bims-mascan Biomed News
on Mass spectrometry in cancer research
Issue of 2025–06–08
eighteen papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz
  1. DuReS: An R Package for Denoising Experimental Tandem Mass Spectra and Metabolite Annotation.
  2. Multiplexed Data-Independent Acquisition (mDIA) to Profile Extracellular Vesicle Proteomes.
  3. Improving proteomic dynamic range with Multiple Accumulation Precursor Mass Spectrometry (MAP-MS).
  4. MALDI-TIMS-MS 2 Imaging and Annotation of Natural Products in Fungal-Bacterial Co-Culture.
  5. msqrob2TMT: robust linear mixed models for inferring differential abundant proteins in labelled experiments with arbitrarily complex design.
  6. Unraveling Posttranslational Modification Complexity: Advances in Quantitative Histone Proteoform Mass Spectrometry.
  7. Subcellular Mass Spectrometry Reveals Proteome Remodeling in an Asymmetrically Dividing (Frog) Embryonic Stem Cell.
  8. Charting unknown metabolic reactions by mass spectrometry-resolved stable-isotope tracing metabolomics.
  9. Pathway metabolite ratios reveal distinctive glutamine metabolism in a subset of proliferating cells.
  10. Analytical Techniques as Indicators of Biomarkers in Proteomics Cancer Diagnosis.
  11. Corrigendum to "A liquid chromatography-high-resolution mass spectrometry method for separation and identification of hemoglobin variant subunits with mass shifts less than 1 Da" [J. Mass Spectromet. Adv. Clin. Lab 35 (2025) 1-7].
  12. UniScore, a unified and universal measure for peptide identification by multiple search engines.
  13. Metabolomic profiling reveals grade-specific niacinamide accumulation and its therapeutic potential via SIRT1-CD38-EMT axis modulation in cervical cancer progression.
  14. PLK1-mediated phosphorylation of PHGDH reprograms serine metabolism in advanced prostate cancer.
  15. Deep Blood Proteomics Identifies over 12,000 Proteins Providing Valuable Information about the State of the Human Body.
  16. Sustained Glucose Turnover Flux Distinguishes Cancer Cachexia from Nutrient Limitation.
  17. Mass spectrometry-based lipidomics identifies select triacylglycerol species as differentially abundant in the bone marrow microenvironment of patients with multiple myeloma compared to monoclonal gammopathy of unknown significance.
  18. PEG Precipitation Followed by Albumin Depletion for Plasma Proteomics Analysis.
  1. Anal Chem. 2025 Jun 06.
    DuReS: An R Package for Denoising Experimental Tandem Mass Spectra and Metabolite Annotation.
    Shayantan Banerjee, Prajval Nakrani, Aviral Singh, Pramod P Wangikar.
      Mass spectrometry-based untargeted metabolomics is a powerful technique for profiling small molecules in biological samples, yet accurate metabolite identification remains challenging. The presence of random noise peaks in tandem mass spectra can lead to false annotations and necessitate time-consuming manual verification. A common method for removing noise from mass spectra is intensity thresholding, where low-intensity peaks are discarded by applying a user-defined cutoff. However, determining an optimal threshold is often data set-specific and may still retain many noisy peaks. We hypothesize that true signal peaks consistently recur across replicate tandem spectra generated from the same precursor ion, unlike random noise. Here, we present a freely available R package, Denoising Using Replicate Spectra (DuReS) (https://github.com/BiosystemEngineeringLab-IITB/dures), which accepts mzML files and feature lists and returns high-quality annotations and denoised mzML files, enabling users to integrate the denoising pipeline into their workflow seamlessly. This package is designed for data-dependent acquisition mode (DDA) data. It has (i) the main denoising module and (i) an optional tuning module to determine each data set's optimal recurrence frequency cutoff (Fthreshold), considering variations in the intrinsic noise characteristics. We tested the tool on eight representative data sets selected from those available in metabolomics repositories. Our approach minimizes signal loss while maximizing noise reduction, effectively preserving diagnostically significant low-intensity fragments that would otherwise be lost through conventional intensity thresholding. This improves spectral matching metrics, leading to more accurate annotations and fewer false positives.
