10.1016/j.mcpro.2025.101002","url":null,"abstract":"<p><p>Labelling strategies in mass spectrometry (MS)-based proteomics enhance sample throughput by enabling the acquisition of multiplexed samples within a single run. However, contemporary experiments often involve increasingly complex designs, where the number of samples exceeds the capacity of a single run, resulting in a complex correlation structure that must be addressed for accurate statistical inference and reliable biomarker discovery. To this end, we introduce msqrob2TMT, a suite of mixed model-based workflows specifically designed for differential abundance analysis in labelled MS-based proteomics data. msqrob2TMT accommodates both sample-specific and feature-specific (e.g., peptide or protein) covariates, facilitating inference in experiments with arbitrarily complex designs and allowing for explicit correction of feature-specific covariates. We benchmark our innovative workflows against state-of-the-art tools, including DEqMS, MSstatsTMT, and msTrawler, using two spike-in studies. Our findings demonstrate that msqrob2TMT offers greater flexibility, improved modularity, and enhanced performance, particularly through the application of robust ridge regression. Finally, we demonstrate the practical relevance of msqrob2TMT in a real mouse study, highlighting its capacity to effectively account for the complex correlation structure in the data.</p>","PeriodicalId":18712,"journal":{"name":"Molecular & Cellular Proteomics","volume":" ","pages":"101002"},"PeriodicalIF":6.1,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144199619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deciphering the proteomic profile of the parasite Fasciola hepatica after initial vertebrate host infection reveals new insights into its early-stages.","authors":"Marta López-García, Krystyna Cwiklinski, David Becerro-Recio, María Teresa Ruiz-Campillo, Verónica Molina-Hernández, José Pérez-Arévalo, Álvaro Martínez-Moreno, Javier González-Miguel, Mar Siles-Lucas","doi":"10.1016/j.mcpro.2025.101005","DOIUrl":"https://doi.org/10.1016/j.mcpro.2025.101005","url":null,"abstract":"<p><p>The migration of the trematode parasite Fasciola hepatica within its vertebrate host following infection by ingestion of the metacercariae represents a critical event in its establishment and survival. The early stages of infection, during which F. hepatica crosses the intestinal barrier and advances to the liver through the peritoneum, initiate changes in the parasite that drive its development from a free-living state on pasture to an obligate blood-feeding parasite. Using an in vivo mouse model, this study explores the proteomic changes in the parasite as it crosses the intestinal barrier and migrates to the peritoneal cavity (24 h post-infection (p.i.)) and liver parenchyma (8 days p.i.). This model was coupled with SWATH-MS, enabling a comparative evaluation of parasite protein abundance during the early stages of infection. We identified a total of 1,180 F. hepatica proteins from three developmental timepoints: newly excysted juveniles (FhNEJ) at 3 h post-excystment in vitro, and parasites collected in vivo at 24 h and 8 days p.i., separated into two different parasite compartments (somatic and tegumental). These extracts exhibited differentially expressed proteins (DEP) across the analysed timepoints, with 274 and 463 DEP identified in parasites obtained at 24 h and 8 days p.i., respectively. Our findings further highlight the adaptations F. hepatica undergoes within the first week of infection, including a shift towards anaerobic metabolic pathways, induction of signal transduction pathways involved in growth, and enrichment of crucial cysteine peptidases associated with feeding and immunomodulation. This study represents the first in-depth proteome analysis of parasites recovered 8 days into infection, adding to the wealth of molecular data available for Fasciola spp. to enhance our understanding of early host-parasite interactions. These data are crucial for the development of future in vitro models of fasciolosis and for identifying vaccine candidates targeting the early parasite stages.</p>","PeriodicalId":18712,"journal":{"name":"Molecular & Cellular Proteomics","volume":" ","pages":"101005"},"PeriodicalIF":6.1,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144199618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recent Advances in Mass Spectrometry-Based Studies of Post-translational Modifications in Alzheimer's Disease.","authors":"Feixuan Wu, Wei Li, Haiyan Lu, Lingjun Li","doi":"10.1016/j.mcpro.2025.101003","DOIUrl":"https://doi.org/10.1016/j.mcpro.2025.101003","url":null,"abstract":"<p><p>Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline. There are over 10 million new cases of AD each year worldwide, implying one new case every 3.2 seconds. Post-translational modifications (PTMs) such as phosphorylation, glycosylation, and citrullination have emerged as key modulators of protein function in AD, influencing protein aggregation, clearance, and toxicity. Mass spectrometry (MS) has become an indispensable tool for detecting and quantifying these PTMs, offering valuable insights into their role in AD pathogenesis. This review explores recent advancements in MS-based studies of PTMs in AD, with emphasis on MS techniques like data-dependent acquisition (DDA) and data-independent acquisition (DIA), as well as enrichment methods used to characterize PTMs. The applications of these MS-based approaches to the study of various PTMs are highlighted, which have significantly accelerated the biomarker discovery process, providing new avenues for early diagnosis and therapeutic targeting. Advances in biological understanding and analytical techniques, while addressing the challenges and future directions, will be discussed.