    DOI:  https://doi.org/10.1021/acs.analchem.5c01726
  2. bioRxiv. 2025 May 15. pii: 2025.05.12.653483. [Epub ahead of print]
    Multiplexed Data-Independent Acquisition (mDIA) to Profile Extracellular Vesicle Proteomes.
    Yi-Kai Liu, Nathaniel Miller, Marco Hadisurya, Zheng Zhang, W Andy Tao.
      Extracellular vesicles (EVs) have gained increasing attention with their intriguing biological functions and their molecular cargoes serving as potential biomarkers for various diseases, including cancers. A relatively lower abundance of EV proteins compared to cellular counterparts necessitates sensitive and accurate quantitative proteomic strategies. Multiplexed proteomics combined with data-independent acquisition (mDIA) has shown promise for improving sensitivity and quantification over traditional DDA and label-free methods. Despite this, mDIA pipelines that utilize various types of spectral libraries and search software suites have not been thoroughly evaluated with EV proteome samples. In this study, we aim to establish a robust mDIA pipeline based on dimethyl labeling for quantitative proteomics of EVs. EVs were isolated using the extracellular vesicle total recovery and purification (EVtrap) technique and processed directly through an on-bead one-pot sample preparation workflow to obtain digested peptides. We evaluated different mDIA pipelines, including library-free and library-based DIA on the timsTOF HT platform. Results showed that library-based DIA, with project-specific spectral libraries generated from StageTip-based fractionation, outperformed other pipelines in protein identification and quantification. We demonstrated for the first time EV proteome landscape changes caused by the IDH1 mutation and inhibitor treatment in intrahepatic cholangiocarcinoma, highlighting the utility of mDIA in EV-based biomarker discovery.
    DOI:  https://doi.org/10.1101/2025.05.12.653483
  3. bioRxiv. 2025 May 17. pii: 2025.05.14.653938. [Epub ahead of print]
    Improving proteomic dynamic range with Multiple Accumulation Precursor Mass Spectrometry (MAP-MS).
    Teeradon Phlairaharn, Ariana E Shannon, Xinlei Zeng, Dong-Jiunn Jeffery Truong, Erwin M Schoof, Zilu Ye, Brian C Searle.
      Orbitrap (OT) -based mass spectrometer platforms are a gold standard in high-resolution mass spectrometry, where their primary disadvantage is slower-scanning speed in comparison to time-of-flight or linear ion trap mass analyzers. In this study, we utilize long OT transients to extend the precursor dynamic range by modifying the selected ion monitoring method to multiplex several precursor m/z ranges from 400 to 1000 m/z into a single scan called " M ultiple A ccumulation P recursor M ass S pectrometry" (MAP-MS). Our approach requires no software or hardware modifications and hides the additional ion accumulation steps during the time it takes to make other Orbitrap measurements, producing precursor spectra with nearly 2× dynamic range and essentially no consequences. We collected data using both data-dependent acquisition (DDA) and data-independent acquisition (DIA) methods to evaluate a range of approaches. With DDA, MAP-MS precursor quantification improves with higher quality measurements. At the same time, DIA detection is enhanced by up to 11% when combining precursor and tandem mass spectra for peptide detection.
    DOI:  https://doi.org/10.1101/2025.05.14.653938
  4. bioRxiv. 2025 May 13. pii: 2025.05.11.653367. [Epub ahead of print]
    MALDI-TIMS-MS 2 Imaging and Annotation of Natural Products in Fungal-Bacterial Co-Culture.
    Robert A Shepherd, Gordon T Luu, Laura M Sanchez.
      Mass spectrometry imaging (MSI) is a powerful tool for monitoring the spatial distributions of microbial metabolites directly from culture. MSI can identify secretion and retention patterns for microbial metabolites, allowing for the assessment of chemical communication within complex microbial communities. Microbial imaging via matrix-assisted laser desorption/ionization (MALDI) MSI remains challenging due to high sample complexity and heterogeneity associated with the required sample preparation, making annotation of molecules by MS 1 alone challenging. The implementation of trapped ion mobility spectrometry (TIMS) has increased the dimensionality of MALDI-MSI experiments, allowing for the resolution of isomers and isobars, and can increase sensitivity of metabolite detection within complex samples. Parallel reaction monitoring - parallel accumulation serial fragmentation (prm-PASEF) leverages TIMS to enhance the targeted acquisition of MS 2 data by increasing the number of precursors that can be fragmented in a single acquisition. Recently, imaging prm-PASEF (iprm-PASEF) has been developed to provide more accurate annotation from MALDI-TIMS-MSI datasets through the inclusion of MS 2 . Here, we showcase the use of MALDI iprm-PASEF to provide rapid and accurate annotation coproporphyrin III directly from a bacterial-fungal co-culture between Glutamicibacter arilaitensis (strain JB182) and Penicillium solitum (strain #12). Additionally, we present a workflow for untargeted iprm-PASEF precursor selection directly in SCiLS Lab, followed by direct export for iprm-PASEF acquisition.
    Abstract Figure:
    DOI:  https://doi.org/10.1101/2025.05.11.653367
  5. Mol Cell Proteomics. 2025 May 30. pii: S1535-9476(25)00101-X. [Epub ahead of print] 101002
    msqrob2TMT: robust linear mixed models for inferring differential abundant proteins in labelled experiments with arbitrarily complex design.
    Stijn Vandenbulcke, Christophe Vanderaa, Oliver Crook, Lennart Martens, Lieven Clement.
      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.
    DOI:  https://doi.org/10.1016/j.mcpro.2025.101002
  6. Mass Spectrom Rev. 2025 Jun 01.
    Unraveling Posttranslational Modification Complexity: Advances in Quantitative Histone Proteoform Mass Spectrometry.
    Karl F Poncha, Alyssa T Paparella, Nicolas L Young.
      Histone proteins and their posttranslational modifications are central to chromatin structure and function. These modifications often occur in combinations, generating a diverse array of histone proteoforms that contribute to the dynamic regulation of chromatin architecture. Advancements in mass spectrometry-based proteomics, particularly top-down and middle-down approaches, have significantly enhanced our ability to characterize these proteoforms and elucidate PTM crosstalk. This review provides an analysis of the epigenetic machinery involved in the addition, recognition, and removal of histone PTMs, emphasizing the complexity introduced by histone variants and combinatorial PTM patterns. We examine the challenges and limitations of traditional antibody-based methods for PTM analysis and highlight the advantages of mass spectrometry techniques in providing comprehensive and quantitative insights into histone proteoforms. Key considerations in experimental design, sample preparation, chromatographic separation, and data analysis are outlined for the effective application of mass spectrometry for histone proteoform studies. By integrating these technological advancements on the side of sample preparation, instrumentation, and data processing a deeper understanding of chromatin regulation through PTM crosstalk is achieved, paving the way for mass spectrometry-based proteomics to spearhead the discovery of novel therapeutic strategies with proteoform level specificity.
    Keywords:  PTM crosstalk; epigenetics; histone; proteoform; top‐down mass spectrometry
    DOI:  https://doi.org/10.1002/mas.21941
  7. bioRxiv. 2025 May 12. pii: 2025.05.12.652541. [Epub ahead of print]
    Subcellular Mass Spectrometry Reveals Proteome Remodeling in an Asymmetrically Dividing (Frog) Embryonic Stem Cell.
    Bowen Shen, Leena R Pade, Fei Zhou, Peter Nemes.