</p>","PeriodicalId":18712,"journal":{"name":"Molecular & Cellular Proteomics","volume":" ","pages":"101003"},"PeriodicalIF":6.1,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144192024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"P4PP: an universal shotgun proteomics data analysis pipeline for virus identification.","authors":"Armand Paauw, Evgeni Levin, Ingrid A I Voskamp-Visser, Ilka M F Marissen, Vincent Ramisse, Marine Eschlimann, Jiří Dresler, Petr Pajer, Christoph Stingl, Hans C van Leeuwen, Theo M Luider, Luc M Hornstra","doi":"10.1016/j.mcpro.2025.101004","DOIUrl":"https://doi.org/10.1016/j.mcpro.2025.101004","url":null,"abstract":"<p><p>Humans can be infected by a wide variety of virus species. We developed a data analysis approach for shotgun proteomic data to detect these viruses. A proteome for pandemic preparedness (P4PP) pipeline, a corresponding database (P4PP v01), and a web application (P4PP) were constructed. The P4PP pipeline enables the identification of 1896 virus species from the 32 virus families, based on multiple identified discriminatory peptides, in which at least one human-infectious virus is described. P4PP was evaluated using different datasets of cell-cultivated viruses, generated at different institutes, measured with different instruments, and prepared with different sample preparation methods. In total, 174 MS datasets of 160 and 14 protein trypsin digests of virus-infected and non-infected cell lines were analyzed, respectively. Of the 160 samples, 146 were correctly identified at the species level, and an additional 4 samples were identified at the family level. In the remaining 10 samples, no virus was detected. However, all these 10 samples tested positive in follow-up samples obtained later in time series were negative samples were measured, indicating that the number of peptides derived from the virus was initially too low in the samples obtained at the start of the experiment. Furthermore, results show that Influenza A or SARS-CoV-2 can be subtyped if enough discriminative peptides of the virus are identified. In the non-infected cell lines, no virus was detected except in one sample where the in that experiment studied virus was detected. Shotgun proteomics, in combination with the developed data analysis approach, can identify all types of virus species after cultivation in a cell line. Implementing this agnostic virus proteome analysis capability in viral diagnostic laboratories has the potential to improve their capabilities to cope with unexpected, mutated or re-emerging viruses.</p>","PeriodicalId":18712,"journal":{"name":"Molecular & Cellular Proteomics","volume":" ","pages":"101004"},"PeriodicalIF":6.1,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144192023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MSstatsTMT improves accuracy of thermal proteome profiling by trading off temperature treatments and biological replicates.","authors":"Amanda M Figueroa-Navedo, Rohan Kapre, Tushita Gupta, Yingrong Xu, Clifford G Phaneuf, Pierre M Jean Beltran, Liang Xue, Alexander R Ivanov, Olga Vitek","doi":"10.1016/j.mcpro.2025.100999","DOIUrl":"https://doi.org/10.1016/j.mcpro.2025.100999","url":null,"abstract":"<p><p>Thermal proteome profiling investigates protein-protein, protein-nucleic acid, or protein-drug interactions, and the impact of metabolite binding and post-translational modifications on these interactions. The experiments quantitatively characterize biological samples treated with small molecules versus controls, and subjected to timed exposures to multiple temperatures. Typically, each enzymatically digested sample is labeled with a tandem mass tag (TMT), where each TMT channel corresponds to a specific temperature treatment, and profiled using liquid chromatography coupled with mass spectrometry in data-dependent data acquisition mode. The resulting mass spectra are processed with computational tools to identify and quantify proteins, and filter out noise. Protein-drug interactions are detected by fitting curves to the protein-level reporter ion abundances across the temperatures. Interacting proteins are identified by shifts in the fitted curves between treated samples and controls. In this manuscript, we focus on data processing and curve fitting in thermal proteome profiling. We review the statistical methods currently used for thermal proteome profiling, and demonstrate that such methods can yield substantially different results. We advocate for the statistical analysis strategy implemented in the open-source R package