      Subcellular proteomics holds the potential to reveal the molecular architecture of cellular processes with unprecedented spatial resolution. Performing these analyses deeply, at the level of hundreds to thousands of proteins in subcellular resolution, is a high and still unmet technical need. Here, we advance microprobe capillary electrophoresis-mass spectrometry (CE-MS) to achieve deep proteomic coverage-quantifying over 1,000 proteins within opposing poles of an asymmetrically dividing embryonic stem cell (blastomere). We integrated CE-electrospray ionization (CE-ESI) with trapped ion mobility spectrometry time-of-flight (timsTOF) MS, implementing data-independent acquisition (DIA) via parallel accumulation-serial fragmentation (diaPASEF). This CE-diaPASEF workflow identified 1,035 proteins from ∼200 pg of proteome digest, equivalent to ∼80% of the HeLa cell's content, with high reproducibility (coefficient of variation <15% across technical triplicates). With microprobe sampling, this technology quantified 808 to 1,022 proteins in opposing poles of a dorsal-animal (D1) blastomere in the 8-cell Xenopus laevis embryo. Comparative proteomic analysis of the D1 blastomere and its descendants-the dorsal-animal-midline (D11) and dorsal-animal-lateral (D12) cells-revealed diverse molecular outcomes of asymmetric division: some protein profiles remained conserved, while others underwent significant or even reversed changes as these lineages descended into neural tissue and epidermal trajectories. Ultraviolet light-induced ventralization was performed to help disentangle subcellular gradients from dorsal-ventral patterning. Collectively, this work establishes microprobe CE-diaPASEF as a powerful platform for deep subcellular proteomics, enabling new insights into spatial proteome organization during key developmental processes.
    DOI:  https://doi.org/10.1101/2025.05.12.652541
  8. Nat Commun. 2025 May 31. 16(1): 5059
    Charting unknown metabolic reactions by mass spectrometry-resolved stable-isotope tracing metabolomics.
    Yang Gao, Mingdu Luo, Hongmiao Wang, Zhiwei Zhou, Yandong Yin, Ruohong Wang, Beizi Xing, Xiaohua Yang, Yuping Cai, Zheng-Jiang Zhu.
      Metabolic reactions play important roles in organisms such as providing energy, transmitting signals, and synthesizing biomacromolecules. Charting unknown metabolic reactions in cells is hindered by limited technologies, restricting the holistic understanding of cellular metabolism. Using mass spectrometry-resolved stable-isotope tracing metabolomics, we develop an isotopologue similarity networking strategy, namely IsoNet, to effectively deduce previously unknown metabolic reactions. The strategy uncovers ~300 previously unknown metabolic reactions in living cells and mice. Specifically, we elaborately chart the metabolic reaction network related to glutathione, unveiling three previously unreported reactions nestled within glutathione metabolism. Among these, a transsulfuration reaction, synthesizing γ-glutamyl-seryl-glycine directly from glutathione, underscores the role of glutathione as a sulfur donor. Functional metabolomics studies systematically characterize biochemical effects of previously unknown reactions in glutathione metabolism, showcasing their diverse functions in regulating cellular metabolism. Overall, these newly uncovered metabolic reactions fill gaps in the metabolic network maps, facilitating exploration of uncharted territories in cellular biochemistry.
    DOI:  https://doi.org/10.1038/s41467-025-60258-7
  9. Mol Syst Biol. 2025 Jun 05.
    Pathway metabolite ratios reveal distinctive glutamine metabolism in a subset of proliferating cells.
    Nancy T Santiappillai, Yue Cao, Mariam F Hakeem-Sanni, Jean Yang, Lake-Ee Quek, Andrew J Hoy.
      Large-scale metabolomic analyses of pan-cancer cell line panels have provided significant insights into the relationships between metabolism and cancer cell biology. Here, we took a pathway-centric approach by transforming targeted metabolomic data into ratios to study associations between reactant and product metabolites in a panel of cancer and non-cancer cell lines. We identified five clusters of cells from various tissue origins. Of these, cells in Cluster 4 had high ratios of TCA cycle metabolites relative to pyruvate, produced more lactate yet consumed less glucose and glutamine, and greater OXPHOS activity compared to Cluster 3 cells with low TCA cycle metabolite ratios. This was due to more glutamine cataplerotic efflux and not glycolysis in cells of Cluster 4. In silico analyses of loss-of-function and drug sensitivity screens showed that Cluster 4 cells were more susceptible to gene deletion and drug targeting of glutamine metabolism and OXPHOS than cells in Cluster 3. Our results highlight the potential of pathway-centric approaches to reveal new aspects of cellular metabolism from metabolomic data.
    Keywords:  Cancer Cell Lines; Glucose Metabolism; Glutamine Metabolism; Metabolic Pathways; Metabolomics
    DOI:  https://doi.org/10.1038/s44320-025-00099-0
  10. Anticancer Agents Med Chem. 2025 May 29.
    Analytical Techniques as Indicators of Biomarkers in Proteomics Cancer Diagnosis.
    Pawan Kumar Goswami, Ranjeet Kumar, Dharmendra Kumar, Shubham Dhiman.
       BACKGROUND: Cancer is a complex disease marked by changes in the levels and functions of key cellular proteins, including oncogenes and tumor suppressors. Proteomics technology enables the identification of crucial protein targets and signaling pathways involved in cancer cell proliferation and metastasis. Various proteomics techniques have been employed to investigate the molecular mechanisms of cancer, aiding in the confirmation and characterization of heritable disorders.
    METHOD: A comprehensive literature search was conducted using PubMed, ScienceDirect, and Google Scholar with search terms like "Cancer and proteomics" and "Mass spectrometry in oncology," utilizing Boolean operators for refinement. Selection criteria included peer-reviewed articles in English on MS-based biomarker detection, tumor-specific proteins, and drug resistance markers, excluding non-peer-reviewed works and pre-2000 publications unless foundational. Extracted data focused on MS methodologies, biomarker sensitivity, and clinical applications, particularly advances in detecting low-abundance biomarkers and monitoring treatment response. Methodological quality was assessed using PRISMA, evaluating study design, sample size, reproducibility, and statistical analysis. Ethical approval was not required, but adherence to systematic review guidelines and proper citation were ensured.
    RESULT: In this review, we highlighted the advanced analytical technique for cancer diagnosis and management of cancer, and described the objective of novel cancer biomarkers. Mass spectrometry (MS) is transforming cancer diagnostics and personalized medicine by enabling precise biomarker detection and monitoring. Unlike traditional antibody-based methods, MS provides high-throughput, quantitative analysis of tumor-specific proteins in clinical samples like blood and tissue. Advanced MS techniques improve sensitivity, allowing for the identification of low-abundance biomarkers and tumor-associated proteoforms, including post-translational modifications and drug resistance markers. In research, MS-based proteomics supports multi-center biomarker validation studies with standardized protocols, enhancing reproducibility. The integration of proteomic data with genomic and transcriptomic datasets through proteogenomics is refining precision oncology strategies. These advancements are bridging the gap between research and clinical application, making MS a critical tool for early cancer detection, prognosis, and therapy selection.
    CONCLUSION: Advancements in technology and analytical techniques have helped to produce more accurate and sensitive cancer-specific biomarkers. These methods are advancing rapidly, and developing high-throughput platforms has yielded great results. However, The substantial variation in protein concentrations makes cancer protein profiling extremely complicated. This shows that more technical developments are required in the future to improve proteome broad screening of cancer cells.
    Keywords:  Cancer; analytical techniques; biomarkers; database.; proteomics
    DOI:  https://doi.org/10.2174/0118715206377391250526054417
  11. J Mass Spectrom Adv Clin Lab. 2025 Aug;37 14-15
    Corrigendum to "A liquid chromatography-high-resolution mass spectrometry method for separation and identification of hemoglobin variant subunits with mass shifts less than 1 Da" [J. Mass Spectromet. Adv. Clin. Lab 35 (2025) 1-7].
    Ainslie Chen, Ryan M Aquino, Hector A Vidal, Carolyn V Wong, Ruben Y Luo.
      [This corrects the article DOI: 10.1016/j.jmsacl.2025.01.002.].
    DOI:  https://doi.org/10.1016/j.jmsacl.2025.04.009
  12. Mol Cell Proteomics. 2025 Jun 02. pii: S1535-9476(25)00109-4. [Epub ahead of print] 101010
    UniScore, a unified and universal measure for peptide identification by multiple search engines.
    Tsuyoshi Tabata, Akiyasu C Yoshizawa, Kosuke Ogata, Chih-Hsiang Chang, Norie Araki, Naoyuki Sugiyama, Yasushi Ishihama.