MSstatsTMT, as it does not require subjective pre-filtering of the data or curve fitting, and appropriately represents all the sources of variation. It supports experimental designs that trade-off temperatures for a larger number of biological replicates, and handles multiple drug concentrations or pools of samples treated with multiple temperatures, thus increasing the sensitivity of the results. We demonstrate these advantages of MSstatsTMT as compared to the currently used alternatives in a series of simulated and experimental datasets, which include conventional thermal proteome profiling and its OnePot counterpart that pools the samples treated at multiple temperatures into one sample, and incorporates multiple doses of a drug. The suggested MSstatsTMT-based workflow is documented in publicly available and fully reproducible R vignettes.</p>","PeriodicalId":18712,"journal":{"name":"Molecular & Cellular Proteomics","volume":" ","pages":"100999"},"PeriodicalIF":6.1,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144180265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Method for Comparing Proteins Measured in Serum and Plasma by Olink® Proximity Extension Assay.","authors":"Rawan Shraim, Caroline Diorio, Scott W Canna, Erin Macdonald-Dunlop, Hamid Bassiri, Zachary Martinez, Anders Mälarstig, Afrouz Abbaspour, David T Teachey, Robert B Lindell, Edward M Behrens","doi":"10.1016/j.mcpro.2025.101000","DOIUrl":"https://doi.org/10.1016/j.mcpro.2025.101000","url":null,"abstract":"<p><p>Accurate measurement of secreted proteins in serum and plasma is essential for understanding mechanisms and developing reliable biomarkers. Recent technological advancements, such as proximity extension assay (PEA), have enabled high-throughput multiplex protein analyses from small sample volumes in either serum or plasma. Despite the increasing use of PEA-based proteomics and the generation of extensive datasets, integrated data from these two mediums remains challenging due to inherent differences in sample processing. To address this issue, we developed and validated protein-specific transformation factors using linear modeling to normalize protein measurements between serum and plasma proteins quantified using Olink®. Our analysis surveyed 1463 proteins across matched serum and plasma samples, identifying 686 transformation factors. The transformation factors were further validated using independent datasets generated from patients with different disease phenotypes and ages, and 551 of the models and transformation factors were reproducible. These transformation factors provide a valuable resource for normalizing PEA-based proteomic data across serum and plasma, ultimately enhancing the capacity for collaborative analyses and facilitating comprehensive insights across diverse disease phenotypes.</p>","PeriodicalId":18712,"journal":{"name":"Molecular & Cellular Proteomics","volume":" ","pages":"101000"},"PeriodicalIF":6.1,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144182517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Employing Expression-Matched Controls Enables High Confidence Proximity-Based Interactome Classification.","authors":"Fulin Jiang, Xuezhen Ge, Eric J Bennett","doi":"10.1016/j.mcpro.2025.101001","DOIUrl":"https://doi.org/10.1016/j.mcpro.2025.101001","url":null,"abstract":"<p><p>Proximity labeling approaches have been widely utilized to define protein interactomes. Due to the inherent promiscuity of proximity labeling using TurboID-based approaches, identification and adoption of appropriate labeling controls is a pivotal step to mitigate background interference and enhance interactome assignment accuracy. Here, we evaluate the effectiveness of both expression controls and data normalization strategies in generating high confidence interactome maps. We demonstrate that the extent of control TurboID protein expression is strongly correlated with overall signal intensity and the number of identified proteins from streptavidin-enrichments. Discordant expression levels between the bait and control samples results in high frequency false-negative and false-positive identifications. Data normalization strategies help correct these expression differences but also introduce data distortion for proteins with high or low endogenous expression. Using the ubiquitin ligases RNF10 and HUWE1 as bait proteins, we demonstrate that matching TurboID expression between control and bait proteins allows for similar sampling of non-specific interactions. Using a matched expression strategy results in significantly reduced background interference and increases the accuracy of interactome assignments. These results document the need to alter proximity-labeling experimental workflows to include the generation of matched expression controls to enhance proximity labeling proteomics interactome mapping robustness and reproducibility.