      We propose UniScore as a metric for integrating and standardizing the outputs of multiple search engines in the analysis of data-dependent acquisition (DDA) data from LC/MS/MS-based bottom-up proteomics. UniScore is calculated from the annotation information attached to the product ions alone by matching the amino acid sequences of candidate peptides suggested by the search engine with the product ion spectrum. The acceptance criteria are controlled independently of the score values by using the false discovery rate based on the target-decoy approach. Compared to other rescoring methods that use deep learning-based spectral prediction, larger amounts of data can be processed using minimal computing resources. When applied to large-scale global proteome data and phosphoproteome data, the UniScore approach outperformed each of the conventional single search engines examined (Comet, X! Tandem, Mascot and MaxQuant). Furthermore, UniScore could also be directly applied to peptide matching in chimeric spectra without any additional filters.
    Keywords:  Chimera spectrum; False discovery rate; Peptide identification; Reanalysis; Search engine; UniScore
    DOI:  https://doi.org/10.1016/j.mcpro.2025.101010
  13. Biochim Biophys Acta Mol Cell Res. 2025 Jun 01. pii: S0167-4889(25)00099-0. [Epub ahead of print]1872(7): 119994
    Metabolomic profiling reveals grade-specific niacinamide accumulation and its therapeutic potential via SIRT1-CD38-EMT axis modulation in cervical cancer progression.
    Shivani Jaiswal, Vivek Mishra, Srija Majumder, Pramod P Wangikar, Shinjinee Sengupta.
      Despite new therapies for cervical cancer, innovative strategies are essential to overcome drug resistance and high toxicity. The present study focuses on the metabolic profiling of cervical carcinoma using a non-targeted metabolomics approach using liquid chromatography-mass spectrometry. Our study identified over 70 metabolites in cervical tissue samples (both cancerous and adjacent normal) using HILIC and reversed-phase chromatography in the positive and negative ionization modes. Major metabolic alterations included changes in nicotinamide metabolism, ammonia recycling, amino acid metabolism and nucleotide metabolism, in a grade-dependent manner. Compared to normal tissue, HPV-positive tumors showed elevated nicotinamide metabolism, and phosphatidylethanolamine biosynthesis, whereas HPV-negative tumors showed enriched purine and pyrimidine metabolism. We validated our findings by analyzing transcriptomics datasets from the Gene Expression Omnibus database to understand the expression patterns of the underlying genes involved in the dysregulated pathways. We observed that nicotinamide metabolism exhibits significant effects in lower-grade cervical cancers and specific HPV genotypes. We treated cervical cancer cell lines with niacinamide (NAM), an amide form of niacin, to evaluate its therapeutic efficacy. NAM treatment modulated NAD+ metabolism, affecting key players such as CD38, PARP, NAMPT, and SIRT1, promoting apoptosis and inhibiting cell proliferation in cervical cancer cells. Importantly, HPV-positive SiHa cells showed elevated NAD+ metabolism relative to HPV-negative C33A cells, reflecting distinct metabolic adaptations that may influence tumor progression. The study highlights the metabolic shifts in cancer progression and provides insights into NAM's molecular mechanisms and therapeutic potential for precision medicine in cervical cancer.
    Keywords:  Cervical Cancer; EMT; HPV; Metabolomics; Nicotinamide; SIRT1
    DOI:  https://doi.org/10.1016/j.bbamcr.2025.119994
  14. bioRxiv. 2025 May 24. pii: 2025.05.21.655274. [Epub ahead of print]
    PLK1-mediated phosphorylation of PHGDH reprograms serine metabolism in advanced prostate cancer.
    Xiongjian Rao, Derek B Allison, Robert M Flight, Penghui Lin, Daheng He, Zhiguo Li, Yanquan Zhang, Ruixin Wang, Chaohao Li, Jianlin Wang, Xinyi Wang, Jia Peng, Ka Wing Fong, Qing Shao, Chi Wang, Eunus S Ali, Hunter Nb Moseley, Xiaoqi Liu.
      Metabolic reprogramming is a hallmark of cancer, enabling tumor cells to meet their increased biosynthetic and energetic demands. While cells possess the capacity for de novo serine biosynthesis, most transformed cancer cells heavily depend on exogenous serine uptake to sustain their growth, yet the regulatory mechanisms driving this metabolic dependency remain poorly understood. Here, we uncover a novel mechanism by which Polo-like kinase 1 (PLK1), often overexpressed in prostate cancer, orchestrates a metabolic shift in serine and lipid metabolism through the phosphorylation of phosphoglycerate dehydrogenase (PHGDH), the rate-limiting enzyme of the serine synthesis pathway (SSP). We demonstrate that PLK1 phosphorylates PHGDH at three specific sites (S512, S513, S517), leading to a marked reduction in its protein level and enzymatic activity. This downregulation of SSP forces cancer cells to increase their reliance on exogenous serine uptake via the ASCT2 transporter, which, in turn, fuels the biosynthesis of lipids, including sphingolipids essential for tumor growth and survival. Targeting the SSP, serine uptake, or downstream lipid biosynthetic pathways may offer promising therapeutic avenues in PLK1-high advanced cancers.
    DOI:  https://doi.org/10.1101/2025.05.21.655274
  15. bioRxiv. 2025 May 20. pii: 2025.05.15.654311. [Epub ahead of print]
    Deep Blood Proteomics Identifies over 12,000 Proteins Providing Valuable Information about the State of the Human Body.
    Zhenyu Sun, T Mamie Lih, Trung Hoàng, Shao-Yung Chen, Joanne Xu, Ding Chiao Lin, Yuefan Wang, Jongmin Woo, Yuanyu Huang, Lijun Chen, Hongyi Liu, Marissa Alpern, Jadranka Milosevic, Hong-Zhang He, Raghothama Chaerkady, Qing Wang, Hui Zhang.
      Blood is a valuable resource for clinical research, offering insight into physiological and pathological states. However, the specific proteins detectable in blood and the optimal proteomic methods for their detection have not been rigorously investigated and documented. To address this, we conducted various blood proteomic strategies, including directly blood proteomic analysis, high-abundance protein depletion, low-abundance protein enrichment, and extracellular vesicle enrichment using data-independent acquisition or targeted proteomics. These approaches identified 11,679 protein groups in plasma from healthy individuals. In 136 pancreatic ductal adenocarcinoma whole blood samples, 6,956 protein groups were found, including 678 not seen in healthy samples, expanding the total to 12,357 blood proteins. This represents the most comprehensive blood proteome to date. To support broader access and analysis, we developed the Human Blood Proteome (HuBP) database, detailing protein detectability, abundance, and reproducibility across workflows, sample types, and disease contexts.
    DOI:  https://doi.org/10.1101/2025.05.15.654311
  16. bioRxiv. 2025 May 20. pii: 2025.05.15.654370. [Epub ahead of print]
    Sustained Glucose Turnover Flux Distinguishes Cancer Cachexia from Nutrient Limitation.
    Young-Yon Kwon, Yanshan Liang, Maria Gomez-Jenkins, Mujmmail Ahmed, Guangru Jiang, Juliya Hsiang, David Y Lewis, Tobias Janowitz, Marcus D Goncalves, Eileen White, Sheng Hui.
      Cancer cachexia is an involuntary weight loss condition characterized by systemic metabolic disorder. A comprehensive flux characterization of this condition however is lacking. Here, we systematically isotope traced eight major circulating nutrients in mice bearing cachectic C26 tumors (cxC26) and food intake-matched mice bearing non-cachectic C26 tumors (ncxC26). We found no difference in whole-body lipolysis and proteolysis, ketogenesis, or fatty acid and ketone oxidation by tissues between the two groups. In contrast, compared to ncxC26 mice ad libitum, glucose turnover flux decreased in food intake-controlled ncxC26 mice but not in cxC26 mice. Similarly, sustained glucose turnover flux was observed in two autochthonous cancer cachexia models despite reduced food intake. We identified glutamine and alanine as responsible for sustained glucose production and tissues with altered use of glucose and lactate in cxC26 mice. We provide a comprehensive view of metabolic alterations in cancer cachexia revealing those distinct from decreased nutrient intake.