</p>","PeriodicalId":18712,"journal":{"name":"Molecular & Cellular Proteomics","volume":" ","pages":"101001"},"PeriodicalIF":6.1,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144181299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Multi-Omics Framework for Decoding Disease Mechanisms: Insights from Methylmalonic Aciduria.","authors":"Jianbo Fu, Vito R T Zanotelli, Cedric Howald, Nylsa Chammartin, Ilya Kolpakov, Ioannis Xenarios, D Sean Froese, Bernd Wollscheid, Patrick G A Pedrioli, Sandra Goetze","doi":"10.1016/j.mcpro.2025.100998","DOIUrl":"https://doi.org/10.1016/j.mcpro.2025.100998","url":null,"abstract":"<p><p>The diverse perspectives offered by multi-omics data analysis can aid in identifying the most relevant molecular pathways involved in disease processes, and findings in one layer can substantiate findings in other layers of information. Integrating data from multiple omics sources is becoming increasingly important to improve disease diagnosis and treatment, especially for conditions with complex and poorly understood underlying pathomechanisms. Methylmalonic aciduria (MMA), an inherited metabolic disorder, serves as an illustrative example of such a disease with poorly understood pathogenesis for which published multi-omics data are readily available. Re-using these FAIR data, obtained from the multi-omics digitization of 230 MMA patient samples, we pursued advanced data integration and analysis strategies to integrate different levels of biological information, combining genomic, transcriptomic, proteomic, and metabolomic profiling with biochemical and clinical data, with the aim of elucidating molecular perturbations in individuals affected by MMA. The analysis of protein-quantitative trait loci highlighted the importance of glutathione metabolism in the pathogenesis of methylmalonic acidemia (MMA). This finding was supported by correlation network analyses that integrated proteomics and metabolomics data, alongside gene set enrichment and transcription factor analyses based on disease severity from transcriptomic data. The correlation network analysis also revealed that lysosomal function is compromised in MMA patients, which is critical for maintaining metabolic balance. Our research introduces a comprehensive data analysis framework that effectively addresses the challenge of prioritizing disruptions in molecular pathways by accumulating evidence from multiple omics levels.</p>","PeriodicalId":18712,"journal":{"name":"Molecular & Cellular Proteomics","volume":" ","pages":"100998"},"PeriodicalIF":6.1,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144172821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LncRNA MALAT1 facilitates HIV-1 replication by upregulation of CHCHD2 and downregulation of IFN-I expression.","authors":"Mei-Rong Wang, Cheng-Si Bai, Jian-Wei Dai, Lan Yang, Fang-Yi Quan, Jian-Chun Ma, Xing-Yuan Chen, Shao-Wei Zhu, Ying-Qi Xu, Zhou-Fu Xiang, Ya-le Jiang, Qi Cheng, Wei-Hao Zhang, Ke-Han Chen, Jian-Hua Wang, Yong Feng, Xiao-Ping Chen, Yong Xiong, Shu-Liang Chen, Wei Hou, Hai-Rong Xiong","doi":"10.1016/j.mcpro.2025.100997","DOIUrl":"https://doi.org/10.1016/j.mcpro.2025.100997","url":null,"abstract":"<p><p>Long noncoding RNAs (lncRNAs) are effective regulators of both RNA and protein functions throughout cell biology, including viral replication. Emerging studies have shown that lncRNAs activate or inhibit the replication and latency of HIV-1 by regulating different cellular mechanisms. Metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) is an oncogenic lncRNA required for paraspeckle integrity and has been proven to be linked to viral infection. However, the mechanisms by which it influences HIV-1 infection in macrophages remain unclear. In this study, we performed RNA-deep sequencing to compare the profiles of lncRNAs in macrophages with or without HIV-1 and found that MALAT1 was dramatically upregulated in HIV-1-infected macrophages. MALAT1 knockdown inhibited HIV-1 infection, whereas MALAT1 overexpression enhanced viral replication, indicating that MALAT1 promotes HIV-1 replication. We further performed proteomics analysis and found that coiled-coil-helix-coiled-coil-helix domain-containing 2 (CHCHD2) was the most downregulated protein affected by RNAi-mediated knockdown of MALAT1. We next demonstrated that MALAT1 favored HIV-1 replication in a CHCHD2-dependent manner and functioned as a competing endogenous RNA to regulate CHCHD2 expression by sponging miR-145-5p, which could mutually bind the MALAT1 and 3'UTR of chchd2 mRNA. Furthermore, knockdown of endogenous MALAT1 or CHCHD2 with specific small interfering RNAs (siRNAs) promoted the expression of IRF7, and enhanced the promoter activities of interferons-α and -β, increasing their production as well as that of a critical interferon-stimulated gene (ISG), myxovirus resistance protein B (MxB). Moreover, MALAT1 or CHCHD2 knockdown promoted the expression of STAT2 to enhance the production of downstream MxB, which expanded the role of CHCHD2 as a negative regulator of the innate immune response. These findings improve our understanding of MALAT1/miR-145-5p/CHCHD2 pathway regulation of HIV-1 replication in macrophages, providing new insights into potential targeted therapeutic interventions.</p>","PeriodicalId":18712,"journal":{"name":"Molecular & Cellular Proteomics","volume":" ","pages":"100997"},"PeriodicalIF":6.1,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144143009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}