    Highlights: Quantitative fluxomics of cancer cachexia under matched food intake and body weightIntact lipolysis, proteolysis, ketogenesis, and lipid oxidation in cachectic miceSustained glucose consumption in cachectic mice despite reduced food intakeIncreased glucose production from glutamine and alanine in cachectic mice.
    DOI:  https://doi.org/10.1101/2025.05.15.654370
  17. Metabolomics. 2025 Jun 01. 21(3): 73
    Mass spectrometry-based lipidomics identifies select triacylglycerol species as differentially abundant in the bone marrow microenvironment of patients with multiple myeloma compared to monoclonal gammopathy of unknown significance.
    Yogesh Chawla, Emilie I Anderson, Dragan Jevremovic, Shaji K Kumar, Taro Hitosugi, Wilson I Gonsalves.
       INTRODUCTION: Alterations in the bone marrow (BM) tumor microenvironment (TME) are closely associated with the progression of premalignant clonal plasma cells in monoclonal gammopathy of undetermined significance (MGUS) to malignant clonal plasma cells in multiple myeloma (MM), though the extent to which these alterations are causative remains unclear. Whether lipidomic changes are part of the BM TME alterations associated with this disease progression remains poorly understood.
    OBJECTIVES: To investigate and compare the levels of individual lipid species across different lipid classes in the BM-TME, specifically BM plasma, of MGUS and MM patients.
    METHODS: A mass spectrometry-based targeted lipidomics approach was employed to quantify individual lipid species from various lipid classes in BM plasma samples obtained from 22 MGUS and 24 MM patients.
    RESULTS: Significant differences in lipid species were observed between the BM-TME of MGUS and MM patients. MM patients exhibited elevated levels of triacylglycerol (TAG) species containing long-chain fatty acids (FAs) with higher degrees of unsaturation. In contrast, MGUS patients had higher levels of TAG species with shorter-chain FAs and lower unsaturation. Additionally, MM patients with a higher percentage of clonal plasma cells and increased proliferation rates displayed lipid profiles more closely resembling MM-associated signatures than MGUS.
    CONCLUSION: The differential abundance of individual lipid species within the BM-TME highlights metabolic changes associated with the progression from MGUS to MM. These findings provide valuable insights into the lipidomic underpinnings of MM pathogenesis, offering a foundation for future research into metabolic biomarkers and therapeutic targets.
    Keywords:  Lipid-species; Lipidomics; MGUS; Multiple myeloma; TAGs; Triacylglycerol; Triglyceride
    DOI:  https://doi.org/10.1007/s11306-025-02271-x
  18. Anal Chem. 2025 Jun 04.
    PEG Precipitation Followed by Albumin Depletion for Plasma Proteomics Analysis.
    Yanyang Wang, Minqi Cai, Yuning Song, Kun Jiang, Ting Liu, Xue Zhang, Huimin Zhu, Jiaojiao Sha, Liangjia Du, Shanyu Qi, Yiqiang Chen, Siyuan Liu, Zijin Geng, Qianqian Hu, Tianze Ling, Dezhu Chen, Hongyan Song, Jie Pan, Qian Zheng, Cheng Chang, Bing Bai.
      The mass spectrometry (MS)-based blood plasma or serum proteomic analysis is limited by interference from albumin, immunoglobins, and other highly abundant proteins. We have found that poly(ethylene glycol) (PEG) can efficiently precipitate some of these proteins except albumin. By PEG precipitation followed by albumin depletion, additional proteins and N-glycoproteins can be identified by liquid chromatography-tandem mass spectrometry (LC-MS/MS) in the plasma. In-depth LC-MS/MS proteomic analyses of the whole plasma, its 10% and 20% PEG-precipitated pellets, and albumin-depleted supernatants have profiled 2943, 2242, 3162, 2187, and 2028 proteins respectively, yielding 5040 proteins in total and thus expanding the plasma proteome coverage. Therefore, PEG precipitation and albumin depletion should be used as a general plasma processing method for successful proteomic discoveries of blood biomarkers.
    DOI:  https://doi.org/10.1021/acs.analchem.5c00593
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